Split (1)

test · 21.3k rows

instance_id stringlengths 10 57	base_commit stringlengths 40 40	created_at stringdate 2014-04-30 14:58:36 2025-04-30 20:14:11	environment_setup_commit stringlengths 40 40	hints_text stringlengths 0 273k	patch stringlengths 251 7.06M	problem_statement stringlengths 11 52.5k	repo stringlengths 7 53	test_patch stringlengths 231 997k	meta dict	version stringclasses 864 values	install_config dict	requirements stringlengths 93 34.2k ⌀	environment stringlengths 760 20.5k ⌀	FAIL_TO_PASS listlengths 1 9.39k	FAIL_TO_FAIL listlengths 0 2.69k	PASS_TO_PASS listlengths 0 7.87k	PASS_TO_FAIL listlengths 0 192	license_name stringclasses 56 values	docker_image stringlengths 42 89 ⌀
BrandonLMorris__auacm-cli-11	5c13a4843e281aa1470d2bd28fe39c07f4e39e92	2016-03-11 15:15:59	5c13a4843e281aa1470d2bd28fe39c07f4e39e92		diff --git a/src/auacm/competition.py b/src/auacm/competition.py index f2d9561..794f8a4 100644 --- a/src/auacm/competition.py +++ b/src/auacm/competition.py @@ -1,6 +1,6 @@ """Subcommands related to competitions""" -import auacm, requests, textwrap +import auacm, requests, textwrap, argparse from datetime import datetime from auacm.utils import subcommand from auacm.exceptions import CompetitionNotFoundError @@ -10,7 +10,7 @@ from auacm.exceptions import CompetitionNotFoundError def get_comps(args=None): """Retrieve one or more competitions from the server""" if args: - return _get_one_comp(args) + return get_one_comp(args) response = requests.get(auacm.BASE_URL + 'competitions') @@ -35,13 +35,30 @@ def get_comps(args=None): {} ''').format(current, upcoming, past).strip() -def _get_one_comp(args): +def get_one_comp(args): """Retrieve info on one specific competition""" - response = requests.get(auacm.BASE_URL + 'competitions/' + str(args[0])) + parser = argparse.ArgumentParser( + add_help=False, + usage='competition [-i/--id] <competition>' + ) + parser.add_argument('-i', '--id', action='store_true') + parser.add_argument('competition') + args = parser.parse_args(args) + + if not args.id: + cid = _cid_from_name(args.competition) + if cid == -1: + raise CompetitionNotFoundError( + 'Could not find a competition with the name ' + + args.competition) + else: + cid = args.competition + + response = requests.get(auacm.BASE_URL + 'competitions/' + str(cid)) if not response.ok or response.status_code == 404: raise CompetitionNotFoundError( - 'Could not find competition with id: ' + str(args[0])) + 'Could not find competition with id: ' + str(args.competition)) comp = response.json()['data'] @@ -62,6 +79,21 @@ def _get_one_comp(args): {} ''').format(comp_str, teams, problems) +def _cid_from_name(comp_name): + """Return the competition of an id based on it's name""" + comps = requests.get(auacm.BASE_URL + 'competitions').json()['data'] + for comp in comps['upcoming']: + if comp_name.lower() in comp['name'].lower(): + return int(comp['cid']) + for comp in comps['ongoing']: + if comp_name.lower() in comp['name'].lower(): + return int(comp['cid']) + for comp in comps['past']: + if comp_name.lower() in comp['name'].lower(): + return int(comp['cid']) + + return -1 + def _format_comps(comps): """Return a formatted string for a list of competitions""" result = list() @@ -85,7 +117,7 @@ def _format_teams(teams): def _format_problems(probs): """Return a formatted string of the problems passed in""" result = '' - for label, prob in probs.items(): + for label, prob in sorted(probs.items()): result += '{}\t{} ({})\n'.format(label, prob['name'], prob['pid']) return result.strip() diff --git a/src/auacm/main.py b/src/auacm/main.py index 32d55e0..35e463b 100644 --- a/src/auacm/main.py +++ b/src/auacm/main.py @@ -7,7 +7,7 @@ The central entry point of the auacm app. import requests, sys, textwrap import auacm import auacm.utils as utils -from auacm.exceptions import ConnectionError, ProblemNotFoundError, UnauthorizedException, InvalidSubmission +from auacm.exceptions import ConnectionError, ProblemNotFoundError, UnauthorizedException, InvalidSubmission, CompetitionNotFoundError def main(args): """ @@ -44,7 +44,8 @@ def main(args): print(utils.callbacks[args[0]](args[1:]) or '') except (ProblemNotFoundError, UnauthorizedException, - InvalidSubmission) as exp: + InvalidSubmission, + CompetitionNotFoundError) as exp: print(exp.message) exit(1) except (requests.exceptions.ConnectionError, ConnectionError):	List recent and ongoing competitions A `comp[etition]` command that will simply list (chronologically) the recent and ongoing competitions.	BrandonLMorris/auacm-cli	diff --git a/tests/competition_tests.py b/tests/competition_tests.py index 7f7c381..b89e62e 100644 --- a/tests/competition_tests.py +++ b/tests/competition_tests.py @@ -21,16 +21,38 @@ class CompetitionTests(unittest.TestCase): self.assertTrue('past fake mock' in result.lower()) @patch('requests.get') - def testGetOneCompetition(self, mock_get): - """Successfully get one competition by it's id""" + def testGetOneCompetitionById(self, mock_get): + """Successfully get one competition by its id""" mock_get.return_value = MockResponse(json=COMPETITION_DETAIL) - result = auacm.competition.get_comps(['2']) + result = auacm.competition.get_comps(['-i', '2']) self.assertTrue('ongoing fake mock' in result.lower()) self.assertTrue('fake problem a' in result.lower()) self.assertTrue('brando the mando' in result.lower()) + @patch('requests.get') + def testGetOneCompetitionByName(self, mock_get): + """Successfully get one competition by its name""" + mock_get.side_effect = [ + MockResponse(json=COMPETITIONS_RESPONSE), + MockResponse(json=COMPETITION_DETAIL)] + + result = auacm.competition.get_comps(['ongoing']) + + self.assertTrue('ongoing fake mock' in result.lower()) + self.assertTrue('fake problem a' in result.lower()) + self.assertTrue('brando the mando' in result.lower()) + + @patch('requests.get') + def testGetOneCompetitionBadName(self, mock_get): + """Attempt to get a competition that doesn't exist by name""" + mock_get.side_effect = [ + MockResponse(json=COMPETITIONS_RESPONSE)] + + self.assertRaises( + auacm.exceptions.CompetitionNotFoundError, + auacm.competition.get_comps, ['not real']) @patch('requests.get') def testGetOneCompetitionBad(self, mock_get): @@ -39,7 +61,7 @@ class CompetitionTests(unittest.TestCase): self.assertRaises( auacm.exceptions.CompetitionNotFoundError, - auacm.competition.get_comps, ['99999999']) + auacm.competition.get_comps, ['-i', '99999999']) if __name__ == '__main__':	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 2 }	0.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requests", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	-e git+https://github.com/BrandonLMorris/auacm-cli.git@5c13a4843e281aa1470d2bd28fe39c07f4e39e92#egg=auacm Brotli @ file:///croot/brotli-split_1736182456865/work certifi @ file:///croot/certifi_1738623731865/work/certifi charset-normalizer @ file:///croot/charset-normalizer_1721748349566/work exceptiongroup==1.2.2 idna @ file:///croot/idna_1714398848350/work iniconfig==2.1.0 packaging==24.2 pluggy==1.5.0 PySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work pytest==8.3.5 requests @ file:///croot/requests_1730999120400/work tomli==2.2.1 urllib3 @ file:///croot/urllib3_1737133630106/work	name: auacm-cli channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - brotli-python=1.0.9=py39h6a678d5_9 - ca-certificates=2025.2.25=h06a4308_0 - certifi=2025.1.31=py39h06a4308_0 - charset-normalizer=3.3.2=pyhd3eb1b0_0 - idna=3.7=py39h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - pysocks=1.7.1=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - requests=2.32.3=py39h06a4308_1 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - urllib3=2.3.0=py39h06a4308_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - exceptiongroup==1.2.2 - iniconfig==2.1.0 - packaging==24.2 - pluggy==1.5.0 - pytest==8.3.5 - tomli==2.2.1 prefix: /opt/conda/envs/auacm-cli	[ "tests/competition_tests.py::CompetitionTests::testGetOneCompetitionBadName", "tests/competition_tests.py::CompetitionTests::testGetOneCompetitionByName" ]	[]	[ "tests/competition_tests.py::CompetitionTests::testGetAllCompetitons", "tests/competition_tests.py::CompetitionTests::testGetOneCompetitionBad", "tests/competition_tests.py::CompetitionTests::testGetOneCompetitionById" ]	[]	MIT License	null
BrandwatchLtd__api_sdk-62	0570363bdee1621b85f8290599e0c86c75bcf802	2018-09-13 12:45:58	0570363bdee1621b85f8290599e0c86c75bcf802		diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index b33d109..0000000 --- a/.travis.yml +++ /dev/null @@ -1,24 +0,0 @@ -language: python - -matrix: - include: - - python: 3.3 - - python: 3.4 - - python: 3.5 - - python: 3.6 - - python: 3.7 - dist: xenial - sudo: true - - python: 3.7-dev - dist: xenial - sudo: true - - python: 3.8-dev - dist: xenial - sudo: true - -install: - - python setup.py install - -script: - - python setup.py test - diff --git a/README.md b/README.md index 7084960..15c6827 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ -[![Build Status](https://travis-ci.com/BrandwatchLtd/api_sdk.svg?branch=master)](https://travis-ci.com/BrandwatchLtd/api_sdk) - # Brandwatch API SDK ## Introduction diff --git a/bwproject.py b/bwproject.py index c879cae..3a41d39 100644 --- a/bwproject.py +++ b/bwproject.py @@ -59,7 +59,7 @@ class BWUser: if "username" in user: if username is None: return user["username"], token - elif user["username"] == username: + elif user["username"].lower() == username.lower(): return username, token else: raise KeyError("Username " + username + " does not match provided token", user) @@ -84,8 +84,8 @@ class BWUser: def _read_auth(self, username, token_path): user_tokens = self._read_auth_file(token_path) - if username in user_tokens: - return self._test_auth(username, user_tokens[username]) + if username.lower() in user_tokens: + return self._test_auth(username, user_tokens[username.lower()]) else: raise KeyError("Token not found in file: " + token_path) diff --git a/setup.py b/setup.py index 9c1d705..56c9ab2 100644 --- a/setup.py +++ b/setup.py @@ -23,15 +23,16 @@ setup( 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', - 'Programming Language :: Python :: 3.5', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7' + 'Programming Language :: Python :: 3.5' ], py_modules=['bwproject', 'bwresources', 'bwdata', 'filters'], - install_requires=['requests'] + install_requires=['requests'], + + tests_require=['responses'] )	username with capital letters I came across an issue when I use the token_path instead of the password to request my project: BWProject(username=YOUR_ACCOUNT, project=YOUR_PROJECT) When at the beginning I used my username and password, the tokens.txt was generated with the username in lowercase (_read_auth_file). On the other hand, my username contains capital letters, meaning my user´s json will be returned with these capital letters in the username field, so the _test_auth() method will fail in bwproject.py. I can think of ways to get around it by making it case sensitive, but I would appreciate some suggestions. Many thanks in advance!	BrandwatchLtd/api_sdk	diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/test_bwproject.py b/test/test_bwproject.py new file mode 100644 index 0000000..4820262 --- /dev/null +++ b/test/test_bwproject.py @@ -0,0 +1,69 @@ +import unittest +import responses +import os +import tempfile + +from bwproject import BWProject + + +class TestBWProjectUsernameCaseSensitivity(unittest.TestCase): + + USERNAME = "[email protected]" + ACCESS_TOKEN = "00000000-0000-0000-0000-000000000000" + PROJECT_NAME = "Example project" + PROJECTS = [ + { + "id": 0, + "name": PROJECT_NAME, + "description": "", + "billableClientId": 0, + "billableClientName": "My company", + "timezone": "Africa/Abidjan", + "billableClientIsPitch": False + } + ] + + def setUp(self): + self.token_path = tempfile.NamedTemporaryFile(suffix='-tokens.txt').name + + responses.add(responses.GET, 'https://api.brandwatch.com/projects', + json={ + "resultsTotal": len(self.PROJECTS), + "resultsPage": -1, + "resultsPageSize": -1, + "results": self.PROJECTS + }, status=200) + + responses.add(responses.POST, 'https://api.brandwatch.com/oauth/token', + json={'access_token': self.ACCESS_TOKEN}, status=200) + + def tearDown(self): + os.unlink(self.token_path) + responses.reset() + + + @responses.activate + def test_lowercase_username(self): + self._test_username("[email protected]") + + @responses.activate + def test_uppercase_username(self): + self._test_username("[email protected]") + + @responses.activate + def test_mixedcase_username(self): + self._test_username("[email protected]") + + def _test_username(self, username): + + responses.add(responses.GET, 'https://api.brandwatch.com/me', json={"username": username}, status=200) + + BWProject(username=username, project=self.PROJECT_NAME, password="", token_path=self.token_path) + try: + BWProject(username=username, project=self.PROJECT_NAME, token_path=self.token_path) + except KeyError as e: + self.fail(e) + + +if __name__ == '__main__': + unittest.main()	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_removed_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 3 }	unknown	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "responses" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	-e git+https://github.com/BrandwatchLtd/api_sdk.git@0570363bdee1621b85f8290599e0c86c75bcf802#egg=bwapi certifi==2025.1.31 charset-normalizer==3.4.1 exceptiongroup==1.2.2 idna==3.10 iniconfig==2.1.0 packaging==24.2 pluggy==1.5.0 pytest==8.3.5 PyYAML==6.0.2 requests==2.32.3 responses==0.25.7 tomli==2.2.1 urllib3==2.3.0	name: api_sdk channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - certifi==2025.1.31 - charset-normalizer==3.4.1 - exceptiongroup==1.2.2 - idna==3.10 - iniconfig==2.1.0 - packaging==24.2 - pluggy==1.5.0 - pytest==8.3.5 - pyyaml==6.0.2 - requests==2.32.3 - responses==0.25.7 - tomli==2.2.1 - urllib3==2.3.0 prefix: /opt/conda/envs/api_sdk	[ "test/test_bwproject.py::TestBWProjectUsernameCaseSensitivity::test_mixedcase_username", "test/test_bwproject.py::TestBWProjectUsernameCaseSensitivity::test_uppercase_username" ]	[]	[ "test/test_bwproject.py::TestBWProjectUsernameCaseSensitivity::test_lowercase_username" ]	[]	MIT License	swerebench/sweb.eval.x86_64.brandwatchltd_1776_api_sdk-62
Brown-University-Library__bdrxml-109	aadc3090ec068103865002d5adc8f39ad600c080	2024-02-08 18:58:33	aadc3090ec068103865002d5adc8f39ad600c080		diff --git a/bdrxml/mods.py b/bdrxml/mods.py index a5c9290..4daa10f 100644 --- a/bdrxml/mods.py +++ b/bdrxml/mods.py @@ -1,16 +1,36 @@ import re import unicodedata + +from .darwincore import get_schema_validation_errors +from eulxml import xmlmap from eulxml.xmlmap import StringField as SF, SchemaField, NodeListField, NodeField + #import everything from eulxml.xmlmap.mods because clients have to use a lot of # those classes, and we're just overriding a few of them here. from eulxml.xmlmap.mods import * -from .darwincore import get_schema_validation_errors +from eulxml.xmlmap.mods import BaseMods as EulXmlBaseMods +from eulxml.xmlmap.mods import Common, Date, Note, TitleInfo +from eulxml.xmlmap.mods import Genre as EulXmlGenre +from eulxml.xmlmap.mods import Language as EulXmlLanguage +from eulxml.xmlmap.mods import LanguageTerm as EulXmlLanguageTerm +from eulxml.xmlmap.mods import Location as EulXmlLocation +from eulxml.xmlmap.mods import Name as EulXmlName +from eulxml.xmlmap.mods import OriginInfo as EulXmlOriginInfo +from eulxml.xmlmap.mods import PartDetail as EulXmlPartDetail +from eulxml.xmlmap.mods import PhysicalDescription as EulXmlPhysicalDescription +from eulxml.xmlmap.mods import RecordInfo as EulXmlRecordInfo +from eulxml.xmlmap.mods import RelatedItem as EulXmlRelatedItem +from eulxml.xmlmap.mods import Role as EulXmlRole +from eulxml.xmlmap.mods import Subject as EulXmlSubject + XLINK_NAMESPACE = 'http://www.w3.org/1999/xlink' XSI_NAMESPACE = 'http://www.w3.org/2001/XMLSchema-instance' -XSI_LOCATION = 'http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-7.xsd' +# XSI_LOCATION = 'http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-7.xsd' +XSI_LOCATION = 'http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd' MODSv35_SCHEMA = "http://www.loc.gov/standards/mods/v3/mods-3-5.xsd" MODSv37_SCHEMA = "http://www.loc.gov/standards/mods/v3/mods-3-7.xsd" +MODSv38_SCHEMA = "http://www.loc.gov/standards/mods/v3/mods-3-8.xsd" FAST = 'http://id.worldcat.org/fast' @@ -22,12 +42,14 @@ class CommonField(Common): text = SF('text()') -class PartDetail(PartDetail): +class PartDetail(EulXmlPartDetail): caption = SF('mods:caption') -class Part(Part): + +class Part(EulXmlPartDetail): details = NodeListField('mods:detail', PartDetail) + class PlaceTerm(CommonField): ROOT_NAME = 'placeTerm' type = SF('@type') @@ -37,21 +59,23 @@ class Place(Common): ROOT_NAME = 'place' place_terms = NodeListField('mods:placeTerm', PlaceTerm) -class OriginInfo(OriginInfo): + +class OriginInfo(EulXmlOriginInfo): label = SF('@displayLabel') places = NodeListField('mods:place', Place) -class Collection(RelatedItem): +class Collection(EulXmlRelatedItem): name = SF('mods:titleInfo/mods:title') id = SF('mods:identifier[@type="COLID"]') -class LanguageTerm(LanguageTerm): +class LanguageTerm(EulXmlLanguageTerm): authority_uri = SF('@authorityURI') value_uri = SF('@valueURI') -class Language(Language): + +class Language(EulXmlLanguage): terms = NodeListField('mods:languageTerm', LanguageTerm) @@ -60,7 +84,7 @@ class PhysicalDescriptionForm(CommonField): type = SF('@type') -class PhysicalDescription(PhysicalDescription): +class PhysicalDescription(EulXmlPhysicalDescription): digital_origin = SF('mods:digitalOrigin') note = SF('mods:note') forms = NodeListField('mods:form', PhysicalDescriptionForm) @@ -76,17 +100,19 @@ class SubLocation(Common): script = SF('@script') transliteration = SF('@transliteration') + class CopyInformation(Common): ROOT_NAME = 'copyInformation' notes = NodeListField('mods:note', Note) sublocations = NodeListField('mods:subLocation', SubLocation) + class HoldingSimple(Common): ROOT_NAME = 'holdingSimple' copy_information = NodeListField('mods:copyInformation', CopyInformation) -class Location(Location): +class Location(EulXmlLocation): physical = NodeField('mods:physicalLocation', PhysicalLocation) holding_simple = NodeField('mods:holdingSimple', HoldingSimple) @@ -116,7 +142,7 @@ class Topic(CommonField): ROOT_NAME = 'topic' -class Subject(Subject): +class Subject(EulXmlSubject): label = SF('@displayLabel') authority_uri = SF('@authorityURI') value_uri = SF('@valueURI') @@ -136,7 +162,7 @@ class Classification(CommonField): label = SF('@displayLabel') -class Genre(Genre): +class Genre(EulXmlGenre): authority_uri = SF('@authorityURI') value_uri = SF('@valueURI') type = SF('@type') @@ -156,18 +182,18 @@ class RecordContentSource(CommonField): ROOT_NAME = 'recordContentSource' -class RecordInfo(RecordInfo): +class RecordInfo(EulXmlRecordInfo): record_identifier_list = NodeListField('mods:recordIdentifier', RecordIdentifier) record_creation_date = NodeField('mods:recordCreationDate', RecordCreationDate) record_content_source = NodeField('mods:recordContentSource', RecordContentSource) -class Role(Role): +class Role(EulXmlRole): authority_uri = SF('mods:roleTerm/@authorityURI') value_uri = SF('mods:roleTerm/@valueURI') -class Name(Name): +class Name(EulXmlName): roles = NodeListField('mods:role', Role) authority_uri = SF('@authorityURI') value_uri = SF('@valueURI') @@ -182,7 +208,7 @@ class ResourceType(Common): text = SF('text()') -class BaseMods(BaseMods): +class BaseMods(EulXmlBaseMods): classifications = NodeListField('mods:classification', Classification) origin_info = NodeField('mods:originInfo', OriginInfo) subjects = NodeListField('mods:subject', Subject) @@ -210,7 +236,8 @@ class Mods(BaseMods): http://www.loc.gov/standards/mods/mods-outline.html """ ROOT_NAME = 'mods' - XSD_SCHEMA = MODSv37_SCHEMA + # XSD_SCHEMA = MODSv37_SCHEMA + XSD_SCHEMA = MODSv38_SCHEMA Common.ROOT_NAMESPACES['xlink'] = XLINK_NAMESPACE Common.ROOT_NAMESPACES['xsi'] = XSI_NAMESPACE xsi_schema_location = SF('@xsi:schemaLocation') @@ -223,8 +250,9 @@ class Mods(BaseMods): def validation_errors(self): '''see notes on SimpleDarwinRecordSet.validation_errors()''' - return get_schema_validation_errors(schema_name='mods-3-7.xsd', lxml_node=self.node) - + #return get_schema_validation_errors(schema_name='mods-3-7.xsd', lxml_node=self.node) + return get_schema_validation_errors(schema_name='mods-3-8.xsd', lxml_node=self.node) + def make_mods(): """Helper that returns Mods object.""" diff --git a/bdrxml/schemas/mods-3-8.xsd b/bdrxml/schemas/mods-3-8.xsd new file mode 100644 index 0000000..72f60c6 --- /dev/null +++ b/bdrxml/schemas/mods-3-8.xsd @@ -0,0 +1,1774 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + Library of Congress + +Editor: Ray Denenberg [email protected] + --> +<xs:schema xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xs="http://www.w3.org/2001/XMLSchema" + xmlns="http://www.loc.gov/mods/v3" targetNamespace="http://www.loc.gov/mods/v3" + elementFormDefault="qualified" attributeFormDefault="unqualified"> + <!-- --> + <xs:import namespace="http://www.w3.org/XML/1998/namespace" + schemaLocation="http://www.loc.gov/mods/xml.xsd"/> + <xs:import namespace="http://www.w3.org/1999/xlink" + schemaLocation="http://www.loc.gov/standards/xlink/xlink.xsd"/> + <!-- +MODS: Metadata Object Description Schema. See http://www.loc.gov/standards/mods/ + + ******************************************************************* + * + * MODS 3.8 + * + * September 16, 2022 + * + ******************************************************************* + +***** November 10, 2022 the attribute @supplied has been added to the <name> definition. +***** August 11, 2023 removed duplicate IDAttributeGroup declaration from accessCondition. + + +*********** Changes in version 3.8 + +1. Controlled-list restriction removed on authority attribute + for <placeTerm> <geographic Code> and <languageTerm>. + +2. Attributes @otherTypeAuth, @otherTypeAuthURI, and @otherTypeURI added for <titleInfo>. + +3. Element <agent> added, subelement of <originInfo>. + +4. Authority attributes added for <accessCondition> and for <affiliation>. + +5. Attribute @usage added for <recordInfo>, value "primary". + +6. Attribute @IDref added to any element that has the @ID attribute; + attributes @ID and @idref added to top-level elements that have neither. + +7. Element <nameIdentifier> element added, subelement of <place>. + +8. Element <cartographics> added, subelement of <place>. + +9. Attribute @stateType added, subelement of <subject><hierarchicalGeographic><state>. + +10. Element <displayDate> added, subelement of <originInfo>. + +11. Attribute @eventTypeURI added to <originInfo>. + +12. Attribute @usage of <location><url>, value "primary display" deprecated. + +13. All occurrences of @fixed removed, replaced by a single-value controlled list, + where the value is the previous value of @fixed. + +14. @type added to <extension>. + +*********************************************** +*********************************************** + + + + ********************************* + Organization of this schema + ********************************* + +The schema has three parts: + +1. Structural declarations and definitions +2. Elements (top level elements and their subelements) +3. Auxiliary Definitions + + ******************************************************************* + ******************************************************************* + Part 1: Structural Declarations and Definitions + ******************************************************************* + ******************************************************************* +- Definition of a single MODS record and a MODS collection +- modsGroup, listing the top level MODS elements + +******************************************************************* + Definition of a single MODS record +****************************************************************** +--> + <xs:element name="mods" type="modsDefinition"/> + <!-- --> + <xs:complexType name="modsDefinition"> + <xs:group ref="modsGroup" maxOccurs="unbounded"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + <xs:attribute name="version"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="3.8"/> + <xs:enumeration value="3.7"/> + <xs:enumeration value="3.6"/> + <xs:enumeration value="3.5"/> + <xs:enumeration value="3.4"/> + <xs:enumeration value="3.3"/> + <xs:enumeration value="3.2"/> + <xs:enumeration value="3.1"/> + <xs:enumeration value="3.0"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + </xs:complexType> + <!-- +******************************************************************* + Definition of a MODS collection +****************************************************************** +--> + <xs:element name="modsCollection" type="modsCollectionDefinition"/> + <!-- --> + <xs:complexType name="modsCollectionDefinition"> + <xs:sequence> + <xs:element ref="mods" maxOccurs="unbounded"/> + </xs:sequence> + </xs:complexType> + <!-- + +******************************************** + Group Definition +********************************************* +This forms the basis of the mods record definition, and also relatedItem. +The difference between a MODS record and a relatedItem +(as they pertain to their usage of the group definition) +is that mods requires at least one element and relatedItem does not. +The group definition is used by both, where relatedItem says +minOccurs="0" and for the mods record definition minOccurs="1" (default). + +--> + <xs:group name="modsGroup"> + <xs:choice> + <!-- +******************************************************************* + These are the "top level" MODS elements +****************************************************************** +--> + <xs:element ref="abstract"/> + <xs:element ref="accessCondition"/> + <xs:element ref="classification"/> + <xs:element ref="extension"/> + <xs:element ref="genre"/> + <xs:element ref="identifier"/> + <xs:element ref="language"/> + <xs:element ref="location"/> + <xs:element ref="name"/> + <xs:element ref="note"/> + <xs:element ref="originInfo"/> + <xs:element ref="part"/> + <xs:element ref="physicalDescription"/> + <xs:element ref="recordInfo"/> + <xs:element ref="relatedItem"/> + <xs:element ref="subject"/> + <xs:element ref="tableOfContents"/> + <xs:element ref="targetAudience"/> + <xs:element ref="titleInfo"/> + <xs:element ref="typeOfResource"/> + <!-- +End list of "top level" MODS elements +--> + </xs:choice> + </xs:group> + <!-- + ******************************************************************* + ******************************************************************* + Part 2: Elements (top level elements and their subelements) + ******************************************************************** + ******************************************************************* +--> + <!-- +******************************************* +* Top Level Element <abstract> * +******************************************* + --> + <xs:element name="abstract" type="abstractDefinition"/> + <!-- --> + <xs:complexType name="abstractDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <!-- + @sharable definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="shareable" type="no"/> + <!-- --> + <xs:attribute name="altRepGroup" type="xs:string"/> + <xs:attributeGroup ref="altFormatAttributeGroup"/> + <!-- +************** following attribute group added in 3.8--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + +************************************************** +* Top Level Element <accessCondition> * +*************************************************** + --> + <xs:element name="accessCondition" type="accessConditionDefinition"/> + <!-- --> + <xs:complexType name="accessConditionDefinition" mixed="true"> + <xs:complexContent mixed="true"> + <xs:extension base="extensionDefinition"> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <!-- + Previously attribute @type was here. It is + removed in 3.8 because it has been added + to the "extension" defintion; "accessCondition" + is based on the "extension definition" so @type is + thereby implicitly included in this definition. + --> + <xs:attribute name="altRepGroup" type="xs:string"/> + <xs:attributeGroup ref="altFormatAttributeGroup"/> + <!-- +************** following two attribute groups added in 3.8 --> + <!--ID attribute group removed as duplicate. See August 11, 2023 note --> + <!-- <xs:attributeGroup ref="IDAttributeGroup"/> --> + <xs:attributeGroup ref="authorityAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:complexContent> + </xs:complexType> + <!-- +************************************************** +* Top Level Element <classification> * +*************************************************** +--> + <xs:element name="classification" type="classificationDefinition"/> + <!-- --> + <xs:complexType name="classificationDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <xs:attribute name="edition" type="xs:string"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- + @usage definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="usage" type="usagePrimary"/> + <!-- --> + <xs:attribute name="generator" type="xs:string"/> + <!-- +************** following attribute group added in 3.8--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +************************************************** +* Top Level Element <extension> * +*************************************************** + --> + <xs:element name="extension" type="extensionDefinition"/> + <!-- --> + <xs:complexType name="extensionDefinition" mixed="true"> + <xs:sequence> + <xs:any processContents="lax" minOccurs="0" maxOccurs="unbounded"/> + </xs:sequence> + <xs:attribute name="displayLabel" type="xs:string"/> + <!-- +************** following attribute added in 3.8 --> + <xs:attribute name="type" type="xs:string"/> + <!-- +************** following attribute group added in 3.8--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- +************************************************** +* Top Level Element <genre> * +*************************************************** +--> + <xs:element name="genre" type="genreDefinition"/> + <!-- --> + <xs:complexType name="genreDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <xs:attribute name="type" type="xs:string"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- + @usage definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="usage" type="usagePrimary"/> + <!-- +************** following attribute group added in 3.8 --> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +************************************************** +* Top Level Element <identifier> * +*************************************************** +--> + <xs:element name="identifier" type="identifierDefinition"/> + <!-- --> + <xs:complexType name="identifierDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + <xs:attribute name="typeURI" type="xs:anyURI"/> + <!-- + @invalid definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="invalid" type="yes"/> + <!-- --> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- +************** following attribute group added in 3.8 --> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +************************************************** +* Top Level Element <language> * +*************************************************** +--> + <xs:element name="language" type="languageDefinition"/> + <!-- --> + <xs:complexType name="languageDefinition"> + <xs:sequence> + <xs:element ref="languageTerm" maxOccurs="unbounded"/> + <xs:element ref="scriptTerm" minOccurs="0" maxOccurs="unbounded"/> + </xs:sequence> + <xs:attribute name="objectPart" type="xs:string"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- + @usage definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="usage" type="usagePrimary"/> + <!-- +************** following attribute group added in 3.8 --> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <language> **** + + +*************languageTerm ******************** + --> + <xs:element name="languageTerm" type="languageTermDefinition"/> + <!-- --> + <xs:complexType name="languageTermDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <!-- ** changed in 3.8 from "stringPlusLanguage" to + ** "stringPlusLanguagePlusAuthority". Previously, + ** @authority was restricted to specific values. + ** That restriction is removed in 3.8 + --> + <xs:attribute name="type" type="codeOrText"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +*************scriptTerm ******************** +--> + <xs:element name="scriptTerm" type="scriptTermDefinition"/> + <!-- --> + <xs:complexType name="scriptTermDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <xs:attribute name="type" type="codeOrText"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + +************************************************** +* Top Level Element <location> * +*************************************************** + --> + <xs:element name="location" type="locationDefinition"/> + <!-- --> + <xs:complexType name="locationDefinition"> + <xs:sequence> + <xs:element ref="physicalLocation" minOccurs="0" maxOccurs="unbounded"/> + <xs:element ref="shelfLocator" minOccurs="0" maxOccurs="unbounded"/> + <xs:element ref="url" minOccurs="0" maxOccurs="unbounded"/> + <xs:element ref="holdingSimple" minOccurs="0"/> + <xs:element ref="holdingExternal" minOccurs="0"/> + </xs:sequence> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- +************** following attribute group added in 3.8 --> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <location> + --> + <!-- +****** physicalLocation ****** +--> + <xs:element name="physicalLocation" type="physicalLocationDefinition"/> + <!-- --> + <xs:complexType name="physicalLocationDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- --> + <xs:element name="shelfLocator" type="stringPlusLanguage"/> + <!-- +****** holdingSimple ****** + --> + <xs:element name="holdingSimple" type="holdingSimpleDefinition"/> + <!-- --> + <xs:complexType name="holdingSimpleDefinition"> + <xs:sequence> + <xs:element ref="copyInformation" maxOccurs="unbounded"/> + </xs:sequence> + </xs:complexType> + <!-- +******copyInformation ****** + --> + <xs:element name="copyInformation" type="copyInformationDefinition"/> + <!-- --> + <xs:complexType name="copyInformationDefinition"> + <xs:sequence> + <xs:element ref="form" minOccurs="0"/> + <xs:element ref="subLocation" minOccurs="0" maxOccurs="unbounded"/> + <xs:element ref="shelfLocator" minOccurs="0" maxOccurs="unbounded"/> + <xs:element ref="electronicLocator" minOccurs="0" maxOccurs="unbounded"/> + <xs:element name="note" minOccurs="0" maxOccurs="unbounded"> + <xs:complexType> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + </xs:element> + <xs:element ref="enumerationAndChronology" minOccurs="0" maxOccurs="unbounded"/> + <xs:element ref="itemIdentifier" minOccurs="0" maxOccurs="unbounded"/> + <!-- --> + </xs:sequence> + </xs:complexType> + <!-- +******itemIdentifier ****** + --> + <xs:element name="itemIdentifier" type="itemIdentifierDefinition"/> + <xs:complexType name="itemIdentifierDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="type" type="xs:string"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +******form****** + --> + <xs:element name="form" type="formDefinition"/> + <!-- --> + <xs:complexType name="formDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <xs:attribute name="type" type="xs:string"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- --> + <xs:element name="subLocation" type="stringPlusLanguage"/> + <xs:element name="electronicLocator" type="stringPlusLanguage"/> + <!-- +******enumerationAndChronology ****** + --> + <xs:element name="enumerationAndChronology" type="enumerationAndChronologyDefinition"/> + <!-- --> + <xs:complexType name="enumerationAndChronologyDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="unitType"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="1"/> + <xs:enumeration value="2"/> + <xs:enumeration value="3"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + ****** url ****** + --> + <xs:element name="url" type="urlDefinition"/> + <!-- --> + <xs:complexType name="urlDefinition"> + <xs:simpleContent> + <xs:extension base="xs:anyURI"> + <xs:attribute name="dateLastAccessed" type="xs:string"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="note" type="xs:string"/> + <xs:attribute name="access"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="preview"/> + <xs:enumeration value="raw object"/> + <xs:enumeration value="object in context"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <xs:attribute name="usage"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="primary display"/> + <!-- This value,"primary display”, is deprecated in version 3.8; + “primary” is the only value that should be used in 3.8 and beyond. + ("primary display” remains a legal value in the schema, + for compatibility with earlier versions.) + --> + <xs:enumeration value="primary"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- --> + <xs:element name="holdingExternal" type="extensionDefinition"/> + <!-- +************************************************** +* Top Level Element <name> * +*************************************************** +--> + <xs:element name="name" type="nameDefinition"/> + <!-- --> + <xs:complexType name="nameDefinition"> + <xs:choice> + + <!-- this choice gives two ways to do this. + The second way allows the element <etal>, to express "et. al." + +Choice one. WITHOUT <etal>. +--> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="namePart"/> + <xs:element ref="displayForm"/> + <xs:element ref="affiliation"/> + <xs:element ref="role"/> + <xs:element ref="description"/> + <xs:element ref="nameIdentifier"/> + <xs:element ref="alternativeName"/> + <!-- --> + </xs:choice> + <!-- +Choice two. With <etal>. + The presence of <etal> indicates that there are names that cannot + be explicitily included. It may be empty, or it may have simple content + - e.g. <etal>et al.</etal>. In the latter case the content is what is + suggested for display. + When <etal> occurs: + - <namePart>, <displayForm>, and <identifier> MAY NOT occur; + - <affiliation>, <role>, <description> MAY occur (but are NOT repeatable). + <etal> is not repeatable within a given <name>, however there may be + mutilple <etal> elements, each within in a separate <name> element. +--> + <xs:sequence> + <!-- + <etal> is mandatory, nonrepeatable, and must occur first. + After that <affiliation>, <role>, and <description> may occur, in any order or number. + <nameIdentifier> is not used with <etal> +--> + <xs:element ref="etal"/> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="affiliation"/> + <xs:element ref="role"/> + <xs:element ref="description"/> + </xs:choice> + </xs:sequence> + <!-- --> + </xs:choice> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + <xs:attributeGroup ref="authorityAttributeGroup"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <xs:attribute name="nameTitleGroup" type="xs:string"/> + <!-- + @usage definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="usage" type="usagePrimary"/> + <xs:attribute name="type"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="personal"/> + <xs:enumeration value="corporate"/> + <xs:enumeration value="conference"/> + <xs:enumeration value="family"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <!-- +************** Following line added 11/10/2022. Fixes a flaw. + --> + <xs:attribute name="supplied" type="yes"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <name> + --> + <!-- namePart--> + <xs:element name="namePart" type="namePartDefinition"/> + <!-- --> + <xs:complexType name="namePartDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="type"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="date"/> + <xs:enumeration value="family"/> + <xs:enumeration value="given"/> + <xs:enumeration value="termsOfAddress"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- displayForm, affiliation, description, alternativeName --> + <xs:element name="displayForm" type="stringPlusLanguage"/> + <!-- + affiliation definition changed in 3.8. Authority attributes added --> + <xs:element name="affiliation" type="stringPlusLanguagePlusAuthority"/> + <!-- --> + <xs:element name="description" type="stringPlusLanguage"/> + <xs:element name="nameIdentifier" type="identifierDefinition"/> + <xs:element name="alternativeName" type="alternativeNameDefinition"/> + <!-- + +**** role ***************** + --> + <xs:element name="role" type="roleDefinition"/> + <!-- --> + <xs:complexType name="roleDefinition"> + <xs:sequence maxOccurs="unbounded"> + <xs:element ref="roleTerm"/> + </xs:sequence> + </xs:complexType> + <!-- +***********roleTerm ******************* + --> + <xs:element name="roleTerm" type="roleTermDefinition"/> + <!-- --> + <xs:complexType name="roleTermDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <xs:attribute name="type" type="codeOrText"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +**** etal **** +--> + <xs:element name="etal" type="stringPlusLanguage"/> + <!-- +**** alternativeName **** +--> + <xs:complexType name="alternativeNameDefinition"> + + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="namePart"/> + <xs:element ref="displayForm"/> + <xs:element ref="affiliation"/> + <xs:element ref="role"/> + <xs:element ref="description"/> + <xs:element ref="nameIdentifier"/> + </xs:choice> + <!-- --> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altType" type="xs:string"/> + </xs:complexType> + + <!-- + +************************************************** +* Top Level Element <note> * +*************************************************** +--> + <xs:element name="note" type="noteDefinition"/> + <!-- --> + <xs:complexType name="noteDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + <xs:attribute name="typeURI" type="xs:anyURI"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + +************************************************** +* Top Level Element <originInfo> * +************************************************** +--> + <xs:element name="originInfo" type="originInfoDefinition"/> + <!-- --> + <xs:complexType name="originInfoDefinition"> + <xs:choice maxOccurs="unbounded"> + <xs:element ref="place"/> + <xs:element ref="publisher"/> + <xs:element ref="dateIssued"/> + <xs:element ref="dateCreated"/> + <xs:element ref="dateCaptured"/> + <xs:element ref="dateValid"/> + <xs:element ref="dateModified"/> + <xs:element ref="copyrightDate"/> + <xs:element ref="dateOther"/> + <!-- + following element, "displayDate", added in 3.8 --> + <xs:element ref="displayDate"/> + <!-- --> + <xs:element ref="edition"/> + <xs:element ref="issuance"/> + <xs:element ref="frequency"/> + <!-- + following element, "agent", added in 3.8--> + <xs:element ref="agent"/> + <!-- --> + </xs:choice> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <xs:attribute name="eventType" type="xs:string"/> + <!-- + following attribute, @eventTypeURI, added in 3.8 --> + <xs:attribute name="eventTypeURI" type="xs:anyURI"/> + <!-- +************** following attribute group added in 3.8 --> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <originInfo> --> +<!-- + +* place * +--> + <xs:element name="place" type="placeDefinition"/> + <!-- --> + <xs:complexType name="placeDefinition"> + <xs:choice minOccurs="1" maxOccurs="unbounded"> + <!-- + Previously, place was simply one or more occurrences of placeTerm. + In 3.8, placeIdentifer and cartographics are added to placeDefinition. + placeIdentifier is a new element in 3.8; cartographics uses the + existing cartographicsDefinition. + + So, now place is one or more of the following three subelements where + each may be any of the three. + --> + <xs:element ref="placeTerm"/> + <xs:element ref="placeIdentifier"/> + <xs:element ref="cartographics"/> + <!-- --> + </xs:choice> + <!-- + @supplied definition corrected in 3.8. + See "Changes in version 3.8" #13 --> + <xs:attribute name="supplied" type="yes"/> + <!-- --> + </xs:complexType> + <!-- + +* placeTerm * +--> + <xs:element name="placeTerm" type="placeTermDefinition"/> + <!-- --> + <xs:complexType name="placeTermDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <!-- + ** changed in 3.8 from "stringPlusLanguage" to + ** "stringPlusLanguagePlusAuthority". Previously, + ** @authority was restricted to specific values. + ** That restriction is removed in 3.8 + --> + <xs:attribute name="type" type="codeOrText"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + +* placeIdentifier * (****new in 3.8) +--> + <xs:element name="placeIdentifier" type="xs:anyURI"/> + <!-- --> + <!-- + +* publisher * +--> + <xs:element name="publisher" type="publisherDefinition"/> + <!-- --> + <xs:complexType name="publisherDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusSupplied"> + <xs:attributeGroup ref="authorityAttributeGroup"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + + agent * (******new in 3.8) + + --> + <xs:element name="agent" type="nameDefinition"/> + <!-- + +****** dates ****** --> + <xs:element name="dateIssued" type="dateDefinition"/> + <xs:element name="dateCreated" type="dateDefinition"/> + <xs:element name="dateCaptured" type="dateDefinition"/> + <xs:element name="dateValid" type="dateDefinition"/> + <xs:element name="dateModified" type="dateDefinition"/> + <xs:element name="copyrightDate" type="dateDefinition"/> + <xs:element name="dateOther" type="dateOtherDefinition"/> + + <!-- following definition added 3.8 --> + <xs:element name="displayDate" type="xs:string"/> + <!-- --> + + <xs:complexType name="dateDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="encoding"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="w3cdtf"/> + <xs:enumeration value="iso8601"/> + <xs:enumeration value="marc"/> + <xs:enumeration value="temper"/> + <xs:enumeration value="edtf"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <xs:attribute name="qualifier"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="approximate"/> + <xs:enumeration value="inferred"/> + <xs:enumeration value="questionable"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <xs:attribute name="point"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="start"/> + <xs:enumeration value="end"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <!-- + @keyDate definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="keyDate" type="yes"/> + <!-- --> + <xs:attribute name="calendar" type="xs:string"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + ****** dateOther ****** +--> + <xs:complexType name="dateOtherDefinition"> + <xs:simpleContent> + <xs:extension base="dateDefinition"> + <xs:attribute name="type" type="xs:string"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + ****** edition ****** +--> + <xs:element name="edition" type="stringPlusLanguagePlusSupplied"/> + <!-- +****** issuance ****** + --> + <xs:element name="issuance" type="issuanceDefinition"/> + <!-- --> + <xs:simpleType name="issuanceDefinition"> + <xs:restriction base="xs:string"> + <xs:enumeration value="continuing"/> + <xs:enumeration value="monographic"/> + <xs:enumeration value="single unit"/> + <xs:enumeration value="multipart monograph"/> + <xs:enumeration value="serial"/> + <xs:enumeration value="integrating resource"/> + </xs:restriction> + </xs:simpleType> + <!-- + ****** frequency ****** +--> + <xs:element name="frequency" type="stringPlusLanguagePlusAuthority"/> + + + <!-- +************************************************** +* Top Level Element <part> * +*************************************************** +--> + <xs:element name="part" type="partDefinition"/> + <!-- --> + <xs:complexType name="partDefinition"> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="detail"/> + <xs:element name="extent" type="extentDefinition"/> + <xs:element ref="date"/> + <xs:element ref="text"/> + </xs:choice> + <xs:attribute name="type" type="xs:string"/> + <xs:attribute name="order" type="xs:integer"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <part> + --> + <!-- +****** detail ****** +--> + <xs:element name="detail" type="detailDefinition"/> + <!-- --> + <xs:complexType name="detailDefinition"> + <xs:choice maxOccurs="unbounded"> + <xs:element ref="number"/> + <xs:element ref="caption"/> + <xs:element ref="title"/> + </xs:choice> + <xs:attribute name="type" type="xs:string"/> + <xs:attribute name="level" type="xs:positiveInteger"/> + </xs:complexType> + <!-- --> + <xs:element name="number" type="stringPlusLanguage"/> + <xs:element name="caption" type="stringPlusLanguage"/> + <!-- +****** extent ****** +--> + <xs:complexType name="extentDefinition"> + <xs:sequence> + <xs:element ref="start" minOccurs="0"/> + <xs:element ref="end" minOccurs="0"/> + <xs:element ref="total" minOccurs="0"/> + <xs:element ref="list" minOccurs="0"/> + </xs:sequence> + <xs:attribute name="unit" type="xs:string"/> + </xs:complexType> + <!-- --> + <xs:element name="start" type="stringPlusLanguage"/> + <xs:element name="end" type="stringPlusLanguage"/> + <xs:element name="total" type="xs:positiveInteger"/> + <xs:element name="list" type="stringPlusLanguage"/> + <!-- +************* date * +--> + <xs:element name="date" type="dateDefinition"/> + <!-- +*************** text * +--> + <xs:element name="text"> + <xs:complexType> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + </xs:element> + <!-- + +**************************************************** +* Top Level Element <physicalDescription> * +*************************************************** + --> + <xs:element name="physicalDescription" type="physicalDescriptionDefinition"/> + <!-- --> + <xs:complexType name="physicalDescriptionDefinition"> + <xs:choice maxOccurs="unbounded"> + <xs:element ref="form"/> + <!-- same definition as is used in copyInformation --> + <xs:element ref="reformattingQuality"/> + <xs:element ref="internetMediaType"/> + <xs:element ref="extent"/> + <xs:element ref="digitalOrigin"/> + <xs:element name="note" type="physicalDescriptionNote"/> + </xs:choice> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- +************** following attribute group added in in 3.8--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <physicalDescription> + --> + <!-- +******reformattingQuality ****** + --> + <xs:element name="reformattingQuality" type="reformattingQualityDefinition"/> + <!-- --> + <xs:simpleType name="reformattingQualityDefinition"> + <xs:restriction base="xs:string"> + <xs:enumeration value="access"/> + <xs:enumeration value="preservation"/> + <xs:enumeration value="replacement"/> + </xs:restriction> + </xs:simpleType> + <!-- +******internetMediaType ****** + --> + <xs:element name="internetMediaType" type="stringPlusLanguage"/> + <!-- +****** extent ****** + --> + <xs:element name="extent"> + <xs:complexType> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusSupplied"> + <xs:attribute name="unit"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + </xs:element> + <!-- +****** digitalOrigin ****** + --> + <xs:element name="digitalOrigin" type="digitalOriginDefinition"/> + <!-- --> + <xs:simpleType name="digitalOriginDefinition"> + <xs:restriction base="xs:string"> + <xs:enumeration value="born digital"/> + <xs:enumeration value="reformatted digital"/> + <xs:enumeration value="digitized microfilm"/> + <xs:enumeration value="digitized other analog"/> + </xs:restriction> + </xs:simpleType> + <!-- +****** note ****** + --> + <xs:complexType name="physicalDescriptionNote"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + <xs:attribute name="typeURI" type="xs:anyURI"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +************************************************** +* Top Level Element <recordInfo> * +*************************************************** + +****** recordInfo ****** +--> + <xs:element name="recordInfo" type="recordInfoDefinition"/> + <!-- --> + <xs:complexType name="recordInfoDefinition"> + <xs:choice maxOccurs="unbounded"> + <xs:element ref="recordContentSource"/> + <xs:element ref="recordCreationDate"/> + <xs:element ref="recordChangeDate"/> + <xs:element ref="recordIdentifier"/> + <xs:element ref="languageOfCataloging"/> + <xs:element ref="recordOrigin"/> + <xs:element ref="descriptionStandard"/> + <xs:element ref="recordInfoNote"/> + <!-- --> + </xs:choice> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- + @usage added to recordInfo in 3.8. --> + <xs:attribute name="usage" type="usagePrimary"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following) --> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <recordInfo> + --> + <xs:element name="recordContentSource" type="stringPlusLanguagePlusAuthority"/> + <xs:element name="recordCreationDate" type="dateDefinition"/> + <xs:element name="recordChangeDate" type="dateDefinition"/> + <xs:element name="recordInfoNote" type="noteDefinition"/> + <!-- +****** recordIdentifier +--> + <xs:element name="recordIdentifier" type="recordIdentifierDefinition"/> + <!-- --> + <xs:complexType name="recordIdentifierDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="source" type="xs:string"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- --> + <xs:element name="languageOfCataloging" type="languageDefinition"/> + <xs:element name="recordOrigin" type="stringPlusLanguage"/> + <xs:element name="descriptionStandard" type="stringPlusLanguagePlusAuthority"/> + <!-- + +************************************************** +* Top Level Element <relatedItem> * +*************************************************** + +****** relatedItem ****** +--> + <xs:element name="relatedItem" type="relatedItemDefinition"/> + <!-- --> + <xs:complexType name="relatedItemDefinition"> + <xs:group ref="modsGroup" minOccurs="0" maxOccurs="unbounded"/> + <xs:attribute name="type"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="preceding"/> + <xs:enumeration value="succeeding"/> + <xs:enumeration value="original"/> + <xs:enumeration value="host"/> + <xs:enumeration value="constituent"/> + <xs:enumeration value="series"/> + <xs:enumeration value="otherVersion"/> + <xs:enumeration value="otherFormat"/> + <xs:enumeration value="isReferencedBy"/> + <xs:enumeration value="references"/> + <xs:enumeration value="reviewOf"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <!-- --> + <xs:attribute name="otherType" type="xs:string"/> + <xs:attribute name="otherTypeAuth" type="xs:string"/> + <xs:attribute name="otherTypeAuthURI" type="xs:string"/> + <xs:attribute name="otherTypeURI" type="xs:string"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +************************************************** +* Top Level Element <subject> * +*************************************************** +--> + <xs:element name="subject" type="subjectDefinition"/> + <!-- --> + <xs:complexType name="subjectDefinition"> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="topic"/> + <xs:element ref="geographic"/> + <xs:element ref="temporal"/> + <xs:element name="titleInfo" type="subjectTitleInfoDefinition"/> + <xs:element name="name" type="subjectNameDefinition"/> + <xs:element ref="geographicCode"/> + <xs:element ref="hierarchicalGeographic"/> + <xs:element ref="cartographics"/> + <xs:element ref="occupation"/> + <xs:element ref="genre"/> + <!-- uses top-level genre definition --> + </xs:choice> + <xs:attributeGroup ref="authorityAttributeGroup"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + <!-- + @usage definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="usage" type="usagePrimary"/> + </xs:complexType> + <!-- + +**** Subordinate Elements for <subject> + --> + <!-- topic, geographic --> + <xs:element name="topic" type="stringPlusLanguagePlusAuthority"/> + <xs:element name="geographic" type="stringPlusLanguagePlusAuthority"/> + <xs:element name="geographicCode" type="stringPlusLanguagePlusAuthority"/> + <!-- changed in 3.8 from "geographicCodeDefinition" to "stringPlusLanguagePlusAuthority". + Previously there had been a separate definition for geographicCode + to accomodate a controlled list restriction. That restriction is + dropped in 3.8 + --> + <!-- +*************temporal ******************** + --> + <xs:element name="temporal" type="temporalDefinition"/> + <!-- --> + <xs:complexType name="temporalDefinition"> + <xs:simpleContent> + <xs:extension base="dateDefinition"> + <xs:attributeGroup ref="authorityAttributeGroup"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +*************subjectTitleInfo ******************** +--> + <xs:complexType name="subjectTitleInfoDefinition"> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="title"/> + <xs:element ref="subTitle"/> + <xs:element ref="partNumber"/> + <xs:element ref="partName"/> + <xs:element ref="nonSort"/> + </xs:choice> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + <xs:attributeGroup ref="authorityAttributeGroup"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="abbreviated"/> + <xs:enumeration value="translated"/> + <xs:enumeration value="alternative"/> + <xs:enumeration value="uniform"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <!-- + Following four attributes added in 3.8 --> + <xs:attribute name="otherType"/> + <xs:attribute name="otherTypeAuth" type="xs:string"/> + <xs:attribute name="otherTypeAuthURI" type="xs:anyURI"/> + <xs:attribute name="otherTypeURI" type="xs:anyURI"/> + <!-- --> + </xs:complexType> + <!-- +*************subjectName ******************** +--> + <xs:complexType name="subjectNameDefinition"> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="namePart"/> + <xs:element ref="displayForm"/> + <xs:element ref="affiliation"/> + <xs:element ref="role"/> + <xs:element ref="description"/> + <xs:element ref="nameIdentifier"/> + <!-- --> + </xs:choice> + <xs:attribute name="type"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="personal"/> + <xs:enumeration value="corporate"/> + <xs:enumeration value="conference"/> + <xs:enumeration value="family"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + <xs:attributeGroup ref="authorityAttributeGroup"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + </xs:complexType> + <!-- + ****** geographicCode ****** + + "geographicCode definition, here, prior to 3.8, is removed in 3.8; + it is now in "subordinate defitions for subject" +--> + <!-- +****** hierarchicalGeographic ****** +--> + <xs:element name="hierarchicalGeographic" type="hierarchicalGeographicDefinition"/> + <!-- --> + <xs:complexType name="hierarchicalGeographicDefinition"> + <xs:choice maxOccurs="unbounded"> + <xs:element ref="extraTerrestrialArea"/> + <xs:element ref="continent"/> + <xs:element ref="country"/> + <!-- --> + <xs:element ref="province"/> + <!-- province has been deprecated (in a previous version). + Use <state> instead of province. + --> + <xs:element ref="region"/> + <xs:element ref="state"/> + <xs:element ref="territory"/> + <xs:element ref="county"/> + <xs:element ref="city"/> + <xs:element ref="citySection"/> + <xs:element ref="island"/> + <xs:element ref="area"/> + </xs:choice> + <xs:attributeGroup ref="authorityAttributeGroup"/> + </xs:complexType> + <!-- --> + <!-- ****** hierarchicalPart * auxiliary definition --> + <xs:complexType name="hierarchicalPart"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="level"/> + <xs:attribute name="period"/> + <xs:attributeGroup ref="authorityAttributeGroup"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + Next, definitions for the place elements, starting with area, region, and citySection + --> + <!-- + +******** area +--> + <xs:element name="area" type="areaDefinition"/> + <!-- --> + <xs:complexType name="areaDefinition"> + <xs:simpleContent> + <xs:extension base="hierarchicalPart"> + <xs:attribute name="areaType"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- --> + <!-- + +****** region +--> + <xs:element name="region" type="regionDefinition"/> + <!-- --> + <xs:complexType name="regionDefinition"> + <xs:simpleContent> + <xs:extension base="hierarchicalPart"> + <xs:attribute name="regionType"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + +****** citySection +--> + <xs:element name="citySection" type="citySectionDefinition"/> + <!-- --> + <xs:complexType name="citySectionDefinition"> + <xs:simpleContent> + <xs:extension base="hierarchicalPart"> + <xs:attribute name="citySectionType"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- --> + <!-- + Next, definitions for state + --> + <!-- + +****** state +--> + <xs:element name="state" type="stateDefinition"/> + <!-- see stateDefinition, revised in 3.8: @stateType added. --> + <!-- + + The rest are all of type "hierarchicalPart" ...... + --> + <xs:element name="extraTerrestrialArea" type="hierarchicalPart"/> + <xs:element name="city" type="hierarchicalPart"/> + <xs:element name="continent" type="hierarchicalPart"/> + <xs:element name="country" type="hierarchicalPart"/> + <xs:element name="county" type="hierarchicalPart"/> + <xs:element name="island" type="hierarchicalPart"/> + <!-- + following removed in 3.8, replaced by new definition for state, below. + REMOVED: <xs:element name="state" type="hierarchicalPart"/> REMOVED + --> + <xs:element name="territory" type="hierarchicalPart"/> + <!-- + ..... except for province (which was depricated in a previous version) --> + <xs:element name="province" type="stringPlusLanguage"/> + <!-- + .... and (in 3.8) for state: + + new state definition, 3.8. Before, state was simply hierarchicalPart. + Now @stateType is added--> + <xs:complexType name="stateDefinition"> + <xs:simpleContent> + <xs:extension base="hierarchicalPart"> + <xs:attribute name="stateType"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + + ****** cartographics ****** +--> + <xs:element name="cartographics" type="cartographicsDefinition"/> + <!-- --> + <xs:complexType name="cartographicsDefinition"> + + <xs:sequence> + <xs:element ref="scale" minOccurs="0"/> + <xs:element ref="projection" minOccurs="0"/> + <xs:element ref="coordinates" minOccurs="0" maxOccurs="unbounded"/> + <xs:element ref="cartographicExtension" minOccurs="0" maxOccurs="unbounded"/> + <!-- --> + </xs:sequence> + <!-- --> + <xs:attributeGroup ref="authorityAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- --> + <xs:element name="scale" type="stringPlusLanguage"/> + <xs:element name="projection" type="stringPlusLanguage"/> + <xs:element name="coordinates" type="stringPlusLanguage"/> + <xs:element name="cartographicExtension" type="extensionDefinition"/> + <!-- + + ****** occupation ****** +--> + <xs:element name="occupation" type="stringPlusLanguagePlusAuthority"/> + <!-- +************************************************** +* Top Level Element <tableOfContents> * +*************************************************** + --> + <xs:element name="tableOfContents" type="tableOfContentsDefinition"/> + <!-- --> + <xs:complexType name="tableOfContentsDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="type" type="xs:string"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <!-- + @sharable definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="shareable" type="no"/> + <!-- --> + <xs:attribute name="altRepGroup" type="xs:string"/> + <xs:attributeGroup ref="altFormatAttributeGroup"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- + +************************************************** +* Top Level Element <targetAudience> * +*************************************************** + --> + <xs:element name="targetAudience" type="targetAudienceDefinition"/> + <!-- --> + <xs:complexType name="targetAudienceDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- +************** following attribute group added in 3.8--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +************************************************** +* Top Level Element <titleInfo> * +*************************************************** + --> + <xs:element name="titleInfo" type="titleInfoDefinition"/> + <!-- --> + <xs:complexType name="titleInfoDefinition"> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="title"/> + <xs:element ref="subTitle"/> + <xs:element ref="partNumber"/> + <xs:element ref="partName"/> + <xs:element ref="nonSort"/> + </xs:choice> + <xs:attribute name="type"> + <xs:simpleType> + <xs:restriction base="xs:string"> + <xs:enumeration value="abbreviated"/> + <xs:enumeration value="translated"/> + <xs:enumeration value="alternative"/> + <xs:enumeration value="uniform"/> + </xs:restriction> + </xs:simpleType> + </xs:attribute> + <xs:attribute name="otherType"/> + <!-- + Following three attributes added in 3.8 + (For consistency with relatedItem.) + --> + <xs:attribute name="otherTypeAuth" type="xs:string"/> + <xs:attribute name="otherTypeAuthURI" type="xs:anyURI"/> + <xs:attribute name="otherTypeURI" type="xs:anyURI"/> + <!-- --> + <!-- + @supplied definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="supplied" type="yes"/> + <!-- --> + <xs:attribute name="altRepGroup" type="xs:string"/> + <xs:attributeGroup ref="altFormatAttributeGroup"/> + <xs:attribute name="nameTitleGroup" type="xs:string"/> + <!-- + @usage definition corrected in 3.8. + See "Changes in version 3.8" #13--> + <xs:attribute name="usage" type="usagePrimary"/> + <!-- --> + <xs:attributeGroup ref="authorityAttributeGroup"/> + <xs:attributeGroup ref="xlink:simpleLink"/> + <xs:attributeGroup ref="languageAttributeGroup"/> + <xs:attribute name="displayLabel" type="xs:string"/> + <!-- +************** @IDREF added in 3.8 (@ID removed and replaced by the following)--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:complexType> + <!-- + +**** Subordinate Elements for <titleInfo> + --> + <xs:element name="title" type="stringPlusLanguage"/> + <xs:element name="subTitle" type="stringPlusLanguage"/> + <xs:element name="partNumber" type="stringPlusLanguage"/> + <xs:element name="partName" type="stringPlusLanguage"/> + <!-- +***** nonSort + --> + <xs:element name="nonSort"> + <xs:complexType> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute ref="xml:space"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + </xs:element> + <!-- +************************************************** +* Top Level Element <typeOfResource> * +*************************************************** + --> + <xs:element name="typeOfResource" type="typeOfResourceDefinition"/> + <!-- --> + <xs:complexType name="typeOfResourceDefinition"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguagePlusAuthority"> + <!-- + + @collection, @manuscript, and @usage definitions changed. + See "Changes in version 3.8" #13--> + <xs:attribute name="collection" type="yes"/> + <xs:attribute name="manuscript" type="yes"/> + <xs:attribute name="usage" type="usagePrimary"/> + <!-- --> + <xs:attribute name="displayLabel" type="xs:string"/> + <xs:attribute name="altRepGroup" type="xs:string"/> + <!-- +************** following attribute group added in 3.8--> + <xs:attributeGroup ref="IDAttributeGroup"/> + <!-- --> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + + <!-- + + * End of top level elements * + + ***************************** + ***************************** + Part 3: Auxiliary definitions + ***************************** + ***************************** + +****************************** +String Definitions +****************************** +--> + <!-- +****** stringPlusLanguage + --> + <xs:complexType name="stringPlusLanguage"> + <xs:simpleContent> + <xs:extension base="xs:string"> + <xs:attributeGroup ref="languageAttributeGroup"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +********************* stringPlusLanguagePlusAuthority ********************* + --> + <xs:complexType name="stringPlusLanguagePlusAuthority"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attributeGroup ref="authorityAttributeGroup"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +********************* stringPlusLanguagePlusSupplied ********************* + --> + <xs:complexType name="stringPlusLanguagePlusSupplied"> + <xs:simpleContent> + <xs:extension base="stringPlusLanguage"> + <xs:attribute name="supplied" type="yes"/> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + <!-- +****************************** + Attribute Group Definitions +****************************** +--> + <!-- + ****** authorityAttributeGroup ****** + --> + <xs:attributeGroup name="authorityAttributeGroup"> + <!-- new in 3.4 --> + <xs:attribute name="authority" type="xs:string"/> + <xs:attribute name="authorityURI" type="xs:anyURI"/> + <xs:attribute name="valueURI" type="xs:anyURI"/> + </xs:attributeGroup> + <!-- + ****** languageAttributeGroup ****** +--> + <xs:attributeGroup name="languageAttributeGroup"> + <xs:attribute name="lang" type="xs:string"/> + <xs:attribute ref="xml:lang"/> + <xs:attribute name="script" type="xs:string"/> + <xs:attribute name="transliteration" type="xs:string"/> + </xs:attributeGroup> + <!-- + ****** altFormatAttributeGroup ****** +--> + <xs:attributeGroup name="altFormatAttributeGroup"> + <xs:attribute name="altFormat" type="xs:anyURI"/> + <xs:attribute name="contentType" type="xs:string"/> + </xs:attributeGroup> + + <!-- ****** IDAttributeGroup ****** +- + This attribute group added in 3.8 + --> + <xs:attributeGroup name="IDAttributeGroup"> + <xs:attribute name="ID" type="xs:ID"/> + <xs:attribute name="IDREF" type="xs:IDREF"/> + </xs:attributeGroup> + <!-- +************************************************ + - Attribute definitions (simpleTypes) +************************************************* +--> + <!-- + ****** codeOrText + **** used by type attribute for elements that distinguish code from text: + ****** <languageTerm>, <placeTerm>, <roleTerm>, <scriptTerm> + --> + <xs:simpleType name="codeOrText"> + <xs:restriction base="xs:string"> + <xs:enumeration value="code"/> + <xs:enumeration value="text"/> + </xs:restriction> + </xs:simpleType> + <!-- + all following attribute definitions added 3.8 + --> + <xs:simpleType name="no"> + <xs:restriction base="xs:string"> + <xs:enumeration value="no"/> + </xs:restriction> + </xs:simpleType> + <!-- --> + <xs:simpleType name="yes"> + <xs:restriction base="xs:string"> + <xs:enumeration value="yes"/> + </xs:restriction> + </xs:simpleType> + <!-- --> + <xs:simpleType name="usagePrimary"> + <xs:restriction base="xs:string"> + <xs:enumeration value="primary"/> + </xs:restriction> + </xs:simpleType> + +</xs:schema> diff --git a/requirements.in b/requirements.in new file mode 100644 index 0000000..10fb6eb --- /dev/null +++ b/requirements.in @@ -0,0 +1,12 @@ +## TEMP -- for development + +## from setup.py +eulxml==1.1.3 +ply==3.8 +# rdflib + +## actual version used by workshop in production +rdflib==6.0.0 + +## actual version used by workshop in production -- newest version fails on some code +lxml==4.7.1 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..b8fbcf4 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,29 @@ +# +# This file is autogenerated by pip-compile with Python 3.8 +# by the following command: +# +# pip-compile ./requirements.in +# +eulxml==1.1.3 + # via -r ./requirements.in +isodate==0.6.1 + # via rdflib +lxml==4.7.1 + # via + # -r ./requirements.in + # eulxml +ply==3.8 + # via + # -r ./requirements.in + # eulxml +pyparsing==3.1.1 + # via rdflib +rdflib==6.0.0 + # via -r ./requirements.in +six==1.16.0 + # via + # eulxml + # isodate + +# The following packages are considered to be unsafe in a requirements file: +# setuptools diff --git a/setup.py b/setup.py index 125dad7..8010727 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='bdrxml', - version='1.4', + version='1.5', url='https://github.com/Brown-University-Library/bdrxml', author='Brown University Library', author_email='[email protected]',	add support for MODS-3.8	Brown-University-Library/bdrxml	diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 3ebe023..2a9b0bb 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -14,7 +14,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: [3.6.8, 3.7, 3.8, 3.9] + python-version: [3.7, 3.8, 3.9] steps: - uses: actions/checkout@v2 diff --git a/test/mods_test.py b/test/mods_test.py index c2f9932..3dce81b 100644 --- a/test/mods_test.py +++ b/test/mods_test.py @@ -1,5 +1,5 @@ import unittest -from eulxml.xmlmap import load_xmlobject_from_string +from eulxml.xmlmap import load_xmlobject_from_string from bdrxml import mods SAMPLE_MODS = ''' @@ -165,6 +165,13 @@ MODS_35_XML = ''' </mods:titleInfo> </mods:mods> ''' +MODS_38_XML = ''' +<mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd" ID="id101" version="3.8"> + <mods:titleInfo> + <mods:title>A Title</mods:title> + </mods:titleInfo> +</mods:mods> +''' INVALID_MODS_XML = ''' <mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-7.xsd" ID="id101" version="3.7"> <mods:titleInfo> @@ -366,6 +373,10 @@ class ModsReadWrite(unittest.TestCase): loaded = load_xmlobject_from_string(MODS_35_XML, mods.Mods) self.assertTrue(loaded.is_valid()) + def test_validate_mods_38(self): + loaded = load_xmlobject_from_string(MODS_38_XML, mods.Mods) + self.assertTrue(loaded.is_valid()) + def test_validate_created_mods(self): self.mods.title = 'Poétry' self.assertTrue(self.mods.is_valid())	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 3, "test_score": 2 }, "num_modified_files": 2 }	1.4	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	-e git+https://github.com/Brown-University-Library/bdrxml.git@aadc3090ec068103865002d5adc8f39ad600c080#egg=bdrxml eulxml==1.1.3 exceptiongroup==1.2.2 iniconfig==2.1.0 isodate==0.7.2 lxml==5.3.1 packaging==24.2 pluggy==1.5.0 ply==3.8 pyparsing==3.2.3 pytest==8.3.5 rdflib==7.1.4 six==1.17.0 tomli==2.2.1	name: bdrxml channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - eulxml==1.1.3 - exceptiongroup==1.2.2 - iniconfig==2.1.0 - isodate==0.7.2 - lxml==5.3.1 - packaging==24.2 - pluggy==1.5.0 - ply==3.8 - pyparsing==3.2.3 - pytest==8.3.5 - rdflib==7.1.4 - six==1.17.0 - tomli==2.2.1 prefix: /opt/conda/envs/bdrxml	[ "test/mods_test.py::ModsReadWrite::test_validate_mods_38" ]	[]	[ "test/mods_test.py::ModsReadWrite::test_create_dates", "test/mods_test.py::ModsReadWrite::test_create_hierarchical_geographic", "test/mods_test.py::ModsReadWrite::test_create_language", "test/mods_test.py::ModsReadWrite::test_create_mods", "test/mods_test.py::ModsReadWrite::test_fast_uri_equality", "test/mods_test.py::ModsReadWrite::test_geographic_subjects", "test/mods_test.py::ModsReadWrite::test_invalid", "test/mods_test.py::ModsReadWrite::test_load_sample_mods", "test/mods_test.py::ModsReadWrite::test_mods_add_topic", "test/mods_test.py::ModsReadWrite::test_mods_add_topic_already_exists", "test/mods_test.py::ModsReadWrite::test_mods_add_topic_incompatible", "test/mods_test.py::ModsReadWrite::test_mods_add_topic_label_and_fast", "test/mods_test.py::ModsReadWrite::test_mods_add_topic_partial", "test/mods_test.py::ModsReadWrite::test_parts", "test/mods_test.py::ModsReadWrite::test_round_trip", "test/mods_test.py::ModsReadWrite::test_setting_xlink_href", "test/mods_test.py::ModsReadWrite::test_subjects", "test/mods_test.py::ModsReadWrite::test_validate_created_mods", "test/mods_test.py::ModsReadWrite::test_validate_mods_35" ]	[]	MIT License	null
BurnzZ__scrapy-loader-upkeep-3	0b36309d1c9ea814e097e06e9d7220cfef6e6f9e	2019-07-18 07:42:47	0b36309d1c9ea814e097e06e9d7220cfef6e6f9e		diff --git a/README.rst b/README.rst index 6fb0e87..3112d48 100644 --- a/README.rst +++ b/README.rst @@ -58,8 +58,8 @@ This only works for the following ``ItemLoader`` methods: - ``add_xpath()`` - ``replace_xpath()`` -Spider Example -~~~~~~~~~~~~~~ +Basic Spider Example +~~~~~~~~~~~~~~~~~~~~ This is taken from the `examples/ <https://github.com/BurnzZ/scrapy-loader-upkeep/tree/master/examples>`_ directory. @@ -83,6 +83,77 @@ This should output in the stats: In this example, we could see that the 1st css rule for the ``quote`` field has had instances of not being matched at all during the scrape. +New Feature +~~~~~~~~~~~ + +As with the example above, we're limited only to the positional context of when +the ``add_css()``, ``add_xpath()``, etc were called during the execution. + +There will be cases where developers will be maintaining a large spider with a +lot of different parsers to handle varying layouts in the site. It would make +sense to have a better context to what a parser does or is for. + +A new optional ``name`` parameter is supported to provide more context around a +given parser. This supports the two (2) main types of creating fallback parsers: + +1. multiple calls + +.. code-block:: python + + loader.add_css('NAME', 'h1::text', name='Name from h1') + loader.add_css('NAME', 'meta[value="title"]::attr(content)', name="Name from meta tag") + +would result in something like: + +.. code-block:: python + + { ... + 'parser/QuotesItemLoader/NAME/css/1/Name from h1': 8, + 'parser/QuotesItemLoader/NAME/css/1/Name from h1/missing': 2, + 'parser/QuotesItemLoader/NAME/css/2/Name from meta tag': 7, + 'parser/QuotesItemLoader/NAME/css/2/Name from meta tag/missing': 3, + ... + } + +2. grouped parsers in a single call + +.. code-block:: python + + loader.add_css( + 'NAME', + [ + 'h1::text', + 'meta[value="title"]::attr(content)', + ], + name='NAMEs at the main content') + loader.add_css( + 'NAME', + [ + 'footer .name::text', + 'div.page-end span.name::text', + ], + name='NAMEs at the bottom of the page') + +would result in something like: + +.. code-block:: python + + { ... + 'parser/QuotesItemLoader/NAME/css/1/NAMEs at the main content': 8, + 'parser/QuotesItemLoader/NAME/css/1/NAMEs at the main content/missing': 2, + 'parser/QuotesItemLoader/NAME/css/2/NAMEs at the main content': 7, + 'parser/QuotesItemLoader/NAME/css/2/NAMEs at the main content/missing': 3, + 'parser/QuotesItemLoader/NAME/css/3/NAMEs at the bottom of the page': 8, + 'parser/QuotesItemLoader/NAME/css/3/NAMEs at the bottom of the page/missing': 2, + 'parser/QuotesItemLoader/NAME/css/4/NAMEs at the bottom of the page': 7, + 'parser/QuotesItemLoader/NAME/css/4/NAMEs at the bottom of the page/missing': 3, + ... + } + +The latter is useful in grouping fallback parsers together if they are quite +related in terms of layout/arrangement in the page. + + Requirements ~~~~~~~~~~~~ Python 3.6+ diff --git a/examples/README.rst b/examples/README.rst index fc57fdb..037f806 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -1,5 +1,12 @@ Contains examples of using the ``scrapy-loader-upkeep`` package inside Scrapy. +A symlink has been included in the example project dir in order to read the +source code. Run the following in order to test these out: + +.. code-block:: bash + + $ python3 -m scrapy crawl <spider_name> + Simple Example ~~~~~~~~~~~~~~ @@ -41,3 +48,24 @@ This should output in the stats: 'parser/QuotesItemLoader/quote/css/2': 10 ... } + + +Example using 'name' feature +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ scrapy crawl quotestoscrape_use_name + +This should output in the stats: + +.. code-block:: python + + 2019-06-16 14:32:32 [scrapy.statscollectors] INFO: Dumping Scrapy stats: + { ... + 'parser/QuotesItemLoader/author/css/1/basic author class': 10, + 'parser/QuotesItemLoader/quote/css/1/Quotes inside the box/missing': 10, + 'parser/QuotesItemLoader/quote/css/2/Quotes inside the box': 10, + 'parser/QuotesItemLoader/tags/css/1/underneath the author text': 10, + ... + } diff --git a/scrapy_loader_upkeep/loader.py b/scrapy_loader_upkeep/loader.py index cb47f96..805b3ce 100644 --- a/scrapy_loader_upkeep/loader.py +++ b/scrapy_loader_upkeep/loader.py @@ -79,6 +79,9 @@ class ItemLoader(ItemLoaderOG): selector_type = selector.__name__ # either 'css' or 'xpath' + # The optional arg in methods like `add_css()` for context in stats + name = kw.get('name') + # For every call of `add_css()` and `add_xpath()` this is incremented. # We'll use it as the base index of the position of the logged stats. index = self.field_tracker[f'{field_name}_{selector_type}'] @@ -87,10 +90,12 @@ class ItemLoader(ItemLoaderOG): for position, rule in enumerate(arg_to_iter(selector_rules), index): parsed_data = selector(rule).getall() values.append(parsed_data) - self.write_to_stats(field_name, parsed_data, position, selector_type) + self.write_to_stats(field_name, parsed_data, position, + selector_type, name=name) return flatten(values) - def write_to_stats(self, field_name, parsed_data, position, selector_type): + def write_to_stats(self, field_name, parsed_data, position, selector_type, + name=None): """Responsible for logging the parser rules usage. NOTES: It's hard to easily denote which parser rule hasn't produced any @@ -111,6 +116,9 @@ class ItemLoader(ItemLoaderOG): f"parser/{self.loader_name}/{field_name}/{selector_type}/{position}" ) + if name: + parser_label += f'/{name}' + if parsed_data in (None, []): parser_label += "/missing"	Allow paths to be named When having several amounts of xpaths for each field, is really difficult to determine which one was added firsts, specially when there are different workflows on the code. To make easier the task to identify the paths on the stats, it would be great if we could name them. For example: ```python loader.add_css('NAME', 'h1::text', name='Name from h1') loader.add_css('NAME', 'meta[value="title"]::attr(content)', name="Name from meta tag") ```	BurnzZ/scrapy-loader-upkeep	diff --git a/examples/quotestoscrape/items.py b/examples/quotestoscrape/items.py index 360d6f0..2eac930 100644 --- a/examples/quotestoscrape/items.py +++ b/examples/quotestoscrape/items.py @@ -3,3 +3,4 @@ import scrapy class QuotesToScrapeItem(scrapy.Item): quote = scrapy.Field() author = scrapy.Field() + tags = scrapy.Field() diff --git a/examples/quotestoscrape/spiders/quotestoscrape.py b/examples/quotestoscrape/spiders/quotestoscrape.py index 04dd31e..da8ff0e 100644 --- a/examples/quotestoscrape/spiders/quotestoscrape.py +++ b/examples/quotestoscrape/spiders/quotestoscrape.py @@ -5,7 +5,7 @@ from ..loader import QuotesItemLoader class BaseExampleSpider(scrapy.Spider): - start_urls = ['http://quotes.toscrape.com/'] + start_urls = ["http://quotes.toscrape.com/"] class QuotesToScrapeSimpleSpider(BaseExampleSpider): @@ -22,18 +22,17 @@ class QuotesToScrapeSimpleSpider(BaseExampleSpider): been successfully matched. """ - name = 'quotestoscrape_simple' - + name = "quotestoscrape_simple" def parse(self, response): """Notice that in order for this enhanced ItemLoader package to work, we'll need to inject the stats API into it. """ - for quote_div in response.css('div.quote'): + for quote_div in response.css("div.quote"): loader = QuotesItemLoader(selector=quote_div, stats=self.crawler.stats) - loader.add_css('quote', '.quote > span[itemprop="text"]::text') - loader.add_css('author', '.author::text') + loader.add_css("quote", '.quote > span[itemprop="text"]::text') + loader.add_css("author", ".author::text") yield loader.load_item() @@ -50,20 +49,58 @@ class QuotesToScrapeHasMissingSpider(BaseExampleSpider): } """ - name = 'quotestoscrape_has_missing' + name = "quotestoscrape_has_missing" + + def parse(self, response): + """Notice that in order for this enhanced ItemLoader package to work, + we'll need to inject the stats API into it. + """ + + for quote_div in response.css("div.quote"): + loader = QuotesItemLoader(selector=quote_div, stats=self.crawler.stats) + loader.add_css( + "quote", + [ + # This first parser rule doesn't exist at all. + ".this-quote-does-not-exist span::text", + '.quote > span[itemprop="text"]::text', + ], + ) + loader.add_css("author", ".author::text") + yield loader.load_item() + + +class QuotesToScrapeUseName(BaseExampleSpider): + """Demonstrates the new feature of naming the parsers using the 'name' param. + + 2019-06-16 14:32:32 [scrapy.statscollectors] INFO: Dumping Scrapy stats: + { ... + 'parser/QuotesItemLoader/author/css/1/basic author class': 10, + 'parser/QuotesItemLoader/quote/css/1/Quotes inside the box/missing': 10, + 'parser/QuotesItemLoader/quote/css/2/Quotes inside the box': 10, + 'parser/QuotesItemLoader/tags/css/1/underneath the author text': 10, + ... + } + """ + name = "quotestoscrape_use_name" def parse(self, response): """Notice that in order for this enhanced ItemLoader package to work, we'll need to inject the stats API into it. """ - for quote_div in response.css('div.quote'): + for quote_div in response.css("div.quote"): loader = QuotesItemLoader(selector=quote_div, stats=self.crawler.stats) - loader.add_css('quote', [ - # This first parser rule doesn't exist at all. - '.this-quote-does-not-exist span::text', - '.quote > span[itemprop="text"]::text' - ]) - loader.add_css('author', '.author::text') + loader.add_css( + "quote", + [ + # This first parser rule doesn't exist at all. + ".this-quote-does-not-exist span::text", + '.quote > span[itemprop="text"]::text', + ], + name="Quotes inside the box", + ) + loader.add_css("author", ".author::text", name="basic author class") + loader.add_css("tags", ".tag::text", name="underneath the author text") yield loader.load_item() diff --git a/tests/test_loader.py b/tests/test_loader.py index 2a3672b..92a7f58 100644 --- a/tests/test_loader.py +++ b/tests/test_loader.py @@ -50,9 +50,9 @@ def test_get_selector_values(): loader.write_to_stats.assert_has_calls( [ - mock.call(field_name, parsed_data, 1, "css"), - mock.call(field_name, parsed_data, 2, "css"), - mock.call(field_name, parsed_data, 3, "css"), + mock.call(field_name, parsed_data, 1, "css", name=None), + mock.call(field_name, parsed_data, 2, "css", name=None), + mock.call(field_name, parsed_data, 3, "css", name=None), ] ) @@ -205,16 +205,36 @@ def test_multiple_1(loader): mock.call("parser/TestItemLoader/title/css/3"), ] ) + assert loader.stats.inc_value.call_count == 3 -def test_multiple_2(loader): - loader.add_css('title', 'h2::text') - loader.add_xpath('title', '//article/h2/text()') +def test_multiple_1_with_name(loader): + loader.add_css('title', 'h2::text', name='title from h2') + loader.add_css('title', [ + 'article h2::text', + 'article .product-title::text', + ], name='title from article') + loader.stats.inc_value.assert_has_calls( + [ + mock.call("parser/TestItemLoader/title/css/1/title from h2"), + mock.call("parser/TestItemLoader/title/css/2/title from article"), + mock.call("parser/TestItemLoader/title/css/3/title from article"), + ] + ) + assert loader.stats.inc_value.call_count == 3 + + +def test_multiple_2_with_name(loader): + loader.add_css('title', 'h2::text', name='title from h2') + loader.add_xpath('title', '//article/h2/text()', name='title from article') loader.add_css('title', 'article .product-title::text') + loader.add_xpath('title', '//aside/h1/text()', name='title from aside') loader.stats.inc_value.assert_has_calls( [ - mock.call("parser/TestItemLoader/title/css/1"), - mock.call("parser/TestItemLoader/title/xpath/1"), + mock.call("parser/TestItemLoader/title/css/1/title from h2"), + mock.call("parser/TestItemLoader/title/xpath/1/title from article"), mock.call("parser/TestItemLoader/title/css/2"), + mock.call("parser/TestItemLoader/title/xpath/2/title from aside/missing"), ] ) + assert loader.stats.inc_value.call_count == 4	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 3 }	0.12	{ "env_vars": null, "env_yml_path": [], "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "Pipfile", "pip_packages": [ "pytest" ], "pre_install": [], "python": "3.9", "reqs_path": [], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 Automat==24.8.1 certifi==2025.1.31 cffi==1.17.1 charset-normalizer==3.4.1 constantly==23.10.4 cryptography==44.0.2 cssselect==1.3.0 defusedxml==0.7.1 exceptiongroup==1.2.2 filelock==3.18.0 hyperlink==21.0.0 idna==3.10 incremental==24.7.2 iniconfig==2.1.0 itemadapter==0.11.0 itemloaders==1.3.2 jmespath==1.0.1 lxml==5.3.1 packaging==24.2 parsel==1.10.0 pipfile==0.0.2 pluggy==1.5.0 Protego==0.4.0 pyasn1==0.6.1 pyasn1_modules==0.4.2 pycparser==2.22 PyDispatcher==2.0.7 pyOpenSSL==25.0.0 pytest==8.3.5 queuelib==1.8.0 requests==2.32.3 requests-file==2.1.0 Scrapy==2.12.0 -e git+https://github.com/BurnzZ/scrapy-loader-upkeep.git@0b36309d1c9ea814e097e06e9d7220cfef6e6f9e#egg=scrapy_loader_upkeep service-identity==24.2.0 tldextract==5.1.3 toml @ file:///tmp/build/80754af9/toml_1616166611790/work tomli==2.2.1 Twisted==24.11.0 typing_extensions==4.13.0 urllib3==2.3.0 w3lib==2.3.1 zope.interface==7.2	name: scrapy-loader-upkeep channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - pipfile=0.0.2=py_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - toml=0.10.2=pyhd3eb1b0_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - automat==24.8.1 - certifi==2025.1.31 - cffi==1.17.1 - charset-normalizer==3.4.1 - constantly==23.10.4 - cryptography==44.0.2 - cssselect==1.3.0 - defusedxml==0.7.1 - exceptiongroup==1.2.2 - filelock==3.18.0 - hyperlink==21.0.0 - idna==3.10 - incremental==24.7.2 - iniconfig==2.1.0 - itemadapter==0.11.0 - itemloaders==1.3.2 - jmespath==1.0.1 - lxml==5.3.1 - packaging==24.2 - parsel==1.10.0 - pluggy==1.5.0 - protego==0.4.0 - pyasn1==0.6.1 - pyasn1-modules==0.4.2 - pycparser==2.22 - pydispatcher==2.0.7 - pyopenssl==25.0.0 - pytest==8.3.5 - queuelib==1.8.0 - requests==2.32.3 - requests-file==2.1.0 - scrapy==2.12.0 - service-identity==24.2.0 - tldextract==5.1.3 - tomli==2.2.1 - twisted==24.11.0 - typing-extensions==4.13.0 - urllib3==2.3.0 - w3lib==2.3.1 - zope-interface==7.2 prefix: /opt/conda/envs/scrapy-loader-upkeep	[ "tests/test_loader.py::test_get_selector_values", "tests/test_loader.py::test_multiple_1_with_name", "tests/test_loader.py::test_multiple_2_with_name" ]	[]	[ "tests/test_loader.py::test_get_selector_values_with_no_selector", "tests/test_loader.py::test_write_to_stats_with_uninjected_stat_dependency", "tests/test_loader.py::test_write_to_stats_with_no_parsed_data", "tests/test_loader.py::test_write_to_stats_with_no_field_name", "tests/test_loader.py::test_write_to_stats", "tests/test_loader.py::test_add_css_1", "tests/test_loader.py::test_add_css_2", "tests/test_loader.py::test_add_css_3_missing", "tests/test_loader.py::test_multiple_1" ]	[]	BSD 3-Clause "New" or "Revised" License	swerebench/sweb.eval.x86_64.burnzz_1776_scrapy-loader-upkeep-3
CDCgov__PyRenew-412	1a86104f6be3d0d2429e3358f7ea5572f8fa3799	2024-08-22 22:46:05	27258c12661b3bb65ad8fe3f743a01c0267fe533	codecov[bot]: ## [Codecov](https://app.codecov.io/gh/CDCgov/PyRenew/pull/412?dropdown=coverage&src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) Report Attention: Patch coverage is `97.16981%` with `3 lines` in your changes missing coverage. Please review. > Project coverage is 93.52%. Comparing base [(`648e69b`)](https://app.codecov.io/gh/CDCgov/PyRenew/commit/648e69be67b6e26df122caa7aff20962e1365706?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) to head [(`da7f428`)](https://app.codecov.io/gh/CDCgov/PyRenew/commit/da7f4289e7fe1bbd27faaa275674b516b5949215?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov). > Report is 1 commits behind head on main. \| [Files](https://app.codecov.io/gh/CDCgov/PyRenew/pull/412?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| Patch % \| Lines \| \|---\|---\|---\| \| [pyrenew/randomvariable/distributionalvariable.py](https://app.codecov.io/gh/CDCgov/PyRenew/pull/412?src=pr&el=tree&filepath=pyrenew%2Frandomvariable%2Fdistributionalvariable.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov#diff-cHlyZW5ldy9yYW5kb212YXJpYWJsZS9kaXN0cmlidXRpb25hbHZhcmlhYmxlLnB5) \| 95.00% \| [3 Missing :warning: ](https://app.codecov.io/gh/CDCgov/PyRenew/pull/412?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| <details><summary>Additional details and impacted files</summary> ```diff @@ Coverage Diff @@ ## main #412 +/- ## ========================================== + Coverage 93.43% 93.52% +0.09% ========================================== Files 38 41 +3 Lines 1005 1019 +14 ========================================== + Hits 939 953 +14 Misses 66 66 ``` \| [Flag](https://app.codecov.io/gh/CDCgov/PyRenew/pull/412/flags?src=pr&el=flags&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| Coverage Δ \| \| \|---\|---\|---\| \| [unittests](https://app.codecov.io/gh/CDCgov/PyRenew/pull/412/flags?src=pr&el=flag&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| `93.52% <97.16%> (+0.09%)` \| :arrow_up: \| Flags with carried forward coverage won't be shown. [Click here](https://docs.codecov.io/docs/carryforward-flags?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov#carryforward-flags-in-the-pull-request-comment) to find out more. </details> [:umbrella: View full report in Codecov by Sentry](https://app.codecov.io/gh/CDCgov/PyRenew/pull/412?dropdown=coverage&src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov). :loudspeaker: Have feedback on the report? [Share it here](https://about.codecov.io/codecov-pr-comment-feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov). damonbayer: @dylanhmorris @sbidari Do we need to make any changes about what is in the `deterministic` module? sbidari: > @dylanhmorris @sbidari Do we need to make any changes about what is in the `deterministic` module? Potentially move `DeterministicVariable` to the `randomvariable` module? @dylanhmorris mentioned it in the issue comment. But since `DeterministicVariables` are not really random variables, it might make more sense to leave them in the deterministic module? damonbayer: We agreed not to move the `DeterministicVariable` class at this time. dylanhmorris: @sbidari is this ready for review? sbidari: > @sbidari is this ready for review? Waiting for #356, I unintentionally created this branch with changes from #356 damonbayer: @sbidari https://github.com/CDCgov/PyRenew/pull/356 is merged. Please mark this as "ready for review" when it's ready for review.	diff --git a/docs/source/msei_reference/index.rst b/docs/source/msei_reference/index.rst index f7fae05..323874c 100644 --- a/docs/source/msei_reference/index.rst +++ b/docs/source/msei_reference/index.rst @@ -7,6 +7,7 @@ Reference model latent process + randomvariable observation datasets msei diff --git a/docs/source/msei_reference/randomvariable.rst b/docs/source/msei_reference/randomvariable.rst new file mode 100644 index 0000000..3ffe44d --- /dev/null +++ b/docs/source/msei_reference/randomvariable.rst @@ -0,0 +1,7 @@ +Random Variables +=========== + +.. automodule:: pyrenew.randomvariable + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/tutorials/basic_renewal_model.qmd b/docs/source/tutorials/basic_renewal_model.qmd index e9a3fcb..bf26209 100644 --- a/docs/source/tutorials/basic_renewal_model.qmd +++ b/docs/source/tutorials/basic_renewal_model.qmd @@ -25,11 +25,8 @@ from pyrenew.latent import ( from pyrenew.observation import PoissonObservation from pyrenew.deterministic import DeterministicPMF from pyrenew.model import RtInfectionsRenewalModel -from pyrenew.metaclass import ( - RandomVariable, - DistributionalRV, - TransformedRandomVariable, -) +from pyrenew.metaclass import RandomVariable +from pyrenew.randomvariable import DistributionalVariable, TransformedVariable import pyrenew.transformation as t from numpyro.infer.reparam import LocScaleReparam ``` @@ -64,7 +61,7 @@ flowchart LR subgraph latent[Latent module] inf["latent_infections_rv\n(Infections)"] - i0["I0_rv\n(DistributionalRV)"] + i0["I0_rv\n(DistributionalVariable)"] end subgraph process[Process module] @@ -126,7 +123,7 @@ gen_int = DeterministicPMF(name="gen_int", value=pmf_array) # (2) Initial infections (inferred with a prior) I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(2.5, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(2.5, 1)), InitializeInfectionsZeroPad(pmf_array.size), t_unit=1, ) @@ -142,17 +139,17 @@ class MyRt(RandomVariable): def sample(self, n: int, *kwargs) -> tuple: sd_rt = numpyro.sample("Rt_random_walk_sd", dist.HalfNormal(0.025)) - rt_rv = TransformedRandomVariable( + rt_rv = TransformedVariable( name="log_rt_random_walk", base_rv=RandomWalk( name="log_rt", - step_rv=DistributionalRV( + step_rv=DistributionalVariable( name="rw_step_rv", distribution=dist.Normal(0, 0.025) ), ), transforms=t.ExpTransform(), ) - rt_init_rv = DistributionalRV( + rt_init_rv = DistributionalVariable( name="init_log_rt", distribution=dist.Normal(0, 0.2) ) init_rt, _ = rt_init_rv.sample() diff --git a/docs/source/tutorials/day_of_the_week.qmd b/docs/source/tutorials/day_of_the_week.qmd index 4322873..4b4c6f8 100644 --- a/docs/source/tutorials/day_of_the_week.qmd +++ b/docs/source/tutorials/day_of_the_week.qmd @@ -51,7 +51,7 @@ inf_hosp_int_array = inf_hosp_int["probability_mass"].to_numpy() ```{python} # \| label: latent-hosp # \| code-fold: true -from pyrenew import latent, deterministic, metaclass +from pyrenew import latent, deterministic, randomvariable import jax.numpy as jnp import numpyro.distributions as dist @@ -59,7 +59,7 @@ inf_hosp_int = deterministic.DeterministicPMF( name="inf_hosp_int", value=inf_hosp_int_array ) -hosp_rate = metaclass.DistributionalRV( +hosp_rate = randomvariable.DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), jnp.log(1.1)) ) @@ -81,7 +81,7 @@ n_initialization_points = max(gen_int_array.size, inf_hosp_int_array.size) - 1 I0 = InfectionInitializationProcess( "I0_initialization", - metaclass.DistributionalRV( + randomvariable.DistributionalVariable( name="I0", distribution=dist.LogNormal(loc=jnp.log(100), scale=jnp.log(1.75)), ), @@ -113,11 +113,11 @@ class MyRt(metaclass.RandomVariable): sd_rt, _ = self.sd_rv() # Random walk step - step_rv = metaclass.DistributionalRV( + step_rv = randomvariable.DistributionalVariable( name="rw_step_rv", distribution=dist.Normal(0, sd_rt.value) ) - rt_init_rv = metaclass.DistributionalRV( + rt_init_rv = randomvariable.DistributionalVariable( name="init_log_rt", distribution=dist.Normal(0, 0.2) ) @@ -128,7 +128,7 @@ class MyRt(metaclass.RandomVariable): ) # Transforming the random walk to the Rt scale - rt_rv = metaclass.TransformedRandomVariable( + rt_rv = randomvariable.TransformedVariable( name="Rt_rv", base_rv=base_rv, transforms=transformation.ExpTransform(), @@ -139,7 +139,7 @@ class MyRt(metaclass.RandomVariable): rtproc = MyRt( - metaclass.DistributionalRV( + randomvariable.DistributionalVariable( name="Rt_random_walk_sd", distribution=dist.HalfNormal(0.025) ) ) @@ -152,9 +152,9 @@ rtproc = MyRt( # \| code-fold: true # we place a log-Normal prior on the concentration # parameter of the negative binomial. -nb_conc_rv = metaclass.TransformedRandomVariable( +nb_conc_rv = randomvariable.TransformedVariable( "concentration", - metaclass.DistributionalRV( + randomvariable.DistributionalVariable( name="concentration_raw", distribution=dist.TruncatedNormal(loc=0, scale=1, low=0.01), ), @@ -212,16 +212,16 @@ out = hosp_model.plot_posterior( We will re-use the infection to admission interval and infection to hospitalization rate from the previous model. But we will also add a day-of-the-week effect. To do this, we will add two additional arguments to the latent hospital admissions random variable: `day_of_the_week_rv` (a `RandomVariable`) and `obs_data_first_day_of_the_week` (an `int` mapping days of the week from 0:6, zero being Monday). The `day_of_the_week_rv`'s sample method should return a vector of length seven; those values are then broadcasted to match the length of the dataset. Moreover, since the observed data may start in a weekday other than Monday, the `obs_data_first_day_of_the_week` argument is used to offset the day-of-the-week effect. -For this example, the effect will be passed as a scaled Dirichlet distribution. It will consist of a `TransformedRandomVariable` that samples an array of length seven from numpyro's `distributions.Dirichlet` and applies a `transformation.AffineTransform` to scale it by seven. [^note-other-examples]: +For this example, the effect will be passed as a scaled Dirichlet distribution. It will consist of a `TransformedVariable` that samples an array of length seven from numpyro's `distributions.Dirichlet` and applies a `transformation.AffineTransform` to scale it by seven. [^note-other-examples]: [^note-other-examples]: A similar weekday effect is implemented in its own module, with example code [here](periodic_effects.html). ```{python} # \| label: weekly-effect # Instantiating the day-of-the-week effect -dayofweek_effect = metaclass.TransformedRandomVariable( +dayofweek_effect = randomvariable.TransformedVariable( name="dayofweek_effect", - base_rv=metaclass.DistributionalRV( + base_rv=randomvariable.DistributionalVariable( name="dayofweek_effect_raw", distribution=dist.Dirichlet(jnp.ones(7)), ), diff --git a/docs/source/tutorials/extending_pyrenew.qmd b/docs/source/tutorials/extending_pyrenew.qmd index 1461548..f81de65 100644 --- a/docs/source/tutorials/extending_pyrenew.qmd +++ b/docs/source/tutorials/extending_pyrenew.qmd @@ -29,11 +29,8 @@ from pyrenew.deterministic import DeterministicPMF, DeterministicVariable from pyrenew.latent import InfectionsWithFeedback from pyrenew.model import RtInfectionsRenewalModel from pyrenew.process import RandomWalk -from pyrenew.metaclass import ( - RandomVariable, - DistributionalRV, - TransformedRandomVariable, -) +from pyrenew.metaclass import RandomVariable +from pyrenew.randomvariable import DistributionalVariable, TransformedVariable from pyrenew.latent import ( InfectionInitializationProcess, InitializeInfectionsExponentialGrowth, @@ -53,7 +50,7 @@ feedback_strength = DeterministicVariable(name="feedback_strength", value=0.01) I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsExponentialGrowth( gen_int_array.size, DeterministicVariable(name="rate", value=0.05), @@ -75,17 +72,17 @@ class MyRt(RandomVariable): def sample(self, n: int, kwargs) -> tuple: sd_rt = numpyro.sample("Rt_random_walk_sd", dist.HalfNormal(0.025)) - rt_rv = TransformedRandomVariable( + rt_rv = TransformedVariable( name="log_rt_random_walk", base_rv=RandomWalk( name="log_rt", - step_rv=DistributionalRV( + step_rv=DistributionalVariable( name="rw_step_rv", distribution=dist.Normal(0, 0.025) ), ), transforms=t.ExpTransform(), ) - rt_init_rv = DistributionalRV( + rt_init_rv = DistributionalVariable( name="init_log_rt", distribution=dist.Normal(0, 0.2) ) init_rt, _ = rt_init_rv.sample() diff --git a/docs/source/tutorials/hospital_admissions_model.qmd b/docs/source/tutorials/hospital_admissions_model.qmd index c9dca3b..07c3864 100644 --- a/docs/source/tutorials/hospital_admissions_model.qmd +++ b/docs/source/tutorials/hospital_admissions_model.qmd @@ -137,7 +137,7 @@ With these two in hand, we can start building the model. First, we will define t ```{python} # \| label: latent-hosp -from pyrenew import latent, deterministic, metaclass +from pyrenew import latent, deterministic, metaclass, randomvariable import jax.numpy as jnp import numpyro.distributions as dist @@ -145,7 +145,7 @@ inf_hosp_int = deterministic.DeterministicPMF( name="inf_hosp_int", value=inf_hosp_int_array ) -hosp_rate = metaclass.DistributionalRV( +hosp_rate = randomvariable.DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), jnp.log(1.1)) ) @@ -155,7 +155,7 @@ latent_hosp = latent.HospitalAdmissions( ) ``` -The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospital admission interval as input. The `hosp_rate` is a `DistributionalRV` object that takes a numpyro distribution to represent the infection to hospital admission rate. The `HospitalAdmissions` class is a `RandomVariable` that takes two distributions as inputs: the infection to admission interval and the infection to hospital admission rate. Now, we can define the rest of the other components: +The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospital admission interval as input. The `hosp_rate` is a `DistributionalVariable` object that takes a numpyro distribution to represent the infection to hospital admission rate. The `HospitalAdmissions` class is a `RandomVariable` that takes two distributions as inputs: the infection to admission interval and the infection to hospital admission rate. Now, we can define the rest of the other components: ```{python} # \| label: initializing-rest-of-model @@ -171,7 +171,7 @@ latent_inf = latent.Infections() n_initialization_points = max(gen_int_array.size, inf_hosp_int_array.size) - 1 I0 = InfectionInitializationProcess( "I0_initialization", - metaclass.DistributionalRV( + randomvariable.DistributionalVariable( name="I0", distribution=dist.LogNormal(loc=jnp.log(100), scale=jnp.log(1.75)), ), @@ -194,17 +194,17 @@ class MyRt(metaclass.RandomVariable): def sample(self, n: int, *kwargs) -> tuple: sd_rt = numpyro.sample("Rt_random_walk_sd", dist.HalfNormal(0.025)) - rt_rv = metaclass.TransformedRandomVariable( + rt_rv = randomvariable.TransformedVariable( name="log_rt_random_walk", base_rv=process.RandomWalk( name="log_rt", - step_rv=metaclass.DistributionalRV( + step_rv=randomvariable.DistributionalVariable( name="rw_step_rv", distribution=dist.Normal(0, 0.025) ), ), transforms=transformation.ExpTransform(), ) - rt_init_rv = metaclass.DistributionalRV( + rt_init_rv = randomvariable.DistributionalVariable( name="init_log_rt", distribution=dist.Normal(0, 0.2) ) init_rt, _ = rt_init_rv.sample() @@ -218,9 +218,9 @@ rtproc = MyRt() # we place a log-Normal prior on the concentration # parameter of the negative binomial. -nb_conc_rv = metaclass.TransformedRandomVariable( +nb_conc_rv = randomvariable.TransformedVariable( "concentration", - metaclass.DistributionalRV( + randomvariable.DistributionalVariable( name="concentration_raw", distribution=dist.TruncatedNormal(loc=0, scale=1, low=0.01), ), diff --git a/docs/source/tutorials/periodic_effects.qmd b/docs/source/tutorials/periodic_effects.qmd index bfe3e30..1603ed5 100644 --- a/docs/source/tutorials/periodic_effects.qmd +++ b/docs/source/tutorials/periodic_effects.qmd @@ -65,7 +65,7 @@ The `PeriodicBroadcaster` class can also be used to repeat a sequence as a whole ```{python} import numpyro.distributions as dist -from pyrenew import transformation, metaclass +from pyrenew import transformation, randomvariable # Building the transformed prior: Dirichlet * 7 mysimplex = dist.TransformedDistribution( @@ -76,7 +76,7 @@ mysimplex = dist.TransformedDistribution( # Constructing the day of week effect dayofweek = process.DayOfWeekEffect( offset=0, - quantity_to_broadcast=metaclass.DistributionalRV( + quantity_to_broadcast=randomvariable.DistributionalVariable( name="simp", distribution=mysimplex ), t_start=0, diff --git a/pyrenew/metaclass.py b/pyrenew/metaclass.py index 424ffea..63d72de 100644 --- a/pyrenew/metaclass.py +++ b/pyrenew/metaclass.py @@ -5,21 +5,17 @@ pyrenew helper classes """ from abc import ABCMeta, abstractmethod -from typing import Callable, NamedTuple, Self, get_type_hints +from typing import NamedTuple, get_type_hints import jax import jax.random as jr import matplotlib.pyplot as plt import numpy as np -import numpyro -import numpyro.distributions as dist import polars as pl from jax.typing import ArrayLike from numpyro.infer import MCMC, NUTS, Predictive -from numpyro.infer.reparam import Reparam from pyrenew.mcmcutils import plot_posterior, spread_draws -from pyrenew.transformation import Transform def _assert_type(arg_name: str, value, expected_type) -> None: @@ -276,338 +272,6 @@ class RandomVariable(metaclass=ABCMeta): return self.sample(*kwargs) -class DynamicDistributionalRV(RandomVariable): - """ - Wrapper class for random variables that sample - from a single :class:`numpyro.distributions.Distribution` - that is parameterized / instantiated at `sample()` time - (rather than at RandomVariable instantiation time). - """ - - def __init__( - self, - name: str, - distribution_constructor: Callable, - reparam: Reparam = None, - expand_by_shape: tuple = None, - ) -> None: - """ - Default constructor for DynamicDistributionalRV. - - Parameters - ---------- - name : str - Name of the random variable. - distribution_constructor : Callable - Callable that returns a concrete parametrized - numpyro.Distributions.distribution instance. - reparam : numpyro.infer.reparam.Reparam - If not None, reparameterize sampling - from the distribution according to the - given numpyro reparameterizer - expand_by_shape : tuple, optional - If not None, call :meth:`expand_by()` on the - underlying distribution once it is instianted - with the given `expand_by_shape`. - Default None. - - Returns - ------- - None - """ - - self.name = name - self.validate(distribution_constructor) - self.distribution_constructor = distribution_constructor - if reparam is not None: - self.reparam_dict = {self.name: reparam} - else: - self.reparam_dict = {} - if not (expand_by_shape is None or isinstance(expand_by_shape, tuple)): - raise ValueError( - "expand_by_shape must be a tuple or be None ", - f"Got {type(expand_by_shape)}", - ) - self.expand_by_shape = expand_by_shape - - return None - - @staticmethod - def validate(distribution_constructor: any) -> None: - """ - Confirm that the distribution_constructor is - callable. - - Parameters - ---------- - distribution_constructor : any - Putative distribution_constructor to validate. - - Returns - ------- - None or raises a ValueError - """ - if not callable(distribution_constructor): - raise ValueError( - "To instantiate a DynamicDistributionalRV, ", - "one must provide a Callable that returns a " - "numpyro.distributions.Distribution as the " - "distribution_constructor argument. " - f"Got {type(distribution_constructor)}, which " - "does not appear to be callable", - ) - return None - - def sample( - self, - args, - obs: ArrayLike = None, - *kwargs, - ) -> tuple: - """ - Sample from the distributional rv. - - Parameters - ---------- - args : - Positional arguments passed to self.distribution_constructor - obs : ArrayLike, optional - Observations passed as the `obs` argument to - :meth:`numpyro.sample()`. Default `None`. - *kwargs : dict, optional - Keyword arguments passed to self.distribution_constructor - - Returns - ------- - SampledValue - Containing a sample from the distribution. - """ - distribution = self.distribution_constructor(args, kwargs) - if self.expand_by_shape is not None: - distribution = distribution.expand_by(self.expand_by_shape) - with numpyro.handlers.reparam(config=self.reparam_dict): - sample = numpyro.sample( - name=self.name, - fn=distribution, - obs=obs, - ) - return ( - SampledValue( - sample, - t_start=self.t_start, - t_unit=self.t_unit, - ), - ) - - def expand_by(self, sample_shape) -> Self: - """ - Expand the distribution by a given - shape_shape, if possible. Returns a - new DynamicDistributionalRV whose underlying - distribution will be expanded by the given shape - at sample() time. - - Parameters - ---------- - sample_shape : tuple - Sample shape by which to expand the distribution. - Passed to the expand_by() method of - :class:`numpyro.distributions.Distribution` - after the distribution is instantiated. - - Returns - ------- - DynamicDistributionalRV - Whose underlying distribution will be expanded by - the given sample shape at sampling time. - """ - return DynamicDistributionalRV( - name=self.name, - distribution_constructor=self.distribution_constructor, - reparam=self.reparam_dict.get(self.name, None), - expand_by_shape=sample_shape, - ) - - -class StaticDistributionalRV(RandomVariable): - """ - Wrapper class for random variables that sample - from a single :class:`numpyro.distributions.Distribution` - that is parameterized / instantiated at RandomVariable - instantiation time (rather than at `sample()`-ing time). - """ - - def __init__( - self, - name: str, - distribution: numpyro.distributions.Distribution, - reparam: Reparam = None, - ) -> None: - """ - Default constructor for DistributionalRV. - - Parameters - ---------- - name : str - Name of the random variable. - distribution : numpyro.distributions.Distribution - Distribution of the random variable. - reparam : numpyro.infer.reparam.Reparam - If not None, reparameterize sampling - from the distribution according to the - given numpyro reparameterizer - - Returns - ------- - None - """ - - self.name = name - self.validate(distribution) - self.distribution = distribution - if reparam is not None: - self.reparam_dict = {self.name: reparam} - else: - self.reparam_dict = {} - - return None - - @staticmethod - def validate(distribution: any) -> None: - """ - Validation of the distribution. - """ - if not isinstance(distribution, numpyro.distributions.Distribution): - raise ValueError( - "distribution should be an instance of " - "numpyro.distributions.Distribution, got " - "{type(distribution)}" - ) - - return None - - def sample( - self, - obs: ArrayLike \| None = None, - kwargs, - ) -> tuple: - """ - Sample from the distribution. - - Parameters - ---------- - obs : ArrayLike, optional - Observations passed as the `obs` argument to - :meth:`numpyro.sample()`. Default `None`. - kwargs : dict, optional - Additional keyword arguments passed through - to internal sample calls, should there be any. - - Returns - ------- - SampledValue - Containing a sample from the distribution. - """ - with numpyro.handlers.reparam(config=self.reparam_dict): - sample = numpyro.sample( - name=self.name, - fn=self.distribution, - obs=obs, - ) - return ( - SampledValue( - sample, - t_start=self.t_start, - t_unit=self.t_unit, - ), - ) - - def expand_by(self, sample_shape) -> Self: - """ - Expand the distribution by the given sample_shape, - if possible. Returns a new StaticDistributionalRV - whose underlying distribution has been expanded by - the given sample_shape via - :meth:`~numpyro.distributions.Distribution.expand_by()` - - Parameters - ---------- - sample_shape : tuple - Sample shape for the expansion. Passed to the - :meth:`expand_by()` method of - :class:`numpyro.distributions.Distribution`. - - Returns - ------- - StaticDistributionalRV - Whose underlying distribution has been expanded by - the given sample shape. - """ - if not isinstance(sample_shape, tuple): - raise ValueError( - "sample_shape for expand()-ing " - "a DistributionalRV must be a " - f"tuple. Got {type(sample_shape)}" - ) - return StaticDistributionalRV( - name=self.name, - distribution=self.distribution.expand_by(sample_shape), - reparam=self.reparam_dict.get(self.name, None), - ) - - -def DistributionalRV( - name: str, - distribution: numpyro.distributions.Distribution \| Callable, - reparam: Reparam = None, -) -> RandomVariable: - """ - Factory function to generate Distributional RandomVariables, - either static or dynamic. - - Parameters - ---------- - name : str - Name of the random variable. - - distribution: numpyro.distributions.Distribution \| Callable - Either numpyro.distributions.Distribution instance - given the static distribution of the random variable or - a callable that returns a parameterized - numpyro.distributions.Distribution when called, which - allows for dynamically-parameterized DistributionalRVs, - e.g. a Normal distribution with an inferred location and - scale. - - reparam : numpyro.infer.reparam.Reparam - If not None, reparameterize sampling - from the distribution according to the - given numpyro reparameterizer - - Returns - ------- - DynamicDistributionalRV \| StaticDistributionalRV or - raises a ValueError if a distribution cannot be constructed. - """ - if isinstance(distribution, dist.Distribution): - return StaticDistributionalRV( - name=name, distribution=distribution, reparam=reparam - ) - elif callable(distribution): - return DynamicDistributionalRV( - name=name, distribution_constructor=distribution, reparam=reparam - ) - else: - raise ValueError( - "distribution argument to DistributionalRV " - "must be either a numpyro.distributions.Distribution " - "(for instantiating a static DistributionalRV) " - "or a callable that returns a " - "numpyro.distributions.Distribution (for " - "a dynamic DistributionalRV" - ) - - class Model(metaclass=ABCMeta): """Abstract base class for models""" @@ -891,137 +555,3 @@ class Model(metaclass=ABCMeta): ) return predictive(rng_key, kwargs) - - -class TransformedRandomVariable(RandomVariable): - """ - Class to represent RandomVariables defined - by taking the output of another RV's - :meth:`RandomVariable.sample()` method - and transforming it by a given transformation - (typically a :class:`Transform`) - """ - - def __init__( - self, - name: str, - base_rv: RandomVariable, - transforms: Transform \| tuple[Transform], - ): - """ - Default constructor - - Parameters - ---------- - name : str - A name for the random variable instance. - base_rv : RandomVariable - The underlying (untransformed) RandomVariable. - transforms : Transform - Transformation or tuple of transformations - to apply to the output of - `base_rv.sample()`; single values will be coerced to - a length-one tuple. If a tuple, should be the same - length as the tuple returned by `base_rv.sample()`. - - Returns - ------- - None - """ - self.name = name - self.base_rv = base_rv - - if not isinstance(transforms, tuple): - transforms = (transforms,) - self.transforms = transforms - self.validate() - - def sample(self, record=False, kwargs) -> tuple: - """ - Sample method. Call self.base_rv.sample() - and then apply the transforms specified - in self.transforms. - - Parameters - ---------- - record : bool, optional - Whether to record the value of the deterministic - RandomVariable. Defaults to False. - kwargs : - Keyword arguments passed to self.base_rv.sample() - - Returns - ------- - tuple of the same length as the tuple returned by - self.base_rv.sample() - """ - - untransformed_values = self.base_rv.sample(kwargs) - transformed_values = tuple( - SampledValue( - t(uv.value), - t_start=self.t_start, - t_unit=self.t_unit, - ) - for t, uv in zip(self.transforms, untransformed_values) - ) - - if record: - if len(untransformed_values) == 1: - numpyro.deterministic(self.name, transformed_values[0].value) - else: - suffixes = ( - untransformed_values._fields - if hasattr(untransformed_values, "_fields") - else range(len(transformed_values)) - ) - for suffix, tv in zip(suffixes, transformed_values): - numpyro.deterministic(f"{self.name}_{suffix}", tv.value) - - return transformed_values - - def sample_length(self): - """ - Sample length for a transformed - random variable must be equal to the - length of self.transforms or - validation will fail. - - Returns - ------- - int - Equal to the length self.transforms - """ - return len(self.transforms) - - def validate(self): - """ - Perform validation checks on a - TransformedRandomVariable instance, - confirming that all transformations - are callable and that the number of - transformations is equal to the sample - length of the base random variable. - - Returns - ------- - None - on successful validation, or raise a ValueError - """ - for t in self.transforms: - if not callable(t): - raise ValueError( - "All entries in self.transforms " "must be callable" - ) - if hasattr(self.base_rv, "sample_length"): - n_transforms = len(self.transforms) - n_entries = self.base_rv.sample_length() - if not n_transforms == n_entries: - raise ValueError( - "There must be exactly as many transformations " - "specified as entries self.transforms as there are " - "entries in the tuple returned by " - "self.base_rv.sample()." - f"Got {n_transforms} transforms and {n_entries} " - "entries" - ) diff --git a/pyrenew/process/iidrandomsequence.py b/pyrenew/process/iidrandomsequence.py index 2f868ad..10adfa9 100644 --- a/pyrenew/process/iidrandomsequence.py +++ b/pyrenew/process/iidrandomsequence.py @@ -4,7 +4,8 @@ import numpyro.distributions as dist from numpyro.contrib.control_flow import scan -from pyrenew.metaclass import DistributionalRV, RandomVariable, SampledValue +from pyrenew.metaclass import RandomVariable, SampledValue +from pyrenew.randomvariable import DistributionalVariable class IIDRandomSequence(RandomVariable): @@ -130,7 +131,7 @@ class StandardNormalSequence(IIDRandomSequence): see :class:`IIDRandomSequence`. element_rv_name: str Name for the internal element_rv, here a - DistributionalRV encoding a + DistributionalVariable encoding a standard Normal (mean = 0, sd = 1) distribution. @@ -139,7 +140,7 @@ class StandardNormalSequence(IIDRandomSequence): None """ super().__init__( - element_rv=DistributionalRV( + element_rv=DistributionalVariable( name=element_rv_name, distribution=dist.Normal(0, 1) ), ) diff --git a/pyrenew/process/randomwalk.py b/pyrenew/process/randomwalk.py index a9fa472..6b0a763 100644 --- a/pyrenew/process/randomwalk.py +++ b/pyrenew/process/randomwalk.py @@ -3,9 +3,10 @@ import numpyro.distributions as dist -from pyrenew.metaclass import DistributionalRV, RandomVariable +from pyrenew.metaclass import RandomVariable from pyrenew.process.differencedprocess import DifferencedProcess from pyrenew.process.iidrandomsequence import IIDRandomSequence +from pyrenew.randomvariable import DistributionalVariable class RandomWalk(DifferencedProcess): @@ -69,7 +70,7 @@ class StandardNormalRandomWalk(RandomWalk): Parameters ---------- step_rv_name : - Name for the DistributionalRV + Name for the DistributionalVariable from which the Normal(0, 1) steps are sampled. kwargs: @@ -80,7 +81,7 @@ class StandardNormalRandomWalk(RandomWalk): None """ super().__init__( - step_rv=DistributionalRV( + step_rv=DistributionalVariable( name=step_rv_name, distribution=dist.Normal(0.0, 1.0) ), *kwargs, diff --git a/pyrenew/randomvariable/__init__.py b/pyrenew/randomvariable/__init__.py new file mode 100644 index 0000000..4f154b2 --- /dev/null +++ b/pyrenew/randomvariable/__init__.py @@ -0,0 +1,17 @@ +# -- coding: utf-8 -- + +# numpydoc ignore=GL08 + +from pyrenew.randomvariable.distributionalvariable import ( + DistributionalVariable, + DynamicDistributionalVariable, + StaticDistributionalVariable, +) +from pyrenew.randomvariable.transformedvariable import TransformedVariable + +__all__ = [ + "DistributionalVariable", + "StaticDistributionalVariable", + "DynamicDistributionalVariable", + "TransformedVariable", +] diff --git a/pyrenew/randomvariable/distributionalvariable.py b/pyrenew/randomvariable/distributionalvariable.py new file mode 100644 index 0000000..671dde0 --- /dev/null +++ b/pyrenew/randomvariable/distributionalvariable.py @@ -0,0 +1,342 @@ +# numpydoc ignore=GL08 + +from typing import Callable, Self + +import numpyro +import numpyro.distributions as dist +from jax.typing import ArrayLike +from numpyro.infer.reparam import Reparam + +from pyrenew.metaclass import RandomVariable, SampledValue + + +class DynamicDistributionalVariable(RandomVariable): + """ + Wrapper class for random variables that sample + from a single :class:`numpyro.distributions.Distribution` + that is parameterized / instantiated at `sample()` time + (rather than at RandomVariable instantiation time). + """ + + def __init__( + self, + name: str, + distribution_constructor: Callable, + reparam: Reparam = None, + expand_by_shape: tuple = None, + ) -> None: + """ + Default constructor for DynamicDistributionalVariable. + + Parameters + ---------- + name : str + Name of the random variable. + distribution_constructor : Callable + Callable that returns a concrete parametrized + numpyro.Distributions.distribution instance. + reparam : numpyro.infer.reparam.Reparam + If not None, reparameterize sampling + from the distribution according to the + given numpyro reparameterizer + expand_by_shape : tuple, optional + If not None, call :meth:`expand_by()` on the + underlying distribution once it is instianted + with the given `expand_by_shape`. + Default None. + + Returns + ------- + None + """ + + self.name = name + self.validate(distribution_constructor) + self.distribution_constructor = distribution_constructor + if reparam is not None: + self.reparam_dict = {self.name: reparam} + else: + self.reparam_dict = {} + if not (expand_by_shape is None or isinstance(expand_by_shape, tuple)): + raise ValueError( + "expand_by_shape must be a tuple or be None ", + f"Got {type(expand_by_shape)}", + ) + self.expand_by_shape = expand_by_shape + + return None + + @staticmethod + def validate(distribution_constructor: any) -> None: + """ + Confirm that the distribution_constructor is + callable. + + Parameters + ---------- + distribution_constructor : any + Putative distribution_constructor to validate. + + Returns + ------- + None or raises a ValueError + """ + if not callable(distribution_constructor): + raise ValueError( + "To instantiate a DynamicDistributionalVariable, ", + "one must provide a Callable that returns a " + "numpyro.distributions.Distribution as the " + "distribution_constructor argument. " + f"Got {type(distribution_constructor)}, which " + "does not appear to be callable", + ) + return None + + def sample( + self, + args, + obs: ArrayLike = None, + *kwargs, + ) -> tuple: + """ + Sample from the distributional rv. + + Parameters + ---------- + args : + Positional arguments passed to self.distribution_constructor + obs : ArrayLike, optional + Observations passed as the `obs` argument to + :meth:`numpyro.sample()`. Default `None`. + *kwargs : dict, optional + Keyword arguments passed to self.distribution_constructor + + Returns + ------- + SampledValue + Containing a sample from the distribution. + """ + distribution = self.distribution_constructor(args, kwargs) + if self.expand_by_shape is not None: + distribution = distribution.expand_by(self.expand_by_shape) + with numpyro.handlers.reparam(config=self.reparam_dict): + sample = numpyro.sample( + name=self.name, + fn=distribution, + obs=obs, + ) + return ( + SampledValue( + sample, + t_start=self.t_start, + t_unit=self.t_unit, + ), + ) + + def expand_by(self, sample_shape) -> Self: + """ + Expand the distribution by a given + shape_shape, if possible. Returns a + new DynamicDistributionalVariable whose underlying + distribution will be expanded by the given shape + at sample() time. + + Parameters + ---------- + sample_shape : tuple + Sample shape by which to expand the distribution. + Passed to the expand_by() method of + :class:`numpyro.distributions.Distribution` + after the distribution is instantiated. + + Returns + ------- + DynamicDistributionalVariable + Whose underlying distribution will be expanded by + the given sample shape at sampling time. + """ + return DynamicDistributionalVariable( + name=self.name, + distribution_constructor=self.distribution_constructor, + reparam=self.reparam_dict.get(self.name, None), + expand_by_shape=sample_shape, + ) + + +class StaticDistributionalVariable(RandomVariable): + """ + Wrapper class for random variables that sample + from a single :class:`numpyro.distributions.Distribution` + that is parameterized / instantiated at RandomVariable + instantiation time (rather than at `sample()`-ing time). + """ + + def __init__( + self, + name: str, + distribution: numpyro.distributions.Distribution, + reparam: Reparam = None, + ) -> None: + """ + Default constructor for DistributionalVariable. + + Parameters + ---------- + name : str + Name of the random variable. + distribution : numpyro.distributions.Distribution + Distribution of the random variable. + reparam : numpyro.infer.reparam.Reparam + If not None, reparameterize sampling + from the distribution according to the + given numpyro reparameterizer + + Returns + ------- + None + """ + + self.name = name + self.validate(distribution) + self.distribution = distribution + if reparam is not None: + self.reparam_dict = {self.name: reparam} + else: + self.reparam_dict = {} + + return None + + @staticmethod + def validate(distribution: any) -> None: + """ + Validation of the distribution. + """ + if not isinstance(distribution, numpyro.distributions.Distribution): + raise ValueError( + "distribution should be an instance of " + "numpyro.distributions.Distribution, got " + "{type(distribution)}" + ) + + return None + + def sample( + self, + obs: ArrayLike \| None = None, + kwargs, + ) -> tuple: + """ + Sample from the distribution. + + Parameters + ---------- + obs : ArrayLike, optional + Observations passed as the `obs` argument to + :meth:`numpyro.sample()`. Default `None`. + kwargs : dict, optional + Additional keyword arguments passed through + to internal sample calls, should there be any. + + Returns + ------- + SampledValue + Containing a sample from the distribution. + """ + with numpyro.handlers.reparam(config=self.reparam_dict): + sample = numpyro.sample( + name=self.name, + fn=self.distribution, + obs=obs, + ) + return ( + SampledValue( + sample, + t_start=self.t_start, + t_unit=self.t_unit, + ), + ) + + def expand_by(self, sample_shape) -> Self: + """ + Expand the distribution by the given sample_shape, + if possible. Returns a new StaticDistributionalVariable + whose underlying distribution has been expanded by + the given sample_shape via + :meth:`~numpyro.distributions.Distribution.expand_by()` + + Parameters + ---------- + sample_shape : tuple + Sample shape for the expansion. Passed to the + :meth:`expand_by()` method of + :class:`numpyro.distributions.Distribution`. + + Returns + ------- + StaticDistributionalVariable + Whose underlying distribution has been expanded by + the given sample shape. + """ + if not isinstance(sample_shape, tuple): + raise ValueError( + "sample_shape for expand()-ing " + "a DistributionalVariable must be a " + f"tuple. Got {type(sample_shape)}" + ) + return StaticDistributionalVariable( + name=self.name, + distribution=self.distribution.expand_by(sample_shape), + reparam=self.reparam_dict.get(self.name, None), + ) + + +def DistributionalVariable( + name: str, + distribution: numpyro.distributions.Distribution \| Callable, + reparam: Reparam = None, +) -> RandomVariable: + """ + Factory function to generate Distributional RandomVariables, + either static or dynamic. + + Parameters + ---------- + name : str + Name of the random variable. + + distribution: numpyro.distributions.Distribution \| Callable + Either numpyro.distributions.Distribution instance + given the static distribution of the random variable or + a callable that returns a parameterized + numpyro.distributions.Distribution when called, which + allows for dynamically-parameterized DistributionalVariables, + e.g. a Normal distribution with an inferred location and + scale. + + reparam : numpyro.infer.reparam.Reparam + If not None, reparameterize sampling + from the distribution according to the + given numpyro reparameterizer + + Returns + ------- + DynamicDistributionalVariable \| StaticDistributionalVariable or + raises a ValueError if a distribution cannot be constructed. + """ + if isinstance(distribution, dist.Distribution): + return StaticDistributionalVariable( + name=name, distribution=distribution, reparam=reparam + ) + elif callable(distribution): + return DynamicDistributionalVariable( + name=name, distribution_constructor=distribution, reparam=reparam + ) + else: + raise ValueError( + "distribution argument to DistributionalVariable " + "must be either a numpyro.distributions.Distribution " + "(for instantiating a static DistributionalVariable) " + "or a callable that returns a " + "numpyro.distributions.Distribution (for " + "a dynamic DistributionalVariable" + ) diff --git a/pyrenew/randomvariable/transformedvariable.py b/pyrenew/randomvariable/transformedvariable.py new file mode 100644 index 0000000..36519a2 --- /dev/null +++ b/pyrenew/randomvariable/transformedvariable.py @@ -0,0 +1,140 @@ +# numpydoc ignore=GL08 + +import numpyro + +from pyrenew.metaclass import RandomVariable, SampledValue +from pyrenew.transformation import Transform + + +class TransformedVariable(RandomVariable): + """ + Class to represent RandomVariables defined + by taking the output of another RV's + :meth:`RandomVariable.sample()` method + and transforming it by a given transformation + (typically a :class:`Transform`) + """ + + def __init__( + self, + name: str, + base_rv: RandomVariable, + transforms: Transform \| tuple[Transform], + ): + """ + Default constructor + + Parameters + ---------- + name : str + A name for the random variable instance. + base_rv : RandomVariable + The underlying (untransformed) RandomVariable. + transforms : Transform + Transformation or tuple of transformations + to apply to the output of + `base_rv.sample()`; single values will be coerced to + a length-one tuple. If a tuple, should be the same + length as the tuple returned by `base_rv.sample()`. + + Returns + ------- + None + """ + self.name = name + self.base_rv = base_rv + + if not isinstance(transforms, tuple): + transforms = (transforms,) + self.transforms = transforms + self.validate() + + def sample(self, record=False, kwargs) -> tuple: + """ + Sample method. Call self.base_rv.sample() + and then apply the transforms specified + in self.transforms. + + Parameters + ---------- + record : bool, optional + Whether to record the value of the deterministic + RandomVariable. Defaults to False. + kwargs : + Keyword arguments passed to self.base_rv.sample() + + Returns + ------- + tuple of the same length as the tuple returned by + self.base_rv.sample() + """ + + untransformed_values = self.base_rv.sample(kwargs) + transformed_values = tuple( + SampledValue( + t(uv.value), + t_start=self.t_start, + t_unit=self.t_unit, + ) + for t, uv in zip(self.transforms, untransformed_values) + ) + + if record: + if len(untransformed_values) == 1: + numpyro.deterministic(self.name, transformed_values[0].value) + else: + suffixes = ( + untransformed_values._fields + if hasattr(untransformed_values, "_fields") + else range(len(transformed_values)) + ) + for suffix, tv in zip(suffixes, transformed_values): + numpyro.deterministic(f"{self.name}_{suffix}", tv.value) + + return transformed_values + + def sample_length(self): + """ + Sample length for a transformed + random variable must be equal to the + length of self.transforms or + validation will fail. + + Returns + ------- + int + Equal to the length self.transforms + """ + return len(self.transforms) + + def validate(self): + """ + Perform validation checks on a + TransformedVariable instance, + confirming that all transformations + are callable and that the number of + transformations is equal to the sample + length of the base random variable. + + Returns + ------- + None + on successful validation, or raise a ValueError + """ + for t in self.transforms: + if not callable(t): + raise ValueError( + "All entries in self.transforms " "must be callable" + ) + if hasattr(self.base_rv, "sample_length"): + n_transforms = len(self.transforms) + n_entries = self.base_rv.sample_length() + if not n_transforms == n_entries: + raise ValueError( + "There must be exactly as many transformations " + "specified as entries self.transforms as there are " + "entries in the tuple returned by " + "self.base_rv.sample()." + f"Got {n_transforms} transforms and {n_entries} " + "entries" + )	`randomvariable` module I think there are enough concrete but generic `RandomVariable`s that they should have their own module rather than living in `metaclass.py`, especially since they aren't actually abstract Candidates for this include `DeterministicVariable`, `DistributionalRV`, `TransformedRandomVariable`, etc. ^ This list also makes clear that we should also standardize their suffixes (versus having all of `Variable`, `RandomVariable`, and `RV`.	CDCgov/PyRenew	diff --git a/test/test_assert_sample_and_rtype.py b/test/test_assert_sample_and_rtype.py index 69a59f0..d0f9ee8 100644 --- a/test/test_assert_sample_and_rtype.py +++ b/test/test_assert_sample_and_rtype.py @@ -9,11 +9,11 @@ from numpy.testing import assert_equal from pyrenew.deterministic import DeterministicVariable, NullObservation from pyrenew.metaclass import ( - DistributionalRV, RandomVariable, SampledValue, _assert_sample_and_rtype, ) +from pyrenew.randomvariable import DistributionalVariable class RVreturnsTuple(RandomVariable): @@ -93,7 +93,7 @@ def test_input_rv(): # numpydoc ignore=GL08 valid_rv = [ NullObservation(), DeterministicVariable(name="rv1", value=jnp.array([1, 2, 3, 4])), - DistributionalRV(name="rv2", distribution=dist.Normal(0, 1)), + DistributionalVariable(name="rv2", distribution=dist.Normal(0, 1)), ] not_rv = jnp.array([1]) diff --git a/test/test_assert_type.py b/test/test_assert_type.py index 7a41cdc..a885cef 100644 --- a/test/test_assert_type.py +++ b/test/test_assert_type.py @@ -3,7 +3,8 @@ import numpyro.distributions as dist import pytest -from pyrenew.metaclass import DistributionalRV, RandomVariable, _assert_type +from pyrenew.metaclass import RandomVariable, _assert_type +from pyrenew.randomvariable import DistributionalVariable def test_valid_assertion_types(): @@ -15,7 +16,7 @@ def test_valid_assertion_types(): 5, "Hello", (1,), - DistributionalRV(name="rv", distribution=dist.Beta(1, 1)), + DistributionalVariable(name="rv", distribution=dist.Beta(1, 1)), ] arg_names = ["input_int", "input_string", "input_tuple", "input_rv"] input_types = [int, str, tuple, RandomVariable] diff --git a/test/test_differenced_process.py b/test/test_differenced_process.py index 63c2807..ba4e95c 100644 --- a/test/test_differenced_process.py +++ b/test/test_differenced_process.py @@ -10,12 +10,12 @@ import pytest from numpy.testing import assert_array_almost_equal from pyrenew.deterministic import DeterministicVariable, NullVariable -from pyrenew.metaclass import DistributionalRV from pyrenew.process import ( DifferencedProcess, IIDRandomSequence, StandardNormalSequence, ) +from pyrenew.randomvariable import DistributionalVariable @pytest.mark.parametrize( @@ -155,7 +155,7 @@ def test_manual_integrator_correctness(diffs, inits, expected_solution): [ [ IIDRandomSequence( - DistributionalRV("element_dist", dist.Cauchy(0.02, 0.3)), + DistributionalVariable("element_dist", dist.Cauchy(0.02, 0.3)), ), 3, jnp.array([0.25, 0.67, 5]), diff --git a/test/test_distributional_rv.py b/test/test_distributional_rv.py index 0a0b4d2..cebe6f8 100644 --- a/test/test_distributional_rv.py +++ b/test/test_distributional_rv.py @@ -1,6 +1,7 @@ """ Tests for the distributional RV classes """ + import jax.numpy as jnp import numpyro import numpyro.distributions as dist @@ -8,17 +9,17 @@ import pytest from numpy.testing import assert_array_equal from numpyro.distributions import ExpandedDistribution -from pyrenew.metaclass import ( - DistributionalRV, - DynamicDistributionalRV, - StaticDistributionalRV, +from pyrenew.randomvariable import ( + DistributionalVariable, + DynamicDistributionalVariable, + StaticDistributionalVariable, ) class NonCallableTestClass: """ Generic non-callable object to test - callable checking for DynamicDistributionalRV. + callable checking for DynamicDistributionalVariable. """ def __init__(self): @@ -37,9 +38,11 @@ def test_invalid_constructor_args(not_a_dist): """ with pytest.raises( - ValueError, match="distribution argument to DistributionalRV" + ValueError, match="distribution argument to DistributionalVariable" ): - DistributionalRV(name="this should fail", distribution=not_a_dist) + DistributionalVariable( + name="this should fail", distribution=not_a_dist + ) with pytest.raises( ValueError, match=( @@ -47,9 +50,9 @@ def test_invalid_constructor_args(not_a_dist): "numpyro.distributions.Distribution" ), ): - StaticDistributionalRV.validate(not_a_dist) + StaticDistributionalVariable.validate(not_a_dist) with pytest.raises(ValueError, match="must provide a Callable"): - DynamicDistributionalRV.validate(not_a_dist) + DynamicDistributionalVariable.validate(not_a_dist) @pytest.mark.parametrize( @@ -63,18 +66,18 @@ def test_invalid_constructor_args(not_a_dist): def test_factory_triage(valid_static_dist_arg, valid_dynamic_dist_arg): """ Test that passing a numpyro.distributions.Distribution - instance to the DistributionalRV factory instaniates - a StaticDistributionalRV, while passing a callable - instaniates a DynamicDistributionalRV + instance to the DistributionalVariable factory instaniates + a StaticDistributionalVariable, while passing a callable + instaniates a DynamicDistributionalVariable """ - static = DistributionalRV( + static = DistributionalVariable( name="test static", distribution=valid_static_dist_arg ) - assert isinstance(static, StaticDistributionalRV) - dynamic = DistributionalRV( + assert isinstance(static, StaticDistributionalVariable) + dynamic = DistributionalVariable( name="test dynamic", distribution=valid_dynamic_dist_arg ) - assert isinstance(dynamic, DynamicDistributionalRV) + assert isinstance(dynamic, DynamicDistributionalVariable) @pytest.mark.parametrize( @@ -97,12 +100,12 @@ def test_expand_by(dist, params, expand_by_shape): Test the expand_by method for static distributional RVs. """ - static = DistributionalRV(name="static", distribution=dist(params)) - dynamic = DistributionalRV(name="dynamic", distribution=dist) + static = DistributionalVariable(name="static", distribution=dist(params)) + dynamic = DistributionalVariable(name="dynamic", distribution=dist) expanded_static = static.expand_by(expand_by_shape) expanded_dynamic = dynamic.expand_by(expand_by_shape) - assert isinstance(expanded_dynamic, DynamicDistributionalRV) + assert isinstance(expanded_dynamic, DynamicDistributionalVariable) assert dynamic.expand_by_shape is None assert isinstance(expanded_dynamic.expand_by_shape, tuple) assert expanded_dynamic.expand_by_shape == expand_by_shape @@ -112,7 +115,7 @@ def test_expand_by(dist, params, expand_by_shape): == expanded_dynamic.distribution_constructor ) - assert isinstance(expanded_static, StaticDistributionalRV) + assert isinstance(expanded_static, StaticDistributionalVariable) assert isinstance(expanded_static.distribution, ExpandedDistribution) assert expanded_static.distribution.batch_shape == ( expand_by_shape + static.distribution.batch_shape @@ -140,15 +143,15 @@ def test_expand_by(dist, params, expand_by_shape): ) def test_sampling_equivalent(dist, params): """ - Test that sampling a DynamicDistributionalRV + Test that sampling a DynamicDistributionalVariable with a given parameterization is equivalent to - sampling a StaticDistributionalRV with the + sampling a StaticDistributionalVariable with the same parameterization and the same random seed """ - static = DistributionalRV(name="static", distribution=dist(params)) - dynamic = DistributionalRV(name="dynamic", distribution=dist) - assert isinstance(static, StaticDistributionalRV) - assert isinstance(dynamic, DynamicDistributionalRV) + static = DistributionalVariable(name="static", distribution=dist(params)) + dynamic = DistributionalVariable(name="dynamic", distribution=dist) + assert isinstance(static, StaticDistributionalVariable) + assert isinstance(dynamic, DynamicDistributionalVariable) with numpyro.handlers.seed(rng_seed=5): static_samp, _ = static() with numpyro.handlers.seed(rng_seed=5): diff --git a/test/test_forecast.py b/test/test_forecast.py index beef027..d8d1d55 100644 --- a/test/test_forecast.py +++ b/test/test_forecast.py @@ -14,9 +14,9 @@ from pyrenew.latent import ( Infections, InitializeInfectionsZeroPad, ) -from pyrenew.metaclass import DistributionalRV from pyrenew.model import RtInfectionsRenewalModel from pyrenew.observation import PoissonObservation +from pyrenew.randomvariable import DistributionalVariable def test_forecast(): @@ -28,7 +28,7 @@ def test_forecast(): gen_int = DeterministicPMF(name="gen_int", value=pmf_array) I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) diff --git a/test/test_iid_random_sequence.py b/test/test_iid_random_sequence.py index eb6d943..73b683a 100755 --- a/test/test_iid_random_sequence.py +++ b/test/test_iid_random_sequence.py @@ -6,12 +6,12 @@ import numpyro.distributions as dist import pytest from scipy.stats import kstest -from pyrenew.metaclass import ( - DistributionalRV, - SampledValue, - StaticDistributionalRV, -) +from pyrenew.metaclass import SampledValue from pyrenew.process import IIDRandomSequence, StandardNormalSequence +from pyrenew.randomvariable import ( + DistributionalVariable, + StaticDistributionalVariable, +) @pytest.mark.parametrize( @@ -29,7 +29,7 @@ def test_iidrandomsequence_with_dist_rv(distribution, n): a distributional RV, including with array-valued distributions """ - element_rv = DistributionalRV("el_rv", distribution=distribution) + element_rv = DistributionalVariable("el_rv", distribution=distribution) rseq = IIDRandomSequence(element_rv=element_rv) if distribution.batch_shape == () or distribution.batch_shape == (1,): expected_shape = (n,) @@ -63,9 +63,9 @@ def test_standard_normal_sequence(): """ norm_seq = StandardNormalSequence("test_norm_elements") - # should be implemented with a DistributionalRV + # should be implemented with a DistributionalVariable # that is a standard normal - assert isinstance(norm_seq.element_rv, StaticDistributionalRV) + assert isinstance(norm_seq.element_rv, StaticDistributionalVariable) assert isinstance(norm_seq.element_rv.distribution, dist.Normal) assert norm_seq.element_rv.distribution.loc == 0.0 assert norm_seq.element_rv.distribution.scale == 1.0 diff --git a/test/test_infection_initialization_process.py b/test/test_infection_initialization_process.py index afe91ef..069299c 100644 --- a/test/test_infection_initialization_process.py +++ b/test/test_infection_initialization_process.py @@ -11,7 +11,7 @@ from pyrenew.latent import ( InitializeInfectionsFromVec, InitializeInfectionsZeroPad, ) -from pyrenew.metaclass import DistributionalRV +from pyrenew.randomvariable import DistributionalVariable def test_infection_initialization_process(): @@ -20,14 +20,14 @@ def test_infection_initialization_process(): zero_pad_model = InfectionInitializationProcess( "zero_pad_model", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints), t_unit=1, ) exp_model = InfectionInitializationProcess( "exp_model", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsExponentialGrowth( n_timepoints, DeterministicVariable(name="rate", value=0.5) ), diff --git a/test/test_latent_admissions.py b/test/test_latent_admissions.py index 526fbc3..1e82db8 100644 --- a/test/test_latent_admissions.py +++ b/test/test_latent_admissions.py @@ -10,7 +10,8 @@ import numpyro.distributions as dist from pyrenew.deterministic import DeterministicPMF, DeterministicVariable from pyrenew.latent import HospitalAdmissions, Infections -from pyrenew.metaclass import DistributionalRV, SampledValue +from pyrenew.metaclass import SampledValue +from pyrenew.randomvariable import DistributionalVariable def test_admissions_sample(): @@ -64,7 +65,7 @@ def test_admissions_sample(): hosp1 = HospitalAdmissions( infection_to_admission_interval_rv=inf_hosp, - infection_hospitalization_ratio_rv=DistributionalRV( + infection_hospitalization_ratio_rv=DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), 0.05) ), ) diff --git a/test/test_model_basic_renewal.py b/test/test_model_basic_renewal.py index ffe09cd..1b0314f 100644 --- a/test/test_model_basic_renewal.py +++ b/test/test_model_basic_renewal.py @@ -18,9 +18,9 @@ from pyrenew.latent import ( Infections, InitializeInfectionsZeroPad, ) -from pyrenew.metaclass import DistributionalRV from pyrenew.model import RtInfectionsRenewalModel from pyrenew.observation import PoissonObservation +from pyrenew.randomvariable import DistributionalVariable def test_model_basicrenewal_no_timepoints_or_observations(): @@ -36,7 +36,7 @@ def test_model_basicrenewal_no_timepoints_or_observations(): I0_init_rv = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) @@ -72,7 +72,7 @@ def test_model_basicrenewal_both_timepoints_and_observations(): I0_init_rv = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) @@ -111,11 +111,11 @@ def test_model_basicrenewal_no_obs_model(): ) with pytest.raises(ValueError): - _ = DistributionalRV(name="I0", distribution=1) + _ = DistributionalVariable(name="I0", distribution=1) I0_init_rv = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) @@ -186,7 +186,7 @@ def test_model_basicrenewal_with_obs_model(): I0_init_rv = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) @@ -240,7 +240,7 @@ def test_model_basicrenewal_padding() -> None: # numpydoc ignore=GL08 I0_init_rv = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) diff --git a/test/test_model_hosp_admissions.py b/test/test_model_hosp_admissions.py index bb74094..f6d3d3a 100644 --- a/test/test_model_hosp_admissions.py +++ b/test/test_model_hosp_admissions.py @@ -23,9 +23,10 @@ from pyrenew.latent import ( Infections, InitializeInfectionsZeroPad, ) -from pyrenew.metaclass import DistributionalRV, RandomVariable, SampledValue +from pyrenew.metaclass import RandomVariable, SampledValue from pyrenew.model import HospitalAdmissionsModel from pyrenew.observation import PoissonObservation +from pyrenew.randomvariable import DistributionalVariable class UniformProbForTest(RandomVariable): # numpydoc ignore=GL08 @@ -91,7 +92,7 @@ def test_model_hosp_no_timepoints_or_observations(): ), ) - I0 = DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)) + I0 = DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)) latent_infections = Infections() Rt_process = SimpleRt() @@ -100,7 +101,7 @@ def test_model_hosp_no_timepoints_or_observations(): latent_admissions = HospitalAdmissions( infection_to_admission_interval_rv=inf_hosp, - infection_hospitalization_ratio_rv=DistributionalRV( + infection_hospitalization_ratio_rv=DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), 0.05) ), ) @@ -156,7 +157,7 @@ def test_model_hosp_both_timepoints_and_observations(): ), ) - I0 = DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)) + I0 = DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)) latent_infections = Infections() Rt_process = SimpleRt() @@ -164,7 +165,7 @@ def test_model_hosp_both_timepoints_and_observations(): latent_admissions = HospitalAdmissions( infection_to_admission_interval_rv=inf_hosp, - infection_hospitalization_ratio_rv=DistributionalRV( + infection_hospitalization_ratio_rv=DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), 0.05) ), ) @@ -226,7 +227,7 @@ def test_model_hosp_no_obs_model(): I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=n_initialization_points), t_unit=1, ) @@ -236,7 +237,7 @@ def test_model_hosp_no_obs_model(): latent_admissions = HospitalAdmissions( infection_to_admission_interval_rv=inf_hosp, - infection_hospitalization_ratio_rv=DistributionalRV( + infection_hospitalization_ratio_rv=DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), 0.05), ), @@ -338,7 +339,7 @@ def test_model_hosp_with_obs_model(): I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=n_initialization_points), t_unit=1, ) @@ -349,7 +350,7 @@ def test_model_hosp_with_obs_model(): latent_admissions = HospitalAdmissions( infection_to_admission_interval_rv=inf_hosp, - infection_hospitalization_ratio_rv=DistributionalRV( + infection_hospitalization_ratio_rv=DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), 0.05), ), @@ -427,7 +428,7 @@ def test_model_hosp_with_obs_model_weekday_phosp_2(): I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=n_initialization_points), t_unit=1, ) @@ -443,7 +444,7 @@ def test_model_hosp_with_obs_model_weekday_phosp_2(): infection_to_admission_interval_rv=inf_hosp, day_of_week_effect_rv=weekday, hospitalization_reporting_ratio_rv=hosp_report_prob_dist, - infection_hospitalization_ratio_rv=DistributionalRV( + infection_hospitalization_ratio_rv=DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), 0.05) ), ) @@ -518,11 +519,11 @@ def test_model_hosp_with_obs_model_weekday_phosp(): ), ) - n_initialization_points = max(gen_int.size(), inf_hosp.size()) - 1 + n_initialization_points = max(gen_int.size(), inf_hosp.size()) I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=n_initialization_points), t_unit=1, ) @@ -534,6 +535,7 @@ def test_model_hosp_with_obs_model_weekday_phosp(): # Other random components total_length = n_obs_to_generate + pad_size + total_length = n_obs_to_generate + pad_size + 1 # gen_int.size() weekday = jnp.array([1, 1, 1, 1, 2, 2, 2]) weekday = weekday / weekday.sum() @@ -553,7 +555,7 @@ def test_model_hosp_with_obs_model_weekday_phosp(): infection_to_admission_interval_rv=inf_hosp, day_of_week_effect_rv=weekday, hospitalization_reporting_ratio_rv=hosp_report_prob_dist, - infection_hospitalization_ratio_rv=DistributionalRV( + infection_hospitalization_ratio_rv=DistributionalVariable( name="IHR", distribution=dist.LogNormal(jnp.log(0.05), 0.05), ), diff --git a/test/test_predictive.py b/test/test_predictive.py index 5c76b98..636578b 100644 --- a/test/test_predictive.py +++ b/test/test_predictive.py @@ -17,15 +17,15 @@ from pyrenew.latent import ( Infections, InitializeInfectionsZeroPad, ) -from pyrenew.metaclass import DistributionalRV from pyrenew.model import RtInfectionsRenewalModel from pyrenew.observation import PoissonObservation +from pyrenew.randomvariable import DistributionalVariable pmf_array = jnp.array([0.25, 0.1, 0.2, 0.45]) gen_int = DeterministicPMF(name="gen_int", value=pmf_array) I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) diff --git a/test/test_random_key.py b/test/test_random_key.py index 6d6cfd4..0b99816 100644 --- a/test/test_random_key.py +++ b/test/test_random_key.py @@ -19,9 +19,9 @@ from pyrenew.latent import ( Infections, InitializeInfectionsZeroPad, ) -from pyrenew.metaclass import DistributionalRV from pyrenew.model import RtInfectionsRenewalModel from pyrenew.observation import PoissonObservation +from pyrenew.randomvariable import DistributionalVariable def create_test_model(): # numpydoc ignore=GL08 @@ -29,7 +29,7 @@ def create_test_model(): # numpydoc ignore=GL08 gen_int = DeterministicPMF(name="gen_int", value=pmf_array) I0 = InfectionInitializationProcess( "I0_initialization", - DistributionalRV(name="I0", distribution=dist.LogNormal(0, 1)), + DistributionalVariable(name="I0", distribution=dist.LogNormal(0, 1)), InitializeInfectionsZeroPad(n_timepoints=gen_int.size()), t_unit=1, ) diff --git a/test/test_random_walk.py b/test/test_random_walk.py index d7e2cab..6997d67 100755 --- a/test/test_random_walk.py +++ b/test/test_random_walk.py @@ -7,15 +7,16 @@ import pytest from numpy.testing import assert_almost_equal, assert_array_almost_equal from pyrenew.deterministic import DeterministicVariable -from pyrenew.metaclass import DistributionalRV, RandomVariable +from pyrenew.metaclass import RandomVariable from pyrenew.process import RandomWalk, StandardNormalRandomWalk +from pyrenew.randomvariable import DistributionalVariable @pytest.mark.parametrize( ["element_rv", "init_value"], [ - [DistributionalRV("test_normal", dist.Normal(0.5, 1)), 50.0], - [DistributionalRV("test_cauchy", dist.Cauchy(0.25, 0.25)), -3], + [DistributionalVariable("test_normal", dist.Normal(0.5, 1)), 50.0], + [DistributionalVariable("test_cauchy", dist.Cauchy(0.25, 0.25)), -3], ["test standard normal", jnp.array(3)], ], ) @@ -81,7 +82,7 @@ def test_normal_rw_samples_correctly_distributed(step_mean, step_sd): rw_normal = StandardNormalRandomWalk("test standard normal") else: rw_normal = RandomWalk( - step_rv=DistributionalRV( + step_rv=DistributionalVariable( name="rw_step_dist", distribution=dist.Normal(loc=step_mean, scale=step_sd), ), diff --git a/test/test_transformed_rv_class.py b/test/test_transformed_rv_class.py index 353d59e..22dd1c2 100644 --- a/test/test_transformed_rv_class.py +++ b/test/test_transformed_rv_class.py @@ -1,7 +1,7 @@ # -- coding: utf-8 -*- """ -Tests for TransformedRandomVariable class +Tests for TransformedVariable class """ from typing import NamedTuple @@ -13,13 +13,8 @@ import pytest from numpy.testing import assert_almost_equal import pyrenew.transformation as t -from pyrenew.metaclass import ( - DistributionalRV, - Model, - RandomVariable, - SampledValue, - TransformedRandomVariable, -) +from pyrenew.metaclass import Model, RandomVariable, SampledValue +from pyrenew.randomvariable import DistributionalVariable, TransformedVariable class LengthTwoRV(RandomVariable): @@ -129,11 +124,11 @@ class MyModel(Model): def test_transform_rv_validation(): """ - Test that a TransformedRandomVariable validation + Test that a TransformedVariable validation works as expected. """ - base_rv = DistributionalRV( + base_rv = DistributionalVariable( name="test_normal", distribution=dist.Normal(0, 1) ) base_rv.sample_length = lambda: 1 # numpydoc ignore=GL08 @@ -143,41 +138,41 @@ def test_transform_rv_validation(): test_transforms = [t.IdentityTransform(), t.ExpTransform()] for tr in test_transforms: - my_rv = TransformedRandomVariable("test_transformed_rv", base_rv, tr) + my_rv = TransformedVariable("test_transformed_rv", base_rv, tr) assert isinstance(my_rv.transforms, tuple) assert len(my_rv.transforms) == 1 assert my_rv.sample_length() == 1 not_callable_err = "All entries in self.transforms " "must be callable" sample_length_err = "There must be exactly as many transformations" with pytest.raises(ValueError, match=sample_length_err): - _ = TransformedRandomVariable( + _ = TransformedVariable( "should_error_due_to_too_many_transforms", base_rv, (tr, tr) ) with pytest.raises(ValueError, match=sample_length_err): - _ = TransformedRandomVariable( + _ = TransformedVariable( "should_error_due_to_too_few_transforms", l2_rv, tr ) with pytest.raises(ValueError, match=sample_length_err): - _ = TransformedRandomVariable( + _ = TransformedVariable( "should_also_error_due_to_too_few_transforms", l2_rv, (tr,) ) with pytest.raises(ValueError, match=not_callable_err): - _ = TransformedRandomVariable( + _ = TransformedVariable( "should_error_due_to_not_callable", l2_rv, (1,) ) with pytest.raises(ValueError, match=not_callable_err): - _ = TransformedRandomVariable( + _ = TransformedVariable( "should_error_due_to_not_callable", base_rv, (1,) ) def test_transforms_applied_at_sampling(): """ - Test that TransformedRandomVariable + Test that TransformedVariable instances correctly apply their specified transformations at sampling """ - norm_rv = DistributionalRV( + norm_rv = DistributionalVariable( name="test_normal", distribution=dist.Normal(0, 1) ) norm_rv.sample_length = lambda: 1 @@ -190,9 +185,9 @@ def test_transforms_applied_at_sampling(): t.ExpTransform().inv, t.ScaledLogitTransform(5), ]: - tr_norm = TransformedRandomVariable("transformed_normal", norm_rv, tr) + tr_norm = TransformedVariable("transformed_normal", norm_rv, tr) - tr_l2 = TransformedRandomVariable( + tr_l2 = TransformedVariable( "transformed_length_2", l2_rv, (tr, t.ExpTransform()) ) @@ -217,22 +212,24 @@ def test_transforms_applied_at_sampling(): def test_transforms_variable_naming(): """ - Tests TransformedRandomVariable name + Tests TransformedVariable name recording is as expected. """ - transformed_dist_named_base_rv = TransformedRandomVariable( + transformed_dist_named_base_rv = TransformedVariable( "transformed_rv", NamedBaseRV(), (t.ExpTransform(), t.IdentityTransform()), ) - transformed_dist_unnamed_base_rv = TransformedRandomVariable( + transformed_dist_unnamed_base_rv = TransformedVariable( "transformed_rv", - DistributionalRV(name="my_normal", distribution=dist.Normal(0, 1)), + DistributionalVariable( + name="my_normal", distribution=dist.Normal(0, 1) + ), (t.ExpTransform(), t.IdentityTransform()), ) - transformed_dist_unnamed_base_l2_rv = TransformedRandomVariable( + transformed_dist_unnamed_base_l2_rv = TransformedVariable( "transformed_rv", LengthTwoRV(), (t.ExpTransform(), t.IdentityTransform()), diff --git a/test/utils.py b/test/utils.py index be551df..ac345b4 100644 --- a/test/utils.py +++ b/test/utils.py @@ -7,13 +7,9 @@ test utilities import numpyro.distributions as dist import pyrenew.transformation as t -from pyrenew.metaclass import ( - DistributionalRV, - RandomVariable, - SampledValue, - TransformedRandomVariable, -) +from pyrenew.metaclass import RandomVariable, SampledValue from pyrenew.process import RandomWalk +from pyrenew.randomvariable import DistributionalVariable, TransformedVariable class SimpleRt(RandomVariable): @@ -37,17 +33,17 @@ class SimpleRt(RandomVariable): None """ self.name = name - self.rt_rv_ = TransformedRandomVariable( + self.rt_rv_ = TransformedVariable( name=f"{name}_log_rt_random_walk", base_rv=RandomWalk( name="log_rt", - step_rv=DistributionalRV( + step_rv=DistributionalVariable( name="rw_step_rv", distribution=dist.Normal(0, 0.025) ), ), transforms=t.ExpTransform(), ) - self.rt_init_rv_ = DistributionalRV( + self.rt_init_rv_ = DistributionalVariable( name=f"{name}_init_log_rt", distribution=dist.Normal(0, 0.2) )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_added_files", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 9 }	0.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.12", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	contourpy==1.3.1 cycler==0.12.1 fonttools==4.56.0 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work jax==0.5.3 jaxlib==0.5.3 kiwisolver==1.4.8 matplotlib==3.10.1 ml_dtypes==0.5.1 multipledispatch==1.0.0 numpy==2.2.4 numpyro==0.18.0 opt_einsum==3.4.0 packaging @ file:///croot/packaging_1734472117206/work pillow==11.1.0 pluggy @ file:///croot/pluggy_1733169602837/work polars==1.26.0 pyparsing==3.2.3 -e git+https://github.com/CDCgov/PyRenew.git@1a86104f6be3d0d2429e3358f7ea5572f8fa3799#egg=PyRenew pytest @ file:///croot/pytest_1738938843180/work python-dateutil==2.9.0.post0 scipy==1.15.2 setuptools==75.8.0 six==1.17.0 tqdm==4.67.1 wheel==0.45.1	name: PyRenew channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h5eee18b_6 - ca-certificates=2025.2.25=h06a4308_0 - expat=2.6.4=h6a678d5_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.2=py312h06a4308_0 - pip=25.0=py312h06a4308_0 - pluggy=1.5.0=py312h06a4308_0 - pytest=8.3.4=py312h06a4308_0 - python=3.12.9=h5148396_0 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py312h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py312h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - contourpy==1.3.1 - cycler==0.12.1 - fonttools==4.56.0 - jax==0.5.3 - jaxlib==0.5.3 - kiwisolver==1.4.8 - matplotlib==3.10.1 - ml-dtypes==0.5.1 - multipledispatch==1.0.0 - numpy==2.2.4 - numpyro==0.18.0 - opt-einsum==3.4.0 - pillow==11.1.0 - polars==1.26.0 - pyparsing==3.2.3 - pyrenew==0.1.0 - python-dateutil==2.9.0.post0 - scipy==1.15.2 - six==1.17.0 - tqdm==4.67.1 prefix: /opt/conda/envs/PyRenew	[ "test/test_assert_sample_and_rtype.py::test_none_rv", "test/test_assert_sample_and_rtype.py::test_input_rv", "test/test_assert_sample_and_rtype.py::test_sample_return", "test/test_assert_type.py::test_valid_assertion_types", "test/test_assert_type.py::test_invalid_assertion_types", "test/test_differenced_process.py::test_differencing_order_type_validation[test]", "test/test_differenced_process.py::test_differencing_order_type_validation[wrong_type_order1]", "test/test_differenced_process.py::test_differencing_order_type_validation[1.0]", "test/test_differenced_process.py::test_differencing_order_type_validation[wrong_type_order3]", "test/test_differenced_process.py::test_differencing_order_value_validation[0-1]", "test/test_differenced_process.py::test_differencing_order_value_validation[-5-5]", "test/test_differenced_process.py::test_differencing_order_value_validation[-10325235-300]", "test/test_differenced_process.py::test_integrator_init_validation[1-diffs0]", "test/test_differenced_process.py::test_integrator_init_validation[2-diffs1]", "test/test_differenced_process.py::test_integrator_init_validation[3-diffs2]", "test/test_differenced_process.py::test_integrator_init_validation[4-diffs3]", "test/test_differenced_process.py::test_integrator_correctness[1-250]", "test/test_differenced_process.py::test_integrator_correctness[3-10]", "test/test_differenced_process.py::test_integrator_correctness[4-10]", "test/test_differenced_process.py::test_integrator_correctness[5-5]", "test/test_differenced_process.py::test_manual_integrator_correctness[diffs0-inits0-expected_solution0]", "test/test_differenced_process.py::test_manual_integrator_correctness[diffs1-inits1-expected_solution1]", "test/test_differenced_process.py::test_differenced_process_sample[fundamental_process0-3-init_diff_vals0]", "test/test_differenced_process.py::test_differenced_process_sample[fundamental_process1-5-init_diff_vals1]", "test/test_differenced_process.py::test_manual_difference_process_sample[fundamental_process0-inits0-3-expected_solution0]", "test/test_differenced_process.py::test_manual_difference_process_sample[fundamental_process1-inits1-5-expected_solution1]", "test/test_differenced_process.py::test_manual_difference_process_sample[fundamental_process2-inits2-7-expected_solution2]", "test/test_differenced_process.py::test_manual_difference_process_sample[fundamental_process3-inits3-1-expected_solution3]", "test/test_differenced_process.py::test_manual_difference_process_sample[fundamental_process4-inits4-2-expected_solution4]", "test/test_distributional_rv.py::test_invalid_constructor_args[1]", "test/test_distributional_rv.py::test_invalid_constructor_args[test]", "test/test_distributional_rv.py::test_invalid_constructor_args[not_a_dist2]", "test/test_distributional_rv.py::test_factory_triage[valid_static_dist_arg0-Normal]", "test/test_distributional_rv.py::test_factory_triage[valid_static_dist_arg1-Cauchy]", "test/test_distributional_rv.py::test_factory_triage[valid_static_dist_arg2-Poisson]", "test/test_distributional_rv.py::test_expand_by[Normal-params0-expand_by_shape0]", "test/test_distributional_rv.py::test_expand_by[Poisson-params1-expand_by_shape1]", "test/test_distributional_rv.py::test_expand_by[Cauchy-params2-expand_by_shape2]", "test/test_distributional_rv.py::test_sampling_equivalent[Normal-params0]", "test/test_distributional_rv.py::test_sampling_equivalent[Poisson-params1]", "test/test_distributional_rv.py::test_sampling_equivalent[Cauchy-params2]", "test/test_forecast.py::test_forecast", "test/test_iid_random_sequence.py::test_iidrandomsequence_with_dist_rv[distribution0-1000]", "test/test_iid_random_sequence.py::test_iidrandomsequence_with_dist_rv[distribution1-13532]", "test/test_iid_random_sequence.py::test_iidrandomsequence_with_dist_rv[distribution2-622]", "test/test_iid_random_sequence.py::test_standard_normal_sequence", "test/test_infection_initialization_process.py::test_infection_initialization_process", "test/test_latent_admissions.py::test_admissions_sample", "test/test_model_basic_renewal.py::test_model_basicrenewal_no_timepoints_or_observations", "test/test_model_basic_renewal.py::test_model_basicrenewal_both_timepoints_and_observations", "test/test_model_basic_renewal.py::test_model_basicrenewal_no_obs_model", "test/test_model_basic_renewal.py::test_model_basicrenewal_with_obs_model", "test/test_model_basic_renewal.py::test_model_basicrenewal_padding", "test/test_model_hosp_admissions.py::test_model_hosp_no_timepoints_or_observations", "test/test_model_hosp_admissions.py::test_model_hosp_both_timepoints_and_observations", "test/test_model_hosp_admissions.py::test_model_hosp_no_obs_model", "test/test_model_hosp_admissions.py::test_model_hosp_with_obs_model", "test/test_model_hosp_admissions.py::test_model_hosp_with_obs_model_weekday_phosp_2", "test/test_model_hosp_admissions.py::test_model_hosp_with_obs_model_weekday_phosp", "test/test_predictive.py::test_posterior_predictive_no_posterior", "test/test_random_key.py::test_rng_keys_produce_correct_samples", "test/test_random_walk.py::test_rw_can_be_sampled[element_rv0-50.0]", "test/test_random_walk.py::test_rw_can_be_sampled[element_rv1--3]", "test/test_random_walk.py::test_rw_can_be_sampled[test", "test/test_random_walk.py::test_normal_rw_samples_correctly_distributed[0-1]", "test/test_random_walk.py::test_normal_rw_samples_correctly_distributed[0-0.25]", "test/test_random_walk.py::test_normal_rw_samples_correctly_distributed[2.253-0.025]", "test/test_random_walk.py::test_normal_rw_samples_correctly_distributed[-3.2521-1]", "test/test_random_walk.py::test_normal_rw_samples_correctly_distributed[1052-3]", "test/test_random_walk.py::test_normal_rw_samples_correctly_distributed[1e-06-0.02]", "test/test_transformed_rv_class.py::test_transform_rv_validation", "test/test_transformed_rv_class.py::test_transforms_applied_at_sampling", "test/test_transformed_rv_class.py::test_transforms_variable_naming" ]	[ "test/test_differenced_process.py::test_integrator_correctness[2-40]" ]	[]	[]	Apache License 2.0	null
CDCgov__PyRenew-482	bbaf1b9b629d861ba534dd5b4c5693f84676bfe8	2025-02-11 20:45:19	bbaf1b9b629d861ba534dd5b4c5693f84676bfe8	codecov[bot]: ## [Codecov](https://app.codecov.io/gh/CDCgov/PyRenew/pull/482?dropdown=coverage&src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) Report Attention: Patch coverage is `35.71429%` with `9 lines` in your changes missing coverage. Please review. > Project coverage is 95.69%. Comparing base [(`bbaf1b9`)](https://app.codecov.io/gh/CDCgov/PyRenew/commit/bbaf1b9b629d861ba534dd5b4c5693f84676bfe8?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) to head [(`6c2bd88`)](https://app.codecov.io/gh/CDCgov/PyRenew/commit/6c2bd88ffbb62b87d3134ccdf6b8471a81c01cad?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov). \| [Files with missing lines](https://app.codecov.io/gh/CDCgov/PyRenew/pull/482?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| Patch % \| Lines \| \|---\|---\|---\| \| [pyrenew/math.py](https://app.codecov.io/gh/CDCgov/PyRenew/pull/482?src=pr&el=tree&filepath=pyrenew%2Fmath.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov#diff-cHlyZW5ldy9tYXRoLnB5) \| 35.71% \| [9 Missing :warning: ](https://app.codecov.io/gh/CDCgov/PyRenew/pull/482?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| <details><summary>Additional details and impacted files</summary> ```diff @@ Coverage Diff @@ ## main #482 +/- ## ========================================== - Coverage 96.56% 95.69% -0.88% ========================================== Files 41 41 Lines 962 975 +13 ========================================== + Hits 929 933 +4 - Misses 33 42 +9 ``` \| [Flag](https://app.codecov.io/gh/CDCgov/PyRenew/pull/482/flags?src=pr&el=flags&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| Coverage Δ \| \| \|---\|---\|---\| \| [unittests](https://app.codecov.io/gh/CDCgov/PyRenew/pull/482/flags?src=pr&el=flag&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) \| `95.69% <35.71%> (-0.88%)` \| :arrow_down: \| Flags with carried forward coverage won't be shown. [Click here](https://docs.codecov.io/docs/carryforward-flags?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov#carryforward-flags-in-the-pull-request-comment) to find out more. </details> [:umbrella: View full report in Codecov by Sentry](https://app.codecov.io/gh/CDCgov/PyRenew/pull/482?dropdown=coverage&src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov). :loudspeaker: Have feedback on the report? [Share it here](https://about.codecov.io/codecov-pr-comment-feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov).	diff --git a/pyrenew/math.py b/pyrenew/math.py index db94200..b8bfd74 100755 --- a/pyrenew/math.py +++ b/pyrenew/math.py @@ -12,6 +12,146 @@ from jax.typing import ArrayLike from pyrenew.distutil import validate_discrete_dist_vector +def _positive_ints_like(vec: ArrayLike) -> jnp.ndarray: + """ + Given an array of size n, return the 1D Jax array + ``[1, ... n]``. + + Parameters + ---------- + vec: ArrayLike + The template array + + Returns + ------- + jnp.ndarray + The resulting array ``[1, ..., n]``. + """ + return jnp.arange(1, jnp.size(vec) + 1) + + +def neg_MGF(r: float, w: ArrayLike) -> float: + """ + Compute the negative moment generating function (MGF) + for a given rate ``r`` and weights ``w``. + + Parameters + ---------- + r: float + The rate parameter. + + w: ArrayLike + An array of weights. + + Returns + ------- + float + The value of the negative MGF evaluated at ``r`` + and ``w``. + + Notes + ----- + For a finite discrete random variable :math:`X` supported on + the first :math:`n` positive integers (:math:`\\{1, 2, ..., n \\}`), + the moment generating function (MGF) :math:`M_+(r)` is defined + as the expected value of :math:`\\exp(rX)`. Similarly, the negative + moment generating function :math:`M_-(r)` is the expected value of + :math:`\\exp(-rX)`. So if we represent the PMF of :math:`X` as a + "weights" vector :math:`w` of length :math:`n`, the negative MGF + :math:`M_-(r)` is given by: + + .. math:: + M_-(r) = \\sum_{t = 1}^{n} w_i \\exp(-rt) + """ + return jnp.sum(w * jnp.exp(-r * _positive_ints_like(w))) + + +def neg_MGF_del_r(r: float, w: ArrayLike) -> float: + """ + Compute the value of the partial deriative of + :func:`neg_MGF` with respect to ``r`` + evaluated at a particular ``r`` and ``w`` pair. + + Parameters + ---------- + r: float + The rate parameter. + + w: ArrayLike + An array of weights. + + Returns + ------- + float + The value of the partial derivative evaluated at ``r`` + and ``w``. + """ + t_vec = _positive_ints_like(w) + return -jnp.sum(w * t_vec * jnp.exp(-r * t_vec)) + + +def r_approx_from_R(R: float, g: ArrayLike, n_newton_steps: int) -> ArrayLike: + """ + Get the approximate asymptotic geometric growth rate ``r`` + for a renewal process with a fixed reproduction number ``R`` + and discrete generation interval PMF ``g``. + + Uses Newton's method with a fixed number of steps. + + Parameters + ---------- + R: float + The reproduction number + + g: ArrayLike + The probability mass function of the generation + interval. + + n_newton_steps: int + Number of steps to take when performing Newton's method. + + Returns + ------- + float + The approximate value of ``r``. + + Notes + ----- + For a fixed value of :math:`\\mathcal{R}`, a renewal process + has an asymptotic geometric growth rate :math:`r` that satisfies + + .. math:: + M_{-}(r) - \\frac{1}{\\mathcal{R}} = 0 + + where :math:`M_-(r)` is the negative moment generating function + for a random variable :math:`\\tau` representing the (discrete) + generation interval. See :func:`neg_MGF` for details. + + We obtain a value for :math:`r` via approximate numerical solution + of this implicit equation. + + We first make an initial guess based on the mean generation interval + :math:`\\bar{\\tau} = \\mathbb{E}(\\tau)`: + + .. math:: + r \\approx \\frac{\\mathcal{R} - 1}{\\mathcal{R} \\bar{\\tau}} + + We then refine this approximation by applying Newton's method for + a fixed number of steps. + """ + mean_gi = jnp.dot(g, _positive_ints_like(g)) + init_r = (R - 1) / (R * mean_gi) + + def _r_next(r, _) -> tuple[ArrayLike, None]: # numpydoc ignore=GL08 + return ( + r - ((R * neg_MGF(r, g) - 1) / (R * neg_MGF_del_r(r, g))), + None, + ) + + result, _ = scan(f=_r_next, init=init_r, xs=None, length=n_newton_steps) + return result + + def get_leslie_matrix( R: float, generation_interval_pmf: ArrayLike ) -> ArrayLike:	Add infection initialization at stable age distribution given R	CDCgov/PyRenew	diff --git a/test/test_leslie_matrix.py b/test/test_leslie_matrix.py deleted file mode 100755 index 3610685..0000000 --- a/test/test_leslie_matrix.py +++ /dev/null @@ -1,38 +0,0 @@ -# numpydoc ignore=GL08 - -import jax.numpy as jnp -from numpy.testing import assert_array_almost_equal - -import pyrenew.math as pmath - - -def test_get_leslie(): - """ - Test that get_leslie matrix - returns expected Leslie matrices - """ - - gi = jnp.array([0.4, 0.2, 0.2, 0.1, 0.1]) - R_a = 0.4 - R_b = 3.0 - expected_a = jnp.array( - [ - [0.16, 0.08, 0.08, 0.04, 0.04], - [1, 0, 0, 0, 0], - [0, 1, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 1, 0], - ] - ) - expected_b = jnp.array( - [ - [1.2, 0.6, 0.6, 0.3, 0.3], - [1, 0, 0, 0, 0], - [0, 1, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 1, 0], - ] - ) - - assert_array_almost_equal(pmath.get_leslie_matrix(R_a, gi), expected_a) - assert_array_almost_equal(pmath.get_leslie_matrix(R_b, gi), expected_b) diff --git a/test/test_math.py b/test/test_math.py new file mode 100644 index 0000000..bace5d4 --- /dev/null +++ b/test/test_math.py @@ -0,0 +1,142 @@ +""" +Unit tests for the pyrenew.math module. +""" + +import jax.numpy as jnp +import numpy as np +import pytest +from numpy.random import RandomState +from numpy.testing import ( + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) + +import pyrenew.math as pmath + +rng = RandomState(5) + + [email protected]( + "arr, arr_len", + [ + ([3, 1, 2], 3), + (np.ones(50), 50), + ((jnp.nan * jnp.ones(250)).reshape((50, -1)), 250), + ], +) +def test_positive_ints_like(arr, arr_len): + """ + Test the _positive_ints_like helper function. + """ + result = pmath._positive_ints_like(arr) + expected = jnp.arange(1, arr_len + 1) + assert_array_equal(result, expected) + + [email protected]( + "R, G", + [ + (5, rng.dirichlet(np.ones(2))), + (0.2, rng.dirichlet(np.ones(50))), + (1, rng.dirichlet(np.ones(10))), + (1.01, rng.dirichlet(np.ones(4))), + (0.99, rng.dirichlet(np.ones(6))), + ], +) +def test_r_approx(R, G): + """ + Test that r_approx_from_R gives answers + consistent with those gained from a Leslie + matrix approach. + """ + r_val = pmath.r_approx_from_R(R, G, n_newton_steps=5) + e_val, stable_dist = pmath.get_asymptotic_growth_rate_and_age_dist(R, G) + + unnormed = r_val * stable_dist + if r_val != 0: + assert_array_almost_equal(unnormed / np.sum(unnormed), stable_dist) + else: + assert_almost_equal(e_val, 1, decimal=5) + + +def test_asymptotic_properties(): + """ + Check that the calculated + asymptotic growth rate and + age distribution given by + get_asymptotic_growth_rate() + and get_stable_age_distribution() + agree with simulated ones from + just running a process for a + while. + """ + R = 1.2 + gi = np.array([0.2, 0.1, 0.2, 0.15, 0.05, 0.025, 0.025, 0.25]) + A = pmath.get_leslie_matrix(R, gi) + + # check via Leslie matrix multiplication + x = np.array([1, 0, 0, 0, 0, 0, 0, 0]) + for i in range(1000): + x_new = A @ x + rat_x = np.sum(x_new) / np.sum(x) + x = x_new + + assert_almost_equal( + rat_x, pmath.get_asymptotic_growth_rate(R, gi), decimal=5 + ) + assert_array_almost_equal( + x / np.sum(x), pmath.get_stable_age_distribution(R, gi) + ) + + # check via backward-looking convolution + y = np.array([1, 0, 0, 0, 0, 0, 0, 0]) + for j in range(1000): + new_pop = np.dot(y, R * gi) + rat_y = new_pop / y[0] + y = np.hstack([new_pop, y[:-1]]) + assert_almost_equal( + rat_y, pmath.get_asymptotic_growth_rate(R, gi), decimal=5 + ) + assert_array_almost_equal( + y / np.sum(x), pmath.get_stable_age_distribution(R, gi) + ) + + [email protected]( + "R, gi, expected", + [ + ( + 0.4, + np.array([0.4, 0.2, 0.2, 0.1, 0.1]), + np.array( + [ + [0.16, 0.08, 0.08, 0.04, 0.04], + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, 0], + ] + ), + ), + ( + 3, + np.array([0.4, 0.2, 0.2, 0.1, 0.1]), + np.array( + [ + [1.2, 0.6, 0.6, 0.3, 0.3], + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 1, 0], + ] + ), + ), + ], +) +def test_get_leslie(R, gi, expected): + """ + Test that get_leslie matrix + returns expected Leslie matrices + """ + assert_array_almost_equal(pmath.get_leslie_matrix(R, gi), expected) diff --git a/test/test_process_asymptotics.py b/test/test_process_asymptotics.py deleted file mode 100755 index 25079c9..0000000 --- a/test/test_process_asymptotics.py +++ /dev/null @@ -1,49 +0,0 @@ -# numpydoc ignore=GL08 - -import jax.numpy as jnp -from numpy.testing import assert_almost_equal, assert_array_almost_equal - -import pyrenew.math as pmath - - -def test_asymptotic_properties(): - """ - Check that the calculated - asymptotic growth rate and - age distribution given by - get_asymptotic_growth_rate() - and get_stable_age_distribution() - agree with simulated ones from - just running a process for a - while. - """ - R = 1.2 - gi = jnp.array([0.2, 0.1, 0.2, 0.15, 0.05, 0.025, 0.025, 0.25]) - A = pmath.get_leslie_matrix(R, gi) - - # check via Leslie matrix multiplication - x = jnp.array([1, 0, 0, 0, 0, 0, 0, 0]) - for i in range(1000): - x_new = A @ x - rat_x = jnp.sum(x_new) / jnp.sum(x) - x = x_new - - assert_almost_equal( - rat_x, pmath.get_asymptotic_growth_rate(R, gi), decimal=5 - ) - assert_array_almost_equal( - x / jnp.sum(x), pmath.get_stable_age_distribution(R, gi) - ) - - # check via backward-looking convolution - y = jnp.array([1, 0, 0, 0, 0, 0, 0, 0]) - for j in range(1000): - new_pop = jnp.dot(y, R * gi) - rat_y = new_pop / y[0] - y = jnp.hstack([new_pop, y[:-1]]) - assert_almost_equal( - rat_y, pmath.get_asymptotic_growth_rate(R, gi), decimal=5 - ) - assert_array_almost_equal( - y / jnp.sum(x), pmath.get_stable_age_distribution(R, gi) - )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 3, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 1 }	0.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.12", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work jax==0.5.3 jaxlib==0.5.3 ml_dtypes==0.5.1 multipledispatch==1.0.0 numpy==2.2.4 numpyro==0.18.0 opt_einsum==3.4.0 packaging @ file:///croot/packaging_1734472117206/work pluggy @ file:///croot/pluggy_1733169602837/work polars==1.26.0 -e git+https://github.com/CDCgov/PyRenew.git@bbaf1b9b629d861ba534dd5b4c5693f84676bfe8#egg=PyRenew pytest @ file:///croot/pytest_1738938843180/work scipy==1.15.2 setuptools==75.8.0 tqdm==4.67.1 wheel==0.45.1	name: PyRenew channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h5eee18b_6 - ca-certificates=2025.2.25=h06a4308_0 - expat=2.6.4=h6a678d5_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.2=py312h06a4308_0 - pip=25.0=py312h06a4308_0 - pluggy=1.5.0=py312h06a4308_0 - pytest=8.3.4=py312h06a4308_0 - python=3.12.9=h5148396_0 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py312h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py312h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - jax==0.5.3 - jaxlib==0.5.3 - ml-dtypes==0.5.1 - multipledispatch==1.0.0 - numpy==2.2.4 - numpyro==0.18.0 - opt-einsum==3.4.0 - polars==1.26.0 - pyrenew==0.1.0 - scipy==1.15.2 - tqdm==4.67.1 prefix: /opt/conda/envs/PyRenew	[ "test/test_math.py::test_positive_ints_like[arr0-3]", "test/test_math.py::test_positive_ints_like[arr1-50]", "test/test_math.py::test_positive_ints_like[arr2-250]", "test/test_math.py::test_r_approx[5-G0]", "test/test_math.py::test_r_approx[0.2-G1]", "test/test_math.py::test_r_approx[1-G2]", "test/test_math.py::test_r_approx[1.01-G3]", "test/test_math.py::test_r_approx[0.99-G4]" ]	[]	[ "test/test_math.py::test_asymptotic_properties", "test/test_math.py::test_get_leslie[0.4-gi0-expected0]", "test/test_math.py::test_get_leslie[3-gi1-expected1]" ]	[]	Apache License 2.0	null
CDCgov__cfa-viral-lineage-model-51	060d63ef58c3d6ed310c15593aad0475df4a1f43	2024-09-10 20:10:48	317d85ffdb77f6659c9f3ef3aac017d25b60de29	swo: I generally really like where this is going. As you can see, I borrowed a lot of this thinking for https://github.com/CDCgov/cfa-immunization-uptake-projection/pull/3. I think we're facing a design choice about what an "evaluator" is (ie how much it does), versus a "score". My current leaning is toward this setup: 1. A score is a function that works at the level of one unit of analysis. It takes in one (set of) predictions (ie posterior samples), a matching set of real/true/observed data, and returns a single number. - For the typical forecasts, a unit of analysis is a date/geography combination. - For the "time to domination" predictions, the unit of analysis is the geography, over many dates. 2. An "evaluator" is either a function or an object, that does some level of things. There are a few options. - Option A (minimum): The evaluator aggregates the unit-of-analysis-level scores into a model-level score. This could also be a function: that takes in a score function, a prediction dataset, and an evaluation (truth/target) dataset. - For typical forecasts, this might be a sum or a mean or something. - For time-to-domination, the aggregation is the trivial identity operation. - Option B (medium), the evaluator takes one (or more?) fitted models and an evaluation (truth) dataset, tells the models to generate the needed predictions, then scores and aggregates. Again, this could be a function, but it's starting to feel more like an object, because it's doing message passing ("hey object, give me xyz predictions for xyz dates"). - Option C (maximum): At a maximum level, the evaluator takes the unfitted models, a calibration dataset, and an evaluation dataset. It fits the models, then does everything the medium-level evaluator would do. The medium & maximum options are harder to conceptualize as solitary Python objects when we're talking about distributed computing. They're more like elements of the pipeline, that could be realized as Python objects if everything is done in one computer. I do think there's also merit in having data object types (as per https://github.com/CDCgov/cfa-immunization-uptake-projection/pull/3/files). This was a score function can ask "is this thing the right type?" before doing anything with it. (If it wants to be really careful, it can force a `input_data.validate()` call, which someone noted somewhere is confusing because validation might require a `.collect()` call.) That might be mitigated by this "callback" architecture, where the evaluator is asking the fitted model for only exactly the stuff it needs. One advantage here is that the evaluator might ask for `my_model.predict(my_dates, my_counts, metric="count")`. The model might error and say it doesn't know how to do counts. Or it might predict phi and then look at its superclass for a convert-phi-to-counts method. (I guess, if the things the models will output are only ever phi or counts, then we don't need any of that. Or if the models will only ever output phi, and we will always convert to counts with multinomial sampling a la #50, then we don't need this.) afmagee42: Also flagging for later that we may want to play with the choice of multinomial sampler here. In #50 I used numpyro's and it was somewhat ponderous for this many draws. There may not be much getting around that, but if we're going to do this many times on many datasets, it could be worth a few hour's testing between different library options and different batching schemes to save the down-stream computation. swo: > Also flagging for later that we may want to play with the choice of multinomial sampler here. In #50 I used numpyro's and it was somewhat ponderous for this many draws. There may not be much getting around that, but if we're going to do this many times on many datasets, it could be worth a few hour's testing between different library options and different batching schemes to save the down-stream computation. See #70	diff --git a/README.md b/README.md index 5580414..95758a3 100644 --- a/README.md +++ b/README.md @@ -57,17 +57,6 @@ Rows are uniquely identified by `(fd_offset, division, lineage, sample_index)`. - [ ] (9) Study more on how to set priors on the logit scale to induce priors on the probability simplex - [ ] (10) Does our ability to identify "good" models change if we evaluate daily vs weekly predictions? -\| Sprint \| Start Date \| Target milestones \| Notes \| -\| ------ \| ---------- \| ----------------- \| -------------------------------- \| -\| L \| Jun 10 \| 1 \| \| -\| M \| Jun 24 \| 1, 2 \| \| -\| N \| Jul 08 \| 2, 3 \| \| -\| O \| Jul 22 \| 3 \| \| -\| P \| Aug 05 \| 3, 4 \| Thanasi at JSM for one week here \| -\| Q \| Aug 19 \| 4, 5, 8 \| \| -\| R \| Sep 2 \| \| \| -\| S \| Sep 16 \| \| \| - ### A ladder of (multinomial logistic regression) models for consideration It seems useful to start at the bottom of the ladder, both for debugging purposes, but also to get a sense of the added predictive power of each step. diff --git a/linmod/data.py b/linmod/data.py index 9b42408..65f977f 100755 --- a/linmod/data.py +++ b/linmod/data.py @@ -31,7 +31,7 @@ import polars as pl import yaml import zstandard -from .utils import ValidPath, print_message +from .utils import ValidPath, expand_grid, print_message DEFAULT_CONFIG = { "data": { @@ -289,18 +289,39 @@ def main(cfg: Optional[dict]): .then(pl.col("lineage")) .otherwise(pl.lit("other")) ) + .collect() + ) + + # Generate every combination of date-division-lineage, so that: + # 1. The evaluation dataset will be evaluation-ready, with 0 counts + # where applicable + # 2. The modeling dataset will have every lineage of interest represented, + # even if a lineage was only sampled in the evaluation period + observations_key = expand_grid( + date=full_df["date"].unique(), + division=full_df["division"].unique(), + lineage=full_df["lineage"].unique(), ) eval_df = ( full_df.group_by("lineage", "date", "division") .agg(count=pl.len()) + .join( + observations_key, + on=("date", "division", "lineage"), + how="right", + ) .with_columns( - fd_offset=(pl.col("date") - forecast_date).dt.total_days() + fd_offset=(pl.col("date") - forecast_date).dt.total_days(), + count=pl.col("count").fill_null(0), ) .select("date", "fd_offset", "division", "lineage", "count") - .collect() ) + assert ( + eval_df.null_count().sum_horizontal().item() == 0 + ), "Null values detected in evaluation dataset." + eval_df.write_parquet(ValidPath(config["data"]["save_file"]["eval"])) print_message(" done.") @@ -310,13 +331,24 @@ def main(cfg: Optional[dict]): full_df.filter(pl.col("date_submitted") <= forecast_date) .group_by("lineage", "date", "division") .agg(count=pl.len()) + .join( + observations_key, + on=("date", "division", "lineage"), + how="right", + ) .with_columns( - fd_offset=(pl.col("date") - forecast_date).dt.total_days() + fd_offset=(pl.col("date") - forecast_date).dt.total_days(), + count=pl.col("count").fill_null(0), ) .select("date", "fd_offset", "division", "lineage", "count") - .collect() + # Remove division-days where no samples were collected, for brevity + .filter(pl.sum("count").over("date", "division") > 0) ) + assert ( + model_df.null_count().sum_horizontal().item() == 0 + ), "Null values detected in modeling dataset." + model_df.write_parquet(ValidPath(config["data"]["save_file"]["model"])) print_message(" done.") diff --git a/linmod/eval.py b/linmod/eval.py index 53fe51f..78cb55b 100644 --- a/linmod/eval.py +++ b/linmod/eval.py @@ -1,124 +1,264 @@ +import numpy as np import polars as pl +from numpy.typing import ArrayLike from linmod.utils import pl_list_cycle, pl_norm -def _merge_samples_and_data(samples, data): - r""" - Join the forecast samples and raw data dataframes, assuming they have the - standard format. +def multinomial_count_sampler( + n: ArrayLike, + p: ArrayLike, + rng: np.random.Generator, +) -> np.ndarray: + """ + Samples from a `multinomial(n, p)` distribution. - Also compute the true proportions from the raw data. + Compatible shapes of `n` and `p` include: + - `n` is a scalar, `p` is a vector + - `n` is a vector, `p` is a matrix with rows corresponding to entries in `n` """ - result = ( - data.with_columns( - phi=( - pl.col("count") / pl.sum("count").over("fd_offset", "division") - ), + + return rng.multinomial(n, p) + + +class ProportionsEvaluator: + def __init__(self, samples: pl.LazyFrame, data: pl.LazyFrame): + """ + `samples` should have the standard model output format. + `data` should have the standard model input format. + """ + + # Join the forecast samples and raw data dataframes. + # Also compute the true proportions from the raw data. + self.df = ( + data.with_columns( + phi=( + pl.col("count") + / pl.sum("count").over("fd_offset", "division") + ), + ) + .drop("count") + # NaN will appear on division-days where none of the lineages + # were sampled. We will filter these out. + .filter(pl.col("phi").is_not_nan()) + .join( + samples, + on=("fd_offset", "division", "lineage"), + how="left", + suffix="_sampled", + ) ) - .drop("count") - .join( - samples, - on=("fd_offset", "division", "lineage"), - how="left", - suffix="_sampled", + + def _mean_norm_per_division_day(self, p=1) -> pl.LazyFrame: + r""" + The expected norm of proportion forecast error for each division-day. + + $E[ \|\| f_{tg} - \phi_{tg} \|\|_p ]$ + + Returns a data frame with columns `(division, fd_offset, mean_norm)`. + """ + + return ( + self.df.group_by("fd_offset", "division", "sample_index") + .agg(norm=pl_norm(pl.col("phi") - pl.col("phi_sampled"), p)) + .group_by("fd_offset", "division") + .agg(mean_norm=pl.mean("norm")) ) - ) - return result + def mean_norm(self, p=1) -> float: + r""" + The expected norm of proportion forecast error, summed over all divisions + and days. + $\sum_{t, g} E[ \|\| f_{tg} - \phi_{tg} \|\|_p ]$ + """ -def proportions_mean_norm_per_division_day(samples, data, p=1): - r""" - The expected norm of proportion forecast error for each division-day. + return ( + self._mean_norm_per_division_day(p=p) + .collect() + .get_column("mean_norm") + .sum() + ) - $E[ \|\| f_{tg} - \phi_{tg} \|\|_p ]$ + def _energy_score_per_division_day(self, p=2) -> pl.LazyFrame: + r""" + Monte Carlo approximation to the energy score (multivariate generalization of + CRPS) of proportion forecasts for each division-day. - `samples` should have the standard model output format. - `data` should have the standard model input format. + $E[ \|\| f_{tg} - \phi_{tg} \|\|_p ] - \frac{1}{2} E[ \|\| f_{tg} - f_{tg}' \|\|_p ]$ - Returns a DataFrame with columns `(division, fd_offset, mean_norm)`. - """ + Returns a data frame with columns `(division, fd_offset, energy_score)`. + """ - return ( - _merge_samples_and_data(samples, data) - .group_by("fd_offset", "division", "sample_index") - .agg(norm=pl_norm(pl.col("phi") - pl.col("phi_sampled"), p)) - .group_by("fd_offset", "division") - .agg(mean_norm=pl.mean("norm")) - ) + return ( + # First, we will gather the values of phi' we will use for (phi-phi') + self.df.group_by("date", "fd_offset", "division", "lineage") + .agg(pl.col("sample_index"), pl.col("phi"), pl.col("phi_sampled")) + .with_columns(replicate=pl_list_cycle(pl.col("phi_sampled"), 1)) + .explode("sample_index", "phi", "phi_sampled", "replicate") + # Now we can compute the score + .group_by("fd_offset", "division", "sample_index") + .agg( + term1=pl_norm(pl.col("phi") - pl.col("phi_sampled"), p), + term2=pl_norm( + (pl.col("phi_sampled") - pl.col("replicate")), p + ), + ) + .group_by("fd_offset", "division") + .agg( + energy_score=pl.col("term1").mean() + - 0.5 * pl.col("term2").mean() + ) + ) + def energy_score(self, p=2) -> float: + r""" + The energy score of proportion forecasts, summed over all divisions and days. -def proportions_mean_norm( - samples: pl.LazyFrame, data: pl.LazyFrame, p=1 -) -> float: - r""" - The expected norm of proportion forecast error, summed over all divisions and days. + $$ + \sum_{t, g} E[ \|\| f_{tg} - \phi_{tg} \|\|_p ] + - \frac{1}{2} E[ \|\| f_{tg} - f_{tg}' \|\|_p ] + $$ + """ - $\sum_{t, g} E[ \|\| f_{tg} - \phi_{tg} \|\|_p ]$ - """ + return ( + self._energy_score_per_division_day(p=p) + .collect() + .get_column("energy_score") + .sum() + ) - return ( - proportions_mean_norm_per_division_day(samples, data, p=p) - .collect() - .get_column("mean_norm") - .sum() - ) +class CountsEvaluator: + _count_samplers = { + "multinomial": multinomial_count_sampler, + } -def proportions_energy_score_per_division_day(samples, data, p=2): - r""" - Monte Carlo approximation to the energy score (multivariate generalization of CRPS) - of proportion forecasts for each division-day. + def __init__( + self, + samples: pl.LazyFrame, + data: pl.LazyFrame, + count_sampler: str = "multinomial", + seed: int = None, + ): + r""" + Evaluates count forecasts $\hat{Y}$ sampled from a specified observation model given model + proportion forecasts. - $E[ \|\| f_{tg} - \phi_{tg} \|\|_p ] - \frac{1}{2} E[ \|\| f_{tg} - \f_{tg}' \|\|_p ]$ + `samples` should have the standard model output format. + `data` should have the standard model input format. + `count_sampler` should be one of the keys in `CountsEvaluator._count_samplers`. + `seed` is an optional random seed for the count sampler. + """ - `samples` should have the standard model output format. - `data` should have the standard model input format. + assert count_sampler in type(self)._count_samplers, ( + f"Count sampler '{count_sampler}' not found. " + f"Available samplers: {', '.join(type(self)._count_samplers)}" + ) + count_sampler = type(self)._count_samplers[count_sampler] - Returns a DataFrame with columns `(division, fd_offset, energy_score)`. - """ + rng = np.random.default_rng(seed) - # First, we will gather the values of phi' we will use for (phi-phi') - samples = ( - samples.group_by("fd_offset", "division", "lineage") - .agg(pl.col("sample_index"), pl.col("phi")) - .with_columns( - sample_index=pl_list_cycle(pl.col("sample_index"), 1), - replicate=pl_list_cycle(pl.col("phi"), 1), + self.df = ( + data.join( + samples.rename({"phi": "phi_sampled"}), + on=("fd_offset", "division", "lineage"), + how="left", + ) + .group_by("date", "fd_offset", "division", "sample_index") + .agg(pl.col("lineage"), pl.col("phi_sampled"), pl.col("count")) + .with_columns( + count_sampled=pl.struct( + pl.col("phi_sampled"), N=pl.col("count").list.sum() + ).map_elements( + lambda struct: list( + count_sampler(struct["N"], struct["phi_sampled"], rng) + ), + return_dtype=pl.List(pl.Int64), + ) + ) + .explode("lineage", "phi_sampled", "count", "count_sampled") + .drop("phi_sampled") ) - .explode("sample_index", "phi", "replicate") - ) - - return ( - _merge_samples_and_data(samples, data) - .group_by("fd_offset", "division", "sample_index") - .agg( - term1=pl_norm(pl.col("phi") - pl.col("phi_sampled"), p), - term2=pl_norm((pl.col("phi_sampled") - pl.col("replicate")), p), + + def _mean_norm_per_division_day(self, p=1) -> pl.LazyFrame: + r""" + The expected norm of count forecast error for each division-day. + + $E[ \|\| \hat{Y}_{tg} - Y_{tg} \|\|_p ]$ + + Returns a data frame with columns `(division, fd_offset, mean_norm)`. + """ + + return ( + self.df.group_by("fd_offset", "division", "sample_index") + .agg(norm=pl_norm(pl.col("count") - pl.col("count_sampled"), p)) + .group_by("fd_offset", "division") + .agg(mean_norm=pl.mean("norm")) ) - .group_by("fd_offset", "division") - .agg( - energy_score=pl.col("term1").mean() - 0.5 * pl.col("term2").mean() + + def mean_norm(self, p=1) -> float: + r""" + The expected norm of count forecast error, summed over all divisions + and days. + + $\sum_{t, g} E[ \|\| \hat{Y}_{tg} - Y_{tg} \|\|_p ]$ + """ + + return ( + self._mean_norm_per_division_day(p=p) + .collect() + .get_column("mean_norm") + .sum() ) - ) + def _energy_score_per_division_day(self, p=2) -> pl.LazyFrame: + r""" + Monte Carlo approximation to the energy score (multivariate generalization of + CRPS) of count forecasts for each division-day. -def proportions_energy_score( - samples: pl.LazyFrame, data: pl.LazyFrame, p=2 -) -> float: - r""" - The energy score of proportion forecasts, summed over all divisions and days. + $E[ \|\| \hat{Y}_{tg} - Y_{tg} \|\|_p ] - \frac{1}{2} E[ \|\| \hat{Y}_{tg} - \hat{Y}_{tg}' \|\|_p ]$ - $$ - \sum_{t, g} E[ \|\| f_{tg} - \phi_{tg} \|\|_p ] - - \frac{1}{2} E[ \|\| f_{tg} - f_{tg}' \|\|_p ] - $$ - """ + Returns a data frame with columns `(division, fd_offset, energy_score)`. + """ - return ( - proportions_energy_score_per_division_day(samples, data, p=p) - .collect() - .get_column("energy_score") - .sum() - ) + return ( + # First, we will gather the values of count' we will use for (count-count') + self.df.group_by("date", "fd_offset", "division", "lineage") + .agg( + pl.col("sample_index"), + pl.col("count"), + pl.col("count_sampled"), + ) + .with_columns(replicate=pl_list_cycle(pl.col("count_sampled"), 1)) + .explode("sample_index", "count", "count_sampled", "replicate") + # Now we can compute the score + .group_by("fd_offset", "division", "sample_index") + .agg( + term1=pl_norm(pl.col("count") - pl.col("count_sampled"), p), + term2=pl_norm( + (pl.col("count_sampled") - pl.col("replicate")), p + ), + ) + .group_by("fd_offset", "division") + .agg( + energy_score=pl.col("term1").mean() + - 0.5 * pl.col("term2").mean() + ) + ) + + def energy_score(self, p=2) -> float: + r""" + The energy score of count forecasts, summed over all divisions and days. + + $$ + \sum_{t, g} E[ \|\| \hat{Y}_{tg} - Y_{tg} \|\|_p ] + - \frac{1}{2} E[ \|\| \hat{Y}_{tg} - \hat{Y}_{tg}' \|\|_p ] + $$ + """ + return ( + self._energy_score_per_division_day(p=p) + .collect() + .get_column("energy_score") + .sum() + ) diff --git a/retrospective-forecasting/config/early-21L.yaml b/retrospective-forecasting/config/early-21L.yaml index 4d3c95d..73dcea3 100644 --- a/retrospective-forecasting/config/early-21L.yaml +++ b/retrospective-forecasting/config/early-21L.yaml @@ -66,5 +66,6 @@ evaluation: # How should forecasts be evaluated? metrics: - - proportions_mean_norm - - proportions_energy_score + - ProportionsEvaluator + - CountsEvaluator: + - count_sampler: multinomial diff --git a/retrospective-forecasting/config/late-21L.yaml b/retrospective-forecasting/config/late-21L.yaml index 39d2274..44d4dec 100644 --- a/retrospective-forecasting/config/late-21L.yaml +++ b/retrospective-forecasting/config/late-21L.yaml @@ -66,5 +66,6 @@ evaluation: # How should forecasts be evaluated? metrics: - - proportions_mean_norm - - proportions_energy_score + - ProportionsEvaluator + - CountsEvaluator: + - count_sampler: multinomial diff --git a/retrospective-forecasting/config/mid-21L.yaml b/retrospective-forecasting/config/mid-21L.yaml index 2ab4fef..20bf414 100644 --- a/retrospective-forecasting/config/mid-21L.yaml +++ b/retrospective-forecasting/config/mid-21L.yaml @@ -66,5 +66,6 @@ evaluation: # How should forecasts be evaluated? metrics: - - proportions_mean_norm - - proportions_energy_score + - ProportionsEvaluator + - CountsEvaluator: + - count_sampler: multinomial diff --git a/retrospective-forecasting/main.py b/retrospective-forecasting/main.py index 61548d9..dd856b7 100755 --- a/retrospective-forecasting/main.py +++ b/retrospective-forecasting/main.py @@ -56,7 +56,7 @@ with open(plot_script_file, "w") as plot_script: plot_script.write("#/usr/bin/sh\n") for model_name in config["forecasting"]["models"]: print_message(f"Fitting {model_name} model...") - model_class = linmod.models.__dict__[model_name] + model_class = getattr(linmod.models, model_name) model = model_class(model_data) mcmc = MCMC( @@ -69,42 +69,29 @@ with open(plot_script_file, "w") as plot_script: mcmc.run(jax.random.key(0)) try: + convergence_config = config["forecasting"]["mcmc"]["convergence"] + convergence = linmod.models.get_convergence( mcmc, ignore_nan_in=model.ignore_nan_in(), drop_ignorable_nan=False, ) - if ( - config["forecasting"]["mcmc"]["convergence"]["report_mode"] - == "failing" - ): + if convergence_config["report_mode"] == "failing": convergence = convergence.filter( - ( - pl.col("n_eff") - < config["forecasting"]["mcmc"]["convergence"][ - "ess_cutoff" - ] - ) - \| ( - pl.col("r_hat") - > config["forecasting"]["mcmc"]["convergence"][ - "psrf_cutoff" - ] - ) + (pl.col("n_eff") < convergence_config["ess_cutoff"]) + \| (pl.col("r_hat") > convergence_config["psrf_cutoff"]) ) convergence.write_parquet( forecast_dir / f"convergence_{model_name}.parquet" ) - if ( - config["forecasting"]["mcmc"]["convergence"]["plot"] - and convergence.shape[0] > 0 - ): + if convergence_config["plot"] and convergence.shape[0] > 0: plot_dir = forecast_dir / ("convergence_" + model_name) plots = linmod.models.plot_convergence( mcmc, convergence["param"] ) + for plot, par in zip(plots, convergence["param"].to_list()): plot.save(plot_dir / (par + ".png"), verbose=False) @@ -169,7 +156,6 @@ with open(plot_script_file, "w") as plot_script: del model, mcmc, forecast # Load the full evaluation dataset - eval_data = pl.read_parquet(config["data"]["save_file"]["eval"]) viz_data = eval_data.filter( @@ -181,44 +167,67 @@ with open(plot_script_file, "w") as plot_script: eval_dir = ValidPath(config["evaluation"]["save_dir"]) scores = [] - for metric_name in config["evaluation"]["metrics"]: - metric_function = linmod.eval.__dict__[metric_name] - for forecast_path in forecast_dir.glob("forecasts_.parquet"): - model_name = forecast_path.stem.split("_")[1] - print_message( - f"Evaluating {model_name} model using {metric_name}...", end="" + for forecast_path in forecast_dir.glob("forecasts_.parquet"): + model_name = forecast_path.stem.split("_")[1] + forecast = pl.scan_parquet(forecast_path) + + for evaluator_config in config["evaluation"]["metrics"]: + if isinstance(evaluator_config, dict): + assert ( + len(evaluator_config) == 1 + ), "Evaluator config is formatted incorrectly." + + evaluator_config = list(evaluator_config.items()) + evaluator_name = evaluator_config[0][0] + evaluator_args = { + k: v for d in evaluator_config[0][1] for k, v in d.items() + } + + else: + evaluator_name = evaluator_config + evaluator_args = {} + + evaluator = getattr(linmod.eval, evaluator_name)( + samples=forecast, + data=eval_data.lazy(), + **evaluator_args, ) - forecast = pl.read_parquet(forecast_path) + for metric_name, metric_function in vars(type(evaluator)).items(): + if metric_name.startswith("_"): + continue - forecast = forecast.lazy() - scores.append( - ( - metric_name, - model_name, - metric_function(forecast, eval_data.lazy()), + print_message( + ( + f"Evaluating {model_name} model using " + f"{evaluator_name}.{metric_name}..." + ), + end="", ) - ) - if ( - metric_name == config["evaluation"]["metrics"][0] - ): # One plot per model - data_path = config["data"]["save_file"]["eval"] - png_path = ( - eval_dir / "visualizations" / f"eval_{model_name}.png" - ) - plot_script.write( + scores.append( ( - "python3 -m linmod.visualize " - f"-f {forecast_path} " - f"-d {data_path} " - f"-p {png_path} " - "-t eval\n" + f"{evaluator_name}.{metric_name}", + model_name, + metric_function(evaluator), ) ) - print_message(" done.") + print_message(" done.") + + data_path = config["data"]["save_file"]["eval"] + png_path = eval_dir / "visualizations" / f"eval_{model_name}.png" + plot_script.write( + ( + "python3 -m linmod.visualize " + f"-f {forecast_path} " + f"-d {data_path} " + f"-p {png_path} " + "-t eval\n" + ) + ) + print_message("Success!") @@ -226,4 +235,4 @@ pl.DataFrame( scores, schema=["Metric", "Model", "Score"], orient="row", -).write_parquet(eval_dir / "results.parquet") +).write_csv(eval_dir / "results.csv")	Syncing lineages between model and eval data I'm looking at the pair of `model.csv` and `eval.csv` for a 2022-01-01 forecast date, and the evaluation data has one more lineage than the model data, namely `22C`. We need to make sure that the set of lineages is consistent across modeling and evaluation. Part of the awkwardness here is hindsight, in that we have said 22C is a lineage we want to model for all three of the forecast dates in `retrospective-forecasting/config` even though it emerges after the first calibration period. I think the generally correct thing to do is to make sure such lineages get labeled as "other" but there may be the possibility that we know we want to model a lineage but just don't happen to see it? (Something which has attracted attention elsewhere, but not been imported yet, for instance.)	CDCgov/cfa-viral-lineage-model	diff --git a/linmod/tests/test_eval.py b/linmod/tests/test_eval.py index 51767a5..33e1f5c 100644 --- a/linmod/tests/test_eval.py +++ b/linmod/tests/test_eval.py @@ -32,18 +32,27 @@ def _generate_fake_samples_and_data( lineage=range(num_lineages), ) - data = data.with_columns(count=rng.integers(0, 100, data.shape[0])) + data = data.with_columns( + count=rng.integers(0, 100, data.shape[0]), + date=pl.col("fd_offset"), + ) # Generate fake forecasts of population proportions - samples = eval._merge_samples_and_data( - expand_grid( - fd_offset=range(num_days), - division=range(num_divisions), - lineage=range(num_lineages), - sample_index=range(num_samples), - ), - data, - ).rename({"phi": "phi_mean"}) + samples = expand_grid( + fd_offset=range(num_days), + division=range(num_divisions), + lineage=range(num_lineages), + sample_index=range(num_samples), + ).join( + data.with_columns( + phi_mean=( + pl.col("count") / pl.sum("count").over("fd_offset", "division") + ), + ).drop("count"), + on=("fd_offset", "division", "lineage"), + how="left", + suffix="_sampled", + ) samples = samples.with_columns( phi=rng.normal(samples["phi_mean"], np.sqrt(sample_variance)) @@ -91,7 +100,7 @@ def test_proportions_mean_L1_norm( # $\|X - \mu\| \sim \text{half-normal}(\sigma)$, which has this mean). assert np.isclose( - eval.proportions_mean_norm(samples, data, p=1), + eval.ProportionsEvaluator(samples, data).mean_norm(p=1), np.sqrt(sample_variance * 2 / np.pi) * NUM_DAYS * NUM_DIVISIONS @@ -157,9 +166,11 @@ def test_proportions_mean_L1_norm2(): } ) - result = eval.proportions_mean_norm_per_division_day( - samples, data, p=1 - ).collect() + result = ( + eval.ProportionsEvaluator(samples, data) + ._mean_norm_per_division_day(p=1) + .collect() + ) assert_frame_equal( result, @@ -232,7 +243,7 @@ def test_proportions_L1_energy_score( ) assert np.isclose( - eval.proportions_energy_score(samples, data, p=1), + eval.ProportionsEvaluator(samples, data).energy_score(p=1), term1 - 0.5 * term2, atol=atol, ) @@ -335,9 +346,11 @@ def test_proportions_L1_energy_score2(): } ) - result = eval.proportions_energy_score_per_division_day( - samples, data, p=1 - ).collect() + result = ( + eval.ProportionsEvaluator(samples, data) + ._energy_score_per_division_day(p=1) + .collect() + ) assert_frame_equal( result,	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 1 }, "num_modified_files": 7 }	.	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.12", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	babel==2.17.0 backrefs==5.8 certifi==2025.1.31 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 contourpy==1.3.1 cycler==0.12.1 fonttools==4.56.0 ghp-import==2.1.0 griffe==1.7.1 idna==3.10 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work jax==0.5.3 jaxlib==0.5.3 Jinja2==3.1.6 kiwisolver==1.4.8 -e git+https://github.com/CDCgov/cfa-viral-lineage-model.git@060d63ef58c3d6ed310c15593aad0475df4a1f43#egg=linmod Markdown==3.7 MarkupSafe==3.0.2 matplotlib==3.10.1 mdx-truly-sane-lists==1.3 mergedeep==1.3.4 mizani==0.11.4 mkdocs==1.6.1 mkdocs-autorefs==1.4.1 mkdocs-get-deps==0.2.0 mkdocs-material==9.6.10 mkdocs-material-extensions==1.3.1 mkdocstrings==0.25.2 mkdocstrings-python==1.10.9 ml_dtypes==0.5.1 multipledispatch==1.0.0 numpy==2.2.4 numpyro==0.15.3 opt_einsum==3.4.0 packaging @ file:///croot/packaging_1734472117206/work paginate==0.5.7 pandas==2.2.3 pathspec==0.12.1 patsy==1.0.1 pillow==11.1.0 platformdirs==4.3.7 plotnine==0.13.6 pluggy @ file:///croot/pluggy_1733169602837/work polars==1.26.0 pyarrow==17.0.0 Pygments==2.19.1 pymdown-extensions==10.14.3 pyparsing==3.2.3 pytest @ file:///croot/pytest_1738938843180/work python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 pyyaml_env_tag==0.1 requests==2.32.3 scipy==1.15.2 setuptools==75.8.0 six==1.17.0 statsmodels==0.14.4 tqdm==4.67.1 tzdata==2025.2 urllib3==2.3.0 watchdog==6.0.0 wheel==0.45.1 zstandard==0.22.0	name: cfa-viral-lineage-model channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h5eee18b_6 - ca-certificates=2025.2.25=h06a4308_0 - expat=2.6.4=h6a678d5_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.2=py312h06a4308_0 - pip=25.0=py312h06a4308_0 - pluggy=1.5.0=py312h06a4308_0 - pytest=8.3.4=py312h06a4308_0 - python=3.12.9=h5148396_0 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py312h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py312h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - babel==2.17.0 - backrefs==5.8 - certifi==2025.1.31 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - contourpy==1.3.1 - cycler==0.12.1 - fonttools==4.56.0 - ghp-import==2.1.0 - griffe==1.7.1 - idna==3.10 - jax==0.5.3 - jaxlib==0.5.3 - jinja2==3.1.6 - kiwisolver==1.4.8 - linmod==0.1.0 - markdown==3.7 - markupsafe==3.0.2 - matplotlib==3.10.1 - mdx-truly-sane-lists==1.3 - mergedeep==1.3.4 - mizani==0.11.4 - mkdocs==1.6.1 - mkdocs-autorefs==1.4.1 - mkdocs-get-deps==0.2.0 - mkdocs-material==9.6.10 - mkdocs-material-extensions==1.3.1 - mkdocstrings==0.25.2 - mkdocstrings-python==1.10.9 - ml-dtypes==0.5.1 - multipledispatch==1.0.0 - numpy==2.2.4 - numpyro==0.15.3 - opt-einsum==3.4.0 - paginate==0.5.7 - pandas==2.2.3 - pathspec==0.12.1 - patsy==1.0.1 - pillow==11.1.0 - platformdirs==4.3.7 - plotnine==0.13.6 - polars==1.26.0 - pyarrow==17.0.0 - pygments==2.19.1 - pymdown-extensions==10.14.3 - pyparsing==3.2.3 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - pyyaml-env-tag==0.1 - requests==2.32.3 - scipy==1.15.2 - six==1.17.0 - statsmodels==0.14.4 - tqdm==4.67.1 - tzdata==2025.2 - urllib3==2.3.0 - watchdog==6.0.0 - zstandard==0.22.0 prefix: /opt/conda/envs/cfa-viral-lineage-model	[ "linmod/tests/test_eval.py::test_proportions_mean_L1_norm", "linmod/tests/test_eval.py::test_proportions_mean_L1_norm2", "linmod/tests/test_eval.py::test_proportions_L1_energy_score", "linmod/tests/test_eval.py::test_proportions_L1_energy_score2" ]	[]	[]	[]	Apache License 2.0	null
CLOVER-energy__CLOVER-96	38b1c3e63dc6fb3db8f1406fa8f32db8b39cc71c	2022-07-01 15:19:21	3f6a3e111767e7bf98a0afc3e991526fed11c180		diff --git a/src/clover/simulation/storage.py b/src/clover/simulation/storage.py index 9b4a58a..44061d6 100644 --- a/src/clover/simulation/storage.py +++ b/src/clover/simulation/storage.py @@ -640,16 +640,43 @@ def get_electric_battery_storage_profile( # pylint: disable=too-many-locals, to grid_energy = pd.DataFrame([0] * (end_hour - start_hour)) # as needed for load remaining_profile = (grid_energy[0] <= 0).mul(load_energy[0]) # type: ignore + logger.debug( + "Remainig profile: %s kWh", + round(float(np.sum(remaining_profile)), 2), # type: ignore [arg-type] + ) # Then take energy from PV if generated + logger.debug( + "Renewables profile: %s kWh", + f"{round(float(np.sum(renewables_energy)), 2)}", # type: ignore [arg-type] + ) battery_storage_profile = pd.DataFrame( renewables_energy[0].values - remaining_profile.values # type: ignore ) + logger.debug( + "Storage profile: %s kWh", + f"{round(float(np.sum(battery_storage_profile)), 2)}", # type: ignore [arg-type] + ) + renewables_energy_used_directly = pd.DataFrame( - (battery_storage_profile > 0) # type: ignore - .mul(remaining_profile[0]) # type: ignore - .add((battery_storage_profile < 0).mul(renewables_energy)) # type: ignore + ((renewables_energy[0] > 0) * (remaining_profile > 0)) # type: ignore [call-overload] + * pd.concat( # type: ignore [call-overload] + [renewables_energy[0], remaining_profile], axis=1 # type: ignore [call-overload] + ).min(axis=1) + ) + + logger.debug( + "Grid energy: %s kWh", + f"{round(float(np.sum(grid_energy)), 2)}", # type: ignore [arg-type] + ) + renewables_direct_rounded: float = round( + float(np.sum(renewables_energy_used_directly)), 2 # type: ignore [arg-type] + ) + logger.debug( + "Renewables direct: %s kWh", + round(float(np.sum(renewables_energy_used_directly)), 2), # type: ignore [arg-type] ) + logger.debug("Renewables direct: %s kWh", renewables_direct_rounded) battery_storage_profile.columns = pd.Index([ColumnHeader.STORAGE_PROFILE.value]) grid_energy.columns = pd.Index([ColumnHeader.GRID_ENERGY.value])	Prioitise self-generation bug ### Describe the bug When running CLOVER, there are two possible flows for how the code selects its source of power, which are set within the `scenario_inputs.yaml` file: - `prioiritise_self_generation: true`, where CLOVER should attempt to get power from the solar and storage first, - and `prioritise_self_generation: false`, where CLOVER should get its power from the grid first. However, when running a CLOVER `optimisation`, there is no difference between these two flows, with the same outputs being selected despite the second scenario being expected to produce systems with a lower renewable capacity due to the reliance on the grid as the primary source of power. ### To Reproduce Steps to reproduce the behavior: 1. Set up two identical scenarios in the `scenario_inputs.yaml` file with the only difference between the two being the `prioritise_self_generation` flag; 2. Set up two identical optimisations in the `optimisation_inputs.yaml` file with the only difference between the two being the scenario that each uses; 3. Run an optimisation with `python -u -m src.clover --location <location_name> --optimisation`; 4. See error. ### Expected behaviour The two scenario should produce different results, with the `true` scenario selecting a higher renewable capacity and the `false` scenario selecting a smaller renewable capacity. ### Screenshots Screenshots are not needed to explain this problem. ## Additional Information ### Desktop (please complete the following information) - OS: Linux, applies to all - Installation setup: downloaded source from GitHub - Version: 5.0.1b1 - Branch: `dev` ### Modifications This version is up-to-date with `origin/dev`.	CLOVER-energy/CLOVER	diff --git a/src/clover/tests/integration/test_clover.py b/src/clover/tests/integration/test_clover.py index 38aa070..d2ec24e 100644 --- a/src/clover/tests/integration/test_clover.py +++ b/src/clover/tests/integration/test_clover.py @@ -480,24 +480,24 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods ) self._check_output( info_file_data, - average_daily_diesel=1.65, + average_daily_diesel=0.0, average_daily_grid_energy=7.196, average_daily_grid_times=9.338, - average_daily_renewables_energy=4.262, + average_daily_renewables_energy=3.893, average_daily_storage_energy=7.52, - blackouts=0.1, - cumulative_cost=32249.869, - cumulative_ghgs=91620.383, + blackouts=0.029, + cumulative_cost=31641.481, + cumulative_ghgs=84941.765, cumulative_pv_generation=36685.0, - diesel_capacity=3.0, - diesel_times=0.12, + diesel_capacity=0.0, + diesel_times=0.0, final_pv_size=19.0, final_storage_size=21.34, initial_pv_size=20.0, initial_storage_size=25.0, - lcue=1.124, - renewables_fraction=0.571, - unmet_energy_fraction=0.018, + lcue=1.212, + renewables_fraction=0.613, + unmet_energy_fraction=0.031, ) @pytest.mark.integrest @@ -515,24 +515,24 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods ) self._check_output( info_file_data, - average_daily_diesel=8.821, + average_daily_diesel=6.568, average_daily_grid_energy=7.196, average_daily_grid_times=9.338, - average_daily_renewables_energy=4.262, + average_daily_renewables_energy=3.893, average_daily_storage_energy=0.0, blackouts=0.1, - cumulative_cost=26343.181, - cumulative_ghgs=100650.761, + cumulative_cost=25059.446, + cumulative_ghgs=109974.46, cumulative_pv_generation=36685.0, diesel_capacity=3.0, - diesel_times=0.394, + diesel_times=0.202, final_pv_size=19.0, final_storage_size=0.0, initial_pv_size=20.0, initial_storage_size=0.0, - lcue=0.934, - renewables_fraction=0.21, - unmet_energy_fraction=0.016, + lcue=0.95, + renewables_fraction=0.22, + unmet_energy_fraction=0.062, ) @pytest.mark.integrest @@ -626,24 +626,24 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods ) self._check_output( info_file_data, - average_daily_diesel=5.548, + average_daily_diesel=2.116, average_daily_grid_energy=0.0, average_daily_grid_times=0.0, - average_daily_renewables_energy=4.271, + average_daily_renewables_energy=5.801, average_daily_storage_energy=9.807, - blackouts=0.099, - cumulative_cost=32209.939, - cumulative_ghgs=91493.592, + blackouts=0.1, + cumulative_cost=33821.539, + cumulative_ghgs=96230.431, cumulative_pv_generation=36685.0, diesel_capacity=3.0, - diesel_times=0.356, + diesel_times=0.071, final_pv_size=19.0, final_storage_size=20.227, initial_pv_size=20.0, initial_storage_size=25.0, - lcue=1.179, - renewables_fraction=0.717, - unmet_energy_fraction=0.011, + lcue=1.309, + renewables_fraction=0.881, + unmet_energy_fraction=0.084, ) @pytest.mark.integrest @@ -661,24 +661,24 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods ) self._check_output( info_file_data, - average_daily_diesel=14.879, + average_daily_diesel=12.193, average_daily_grid_energy=0.0, average_daily_grid_times=0.0, - average_daily_renewables_energy=4.271, + average_daily_renewables_energy=5.801, average_daily_storage_energy=0.0, blackouts=0.1, - cumulative_cost=29025.727, - cumulative_ghgs=104115.842, + cumulative_cost=26548.761, + cumulative_ghgs=108666.131, cumulative_pv_generation=36685.0, diesel_capacity=3.0, - diesel_times=0.726, + diesel_times=0.443, final_pv_size=19.0, final_storage_size=0.0, initial_pv_size=20.0, initial_storage_size=0.0, - lcue=1.092, - renewables_fraction=0.223, - unmet_energy_fraction=0.011, + lcue=0.997, + renewables_fraction=0.322, + unmet_energy_fraction=0.045, ) @pytest.mark.integrest @@ -775,11 +775,11 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods average_daily_diesel=0.0, average_daily_grid_energy=7.196, average_daily_grid_times=9.338, - average_daily_renewables_energy=4.262, + average_daily_renewables_energy=3.893, average_daily_storage_energy=7.52, - blackouts=0.22, - cumulative_cost=31728.191, - cumulative_ghgs=85650.33, + blackouts=0.029, + cumulative_cost=31641.481, + cumulative_ghgs=84941.765, cumulative_pv_generation=36685.0, diesel_capacity=0.0, diesel_times=0.0, @@ -787,9 +787,9 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods final_storage_size=21.34, initial_pv_size=20.0, initial_storage_size=25.0, - lcue=1.172, - renewables_fraction=0.621, - unmet_energy_fraction=0.105, + lcue=1.212, + renewables_fraction=0.613, + unmet_energy_fraction=0.031, ) @pytest.mark.integrest @@ -810,11 +810,11 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods average_daily_diesel=0.0, average_daily_grid_energy=7.196, average_daily_grid_times=9.338, - average_daily_renewables_energy=4.262, + average_daily_renewables_energy=3.893, average_daily_storage_energy=0.0, - blackouts=0.493, - cumulative_cost=33980.723, - cumulative_ghgs=196112.02, + blackouts=0.302, + cumulative_cost=33894.013, + cumulative_ghgs=195403.455, cumulative_pv_generation=36685.0, diesel_capacity=0.0, diesel_times=0.0, @@ -822,9 +822,9 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods final_storage_size=0.0, initial_pv_size=20.0, initial_storage_size=0.0, - lcue=1.256, - renewables_fraction=0.372, - unmet_energy_fraction=0.485, + lcue=1.329, + renewables_fraction=0.351, + unmet_energy_fraction=0.411, ) @pytest.mark.integrest @@ -922,11 +922,11 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods average_daily_diesel=0.0, average_daily_grid_energy=0.0, average_daily_grid_times=0.0, - average_daily_renewables_energy=4.271, + average_daily_renewables_energy=5.801, average_daily_storage_energy=9.807, - blackouts=0.454, - cumulative_cost=34386.293, - cumulative_ghgs=102913.66, + blackouts=0.171, + cumulative_cost=34260.245, + cumulative_ghgs=101882.52, cumulative_pv_generation=36685.0, diesel_capacity=0.0, diesel_times=0.0, @@ -934,9 +934,9 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods final_storage_size=20.227, initial_pv_size=20.0, initial_storage_size=25.0, - lcue=1.534, + lcue=1.415, renewables_fraction=1.0, - unmet_energy_fraction=0.306, + unmet_energy_fraction=0.196, ) @pytest.mark.integrest @@ -957,11 +957,11 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods average_daily_diesel=0.0, average_daily_grid_energy=0.0, average_daily_grid_times=0.0, - average_daily_renewables_energy=4.271, + average_daily_renewables_energy=5.801, average_daily_storage_energy=0.0, - blackouts=0.826, - cumulative_cost=41931.345, - cumulative_ghgs=256032.195, + blackouts=0.543, + cumulative_cost=41805.298, + cumulative_ghgs=255001.055, cumulative_pv_generation=36685.0, diesel_capacity=0.0, diesel_times=0.0, @@ -969,9 +969,9 @@ class SimulationTests(_BaseTest): # pylint: disable=too-many-public-methods final_storage_size=0.0, initial_pv_size=20.0, initial_storage_size=0.0, - lcue=3.249, + lcue=2.548, renewables_fraction=1.0, - unmet_energy_fraction=0.801, + unmet_energy_fraction=0.692, ) @pytest.mark.integtest	{ "commit_name": "head_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 1 }	5.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.7", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	astroid==2.15.8 black==23.3.0 certifi @ file:///croot/certifi_1671487769961/work/certifi charset-normalizer==3.4.1 click==8.1.8 -e git+https://github.com/CLOVER-energy/CLOVER.git@38b1c3e63dc6fb3db8f1406fa8f32db8b39cc71c#egg=clover_energy cycler==0.11.0 dill==0.3.7 exceptiongroup==1.2.2 fonttools==4.38.0 idna==3.10 importlib-metadata==6.7.0 iniconfig==2.0.0 isort==5.11.5 joblib==1.3.2 kiwisolver==1.4.5 lazy-object-proxy==1.9.0 matplotlib==3.5.3 mccabe==0.7.0 mypy==1.4.1 mypy-extensions==1.0.0 numpy==1.21.6 packaging==24.0 pandas==1.3.5 pandas-stubs==1.2.0.62 pathspec==0.11.2 Pillow==9.5.0 platformdirs==4.0.0 pluggy==1.2.0 pylint==2.17.7 pyparsing==3.1.4 pytest==7.4.4 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.1 requests==2.31.0 scikit-learn==1.0.2 scipy==1.7.3 seaborn==0.12.2 six==1.17.0 threadpoolctl==3.1.0 tomli==2.0.1 tomlkit==0.12.5 tqdm==4.67.1 typed-ast==1.5.5 types-requests==2.31.0.20231231 typing_extensions==4.7.1 urllib3==2.0.7 wrapt==1.16.0 zipp==3.15.0	name: CLOVER channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - astroid==2.15.8 - black==23.3.0 - charset-normalizer==3.4.1 - click==8.1.8 - clover-energy==5.0.4 - cycler==0.11.0 - dill==0.3.7 - exceptiongroup==1.2.2 - fonttools==4.38.0 - idna==3.10 - importlib-metadata==6.7.0 - iniconfig==2.0.0 - isort==5.11.5 - joblib==1.3.2 - kiwisolver==1.4.5 - lazy-object-proxy==1.9.0 - matplotlib==3.5.3 - mccabe==0.7.0 - mypy==1.4.1 - mypy-extensions==1.0.0 - numpy==1.21.6 - packaging==24.0 - pandas==1.3.5 - pandas-stubs==1.2.0.62 - pathspec==0.11.2 - pillow==9.5.0 - platformdirs==4.0.0 - pluggy==1.2.0 - pylint==2.17.7 - pyparsing==3.1.4 - pytest==7.4.4 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.1 - requests==2.31.0 - scikit-learn==1.0.2 - scipy==1.7.3 - seaborn==0.12.2 - six==1.17.0 - threadpoolctl==3.1.0 - tomli==2.0.1 - tomlkit==0.12.5 - tqdm==4.67.1 - typed-ast==1.5.5 - types-requests==2.31.0.20231231 - typing-extensions==4.7.1 - urllib3==2.0.7 - wrapt==1.16.0 - zipp==3.15.0 prefix: /opt/conda/envs/CLOVER	[ "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_and_pv", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_grid_and_pv", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_grid_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_grid_and_pv", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_grid_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_pv_only" ]	[]	[ "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_and_grid", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_grid_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_diesel_only", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_grid_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_grid_only", "src/clover/tests/integration/test_clover.py::SimulationTests::test_grid_prioritise_storage_only", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_and_grid", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_and_pv", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_grid_and_pv", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_grid_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_grid_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_only", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_diesel_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_grid_and_pv", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_grid_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_grid_only", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_grid_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_pv_and_storage", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_pv_only", "src/clover/tests/integration/test_clover.py::SimulationTests::test_self_prioritise_storage_only" ]	[]	MIT License	null
CMakePP__CMinx-151	ea6d33ebc88237c5f2dd3a1d166d7bc4b078223f	2023-04-09 02:03:19	51558674c3d9bf1f3afaef48bd66b3f74aefb028	codecov[bot]: ## [Codecov](https://codecov.io/gh/CMakePP/CMinx/pull/151?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP) Report Patch coverage: `100.00`% and no project coverage change. > Comparison is base [(`e51acd0`)](https://codecov.io/gh/CMakePP/CMinx/commit/e51acd0bc52fbd974a9ff1931c10f42403e0a0f8?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP) 97.09% compared to head [(`cf8f647`)](https://codecov.io/gh/CMakePP/CMinx/pull/151?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP) 97.10%. <details><summary>Additional details and impacted files</summary> ```diff @@ Coverage Diff @@ ## master #151 +/- ## ======================================= Coverage 97.09% 97.10% ======================================= Files 8 8 Lines 930 932 +2 ======================================= + Hits 903 905 +2 Misses 27 27 ``` \| Flag \| Coverage Δ \| \| \|---\|---\|---\| \| unittests \| `97.10% <100.00%> (+<0.01%)` \| :arrow_up: \| Flags with carried forward coverage won't be shown. [Click here](https://docs.codecov.io/docs/carryforward-flags?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP#carryforward-flags-in-the-pull-request-comment) to find out more. \| [Impacted Files](https://codecov.io/gh/CMakePP/CMinx/pull/151?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP) \| Coverage Δ \| \| \|---\|---\|---\| \| [src/cminx/aggregator.py](https://codecov.io/gh/CMakePP/CMinx/pull/151?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP#diff-c3JjL2NtaW54L2FnZ3JlZ2F0b3IucHk=) \| `95.27% <100.00%> (+0.01%)` \| :arrow_up: \| \| [src/cminx/config.py](https://codecov.io/gh/CMakePP/CMinx/pull/151?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP#diff-c3JjL2NtaW54L2NvbmZpZy5weQ==) \| `100.00% <100.00%> (ø)` \| \| Help us with your feedback. Take ten seconds to tell us [how you rate us](https://about.codecov.io/nps?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP). Have a feature suggestion? [Share it here.](https://app.codecov.io/gh/feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP) </details> [:umbrella: View full report in Codecov by Sentry](https://codecov.io/gh/CMakePP/CMinx/pull/151?src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP). :loudspeaker: Do you have feedback about the report comment? [Let us know in this issue](https://about.codecov.io/codecov-pr-comment-feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CMakePP).	diff --git a/docs/source/developer/api.rst b/docs/source/developer/api.rst index a397b37..e836a7e 100644 --- a/docs/source/developer/api.rst +++ b/docs/source/developer/api.rst @@ -25,10 +25,12 @@ Python Modules .. autosummary:: :toctree: .autosummary - - cminx.rstwriter - cminx.documenter cminx.aggregator + cminx.config + cminx.documenter + cminx.documentation_types + cminx.exceptions + cminx.rstwriter cminx.parser.CMakeLexer cminx.parser.CMakeParser cminx.parser.CMakeListener diff --git a/src/cminx/aggregator.py b/src/cminx/aggregator.py index a7861d3..71d43e0 100755 --- a/src/cminx/aggregator.py +++ b/src/cminx/aggregator.py @@ -12,13 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +This module interfaces with the generated CMake parser. +The primary entrypoint is :class:`~DocumentationAggregator`, +which subclasses :class:`cminx.parser.CMakeListener`. This +class listens to all relevent parser rules, generates +:class:`cminx.documentation_types.DocumentationType` +objects that represent the parsed CMake, and +aggregates them in a single list. + +:Author: Branden Butler +:License: Apache 2.0 +""" + import logging -from typing import List +import re +from dataclasses import dataclass +from typing import List, Union + +from antlr4 import ParserRuleContext from .documentation_types import AttributeDocumentation, FunctionDocumentation, MacroDocumentation, \ VariableDocumentation, GenericCommandDocumentation, ClassDocumentation, TestDocumentation, SectionDocumentation, \ MethodDocumentation, VarType, CTestDocumentation, ModuleDocumentation, AbstractCommandDefinitionDocumentation, \ - OptionDocumentation, DanglingDoccomment + OptionDocumentation, DanglingDoccomment, DocumentationType from .exceptions import CMakeSyntaxException from .parser.CMakeListener import CMakeListener @@ -27,50 +44,64 @@ from .parser.CMakeListener import CMakeListener from .parser.CMakeParser import CMakeParser from cminx import Settings -""" -This module interfaces with the generated CMake parser. -It also subclasses CMakeListener to aggregate and further -process documented commands on-the-fly. - -Processed documentation is stored in two different types of named tuples -depending on the type being documented. VariableDocumentation also stores -what type the variable is, either String or List for most purposes but also -supports Unset in case someone wants to document why something is unset. - -:Author: Branden Butler -:License: Apache 2.0 -""" - +@dataclass class DefinitionCommand: - def __init__(self, documentation: AbstractCommandDefinitionDocumentation, should_document=True): - self.documentation = documentation - self.should_document = should_document + """ + A container for documentation of definition commands, used to update + the parameter list when a :code:`cmake_parse_arguments()` command + is encountered. + + A definition command is a command that defines another command, + so the function() and macro() commands are definition commands. + Instances of this dataclass are placed on the top of a stack + when definition commands are encountered. + """ + documentation: Union[AbstractCommandDefinitionDocumentation, None] + """ + The documentation for the definition command. If None, + this DefinitionCommand will be ignored when popped. + """ + + should_document: bool = True + """ + Whether the contained documentation object should be updated + if a :code:`cmake_parse_arguments()` command is found. + When false, this object is ignored. + """ class DocumentationAggregator(CMakeListener): """ Processes all docstrings and their associated commands, aggregating - them in a list. + them in a list. Uses the given :class:`~cminx.config.Settings` object + to determine aggregation settings, and will document commands without + doccomments if the associated settings are set. + + The method used to generate the documentation object for + a given command is chosen by searching for a method with the name + :code:`"process_<command name>"` with two arguments: :code:`ctx` that is + of type :class:`cminx.parser.CMakeParser.Command_invocationContext`, + and :code:`docstring` that is of type string. """ - def __init__(self, settings: Settings = Settings()): - self.settings = settings + def __init__(self, settings: Settings = Settings()) -> None: + self.settings: Settings = settings """Application settings used to determine what commands to document""" - self.documented = [] + self.documented: List[DocumentationType] = [] """All current documented commands""" - self.documented_classes_stack = [] + self.documented_classes_stack: List[Union[ClassDocumentation, None]] = [] """A stack containing the documented classes and inner classes as they are processed""" - self.documented_awaiting_function_def = None + self.documented_awaiting_function_def: Union[DocumentationType, None] = None """ A variable containing a documented command such as cpp_member() that is awaiting its function/macro definition """ - self.definition_command_stack = [] + self.definition_command_stack: List[DefinitionCommand] = [] """ A stack containing the current definition command that we are inside. A definition command is any command that defines a construct with both a @@ -79,14 +110,14 @@ class DocumentationAggregator(CMakeListener): function(), macro(), and cpp_class(). """ - self.consumed = [] + self.consumed: List[ParserRuleContext] = [] """ A list containing all of the contexts that have already been processed """ - self.logger = logging.getLogger(__name__) + self.logger: logging.Logger = logging.getLogger(__name__) - def process_function(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_function(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts function name and declared parameters. @@ -106,7 +137,9 @@ class DocumentationAggregator(CMakeListener): ctx.start.line ) - params = [p.getText() for p in def_params[1:]] + params = [ + re.sub(self.settings.input.function_parameter_name_strip_regex, "", p.getText()) for p in def_params[1:] + ] function_name = def_params[0].getText() has_kwargs = self.settings.input.kwargs_doc_trigger_string in docstring @@ -115,7 +148,7 @@ class DocumentationAggregator(CMakeListener): self.documented.append(doc) self.definition_command_stack.append(DefinitionCommand(doc)) - def process_macro(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_macro(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts macro name and declared parameters. @@ -135,7 +168,9 @@ class DocumentationAggregator(CMakeListener): ctx.start.line ) - params = [p.getText() for p in def_params[1:]] + params = [ + re.sub(self.settings.input.macro_parameter_name_strip_regex, "", p.getText()) for p in def_params[1:] + ] macro_name = def_params[0].getText() has_kwargs = self.settings.input.kwargs_doc_trigger_string in docstring @@ -144,9 +179,11 @@ class DocumentationAggregator(CMakeListener): self.documented.append(doc) self.definition_command_stack.append(DefinitionCommand(doc)) - def process_cmake_parse_arguments(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_cmake_parse_arguments(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Determines whether a documented function or macro uses args or kwargs. + Accesses the last element in the :code:`definition_command_stack` to + update the documentation's :code:`has_kwargs` field. :param ctx: Documented command context. Constructed by the Antlr4 parser. @@ -158,7 +195,7 @@ class DocumentationAggregator(CMakeListener): AbstractCommandDefinitionDocumentation): last_element.documentation.has_kwargs = True - def process_ct_add_test(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_ct_add_test(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts test name and declared parameters. @@ -198,7 +235,7 @@ class DocumentationAggregator(CMakeListener): self.documented.append(test_doc) self.documented_awaiting_function_def = test_doc - def process_ct_add_section(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_ct_add_section(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts section name and declared parameters. @@ -237,7 +274,7 @@ class DocumentationAggregator(CMakeListener): self.documented.append(section_doc) self.documented_awaiting_function_def = section_doc - def process_set(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_set(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts variable name and values from the documented set command. Also determines the type of set command/variable: String, List, or Unset. @@ -280,7 +317,7 @@ class DocumentationAggregator(CMakeListener): self.documented.append(VariableDocumentation( varname, docstring, VarType.UNSET, None)) - def process_cpp_class(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_cpp_class(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts the name and the declared superclasses from the documented cpp_class command. @@ -315,7 +352,7 @@ class DocumentationAggregator(CMakeListener): self.documented_classes_stack.append(clazz) def process_cpp_member(self, ctx: CMakeParser.Command_invocationContext, docstring: str, - is_constructor: bool = False): + is_constructor: bool = False) -> None: """ Extracts the method name and declared parameter types from the documented cpp_member command. @@ -362,13 +399,13 @@ class DocumentationAggregator(CMakeListener): clazz.members.append(method_doc) self.documented_awaiting_function_def = method_doc - def process_cpp_constructor(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_cpp_constructor(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Alias for calling process_cpp_member() with is_constructor=True. """ self.process_cpp_member(ctx, docstring, is_constructor=True) - def process_cpp_attr(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_cpp_attr(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts the name and any default values from the documented cpp_attr command. @@ -405,7 +442,7 @@ class DocumentationAggregator(CMakeListener): clazz.attributes.append(AttributeDocumentation( name, docstring, parent_class, default_values)) - def process_add_test(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_add_test(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts information from a CTest add_test() command. Note: this is not the processor for the CMakeTest ct_add_test() command, @@ -441,7 +478,7 @@ class DocumentationAggregator(CMakeListener): test_doc = CTestDocumentation(name, docstring, [p for p in params if p != name and p != "NAME"]) self.documented.append(test_doc) - def process_option(self, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_option(self, ctx: CMakeParser.Command_invocationContext, docstring: str) -> None: """ Extracts information from an :code:`option()` command and creates an OptionDocumentation from it. It extracts the option name, @@ -467,7 +504,8 @@ class DocumentationAggregator(CMakeListener): ) self.documented.append(option_doc) - def process_generic_command(self, command_name: str, ctx: CMakeParser.Command_invocationContext, docstring: str): + def process_generic_command(self, command_name: str, ctx: CMakeParser.Command_invocationContext, + docstring: str) -> None: """ Extracts command invocation and arguments for a documented command that does not have a dedicated processor function. @@ -512,7 +550,7 @@ class DocumentationAggregator(CMakeListener): return cleaned_doc - def enterDocumented_command(self, ctx: CMakeParser.Documented_commandContext): + def enterDocumented_command(self, ctx: CMakeParser.Documented_commandContext) -> None: """ Main entrypoint into the documentation processor and aggregator. Called by ParseTreeWalker whenever encountering a documented command. Cleans the docstring and dispatches ctx to other functions for additional @@ -543,7 +581,7 @@ class DocumentationAggregator(CMakeListener): ) raise e - def enterCommand_invocation(self, ctx: CMakeParser.Command_invocationContext): + def enterCommand_invocation(self, ctx: CMakeParser.Command_invocationContext) -> None: """ Visitor for all other commands, used for locating position-dependent elements of documented commands, such as cpp_end_class() that pops the class stack, @@ -566,6 +604,11 @@ class DocumentationAggregator(CMakeListener): # We've found the function/macro def that the previous documented command needed params = [param.getText() for param in ctx.single_argument()] + if isinstance(self.documented_awaiting_function_def, MethodDocumentation): + params = [ + re.sub(self.settings.input.member_parameter_name_strip_regex, "", p.getText()) for p in + ctx.single_argument() + ] self.documented_awaiting_function_def.is_macro = command == "macro" @@ -592,18 +635,18 @@ class DocumentationAggregator(CMakeListener): self.logger.error(f"Caught exception while processing command beginning at line number {line_num}") raise e - def enterDocumented_module(self, ctx: CMakeParser.Documented_moduleContext): + def enterDocumented_module(self, ctx: CMakeParser.Documented_moduleContext) -> None: text = ctx.Module_docstring().getText() cleaned_lines = DocumentationAggregator.clean_doc_lines(text.split("\n")).split("\n") module_name = cleaned_lines[0].replace("@module", "").strip() doc = "\n".join(cleaned_lines[1:]) self.documented.append(ModuleDocumentation(module_name, doc)) - def enterBracket_doccomment(self, ctx: CMakeParser.Bracket_doccommentContext): + def enterBracket_doccomment(self, ctx: CMakeParser.Bracket_doccommentContext) -> None: if ctx not in self.consumed: - text = ctx.Docstring().getText() - cleaned_lines = DocumentationAggregator.clean_doc_lines(text.split("\n")).split("\n") - #self.documented.append(DanglingDoccomment("", "\n".join(cleaned_lines))) + # text = ctx.Docstring().getText() + # cleaned_lines = DocumentationAggregator.clean_doc_lines(text.split("\n")).split("\n") + # self.documented.append(DanglingDoccomment("", "\n".join(cleaned_lines))) self.logger.warning( f"Detected dangling doccomment, ignoring. RST may change depending on CMinx version." ) diff --git a/src/cminx/config.py b/src/cminx/config.py index cb9b1f3..fd42168 100644 --- a/src/cminx/config.py +++ b/src/cminx/config.py @@ -12,6 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +Contains config objects, their defaults, +the config template for confuse, and a function +to convert a dictionary to a :class:`~Settings` object. + +The settings object mirrors the template and +the :code:`config_default.yaml` file. Visit that file +for information on what each setting does. +""" + import confuse import os from typing import List @@ -40,6 +50,9 @@ def config_template(output_dir_relative_to_config: bool = False) -> dict: "auto_exclude_directories_without_cmake": bool, "kwargs_doc_trigger_string": confuse.Optional(confuse.String(), default=":keyword"), "exclude_filters": confuse.Optional(list, default=()), + "function_parameter_name_strip_regex": confuse.Optional(confuse.String(), default=""), + "macro_parameter_name_strip_regex": confuse.Optional(confuse.String(), default=""), + "member_parameter_name_strip_regex": confuse.Optional(confuse.String(), default=""), "recursive": bool, "follow_symlinks": bool }, @@ -76,6 +89,9 @@ class InputSettings: auto_exclude_directories_without_cmake: bool = True kwargs_doc_trigger_string: str = ":param *kwargs:" exclude_filters: List[str] = () + function_parameter_name_strip_regex: str = "" + macro_parameter_name_strip_regex: str = "" + member_parameter_name_strip_regex: str = "" recursive: bool = False follow_symlinks: bool = False diff --git a/src/cminx/config_default.yaml b/src/cminx/config_default.yaml index 0af9d04..f620f81 100644 --- a/src/cminx/config_default.yaml +++ b/src/cminx/config_default.yaml @@ -68,6 +68,30 @@ input: # body of the function or macro. kwargs_doc_trigger_string: ":keyword" + # Strips out any string matching this regex + # for parameter names. This is useful for when + # parameter names have a prefix/postfix to prevent + # name collisions. This affects function definitions. + # + # An example regex that strips the prefix "_abc_" + # from parameter names where "abc" is any combination of letters + # is below: + # function_parameter_name_strip_regex: "^_[a-zA-Z]_" + function_parameter_name_strip_regex: "" + + # Strips out any string matching this regex + # for parameter names. This is useful for when + # parameter names have a prefix/postfix to prevent + # name collisions. This affects macro definitions + macro_parameter_name_strip_regex: "" + + # Strips out any string matching this regex + # for parameter names. This is useful for when + # parameter names have a prefix/postfix to prevent + # name collisions. This affects CMakePP + # class member definitions. + member_parameter_name_strip_regex: "" + # Whether directories should be documented recursively recursive: false diff --git a/src/cminx/documentation_types.py b/src/cminx/documentation_types.py index 399fc90..d202646 100644 --- a/src/cminx/documentation_types.py +++ b/src/cminx/documentation_types.py @@ -23,13 +23,12 @@ representation into an RST representation using :class:`RSTWriter`. :License: Apache 2.0 """ - +import textwrap from abc import ABC, abstractmethod from dataclasses import dataclass, field from enum import Enum from typing import Union, List -from . import Settings from .rstwriter import RSTWriter, Directive, interpreted_text @@ -37,8 +36,16 @@ class VarType(Enum): """The types of variables accepted by the CMake :code:`set()` command""" STRING = 1 + """A String variable, ex: :code:`set(str_var "Hello")`""" + LIST = 2 + """A List variable, ex: :code:`set(list_var item1 item2 item3)`""" + UNSET = 3 + """ + Unsetting a variable, i.e. not an actual variable but a declaration + that a variable no longer exists. Ex: :code:`set(MyVar)` + """ @dataclass @@ -56,13 +63,13 @@ class DocumentationType(ABC): """The full doc comment, cleaned of # characters.""" @abstractmethod - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: """ Processes the data stored in this documentation type, converting it to RST form by using the given RST writer. :param writer: RSTWriter instance that the documentation - will be added to. + will be added to. """ pass @@ -89,7 +96,7 @@ class FunctionDocumentation(AbstractCommandDefinitionDocumentation): directly to a Sphinx :code:`function` directive. """ - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: param_list = self.params if self.has_kwargs: param_list.append("kwargs") @@ -107,7 +114,7 @@ class MacroDocumentation(AbstractCommandDefinitionDocumentation): :code:`note` directive specifying that it is a macro. """ - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: param_list = self.params if self.has_kwargs: param_list.append("kwargs") @@ -125,7 +132,7 @@ class VariableDocumentation(DocumentationType): This class serves as the representation of a documented CMake variable definition (:code:`set()` command). It can only recognize the CMake primitives :code:`str` and :code:`list`, as - well as the :code:`UNSET` subcommand. + well as the implied :code:`unset()` when no value is given. This class translates the definition into a Sphinx :code:`data` directive, with a :code:`Default value` field @@ -138,7 +145,7 @@ class VariableDocumentation(DocumentationType): value: Union[str, None] """A default value that the variable has""" - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: d = writer.directive("data", f"{self.name}") d.text(self.doc) d.field("Default value", self.value) @@ -166,15 +173,15 @@ class OptionDocumentation(VariableDocumentation): help_text: str """The help text that the option has.""" - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: d = writer.directive("data", f"{self.name}") note = d.directive("note") - note.text( + note.text(textwrap.dedent( f""" This variable is a user-editable option, meaning it appears within the cache and can be edited on the command line by the :code:`-D` flag. - """) + """)) d.text(self.doc) d.field("Help text", self.help_text) d.field("Default value", self.value if self.value is not None else "OFF") @@ -196,7 +203,7 @@ class GenericCommandDocumentation(DocumentationType): params: List[str] """Any parameters passed to the command, unfiltered since this documentation type has no knowledge of the command""" - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: d = writer.directive( "function", f"{self.name}({' '.join(self.params)})") d.directive( @@ -216,7 +223,7 @@ class CTestDocumentation(DocumentationType): """ params: List[str] = field(default_factory=lambda: []) - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: d = writer.directive( "function", f"{self.name}({' '.join(self.params)})") @@ -247,7 +254,7 @@ class TestDocumentation(DocumentationType): is_macro: bool = False """Whether the linked command is a macro or a function. If true, a warning stating so is generated.""" - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: d = writer.directive( "function", f"{self.name}({'EXPECTFAIL' if self.expect_fail else ''})") @@ -267,7 +274,7 @@ class SectionDocumentation(TestDocumentation): section. """ - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: d = writer.directive( "function", f"{self.name}({'EXPECTFAIL' if self.expect_fail else ''})") @@ -305,7 +312,7 @@ class MethodDocumentation(DocumentationType): is_macro: bool = False """Whether the linked command is a macro or a function. If true, a note saying so is generated.""" - def process(self, writer: Directive): + def process(self, writer: Directive) -> None: params_pretty = ', '.join( self.params) + ("[, ...]" if "args" in self.param_types else "") d = writer.directive( @@ -344,7 +351,7 @@ class AttributeDocumentation(DocumentationType): default_value: str """The default value of this attribute, if it has one.""" - def process(self, writer: Directive): + def process(self, writer: Directive) -> None: d = writer.directive("py:attribute", f"{self.name}") if self.default_value is not None: d.option("value", self.default_value) @@ -384,7 +391,7 @@ class ClassDocumentation(DocumentationType): attributes: List[AttributeDocumentation] """A list of attribute documentation objects describing the class's attributes.""" - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: d = writer.directive("py:class", f"{self.name}") if len(self.superclasses) > 0: bases = "Bases: " + \ @@ -419,7 +426,7 @@ class ModuleDocumentation(DocumentationType): Represents documentation for an entire CMake module """ - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: module = writer.directive("module", self.name) if self.doc is not None and len(self.doc) != 0: module.text(self.doc) @@ -432,5 +439,5 @@ class DanglingDoccomment(DocumentationType): This documentation type's name is left unused, and the doc contains the cleaned text of the dangling doccomment. """ - def process(self, writer: RSTWriter): + def process(self, writer: RSTWriter) -> None: writer.text(self.doc) diff --git a/src/cminx/documenter.py b/src/cminx/documenter.py index d653bd8..8826a16 100755 --- a/src/cminx/documenter.py +++ b/src/cminx/documenter.py @@ -13,9 +13,9 @@ # limitations under the License. # """ -This file contains the Documenter class, which combines functionality -from parser.aggregator and rstwriter to generate RST documentation -for CMake files. +This file contains the Documenter class, which is the top-level +entrypoint into the CMinx documentation system. It handles parsing, +aggregating, and RST writing. :Author: Branden Butler :License: Apache 2.0 @@ -36,7 +36,9 @@ from .rstwriter import RSTWriter, Directive class Documenter(object): """ - Generates RST documentation from aggregated documentation, combining parser.aggregator and rstwriter. + Generates RST documentation from aggregated documentation, combining + :class:`~cminx.aggregator.DocumentationAggregator` and :class:`~cminx.rstwriter.RSTWriter`. + The entrypoint for this class is the :meth:`process` method. """ def __init__( @@ -44,48 +46,67 @@ class Documenter(object): file: str, title: str = None, module_name: str = None, - settings: Settings = Settings()): + settings: Settings = Settings()) -> None: """ :param file: CMake file to read documentation from. :param title: RST header title to use in the generated document. + :param module_name: The name of the CMake module, used in the module directive if + no other name is given in a module doccomment. :param settings: Dictionary containing application settings for documentation """ - self.settings = settings + self.settings: Settings = settings + """Settings used for aggregation and RST generation, passed down to all downstream components.""" title = file if title is None else title if module_name is None: module_name = title - self.module_name = module_name + self.module_name: str = module_name + """The name of the CMake module, used as a default when no name given via a module doccomment.""" - self.writer = RSTWriter(title, settings=settings) + self.writer: RSTWriter = RSTWriter(title, settings=settings) + """The writer that is passed to the aggregated documentation objects.""" self.module: Directive + """The :code:`.. module::` directive that defines the module's name.""" # We need a string stream of some kind, FileStream is easiest - self.input_stream = FileStream(file) + self.input_stream: InputStream = FileStream(file) + """The string stream used to read the CMake file.""" # Convert those strings into tokens and build a stream from those - self.lexer = CMakeLexer(self.input_stream) - self.stream = CommonTokenStream(self.lexer) + self.lexer: CMakeLexer = CMakeLexer(self.input_stream) + """The lexer used to generate the token stream.""" + + self.stream: TokenStream = CommonTokenStream(self.lexer) + """The stream of tokens from the lexer, should be passed to the parser.""" # We now have a stream of CommonToken instead of strings, parsers # require this type of stream - self.parser = CMakeParser(self.stream) + self.parser: CMakeParser = CMakeParser(self.stream) + """ + The parser used to parse the token stream and call our listener. + By default, an instance of :class:`~cminx.parser.ParserErrorListener` is + added. + """ + self.parser.addErrorListener(ParserErrorListener()) # Hard part is done, we now have a fully usable parse tree, now we just # need to walk it - self.aggregator = DocumentationAggregator(settings) - self.walker = ParseTreeWalker() + self.aggregator: DocumentationAggregator = DocumentationAggregator(settings) + """The aggregator used to listen for parser rules and generate documentation objects.""" + + self.walker: ParseTreeWalker = ParseTreeWalker() + """Walks the parser tree at a given parser rule, used to kick off aggregation.""" def process(self) -> RSTWriter: """ - Process Documenter.aggregator.documented and build RST document from it. + Process :attr:`cminx.aggregator.DocumentationAggregator.documented` and build RST document from it. - :return: Completed RSTWriter document, also located in Documenter.writer + :return: Completed RSTWriter document, also located in :attr:`Documenter.writer` """ # Parse and lex the file, then walk the tree and aggregate the @@ -99,9 +120,10 @@ class Documenter(object): self.process_docs(self.aggregator.documented) return self.writer - def process_docs(self, docs: List[DocumentationType]): + def process_docs(self, docs: List[DocumentationType]) -> None: """ - Loops over document and calls Documentation.process() with self.writer + Loops over document and calls :meth:`cminx.documentation_types.DocumentationType.process` with + :attr:`~Documenter.writer`. :param docs: List of documentation objects. """ diff --git a/src/cminx/exceptions.py b/src/cminx/exceptions.py index e9424c7..2dd54e0 100644 --- a/src/cminx/exceptions.py +++ b/src/cminx/exceptions.py @@ -17,5 +17,20 @@ import dataclasses @dataclasses.dataclass class CMakeSyntaxException(Exception): + """ + Denotes a syntax error in the CMake source code. + This is usually not recoverable and analysis + of the CMake code is not guaranteed to be consistent, + so generation of this exception is expected to + halt the program. + """ + msg: str + """A message describing the syntax error.""" + line: int + """ + The line in the CMake file where the exception was detected. + Note that this may not be the line where the error is actually located + in the source, only where the lexer or parser detected an error. + """ diff --git a/src/cminx/rstwriter.py b/src/cminx/rstwriter.py index 375534c..febadad 100755 --- a/src/cminx/rstwriter.py +++ b/src/cminx/rstwriter.py @@ -31,8 +31,19 @@ from cminx import Settings class ListType(Enum): + """ + Represents the different types of RST + lists. + """ + ENUMERATED = 0 + """ + An enumerated RST list. + """ BULLETED = 1 + """ + A bulleted RST list. + """ def interpreted_text(role: str, text: str) -> str: @@ -55,13 +66,13 @@ class Paragraph(object): Represents an RST paragraph """ - def __init__(self, text: str, indent: str = ""): - self.text = text - self.prefix = indent - self.text_string = "" + def __init__(self, text: str, indent: str = "") -> None: + self.text: str = text + self.prefix: str = indent + self.text_string: str = "" self.build_text_string() - def build_text_string(self): + def build_text_string(self) -> None: """ Populates Paragraph.text_string with the RST string corresponding to this paragraph @@ -69,7 +80,7 @@ class Paragraph(object): self.text_string = "\n".join( [self.prefix + text for text in self.text.split("\n")]) - def __str__(self): + def __str__(self) -> str: return self.text_string @@ -78,14 +89,14 @@ class Field(object): Represents an RST field, such as Author """ - def __init__(self, field_name: str, field_text: str, indent: str = ""): - self.field_name = field_name - self.field_text = field_text - self.field_string = "" - self.indent = indent + def __init__(self, field_name: str, field_text: str, indent: str = "") -> None: + self.field_name: str = field_name + self.field_text: str = field_text + self.field_string: str = "" + self.indent: str = indent self.build_field_string() - def build_field_string(self): + def build_field_string(self) -> None: """ Populates Field.field_string with the RST string corresponding to this field @@ -93,7 +104,7 @@ class Field(object): self.field_string = f"\n{self.indent}:{self.field_name}: {self.field_text}" - def __str__(self): + def __str__(self) -> str: return self.field_string @@ -102,21 +113,21 @@ class DocTest(object): Represents an RST DocTest """ - def __init__(self, test_line: str, expected_output: str, indent=""): - self.test_line = test_line - self.expected_output = expected_output - self.doctest_string = "" - self.indent = indent + def __init__(self, test_line: str, expected_output: str, indent: str = "") -> None: + self.test_line: str = test_line + self.expected_output: str = expected_output + self.doctest_string: str = "" + self.indent: str = indent self.build_doctest_string() - def build_doctest_string(self): + def build_doctest_string(self) -> None: """ Populates DocTest.doctest_string with the RST string corresponding to this DocTest """ self.doctest_string = f"\n{self.indent}>>> {self.test_line}\n{self.expected_output}\n" - def __str__(self): + def __str__(self) -> str: return self.doctest_string @@ -126,17 +137,19 @@ class RSTList(object): Enumerated or Bulleted """ - def __init__(self, items: Tuple[str], list_type: ListType, indent: str = ""): - self.items = items - self.list_type = list_type - self.list_string = "" - self.indent = indent + def __init__(self, items: Tuple[str], list_type: ListType, indent: str = "") -> None: + self.items: Tuple[str] = items + self.list_type: ListType = list_type + self.list_string: str = "" + self.indent: str = indent self.build_list_string() - def build_list_string(self): + def build_list_string(self) -> None: """ Populates RSTList.list_string with the - RST string corresponding to this list + RST string corresponding to this list. + + :raises ValueError: When :py:attr:`~.RSTList.list_type` is unknown. """ self.list_string = "\n" @@ -149,7 +162,7 @@ class RSTList(object): else: raise ValueError("Unknown list type") - def __str__(self): + def __str__(self) -> str: return self.list_string @@ -158,13 +171,13 @@ class Heading(object): Represents a section heading """ - def __init__(self, title: str, header_char: str): + def __init__(self, title: str, header_char: str) -> None: self.title = title self.header_char = header_char self.heading_string = "" self.build_heading_string() - def build_heading_string(self): + def build_heading_string(self) -> None: """ Populates Heading.heading_string with the RST string corresponding to this heading @@ -174,7 +187,7 @@ class Heading(object): heading += self.header_char self.heading_string = f"\n{heading}\n{self.title}\n{heading}" - def __str__(self): + def __str__(self) -> str: return self.heading_string @@ -184,12 +197,12 @@ class SimpleTable(object): """ def __init__(self, tab: List[List[str]], headings: List[str]): - self.table = tab - self.column_headings = headings - self.table_string = "" + self.table: List[List[str]] = tab + self.column_headings: List[str] = headings + self.table_string: str = "" self.build_table_string() - def build_table_string(self): + def build_table_string(self) -> None: """ Populates SimpleTable.table_string with the RST string equivalent of this table @@ -212,7 +225,7 @@ class SimpleTable(object): raise ValueError( "Different number of headings than number of columns in table") - # Find the longest cell to use as the row separator width Yes I know doing it this way is inefficient, + # Find the longest cell to use as the row separator width. Yes I know doing it this way is inefficient, # but readability is more important unless we're dealing with thousands # of cells. for row in self.table: @@ -236,27 +249,25 @@ class SimpleTable(object): row_separator += "=" table_str = "" - # Index zero is heading overline, index 1 is headings, index 2 is - # underline - heading_lines = ["", "", ""] + heading_lines = {"overline": "", "headings": "", "underline": ""} # Build heading overlines/underlines and add correct spacing for heading in self.column_headings: heading_len = len(heading) for i in range(0, row_separator_width): - heading_lines[0] += '=' + heading_lines["overline"] += '=' # If current line position is not greater than the length of the heading, add the character at position # Else, add spaces until end of row separator if i < heading_len: - heading_lines[1] += heading[i] + heading_lines["headings"] += heading[i] else: - heading_lines[1] += " " - heading_lines[0] += " " # Recommended 2 spaces between columns - heading_lines[1] += " " + heading_lines["headings"] += " " + heading_lines["overline"] += " " # Recommended 2 spaces between columns + heading_lines["headings"] += " " # Overline and underline should be the same - heading_lines[2] = heading_lines[0] - table_str += "\n".join(heading_lines) + "\n" + heading_lines["underline"] = heading_lines["overline"] + table_str += "\n".join(heading_lines.values()) + "\n" # Add cells to table string for row in self.table: @@ -272,7 +283,7 @@ class SimpleTable(object): table_str += "\n" self.table_string = table_str - def __str__(self): + def __str__(self) -> str: return self.table_string @@ -281,17 +292,18 @@ class DirectiveHeading(object): Represents the unique heading for a Directive (.. :<name>:) """ - def __init__(self, title: str, indent: str, args: str): - self.title = title - self.indent = indent - self.args = args - self.heading_string = "" + def __init__(self, title: str, indent: str, args: str) -> None: + self.title: str = title + self.indent: str = indent + self.args: str = args + self.heading_string: str = "" self.build_heading_string() - def build_heading_string(self): + def build_heading_string(self) -> None: + """Builds the directive heading string and stores in :py:attr:`~heading_str`""" self.heading_string = f"\n{self.indent}.. {self.title}:: {self.args}" - def __str__(self): + def __str__(self) -> str: return self.heading_string @@ -300,29 +312,34 @@ class Option(object): Represents a directive option, such as maxdepth """ - def __init__(self, name: str, value: str, indent: str): - self.name = name - self.value = value - self.indent = indent - self.option_string = "" + def __init__(self, name: str, value: str, indent: str) -> None: + self.name: str = name + self.value: str = value + self.indent: str = indent + self.option_string: str = "" self.build_option_string() - def build_option_string(self): + def build_option_string(self) -> None: + """Builds the option string and stores in :py:attr:`~option_string`""" self.option_string = f"{self.indent}:{self.name}: {self.value}" - def __str__(self): + def __str__(self) -> str: return self.option_string -def get_indents(num) -> str: +def get_indents(num: int) -> str: """ Get the string containing the necessary indent. + The number of spaces in the returned string equals three times + the input number, because directives require three spaces + so the text lines up with the first letter of the directive name. - :return: A string containing the correct number of whitespace characters, derived from the indent level. + :return: A string containing the correct number of space characters, derived from the indent level. """ indents = "" for i in range(0, num): - # Directives require the first non-whitespace character of every line to line up with the first letter of + # Directives require the first non-whitespace character + # of every line to line up with the first letter of # the directive name indents += ' ' return indents @@ -339,26 +356,26 @@ class RSTWriter(object): or by calling :py:func:`str` on this object. """ - heading_level_chars = ['#', '', '=', '-', '_', '~', '!', '&', '@', '^'] + heading_level_chars: List[str] = ['#', '', '=', '-', '_', '~', '!', '&', '@', '^'] """Characters to use as heading overline/underline, indexed by section_level""" def __init__( self, title: str, section_level: int = 0, - settings=Settings(), indent: int = 0): + settings: Settings = Settings(), indent: int = 0) -> None: self.__title: str = title self.section_level: int = section_level - self.settings = settings + self.settings: Settings = settings if settings.rst.headers is not None: self.heading_level_chars = settings.rst.headers - self.indent = indent + self.indent: int = indent self.header_char: str = self.heading_level_chars[section_level] # Heading must be first in the document tree self.document: List[Any] = [self.build_heading()] - def clear(self): + def clear(self) -> None: """ Clear all document tree elements (besides required heading) """ @@ -369,7 +386,7 @@ class RSTWriter(object): return self.__title @title.setter - def title(self, new_title: str): + def title(self, new_title: str) -> None: """ Rebuild heading and replace the old one in the document tree whenever title is changed @@ -378,7 +395,7 @@ class RSTWriter(object): self.__title = new_title self.document[0] = self.build_heading() - def bulleted_list(self, items: str): + def bulleted_list(self, items: str) -> None: """ Add a bulleted list to the document tree. @@ -387,7 +404,7 @@ class RSTWriter(object): self.document.append(RSTList(items, ListType.BULLETED, indent=get_indents(self.indent))) - def enumerated_list(self, items: str): + def enumerated_list(self, items: str) -> None: """ Add an enumerated list to the document tree; e.g. 1. Item 1 @@ -397,7 +414,7 @@ class RSTWriter(object): """ self.document.append(RSTList(items, ListType.ENUMERATED, indent=get_indents(self.indent))) - def field(self, field_name: str, field_text: str): + def field(self, field_name: str, field_text: str) -> None: """ Add a field, such as Author or Version, to the document tree. @@ -407,7 +424,7 @@ class RSTWriter(object): """ self.document.append(Field(field_name, field_text, get_indents(self.indent))) - def doctest(self, test_line: str, expected_output: str): + def doctest(self, test_line: str, expected_output: str) -> None: """ Adds a doctest segment to the document tree. A doctest segment appears as an interactive python session. @@ -433,7 +450,7 @@ class RSTWriter(object): self.document.append(sect) return sect - def text(self, txt: str): + def text(self, txt: str) -> None: """ Add a paragraph to document tree. @@ -491,7 +508,7 @@ class RSTWriter(object): document_string += f"{element}\n" return document_string - def __str__(self): + def __str__(self) -> str: """ Equivalent to :func: `~cminx.RSTWriter.to_text` @@ -499,7 +516,7 @@ class RSTWriter(object): """ return self.to_text() - def write_to_file(self, file: Union[str, IO]): + def write_to_file(self, file: Union[str, IO]) -> None: """ Write text representation of this document to file. File may be a string (path will be opened in write mode) or a file-like object. @@ -540,7 +557,7 @@ class Directive(RSTWriter): Does not verify that the directive or its arguments are valid. """ - def __init__(self, name: str, indent: int = 0, arguments: str, settings: Settings = Settings()): + def __init__(self, name: str, indent: int = 0, arguments: str, settings: Settings = Settings()) -> None: """ :param name: Name of the directive being constructed, eg. toctree or admonition. @@ -549,8 +566,8 @@ class Directive(RSTWriter): :param arguments: Varargs to be used as the directive arguments, eg. a topic's title. """ - self.arguments = arguments - self.options = [] + self.arguments: Tuple[str] = arguments + self.options: List[Option] = [] super().__init__(name, settings=settings, indent=indent + 1) def build_heading(self) -> DirectiveHeading: @@ -571,7 +588,7 @@ class Directive(RSTWriter): """ return ','.join(map(str, self.arguments)) - def option(self, name: str, value: str = ""): + def option(self, name: str, value: str = "") -> None: """ Add an option, such as toctree's maxdepth. Does not verify if valid option """	Add a regex filter for parameter names to make longer names more readable Is your feature request related to a problem? Please describe. A regex filter was requested in discussions for https://github.com/CMakePP/CMakeTest/issues/5	CMakePP/CMinx	diff --git a/tests/examples/sphinx/source/example.rst b/tests/examples/sphinx/source/example.rst index 2759669..45fc64b 100644 --- a/tests/examples/sphinx/source/example.rst +++ b/tests/examples/sphinx/source/example.rst @@ -346,10 +346,10 @@ examples.example .. note:: - This variable is a user-editable option, - meaning it appears within the cache and can be - edited on the command line by the :code:`-D` flag. - + This variable is a user-editable option, + meaning it appears within the cache and can be + edited on the command line by the :code:`-D` flag. + This is a documented option diff --git a/tests/examples/sphinx/source/example_no_undocumented.rst b/tests/examples/sphinx/source/example_no_undocumented.rst index 9a5a9bb..1b3615f 100644 --- a/tests/examples/sphinx/source/example_no_undocumented.rst +++ b/tests/examples/sphinx/source/example_no_undocumented.rst @@ -146,10 +146,10 @@ examples.example .. note:: - This variable is a user-editable option, - meaning it appears within the cache and can be - edited on the command line by the :code:`-D` flag. - + This variable is a user-editable option, + meaning it appears within the cache and can be + edited on the command line by the :code:`-D` flag. + This is a documented option diff --git a/tests/examples/sphinx/source/example_no_undocumented_diff_header.rst b/tests/examples/sphinx/source/example_no_undocumented_diff_header.rst index 8c55746..9bdbb86 100644 --- a/tests/examples/sphinx/source/example_no_undocumented_diff_header.rst +++ b/tests/examples/sphinx/source/example_no_undocumented_diff_header.rst @@ -146,10 +146,10 @@ examples.example.cmake .. note:: - This variable is a user-editable option, - meaning it appears within the cache and can be - edited on the command line by the :code:`-D` flag. - + This variable is a user-editable option, + meaning it appears within the cache and can be + edited on the command line by the :code:`-D` flag. + This is a documented option diff --git a/tests/examples/sphinx/source/example_prefix.rst b/tests/examples/sphinx/source/example_prefix.rst index e0c8440..0ec5a92 100644 --- a/tests/examples/sphinx/source/example_prefix.rst +++ b/tests/examples/sphinx/source/example_prefix.rst @@ -346,10 +346,10 @@ prefix.example .. note:: - This variable is a user-editable option, - meaning it appears within the cache and can be - edited on the command line by the :code:`-D` flag. - + This variable is a user-editable option, + meaning it appears within the cache and can be + edited on the command line by the :code:`-D` flag. + This is a documented option diff --git a/tests/test_samples/corr_rst/option.rst b/tests/test_samples/corr_rst/option.rst index e984b10..bab7be4 100644 --- a/tests/test_samples/corr_rst/option.rst +++ b/tests/test_samples/corr_rst/option.rst @@ -12,10 +12,10 @@ test_samples.option .. note:: - This variable is a user-editable option, - meaning it appears within the cache and can be - edited on the command line by the :code:`-D` flag. - + This variable is a user-editable option, + meaning it appears within the cache and can be + edited on the command line by the :code:`-D` flag. + This is a documentation of an option. diff --git a/tests/test_samples/corr_rst/undocumented_option_no_default.rst b/tests/test_samples/corr_rst/undocumented_option_no_default.rst index 23f2744..7cf90e6 100644 --- a/tests/test_samples/corr_rst/undocumented_option_no_default.rst +++ b/tests/test_samples/corr_rst/undocumented_option_no_default.rst @@ -12,10 +12,10 @@ test_samples.undocumented_option_no_default .. note:: - This variable is a user-editable option, - meaning it appears within the cache and can be - edited on the command line by the :code:`-D` flag. - + This variable is a user-editable option, + meaning it appears within the cache and can be + edited on the command line by the :code:`-D` flag. + diff --git a/tests/unit_tests/test_aggregator.py b/tests/unit_tests/test_aggregator.py index c0caa2e..9a1a9ef 100755 --- a/tests/unit_tests/test_aggregator.py +++ b/tests/unit_tests/test_aggregator.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # +import textwrap import unittest import pytest @@ -20,13 +21,15 @@ import pytest import context from antlr4 import * +from cminx import Settings +from cminx.config import InputSettings from cminx.exceptions import CMakeSyntaxException from cminx.parser import ParserErrorListener from cminx.parser.CMakeLexer import CMakeLexer from cminx.parser.CMakeParser import CMakeParser from cminx.aggregator import DocumentationAggregator from cminx.documentation_types import FunctionDocumentation, MacroDocumentation, VariableDocumentation, \ - GenericCommandDocumentation, ClassDocumentation, VarType, DocumentationType + GenericCommandDocumentation, ClassDocumentation, VarType, DocumentationType, MethodDocumentation class TestAggregator(unittest.TestCase): @@ -37,7 +40,7 @@ class TestAggregator(unittest.TestCase): self.input_stream = FileStream(self.filename) self.reset() - def reset(self): + def reset(self, settings=Settings()): # Convert those strings into tokens and build a stream from those self.lexer = CMakeLexer(self.input_stream) self.stream = CommonTokenStream(self.lexer) @@ -48,7 +51,7 @@ class TestAggregator(unittest.TestCase): self.tree = self.parser.cmake_file() # Hard part is done, we now have a fully useable parse tree, now we just need to walk it - self.aggregator = DocumentationAggregator() + self.aggregator = DocumentationAggregator(settings) self.walker = ParseTreeWalker() self.walker.walk(self.aggregator, self.tree) @@ -109,13 +112,13 @@ endmacro() var_name = "TEST_VAR" val = "This is a value" - self.input_stream = InputStream(f''' -#[[[ - {docstring} -#]] -set({var_name} "{val}") + self.input_stream = InputStream(textwrap.dedent(f''' + #[[[ + {docstring} + #]] + set({var_name} "{val}") - ''') + ''')) self.reset() self.assertEqual(len(self.aggregator.documented), 1, "Different number of documented commands than expected") self.assertEqual(type(self.aggregator.documented[0]), VariableDocumentation, "Unexpected documentation type") @@ -129,12 +132,12 @@ set({var_name} "{val}") var_name = "listvar" params = ["param1", "param2"] - self.input_stream = InputStream(f''' -#[[[ - {docstring} -#]] -set({var_name} {params[0]} {params[1]}) - ''') + self.input_stream = InputStream(textwrap.dedent(f''' + #[[[ + {docstring} + #]] + set({var_name} {params[0]} {params[1]}) + ''')) self.reset() self.assertEqual(len(self.aggregator.documented), 1, "Different number of documented commands than expected") self.assertEqual(type(self.aggregator.documented[0]), VariableDocumentation, "Unexpected documentation type") @@ -149,12 +152,12 @@ set({var_name} {params[0]} {params[1]}) docstring = "Unsetting a variable" var_name = "myvar" - self.input_stream = InputStream(f''' -#[[[ - {docstring} -#]] -set({var_name}) - ''') + self.input_stream = InputStream(textwrap.dedent(f''' + #[[[ + {docstring} + #]] + set({var_name}) + ''')) self.reset() self.assertEqual(len(self.aggregator.documented), 1, "Different number of documented commands than expected") self.assertEqual(type(self.aggregator.documented[0]), VariableDocumentation, "Unexpected documentation type") @@ -176,27 +179,43 @@ set({var_name}) attribute_docstring = "#[[[\n# This is an attribute\n#]]" class_name = "MyClass" inner_class_definitions = '\n'.join( - [f'{inner_class_docstring}\ncpp_class({inner_class_name})\ncpp_end_class()' for inner_class_name in - inner_classes]) - method_definitions = '\n'.join([ - f'{method_docstring}\ncpp_member({method_name} {class_name})\nfunction(' + '${' + method_name + '})\nendfunction()' - for method_name in methods]) + [ + textwrap.dedent(f"""\ + {inner_class_docstring} + cpp_class({inner_class_name}) + cpp_end_class() + """) + for inner_class_name in inner_classes + ] + ) + # Curly brackets in f strings are escaped with two brackets. So we end up with "${<value of method_name>}" + method_definitions = '\n'.join( + [ + textwrap.dedent(f"""\ + {method_docstring} + cpp_member({method_name} {class_name}) + function(${{{method_name}}}) + endfunction() + """) + for method_name in methods + ] + ) attribute_definitions = '\n'.join( [f'{attribute_docstring}\ncpp_attr({class_name} {attr_name})' for attr_name in attributes]) - self.input_stream = InputStream(f''' -#[[[ -# {class_docstring} -#]] -cpp_class({class_name} {' '.join(superclasses)}) + self.input_stream = InputStream(textwrap.dedent(f''' + #[[[ + # {class_docstring} + #]] + cpp_class({class_name} {' '.join(superclasses)}) - {attribute_definitions} + {attribute_definitions} - {method_definitions} + {method_definitions} - {inner_class_definitions} + {inner_class_definitions} -cpp_end_class() - ''') + cpp_end_class() + ''')) self.reset() self.assertEqual(len(self.aggregator.documented), 1 + len(inner_classes), "Different number of documented commands than expected") @@ -214,7 +233,7 @@ cpp_end_class() self.assertEqual(len(self.aggregator.documented[0].members), len(methods), "Members incorrectly found") self.assertEqual(len(self.aggregator.documented[0].attributes), len(attributes), "Attributes incorrectly found") - def test_cpp_class_multi_superclass_no_inner(self, superclasses=["SuperClassA", "SuperClassB", "SuperClassC"]): + def test_cpp_class_multi_superclass_no_inner(self, superclasses=("SuperClassA", "SuperClassB", "SuperClassC")): self.test_cpp_class_multi_superclass_multi_members(superclasses=superclasses, attributes=[], methods=[]) def test_cpp_class_one_superclasses_no_inner(self): @@ -391,6 +410,92 @@ cpp_end_class() with pytest.raises(CMakeSyntaxException): self.reset() + def test_function_param_regex(self): + func_name = "test_func" + stripped_param_names = ["param1", "param2_with_underscores"] + param_names = [f"_tf_{name}" for name in stripped_param_names] + + docstring = "This is a function that has its param name regex-stripped" + + regex = "^_[a-zA-Z]_" + + self.input_stream = InputStream(textwrap.dedent(f""" + #[[[ + # {docstring} + #]] + function({func_name} {' '.join(param_names)}) + endfunction() + """)) + + input_settings = InputSettings(function_parameter_name_strip_regex=regex) + self.reset(settings=Settings(input=input_settings)) + self.assertIsInstance(self.aggregator.documented[0], FunctionDocumentation) + self.assertEqual(func_name, self.aggregator.documented[0].name) + for i in range(0, len(stripped_param_names)): + self.assertEqual( + stripped_param_names[i], self.aggregator.documented[0].params[i], + "Parameter name was not stripped correctly" + ) + + def test_macro_param_regex(self): + func_name = "test_macro" + stripped_param_names = ["param1", "param2_with_underscores"] + param_names = [f"_tm_{name}" for name in stripped_param_names] + + docstring = "This is a macro that has its param name regex-stripped" + + regex = "^_[a-zA-Z]_" + + self.input_stream = InputStream(textwrap.dedent(f""" + #[[[ + # {docstring} + #]] + macro({func_name} {' '.join(param_names)}) + endmacro() + """)) + + input_settings = InputSettings(macro_parameter_name_strip_regex=regex) + self.reset(settings=Settings(input=input_settings)) + self.assertIsInstance(self.aggregator.documented[0], MacroDocumentation) + self.assertEqual(func_name, self.aggregator.documented[0].name) + for i in range(0, len(stripped_param_names)): + self.assertEqual( + stripped_param_names[i], self.aggregator.documented[0].params[i], + "Parameter name was not stripped correctly" + ) + + def test_method_param_regex(self): + func_name = "test_method" + stripped_param_names = ["param1", "param2_with_underscores"] + param_names = [f"_tm_{name}" for name in stripped_param_names] + param_types = ["desc", "str"] + + docstring = "This is a method that has its param name regex-stripped" + + regex = "^_[a-zA-Z]*_" + + self.input_stream = InputStream(textwrap.dedent(f""" + cpp_class(TestClass) + #[[[ + # {docstring} + #]] + cpp_member({func_name} TestClass {' '.join(param_types)}) + function("${{{func_name}}}" self {' '.join(param_names)}) + endfunction() + cpp_end_class() + """)) + + input_settings = InputSettings(member_parameter_name_strip_regex=regex) + self.reset(settings=Settings(input=input_settings)) + self.assertIsInstance(self.aggregator.documented[0], ClassDocumentation) + self.assertIsInstance(self.aggregator.documented[0].members[0], MethodDocumentation) + self.assertEqual(func_name, self.aggregator.documented[0].members[0].name) + for i in range(0, len(stripped_param_names)): + self.assertEqual( + stripped_param_names[i], self.aggregator.documented[0].members[0].params[i], + "Parameter name was not stripped correctly" + ) + if __name__ == '__main__': unittest.main()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 1 }, "num_modified_files": 8 }	1.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[testing]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "docs/requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	alabaster==0.7.16 antlr4-python3-runtime==4.7.2 babel==2.17.0 certifi==2025.1.31 charset-normalizer==3.4.1 -e git+https://github.com/CMakePP/CMinx.git@ea6d33ebc88237c5f2dd3a1d166d7bc4b078223f#egg=CMinx confuse==2.0.1 coverage==7.8.0 docutils==0.21.2 exceptiongroup==1.2.2 idna==3.10 imagesize==1.4.1 importlib_metadata==8.6.1 iniconfig==2.1.0 Jinja2==3.1.6 MarkupSafe==3.0.2 packaging==24.2 pathspec==0.12.1 pluggy==1.5.0 Pygments==2.19.1 pytest==8.3.5 pytest-cov==6.0.0 PyYAML==6.0.2 requests==2.32.3 snowballstemmer==2.2.0 Sphinx==7.4.7 sphinx-rtd-theme==3.0.2 sphinxcontrib-applehelp==2.0.0 sphinxcontrib-devhelp==2.0.0 sphinxcontrib-htmlhelp==2.1.0 sphinxcontrib-jquery==4.1 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==2.0.0 sphinxcontrib-serializinghtml==2.0.0 tomli==2.2.1 urllib3==2.3.0 zipp==3.21.0	name: CMinx channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - alabaster==0.7.16 - antlr4-python3-runtime==4.7.2 - babel==2.17.0 - certifi==2025.1.31 - charset-normalizer==3.4.1 - cminx==1.1.8 - confuse==2.0.1 - coverage==7.8.0 - docutils==0.21.2 - exceptiongroup==1.2.2 - idna==3.10 - imagesize==1.4.1 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - jinja2==3.1.6 - markupsafe==3.0.2 - packaging==24.2 - pathspec==0.12.1 - pluggy==1.5.0 - pygments==2.19.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pyyaml==6.0.2 - requests==2.32.3 - snowballstemmer==2.2.0 - sphinx==7.4.7 - sphinx-rtd-theme==3.0.2 - sphinxcontrib-applehelp==2.0.0 - sphinxcontrib-devhelp==2.0.0 - sphinxcontrib-htmlhelp==2.1.0 - sphinxcontrib-jquery==4.1 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==2.0.0 - sphinxcontrib-serializinghtml==2.0.0 - tomli==2.2.1 - urllib3==2.3.0 - zipp==3.21.0 prefix: /opt/conda/envs/CMinx	[ "tests/unit_tests/test_aggregator.py::TestAggregator::test_function_param_regex", "tests/unit_tests/test_aggregator.py::TestAggregator::test_macro_param_regex", "tests/unit_tests/test_aggregator.py::TestAggregator::test_method_param_regex" ]	[]	[ "tests/unit_tests/test_aggregator.py::TestAggregator::test_aggregated_docs", "tests/unit_tests/test_aggregator.py::TestAggregator::test_cpp_attr_outside_class", "tests/unit_tests/test_aggregator.py::TestAggregator::test_cpp_class_multi_superclass_multi_members", "tests/unit_tests/test_aggregator.py::TestAggregator::test_cpp_class_multi_superclass_no_inner", "tests/unit_tests/test_aggregator.py::TestAggregator::test_cpp_class_no_superclass_no_inner", "tests/unit_tests/test_aggregator.py::TestAggregator::test_cpp_class_one_superclasses_no_inner", "tests/unit_tests/test_aggregator.py::TestAggregator::test_cpp_member_outside_class", "tests/unit_tests/test_aggregator.py::TestAggregator::test_doccomment_function_leading_space", "tests/unit_tests/test_aggregator.py::TestAggregator::test_doccomment_listvar_leading_space", "tests/unit_tests/test_aggregator.py::TestAggregator::test_doccomment_macro_leading_space", "tests/unit_tests/test_aggregator.py::TestAggregator::test_doccomment_stringvar_leading_space", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_cpp_attribute_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_cpp_class_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_cpp_member_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_ct_add_section_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_ct_add_test_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_ctest_add_test_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_function_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_macro_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_option_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_incorrect_set_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_invalid_ct_add_section_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_invalid_ct_add_test_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_invalid_ctest_add_test_params", "tests/unit_tests/test_aggregator.py::TestAggregator::test_invalid_syntax", "tests/unit_tests/test_aggregator.py::TestAggregator::test_unknown_documented_command", "tests/unit_tests/test_aggregator.py::TestAggregator::test_unset" ]	[]	Apache License 2.0	null
CODAIT__exchange-metadata-converter-25	a5432d877fe947a12c169d2d84c22e034d555289	2020-10-14 23:33:51	a5432d877fe947a12c169d2d84c22e034d555289		diff --git a/metadata_converter/apply.py b/metadata_converter/apply.py index f20d451..34cd14f 100644 --- a/metadata_converter/apply.py +++ b/metadata_converter/apply.py @@ -16,6 +16,8 @@ from argparse import ArgumentParser from pathlib import Path from ruamel.yaml import YAML +from ruamel.yaml.tokens import CommentToken +from ruamel.yaml.comments import CommentedMap import re import sys @@ -48,11 +50,70 @@ def replace(yaml_dict, template_dict): :rtype: dict """ + def get_metadata(template_dict, key): + # If d is an instance of ruamel.yaml.comments.CommentedMap + # this method returns the metadata associated with the + # provided property key + + if not isinstance(template_dict, CommentedMap): + return None + + d = template_dict + + metadata = { + 'comment': None, + 'comment_text': None, + 'annotations': set() + } + # raw comment, e.g. "# @optional another comment\n" + if key in d.ca.items: + if (len(d.ca.items.get(key)) > 2) and \ + isinstance(d.ca.items.get(key)[2], CommentToken): + metadata['comment'] = d.ca.items.get(key)[2].value + # comment text, e.g. "another comment" + metadata['comment_text'] = metadata['comment'] + # extract embedded annotations, e.g. "@optional" + if metadata['comment'] is not None: + # Extract @... annotations + for match in re.finditer('@([a-zA-Z]+)', + metadata['comment'], + re.I): + metadata['annotations'].add(match.group(1).lower()) + # Remove noise from the comment, such as annotations + # comment characters and whitespace characters + # - Remove annotations + for annotation in metadata['annotations']: + metadata['comment_text'] =\ + metadata['comment_text'].replace('@{}'.format(annotation), + '') + # - Remove '#' and whitespaces from the beginning of each line + # This needs to be done twice to handle empty comments properly. + metadata['comment_text'] = re.sub(r'^\s#\s', + '', + metadata['comment_text'], + flags=re.MULTILINE) + metadata['comment_text'] = re.sub(r'^\s#\s', + '', + metadata['comment_text'], + flags=re.MULTILINE) + # - remove whitespace chars from the end of each line + metadata['comment_text'] = re.sub(r'\s*$', + '', + metadata['comment_text'], + flags=re.MULTILINE) + + # - remove newline from the end + metadata['comment_text'] = metadata['comment_text'].strip() + + if len(metadata['comment_text']) == 0: + metadata['comment_text'] = None + return metadata + # pre-compile the {{...}} placeholder search expression # outside `do_replace` to avoid doing it multiple times p = re.compile(r'{{([\w\.]+)}}', re.VERBOSE) - def process_string(yaml_dict, str): + def process_string(yaml_dict, str, metadata): # determine whether {{...}} placeholder # replacement is required r = p.search(str) @@ -63,19 +124,25 @@ def replace(yaml_dict, template_dict): for property in r.group(1).split('.'): root = root.get(property) if root is None: - raise PlaceholderNotFoundError(r.group(1)) + # check annotations to determine appropriate + # behavior + if metadata is not None and\ + 'optional' in metadata['annotations']: + return None + else: + raise PlaceholderNotFoundError(r.group(1)) return root - def process_list(yaml_dict, list_in): + def process_list(yaml_dict, list_in, metadata): list_out = [] for v in list_in: # if v is a string process it if(isinstance(v, str)): - list_out.append(process_string(yaml_dict, v)) + list_out.append(process_string(yaml_dict, v, metadata)) elif(isinstance(v, dict)): list_out.append(do_replace(yaml_dict, v)) elif(isinstance(v, list)): - list_out.append(process_list(yaml_dict, v)) + list_out.append(process_list(yaml_dict, v, metadata)) else: raise NotImplementedError( 'Support for properties of type {} in lists ' @@ -103,18 +170,23 @@ def replace(yaml_dict, template_dict): for key in template_dict.keys(): val = template_dict[key] # print('Key: {} Value: {} Type: {}'.format(key, val, type(val))) + metadata = get_metadata(template_dict, key) if val is None: # NoneType - no value template_out[key] = None elif isinstance(val, str): # process string - template_out[key] = process_string(yaml_dict, val) + template_out[key] = process_string(yaml_dict, + val, + metadata) elif isinstance(val, dict): # process the dictionary recursively template_out[key] = do_replace(yaml_dict, val) elif isinstance(val, list): # process each list item - template_out[key] = process_list(yaml_dict, val) + template_out[key] = process_list(yaml_dict, + val, + metadata) else: raise NotImplementedError( 'Support for properties of type {} is not implemented.' diff --git a/setup.py b/setup.py index d3a2bc7..5ab9ac3 100644 --- a/setup.py +++ b/setup.py @@ -22,7 +22,7 @@ with open('README.md') as readme: setup( name='exchange-metadata-converter', packages=find_packages(), - version='0.0.6', + version='0.0.7', license='Apache-2.0', description='exchange metadata converters', long_description=README,	Support optional properties Currently all `{{...}}` placeholders in https://github.com/CODAIT/exchange-metadata-converter/tree/main/templates are considered to be required and therefore each placeholder input file must define them. Annotations would solve this issue. Investigate what it takes to support something like this, where `@annotation_key` serves as a hint to the processing engine and doesn't invalidate the YAML file because it is specified as a comment. ``` property: '{{value}}' # just a comment another: property2: '{{value2}}' # @optional and a comment third_property: # comment only fourth_property: fifth_property: '{{value6}}' # @annotation_only ```	CODAIT/exchange-metadata-converter	diff --git a/tests/inputs/annotations.yaml b/tests/inputs/annotations.yaml new file mode 100644 index 0000000..84678c1 --- /dev/null +++ b/tests/inputs/annotations.yaml @@ -0,0 +1,6 @@ +# This YAML document does not define the following properties: +# noprop1value, noprop2value, noprop3value, noprop3value, +# noprop4value, noprop5value, noprop6value +unused_property: True +hey: + we_are_a_match: 'Then let us pair up!' \ No newline at end of file diff --git a/tests/templates/annotations.yaml b/tests/templates/annotations.yaml new file mode 100644 index 0000000..890c6e3 --- /dev/null +++ b/tests/templates/annotations.yaml @@ -0,0 +1,46 @@ +# test level-0 properties; each one is annotated with +# <at>optional and should therefore not raise an error +property1: '{{noprop1value}}' # @optional +property2: '{{noprop2value}}' # comment @optional +property3: '{{noprop3value}}' # more comments @optional +property4: '{{noprop4value}}' # more @optional comments +property5: '{{noprop5value}}' # more @bogus comments @optional +property6: '{{noprop6value}}' # more + # @bogus comments + # + # @optional +property7: + property8: '{{noprop8value}}' # property8 is + # + # an @optional property + # we are @very @very @lucky +property9: # annotations work in dicts + property10: + property11: '{{noprop11value}}' # this @optional property + # is @fine +property12: # annotations work in lists + - property12a: '{{hey.we_are_a_match}}' # @optional does not apply + property12b: '{{noprop12bvalue}}' # sweet, it's @optional + property12c: a_constant! +property13: # annotations are case insensitive + property14: + property15: + property16a: '{{noprop16avalue}}' # @OPTIONAL + property16b: '{{noprop16bvalue}}' # an @Optional property + property16c: '{{noprop16cvalue}}' # another @OptioNAl property + property17: all is good # Nothing @opTioNAL here + property18: '{{hey.we_are_a_match}}'# nothing @OPTIONal here either +--- +property1: '{{noprop1value}}' # +--- +property1: '{{noprop1value}}' # just a comment +--- +property1: '{{noprop1value}}' # @notarecognizedannotation +--- +property1: '{{noprop1value}}' # more + # is not always @better + # +--- +property1: # @optional does not apply + property1a: '{{noprop1avalue}}' # because we fail here + diff --git a/tests/test_annotations.py b/tests/test_annotations.py new file mode 100644 index 0000000..e11616d --- /dev/null +++ b/tests/test_annotations.py @@ -0,0 +1,114 @@ +# +# Copyright 2020 IBM Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from metadata_converter.apply import replace +from metadata_converter.apply import PlaceholderNotFoundError +from pathlib import Path +from ruamel.yaml import YAML +import unittest + +yaml = YAML() +yaml.default_flow_style = False + +# +# Tests template annotations: @optional +# + + +class TestAnnotations(unittest.TestCase): + + def setUp(self): + + self.placeholder_file = 'tests/inputs/annotations.yaml' + + self.in_yamls = yaml.load(Path(self.placeholder_file)) + + self.template_file = 'tests/templates/annotations.yaml' + + self.template_yamls = list(yaml.load_all(Path(self.template_file))) + self.assertEqual(len(self.template_yamls), 6) + + def test_optional_annotation(self): + + try: + out_dict = replace(self.in_yamls, self.template_yamls[0]) + self.assertTrue(out_dict is not None) + self.assertTrue(isinstance(out_dict, dict)) + self.assertIsNone(out_dict['property1']) + self.assertIsNone(out_dict['property2']) + self.assertIsNone(out_dict['property3']) + self.assertIsNone(out_dict['property4']) + self.assertIsNone(out_dict['property5']) + self.assertIsNone(out_dict['property6']) + self.assertIsNone(out_dict['property7']['property8']) + self.assertIsNone( + out_dict['property9']['property10']['property11']) + self.assertEqual( + out_dict['property12'][0]['property12a'], + self.in_yamls['hey']['we_are_a_match']) + self.assertIsNone( + out_dict['property12'][0]['property12b']) + self.assertEqual( + self.template_yamls[0]['property12'][0]['property12c'], + out_dict['property12'][0]['property12c']) + self.assertIsNone( + out_dict['property13']['property14'] + ['property15']['property16a']) + self.assertIsNone( + out_dict['property13']['property14'] + ['property15']['property16b']) + self.assertIsNone( + out_dict['property13']['property14'] + ['property15']['property16c']) + self.assertEqual(out_dict['property13'] + ['property14']['property17'], + self.template_yamls[0]['property13']['property14'] + ['property17']) + self.assertEqual(out_dict['property13'] + ['property14']['property18'], + self.in_yamls['hey']['we_are_a_match']) + except Exception as e: + self.assertTrue(False, e) + + def test_without_optional_annotation(self): + + # self.template_yamls contains multipe documents with + # references to an undefined placeholder + for index in [1, 2, 3, 4]: + try: + # should fail + replace(self.in_yamls, self.template_yamls[index]) + self.assertTrue(False) + except PlaceholderNotFoundError as pnfe: + self.assertEqual('{{' + pnfe.placeholder + '}}', + self.template_yamls[index]['property1']) + except Exception as e: + self.assertTrue(False, e) + + for index in [5]: + try: + # should fail + replace(self.in_yamls, self.template_yamls[index]) + self.assertTrue(False) + except PlaceholderNotFoundError as pnfe: + self.assertEqual('{{' + pnfe.placeholder + '}}', + self.template_yamls[index] + .get('property1').get('property1a')) + except Exception as e: + self.assertTrue(False, e) + + +if __name__ == '__main__': + unittest.main()	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 2 }	unknown	{ "env_vars": null, "env_yml_path": [], "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [], "python": "3.7", "reqs_path": [ "test_requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	certifi @ file:///croot/certifi_1671487769961/work/certifi exceptiongroup==1.2.2 -e git+https://github.com/CODAIT/exchange-metadata-converter.git@a5432d877fe947a12c169d2d84c22e034d555289#egg=exchange_metadata_converter flake8==5.0.4 importlib-metadata==4.2.0 importlib-resources==5.12.0 iniconfig==2.0.0 mccabe==0.7.0 packaging==24.0 pluggy==1.2.0 pycodestyle==2.9.1 pyflakes==2.5.0 pytest==7.4.4 ruamel.yaml==0.18.10 ruamel.yaml.clib==0.2.8 tomli==2.0.1 typing_extensions==4.7.1 zipp==3.15.0	name: exchange-metadata-converter channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - exceptiongroup==1.2.2 - flake8==5.0.4 - importlib-metadata==4.2.0 - importlib-resources==5.12.0 - iniconfig==2.0.0 - mccabe==0.7.0 - packaging==24.0 - pluggy==1.2.0 - pycodestyle==2.9.1 - pyflakes==2.5.0 - pytest==7.4.4 - ruamel-yaml==0.18.10 - ruamel-yaml-clib==0.2.8 - tomli==2.0.1 - typing-extensions==4.7.1 - zipp==3.15.0 prefix: /opt/conda/envs/exchange-metadata-converter	[ "tests/test_annotations.py::TestAnnotations::test_optional_annotation" ]	[]	[ "tests/test_annotations.py::TestAnnotations::test_without_optional_annotation" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.codait_1776_exchange-metadata-converter-25
CODAIT__exchange-metadata-converter-38	c306552b0a2fc25f868cfc7daee7b26a1c99f31b	2020-11-05 20:45:48	c306552b0a2fc25f868cfc7daee7b26a1c99f31b		diff --git a/setup.py b/setup.py index 1e62eb5..afb9bf4 100644 --- a/setup.py +++ b/setup.py @@ -22,7 +22,7 @@ with open('README.md') as readme: setup( name='exchange-metadata-converter', packages=find_packages(), - version='0.0.8', + version='0.0.9', license='Apache-2.0', description='exchange metadata converters', long_description=README, diff --git a/templates/dlf_out.yaml b/templates/dlf_out.yaml index b440965..0e8427a 100644 --- a/templates/dlf_out.yaml +++ b/templates/dlf_out.yaml @@ -14,7 +14,7 @@ apiVersion: com.ie.ibm.hpsys/v1alpha1 kind: Dataset metadata: - name: '{{name}}' + name: '{{id}}' labels: version: '{{version}}' spec:	Non-compliant metadata.name in the generated DLF YAML The `metadata.name` in the generated DLF YAML does not comply with the Kubernetes spec for DNS-1123 subdomain names. ```JSON { "kind": "Status", "apiVersion": "v1", "metadata": {}, "status": "Failure", "message": "Dataset.com.ie.ibm.hpsys \"Finance Proposition Bank\" is invalid: metadata.name: Invalid value: \"Finance Proposition Bank\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9][a-z0-9])?(\\.[a-z0-9]([-a-z0-9][a-z0-9])?)')", "reason": "Invalid", "details": { "name": "Finance Proposition Bank", "group": "com.ie.ibm.hpsys", "kind": "Dataset", "causes": [ { "reason": "FieldValueInvalid", "message": "Invalid value: \"Finance Proposition Bank\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9][a-z0-9])?(\\.[a-z0-9]([-a-z0-9][a-z0-9])?)')", "field": "metadata.name" } ] }, "code": 422 } ``` @ptitzler	CODAIT/exchange-metadata-converter	diff --git a/tests/test_dlf.py b/tests/test_dlf.py index 9e37f60..e593f79 100644 --- a/tests/test_dlf.py +++ b/tests/test_dlf.py @@ -48,7 +48,7 @@ class TestDLF(unittest.TestCase): 'com.ie.ibm.hpsys/v1alpha1') self.assertEqual(out_dict['kind'], 'Dataset') self.assertEqual(out_dict['metadata']['name'], - self.in_yamls['name']) + self.in_yamls['id']) self.assertEqual(out_dict['metadata']['labels']['version'], self.in_yamls['version']) self.assertEqual(out_dict['spec']['type'],	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 2 }	0.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "flake8", "pytest" ], "pre_install": null, "python": "3.8", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	exceptiongroup @ file:///croot/exceptiongroup_1706031385326/work -e git+https://github.com/CODAIT/exchange-metadata-converter.git@c306552b0a2fc25f868cfc7daee7b26a1c99f31b#egg=exchange_metadata_converter flake8==7.1.2 importlib_resources==6.4.5 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work mccabe==0.7.0 packaging @ file:///croot/packaging_1720101850331/work pluggy @ file:///tmp/build/80754af9/pluggy_1648042571233/work pycodestyle==2.12.1 pyflakes==3.2.0 pytest @ file:///croot/pytest_1717793244625/work ruamel.yaml==0.18.10 ruamel.yaml.clib==0.2.8 tomli @ file:///opt/conda/conda-bld/tomli_1657175507142/work zipp==3.20.2	name: exchange-metadata-converter channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - exceptiongroup=1.2.0=py38h06a4308_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.1=py38h06a4308_0 - pip=24.2=py38h06a4308_0 - pluggy=1.0.0=py38h06a4308_1 - pytest=7.4.4=py38h06a4308_0 - python=3.8.20=he870216_0 - readline=8.2=h5eee18b_0 - setuptools=75.1.0=py38h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tomli=2.0.1=py38h06a4308_0 - wheel=0.44.0=py38h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - flake8==7.1.2 - importlib-resources==6.4.5 - mccabe==0.7.0 - pycodestyle==2.12.1 - pyflakes==3.2.0 - ruamel-yaml==0.18.10 - ruamel-yaml-clib==0.2.8 - zipp==3.20.2 prefix: /opt/conda/envs/exchange-metadata-converter	[ "tests/test_dlf.py::TestDLF::test_dtest_dlf" ]	[]	[ "tests/test_dlf.py::TestDLF::test_dax_dataset_descriptors" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.codait_1776_exchange-metadata-converter-38
CORE-GATECH-GROUP__serpent-tools-100	433d55710b85805c9dee609ad77f81bc3c202b72	2018-03-23 15:07:37	1d475a4dd8982532d097286468b2d616345c8ab8		diff --git a/docs/examples/Branching.rst b/docs/examples/Branching.rst index 32e10ca..7544198 100644 --- a/docs/examples/Branching.rst +++ b/docs/examples/Branching.rst @@ -1,3 +1,9 @@ +.. \|branchReader\| replace:: :py:class:`~serpentTools.parsers.branching.BranchingReader` + +.. \|branchContainer\| replace:: :py:class:`~serpentTools.objects.containers.BranchContainer` + +.. \|homogUniv\| replace:: :py:class:`~serpentTools.objects.containers.HomogUniv` + .. _branching-ex: Branching Reader @@ -34,37 +40,36 @@ The simplest way to read these files is using the >>> import serpentTools >>> branchFile = 'demo.coe' - INFO : serpentTools: Using version 0.2.1 >>> r0 = serpentTools.read(branchFile) INFO : serpentTools: Inferred reader for demo.coe: BranchingReader INFO : serpentTools: Preparing to read demo.coe INFO : serpentTools: Done reading branching file -The branches are stored in custom -:py:class:`~serpentTools.objects.containers.BranchContainer` objects in the -``branches`` dictionary +The branches are stored in custom \|branchContainer\| objects in the +:py:attr:`~serpentTools.parsers.branching.BranchingReader.branches` +dictionary .. code:: >>> r0.branches - {('B1000', 'FT1200'): - <serpentTools.objects.containers.BranchContainer at 0x2220c762438>, + {('B1000', 'FT1200'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8d8c9b00>, ('B1000', 'FT600'): - <serpentTools.objects.containers.BranchContainer at 0x2220c787908>, - ('B1000', 'nom'): - <serpentTools.objects.containers.BranchContainer at 0x2220c737ef0>, - ('B750', 'FT1200'): - <serpentTools.objects.containers.BranchContainer at 0x2220c752cf8>, - ('B750', 'FT600'): - <serpentTools.objects.containers.BranchContainer at 0x2220c77c208>, - ('B750', 'nom'): - <serpentTools.objects.containers.BranchContainer at 0x2220c72c860>, - ('nom', 'FT1200'): - <serpentTools.objects.containers.BranchContainer at 0x2220c7455f8>, - ('nom', 'FT600'): - <serpentTools.objects.containers.BranchContainer at 0x2220c76dac8>, - ('nom', 'nom'): - <serpentTools.objects.containers.BranchContainer at 0x2220c7231d0>} + <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfecfd0>, + ('B1000', 'nom'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8d052b00>, + ('B750', 'FT1200'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8d8cc400>, + ('B750', 'FT600'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfe58d0>, + ('B750', 'nom'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8d041470>, + ('nom', 'FT1200'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfda208>, + ('nom', 'FT600'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfdf1d0>, + ('nom', 'nom'): + <serpentTools.objects.containers.BranchContainer at 0x7f2c8d03eda0>} Here, the keys are tuples of strings indicating what perturbations/branch states were applied for each ``SERPENT`` solution. @@ -83,7 +88,9 @@ Examining a particular case var V1_name V1_value -cards. These are stored in the ``stateData`` attribute +cards. These are stored in the +:py:attr:`~serpentTools.objects.containers.BranchContainer.stateData` +attribute .. code:: @@ -106,9 +113,10 @@ Group Constant Data Group constants are converted from ``SERPENT_STYLE`` to ``mixedCase`` to fit the overall style of the project. -The :py:class:`~serpentTools.objects.containers.BranchContainer` stores group -constant data in :py:class:`~serpentTools.objects.containers.HomogUniv` -objects in the ``universes`` dictionary +The \|branchContainer\| stores group +constant data in \|homogUniv\| objects in the +:py:attr:`~serpentTools.parsers.branching.BranchingReader.branches` +dictionary .. code:: @@ -129,42 +137,43 @@ objects in the ``universes`` dictionary (40, 1.0, 2): <serpentTools.objects.containers.HomogUniv at 0x2220c78bdd8>, (40, 10.0, 3): <serpentTools.objects.containers.HomogUniv at 0x2220c791a58>} -The keys here are vectors indicating the universe ID, burnup [MWd/kgU], -and burnup index corresponding to the point in the burnup schedule. +The keys here are vectors indicating the universe ID, burnup, and burnup +index corresponding to the point in the burnup schedule. ``SERPENT`` +prints negative values of burnup to indicate units of days, which is +reflected in the +:py:attr:`~serpentTools.objects.containers.BranchContainer.hasDays` +attribute. ``hasDays-> True`` indicates +that the values of burnup, second item in the above tuple, are in terms +of days, not MWd/kgU. These universes can be obtained by indexing this dictionary, or by using the :py:meth:`~serpentTools.objects.containers.BranchContainer.getUniv` method .. code:: - >>> univ0 = b0.universes[0, 1, 2] + >>> univ0 = b0.universes[0, 1, 1] >>> print(univ0) + <HomogUniv 0: burnup: 1.000 MWd/kgu, step: 1> >>> print(univ0.name) + 0 >>> print(univ0.bu) + 1.0 >>> print(univ0.step) + 1 >>> print(univ0.day) - <HomogUniv from demo.coe> - 0 - 1.0 - 2 - 0 + None + >>> print(b0.hasDays) + False >>> univ1 = b0.getUniv(0, burnup=1) - >>> univ2 = b0.getUniv(0, index=2) + >>> univ2 = b0.getUniv(0, index=1) >>> assert univ0 is univ1 is univ2 -Since the coefficient files do not store the day value of burnup, all -:py:class:`~serpentTools.objects.containers.HomogUniv` objects created by the -:py:class:`~serpentTools.objects.containers.BranchContainer` default to day -zero. - Group constant data is stored in five dictionaries: -1. ``infExp``: Expected values for infinite medium group constants -2. ``infUnc``: Relative uncertainties for infinite medium group - constants -3. ``b1Exp``: Expected values for leakge-corrected group constants -4. ``b1Unc``: Relative uncertainties for leakge-corrected group - constants -5. ``metaData``: items that do not fit the in the above categories +1. :py:attr:`~serpentTools.objects.containers.HomogUniv.infExp`: Expected values for infinite medium group constants +2. :py:attr:`~serpentTools.objects.containers.HomogUniv.infUnc`: Relative uncertainties for infinite medium group constants +3. :py:attr:`~serpentTools.objects.containers.HomogUniv.b1Exp`: Expected values for leakge-corrected group constants +4. :py:attr:`~serpentTools.objects.containers.HomogUniv.b1Unc`: Relative uncertainties for leakge-corrected group constants +5. :py:attr:`~serpentTools.objects.containers.HomogUniv.metaData`: items that do not fit the in the above categories .. code:: @@ -206,7 +215,7 @@ Iteration The branching reader has a :py:meth:`~serpentTools.parsers.branching.BranchingReader.iterBranches` method that works to yield branch names and their associated -:py:class:`~serpentTools.objects.containers.BranchContainer` objects. This can +\|branchContainer\| objects. This can be used to efficiently iterate over all the branches presented in the file. .. code:: @@ -231,9 +240,8 @@ User Control The ``SERPENT`` `set coefpara <http://serpent.vtt.fi/mediawiki/index.php/Input_syntax_manual#set_coefpara>`_ card already restricts the data present in the coeffient file to user -control, and the :py:class:`~serpentTools.parsers.branching.BranchingReader` -includes similar control. Below are the various settings that the -:py:class:`~serpentTools.parsers.branching.BranchingReader` uses to read and +control, and the \|branchReader\| includes similar control. +Below are the various settings that the \|branchReader\| uses to read and process coefficient files. .. code:: @@ -290,7 +298,7 @@ that the default action is to store all state data variables as strings. As demonstrated in the :ref:`group-const-variables` example, use of ``xs.variableGroups`` and ``xs.variableExtras`` controls what data is -stored on the :py:class:`~serpentTools.objects.containers.HomogUniv` +stored on the \|homogUniv\| objects. By default, all variables present in the coefficient file are stored. .. code:: @@ -327,8 +335,7 @@ variables explicitly requested are present Conclusion ---------- -The :py:class:`~serpentTools.parsers.branching.BranchingReader` is capable of -reading coefficient files created +The \|branchReader\| is capable of reading coefficient files created by the ``SERPENT`` automated branching process. The data is stored according to the branch parameters, universe information, and burnup. This reader also supports user control of the processing by selecting diff --git a/examples/Branching.ipynb b/examples/Branching.ipynb index d2f1836..ca01663 100644 --- a/examples/Branching.ipynb +++ b/examples/Branching.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Copyright (c) 2017 Andrew Johnson, Dan Kotlyar, GTRC\n", + "Copyright (c) 2017-2018 Andrew Johnson, Dan Kotlyar, Stefano Terlizzi, Gavin Ridley, GTRC\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." ] @@ -39,17 +39,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO : serpentTools: Using version 1.0b0+37.ga1e314a.dirty\n" - ] - } - ], + "outputs": [], "source": [ "import serpentTools\n", "branchFile = 'demo.coe'" @@ -66,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -92,33 +84,33 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{('B1000',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x2220c762438>,\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8d8c9b00>,\n", " ('B1000',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x2220c787908>,\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfecfd0>,\n", " ('B1000',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x2220c737ef0>,\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8d052b00>,\n", " ('B750',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x2220c752cf8>,\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8d8cc400>,\n", " ('B750',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x2220c77c208>,\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfe58d0>,\n", " ('B750',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x2220c72c860>,\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8d041470>,\n", " ('nom',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x2220c7455f8>,\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfda208>,\n", " ('nom',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x2220c76dac8>,\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8cfdf1d0>,\n", " ('nom',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x2220c7231d0>}" + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7f2c8d03eda0>}" ] }, - "execution_count": 5, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -136,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -157,15 +149,17 @@ "metadata": {}, "source": [ "`SERPENT` allows the user to define variables for each branch through:\n", - "```\n", + "\n", + "`\n", "var V1_name V1_value\n", - "```\n", + "`\n", + "\n", "cards. These are stored in the `stateData` attribute" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -178,7 +172,7 @@ " 'VERSION': '2.1.29'}" ] }, - "execution_count": 7, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -206,74 +200,76 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `BranchContainer` stores group constant data in `HomogUniv` objects in the `universes` dictionary" + "The `BranchContainer` stores group constant data in `HomogUniv` objects in the `universes` dictionary. " ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{(0, 0.0, 1): <serpentTools.objects.containers.HomogUniv at 0x2220c781ac8>,\n", - " (0, 1.0, 2): <serpentTools.objects.containers.HomogUniv at 0x2220c78b5f8>,\n", - " (0, 10.0, 3): <serpentTools.objects.containers.HomogUniv at 0x2220c791240>,\n", - " (10, 0.0, 1): <serpentTools.objects.containers.HomogUniv at 0x2220c787a58>,\n", - " (10, 1.0, 2): <serpentTools.objects.containers.HomogUniv at 0x2220c78b6a0>,\n", - " (10, 10.0, 3): <serpentTools.objects.containers.HomogUniv at 0x2220c791320>,\n", - " (20, 0.0, 1): <serpentTools.objects.containers.HomogUniv at 0x2220c787cc0>,\n", - " (20, 1.0, 2): <serpentTools.objects.containers.HomogUniv at 0x2220c78b908>,\n", - " (20, 10.0, 3): <serpentTools.objects.containers.HomogUniv at 0x2220c791588>,\n", - " (30, 0.0, 1): <serpentTools.objects.containers.HomogUniv at 0x2220c78b048>,\n", - " (30, 1.0, 2): <serpentTools.objects.containers.HomogUniv at 0x2220c78bb70>,\n", - " (30, 10.0, 3): <serpentTools.objects.containers.HomogUniv at 0x2220c7917f0>,\n", - " (40, 0.0, 1): <serpentTools.objects.containers.HomogUniv at 0x2220c78b1d0>,\n", - " (40, 1.0, 2): <serpentTools.objects.containers.HomogUniv at 0x2220c78bdd8>,\n", - " (40, 10.0, 3): <serpentTools.objects.containers.HomogUniv at 0x2220c791a58>}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, 1.0, 1)\n", + "(10, 1.0, 1)\n", + "(20, 1.0, 1)\n", + "(30, 1.0, 1)\n", + "(20, 0.0, 0)\n", + "(40, 0.0, 0)\n", + "(20, 10.0, 2)\n", + "(10, 10.0, 2)\n", + "(0, 0.0, 0)\n", + "(10, 0.0, 0)\n", + "(0, 10.0, 2)\n", + "(30, 0.0, 0)\n", + "(40, 10.0, 2)\n", + "(40, 1.0, 1)\n", + "(30, 10.0, 2)\n" + ] } ], "source": [ - "b0.universes" + "for key in b0.universes:\n", + " print(key)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The keys here are vectors indicating the universe ID, burnup [MWd/kgU], and burnup index corresponding to the point in the burnup schedule. These universes can be obtained by indexing this dictionary, or by using the `getUniv` method" + "The keys here are vectors indicating the universe ID, burnup, and burnup index corresponding to the point in the burnup schedule. `SERPENT` prints negative values of burnup to indicate units of days, which is reflected in the `hasDays` attribute. `hasDays-> True` indicates that the values of burnup, second item in the above tuple, are in terms of days, not MWd/kgU.\n", + "\n", + "These universes can be obtained by indexing this dictionary, or by using the `getUniv` method." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "<HomogUniv from demo.coe>\n", + "<HomogUniv 0: burnup: 1.000 MWd/kgu, step: 1>\n", "0\n", "1.0\n", - "2\n", - "0\n" + "1\n", + "None\n", + "False\n" ] } ], "source": [ - "univ0 = b0.universes[0, 1, 2]\n", + "univ0 = b0.universes[0, 1, 1]\n", "print(univ0)\n", "print(univ0.name)\n", "print(univ0.bu)\n", "print(univ0.step)\n", - "print(univ0.day)" + "print(univ0.day)\n", + "print(b0.hasDays)" ] }, { @@ -285,34 +281,12 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "univ1 = b0.getUniv(0, burnup=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "univ2 = b0.getUniv(0, index=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ + "univ1 = b0.getUniv(0, burnup=1)\n", + "univ2 = b0.getUniv(0, index=1)\n", "assert univ0 is univ1 is univ2" ] }, @@ -326,12 +300,14 @@ "1. `infUnc`: Relative uncertainties for infinite medium group constants\n", "1. `b1Exp`: Expected values for leakge-corrected group constants\n", "1. `b1Unc`: Relative uncertainties for leakge-corrected group constants\n", - "1. `metaData`: items that do not fit the in the above categories" + "1. `metaData`: items that do not fit the in the above categories, such as energy group structure, k-infinity, etc.\n", + "\n", + "For this problem, the coefficient file does not have uncertainties, nor these metadata arguments. For this reason, the `infUnc`, `b1Unc`, and `metaData` dictionaries are emtpy." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -345,7 +321,7 @@ " 'infTot': array([ 0.310842, 0.618286])}" ] }, - "execution_count": 13, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -356,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -365,7 +341,7 @@ "{}" ] }, - "execution_count": 14, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -376,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -390,7 +366,7 @@ " 'b1Tot': array([ 0.314521, 0.618361])}" ] }, - "execution_count": 15, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -401,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -410,7 +386,7 @@ "{}" ] }, - "execution_count": 16, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -428,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -437,7 +413,7 @@ "array([ 0.00271604, 0.059773 ])" ] }, - "execution_count": 17, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -448,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -476,22 +452,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "('nom', 'nom') <BranchContainer for nom, nom from demo.coe>\n", - "('B750', 'nom') <BranchContainer for B750, nom from demo.coe>\n", - "('B1000', 'nom') <BranchContainer for B1000, nom from demo.coe>\n", - "('nom', 'FT1200') <BranchContainer for nom, FT1200 from demo.coe>\n", - "('B750', 'FT1200') <BranchContainer for B750, FT1200 from demo.coe>\n", "('B1000', 'FT1200') <BranchContainer for B1000, FT1200 from demo.coe>\n", + "('B750', 'FT1200') <BranchContainer for B750, FT1200 from demo.coe>\n", + "('nom', 'FT1200') <BranchContainer for nom, FT1200 from demo.coe>\n", "('nom', 'FT600') <BranchContainer for nom, FT600 from demo.coe>\n", + "('B750', 'nom') <BranchContainer for B750, nom from demo.coe>\n", + "('nom', 'nom') <BranchContainer for nom, nom from demo.coe>\n", + "('B1000', 'FT600') <BranchContainer for B1000, FT600 from demo.coe>\n", "('B750', 'FT600') <BranchContainer for B750, FT600 from demo.coe>\n", - "('B1000', 'FT600') <BranchContainer for B1000, FT600 from demo.coe>\n" + "('B1000', 'nom') <BranchContainer for B1000, nom from demo.coe>\n" ] } ], @@ -511,10 +487,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [], "source": [ "import six\n", @@ -523,33 +497,21 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "branching.areUncsPresent\n", - "\t default: False\n", + "xs.getInfXS\n", + "\t default: True\n", + "\t description: If true, store the infinite medium cross sections.\n", "\t type: <class 'bool'>\n", - "\t description: True if the values in the .coe file contain uncertainties\n", - "branching.intVariables\n", - "\t default: []\n", - "\t description: Name of state data variables to convert to integers for each branch\n", - "\t type: <class 'list'>\n", "branching.floatVariables\n", "\t default: []\n", "\t description: Names of state data variables to convert to floats for each branch\n", "\t type: <class 'list'>\n", - "xs.getInfXS\n", - "\t default: True\n", - "\t description: If true, store the infinite medium cross sections.\n", - "\t type: <class 'bool'>\n", - "xs.getB1XS\n", - "\t default: True\n", - "\t description: If true, store the critical leakage cross sections.\n", - "\t type: <class 'bool'>\n", "xs.variableGroups\n", "\t default: []\n", "\t description: Name of variable groups from variables.yaml to be expanded into SERPENT variable to be stored\n", @@ -557,7 +519,19 @@ "xs.variableExtras\n", "\t default: []\n", "\t description: Full SERPENT name of variables to be read\n", - "\t type: <class 'list'>\n" + "\t type: <class 'list'>\n", + "branching.intVariables\n", + "\t default: []\n", + "\t description: Name of state data variables to convert to integers for each branch\n", + "\t type: <class 'list'>\n", + "branching.areUncsPresent\n", + "\t default: False\n", + "\t description: True if the values in the .coe file contain uncertainties\n", + "\t type: <class 'bool'>\n", + "xs.getB1XS\n", + "\t default: True\n", + "\t description: If true, store the critical leakage cross sections.\n", + "\t type: <class 'bool'>\n" ] } ], @@ -579,10 +553,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "assert isinstance(b0.stateData['BOR'], str)" @@ -592,7 +564,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As demonstrated in the `Settings` example, use of `xs.variableGroups` and `xs.variableExtras` controls what data is stored on the `HomogUniv` objects. By default, all variables present in the coefficient file are stored." + "As demonstrated in the [Settings example](https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/master/examples/Settings.ipynb), use of `xs.variableGroups` and `xs.variableExtras` controls what data is stored on the `HomogUniv` objects. By default, all variables present in the coefficient file are stored." ] }, { @@ -654,9 +626,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "assert isinstance(b1.stateData['BOR'], float)\n", @@ -736,7 +706,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/serpentTools/objects/containers.py b/serpentTools/objects/containers.py index 745a613..8213dd2 100644 --- a/serpentTools/objects/containers.py +++ b/serpentTools/objects/containers.py @@ -4,6 +4,7 @@ Contents -------- :py:class:`~serpentTools.objects.containers.HomogUniv` :py:class:`~serpentTools.objects.containers.BranchContainer +:py:class:`~serpentTools.objects.containers.DetectorBase` :py:class:`~serpentTools.objects.containers.Detector` """ @@ -22,6 +23,17 @@ DET_COLS = ('value', 'energy', 'universe', 'cell', 'material', 'lattice', """Name of the columns of the data""" +__all__ = ('DET_COLS', 'HomogUniv', 'BranchContainer', 'Detector', + 'DetectorBase') + + +def isNonNeg(value): + """Return true if a value is None or non-negative""" + if value is None: + return True + return value >= 0 + + class HomogUniv(NamedObject): """ Class for storing homogenized universe specifications and retrieving them @@ -41,12 +53,12 @@ class HomogUniv(NamedObject): ---------- name: str name of the universe - bu: float - burnup value - step: float + bu: float or int + non-negative burnup value + step: int temporal step - day: float - depletion day + day: float or int + non-negative depletion day infExp: dict Expected values for infinite medium group constants infUnc: dict @@ -57,10 +69,26 @@ class HomogUniv(NamedObject): Relative uncertainties for leakage-corrected group constants metadata: dict Other values that do not not conform to inf/b1 dictionaries + + Raises + ------ + SerpentToolsException: + If a negative value of burnup, step, or burnup days is passed + """ def __init__(self, name, bu, step, day): + if not all(isNonNeg(xx) for xx in (bu, step, day)): + tail = ['{}: {}'.format(name, val) + for name, val in zip(('burnup', 'index', 'days'), + (bu, step, day))] + raise SerpentToolsException( + "Will not create universe with negative burnup\n{}" + .format(', '.join(tail))) NamedObject.__init__(self, name) + if step is not None and step == 0: + bu = bu if bu is not None else 0.0 + day = day if day is not None else 0.0 self.bu = bu self.step = step self.day = day @@ -71,9 +99,22 @@ class HomogUniv(NamedObject): self.infUnc = {} self.metadata = {} + def __str__(self): + extras = [] + if self.bu is not None: + extras.append('burnup: {:5.3f} MWd/kgu'.format(self.bu)) + if self.step: + extras.append('step: {}'.format(self.step)) + if self.day is not None: + extras.append('{:5.3f} days'.format(self.day)) + if extras: + extras = ': ' + ', '.join(extras) + return '<{} {}{}>'.format(self.__class__.__name__, self.name, + extras or '') + def addData(self, variableName, variableValue, uncertainty=False): """ - Sets the value of the variable and, optionally, the associate s.d. + sets the value of the variable and, optionally, the associate s.d. .. warning:: @@ -658,6 +699,8 @@ class BranchContainer(object): Keys are tuples of ``(universeID, burnup, burnIndex)`` """ + __mismatchedBurnup = ("Was not expecting a {} value of burnup. " + "Expect burnup in units of {}") def __init__(self, filePath, branchID, branchNames, stateData): self.filePath = filePath @@ -667,6 +710,7 @@ class BranchContainer(object): self.branchNames = branchNames self.__orderedUniverses = None self.__keys = set() + self.__hasDays = None def __str__(self): return '<BranchContainer for {} from {}>'.format( @@ -693,8 +737,11 @@ class BranchContainer(object): Add a universe to this branch. Data for the universes are produced at specific points in time. - The additional arguments help track when the data for this + The additional arguments help track of when the data for this universe were created. + A negative value of ``burnup`` indicates the units on burnup are + really ``days``. Therefore, the value of ``burnDays`` and ``burnup`` + will be swapped. .. warning:: @@ -705,7 +752,8 @@ class BranchContainer(object): univID: int or str Identifier for this universe burnup: float or int - Value of burnup [MWd/kgU] + Value of burnup [MWd/kgU]. A negative value here indicates + the value is really in units of days. burnIndex: int Point in the depletion schedule burnDays: int or float @@ -713,11 +761,24 @@ class BranchContainer(object): Returns ------- - newUniv: serpentTools.objects.containers.HomogUniv + serpentTools.objects.containers.HomogUniv + Empty new universe + """ + if self.__hasDays is None and burnup: + self.__hasDays = burnup < 0 + if burnup < 0: + if not self.__hasDays: + raise SerpentToolsException(self.__mismatchedBurnup.format( + 'negative', 'MWd/kgU')) + burnup, burnDays = None if burnup else 0, - burnup + else: + if self.__hasDays and not burnDays: + raise SerpentToolsException(self.__mismatchedBurnup.format( + 'positive', 'days')) + burnDays = None if burnup else 0 newUniv = HomogUniv(univID, burnup, burnIndex, burnDays) - key = tuple( - [univID, burnup, burnIndex] + ([burnDays] if burnDays else [])) + key = (univID, burnup or burnDays, burnIndex) if key in self.__keys: warning('Overwriting existing universe {} in {}' .format(key, str(self))) @@ -743,8 +804,8 @@ class BranchContainer(object): Returns ------- - univ: serpentTools.objects.containers.HomogUniv - Requested Universe + :py:class:`~serpentTools.objects.containers.HomogUniv` + Requested universe Raises ------ @@ -755,7 +816,7 @@ class BranchContainer(object): """ if burnup is None and index is None: raise SerpentToolsException('Burnup or index are required inputs') - searchIndex = 2 if index is not None else 1 + searchIndex = 2 if index is not None else 1 searchValue = index if index is not None else burnup for key in self.__keys: if key[0] == univID and key[searchIndex] == searchValue: @@ -766,3 +827,11 @@ class BranchContainer(object): raise KeyError( 'Could not find a universe that matched requested universe {} and ' '{} {}'.format(univID, searchName, searchValue)) + + @property + def hasDays(self): + """Returns True if the burnups in the file are in units of days""" + if self.__hasDays is None: + raise AttributeError("Need to load at least one universe with " + "non-zero burnup first.""") + return self.__hasDays diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index 795036e..8b18e52 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -17,6 +17,10 @@ class BranchingReader(XSReader): ---------- filePath: str path to the depletion file + branches: dict + Dictionary of branch names and their corresponding + :py:class:`~serpentTools.objects.containers.BranchContainer` + objects """ def __init__(self, filePath): @@ -98,7 +102,7 @@ class BranchingReader(XSReader): def _processBranchUniverses(self, branch, burnup, burnupIndex): """Add universe data to this branch at this burnup.""" unvID, numVariables = [int(xx) for xx in self._advance()] - univ = branch.addUniverse(unvID, burnup, burnupIndex) + univ = branch.addUniverse(unvID, burnup, burnupIndex - 1) for step in range(numVariables): splitList = self._advance( possibleEndOfFile=step == numVariables - 1)	Universes from branching file can be stored with negative burnup ## Summary of issue The burnup values in coefficient files can be negative, indicating units of days, not MWd/kgU [Link to SERPENT coef card](http://serpent.vtt.fi/mediawiki/index.php/Input_syntax_manual#coef_.28coefficient_matrix_definition.29). This leads to `HomogUniv` objects being created with negative values of burnup, which is _slightly_ nonsensical. ## Code for reproducing the issue Read in a coefficient file that has negative values of burnup. ## Actual outcome including console output and error traceback if applicable For some branchContainer `b` in the branching reader: ``` >>> b.universes.keys() dict_keys([(3102, 0.0, 1), (3102, -200.0, 6), (3102, -328.5, 9), (3102, -246.375, 7), (3102, -280.0, 8), (3102, -164.25, 5), (3102, -82.125, 3), (3102, -120.0, 4), (3102, -40.0, 2)]) >>> univ = b.universes[(3102, -328.5, 9)] >>> univ.bu -328.5 >>> univ.day 0 ``` ## Expected outcome Universes with non-negative burnup. Maybe implement a better method for retrieving universes given values of days or burnup. ## Versions * ``serpentTools.__version__`` 0.2.1+12.g68709f6	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_branching.py b/serpentTools/tests/test_branching.py index 8bfd381..be967f5 100644 --- a/serpentTools/tests/test_branching.py +++ b/serpentTools/tests/test_branching.py @@ -10,6 +10,7 @@ from numpy.testing import assert_allclose from serpentTools.settings import rc from serpentTools.tests import TEST_ROOT from serpentTools.parsers import BranchingReader +from serpentTools.objects.containers import BranchContainer from serpentTools.messages import SerpentToolsException @@ -22,7 +23,7 @@ class _BranchTesterHelper(unittest.TestCase): cls.expectedBranches = {('nom', 'nom', 'nom')} cls.expectedUniverses = { # universe id, burnup, step - (0, 0, 1), + (0, 0, 0), } with rc: rc['serpentVersion'] = '2.1.29' @@ -76,7 +77,7 @@ class BranchContainerTester(_BranchTesterHelper): def setUpClass(cls): _BranchTesterHelper.setUpClass() cls.refBranchID = ('nom', 'nom', 'nom') - cls.refUnivKey = (0, 0, 1) + cls.refUnivKey = (0, 0, 0) cls.refBranch = cls.reader.branches[cls.refBranchID] cls.refUniv = cls.refBranch.universes[cls.refUnivKey] @@ -108,6 +109,18 @@ class BranchContainerTester(_BranchTesterHelper): with self.assertRaises(SerpentToolsException): self.refBranch.getUniv(key[0]) + def test_cannotAddBurnupDays(self): + """ + Verify that a universe cannot be added with burnup of opposite units. + """ + containerWithBU = BranchContainer(None, None, None, None) + containerWithBU.addUniverse(101, 10, 1) + containerWithDays = BranchContainer(None, None, None, None) + containerWithDays.addUniverse(101, -10, 1) + with self.assertRaises(SerpentToolsException): + containerWithBU.addUniverse(101, -10, 1) + with self.assertRaises(SerpentToolsException): + containerWithDays.addUniverse(101, 10, 1) if __name__ == '__main__': unittest.main()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 4 }	0.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "numpy>=1.11.1 matplotlib>=1.5.0 pyyaml>=3.08 scipy", "pip_packages": [ "pytest", "jupyter", "nbconvert" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	anyio==3.6.2 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 async-generator==1.10 attrs==22.2.0 Babel==2.11.0 backcall==0.2.0 bleach==4.1.0 certifi==2021.5.30 cffi==1.15.1 charset-normalizer==2.0.12 comm==0.1.4 contextvars==2.4 cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work dataclasses==0.8 decorator==5.1.1 defusedxml==0.7.1 entrypoints==0.4 idna==3.10 immutables==0.19 importlib-metadata==4.8.3 iniconfig==1.1.1 ipykernel==5.5.6 ipython==7.16.3 ipython-genutils==0.2.0 ipywidgets==7.8.5 jedi==0.17.2 Jinja2==3.0.3 json5==0.9.16 jsonschema==3.2.0 jupyter==1.1.1 jupyter-client==7.1.2 jupyter-console==6.4.3 jupyter-core==4.9.2 jupyter-server==1.13.1 jupyterlab==3.2.9 jupyterlab-pygments==0.1.2 jupyterlab-server==2.10.3 jupyterlab_widgets==1.1.11 kiwisolver @ file:///tmp/build/80754af9/kiwisolver_1612282412546/work MarkupSafe==2.0.1 matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1613407855456/work mistune==0.8.4 nbclassic==0.3.5 nbclient==0.5.9 nbconvert==6.0.7 nbformat==5.1.3 nest-asyncio==1.6.0 notebook==6.4.10 numpy @ file:///tmp/build/80754af9/numpy_and_numpy_base_1603483703303/work olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work packaging==21.3 pandocfilters==1.5.1 parso==0.7.1 pexpect==4.9.0 pickleshare==0.7.5 Pillow @ file:///tmp/build/80754af9/pillow_1625670622947/work pluggy==1.0.0 prometheus-client==0.17.1 prompt-toolkit==3.0.36 ptyprocess==0.7.0 py==1.11.0 pycparser==2.21 Pygments==2.14.0 pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work pyrsistent==0.18.0 pytest==7.0.1 python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work pytz==2025.2 PyYAML==5.4.1 pyzmq==25.1.2 requests==2.27.1 scipy @ file:///tmp/build/80754af9/scipy_1597686635649/work Send2Trash==1.8.3 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@433d55710b85805c9dee609ad77f81bc3c202b72#egg=serpentTools six @ file:///tmp/build/80754af9/six_1644875935023/work sniffio==1.2.0 terminado==0.12.1 testpath==0.6.0 tomli==1.2.3 tornado @ file:///tmp/build/80754af9/tornado_1606942266872/work traitlets==4.3.3 typing_extensions==4.1.1 urllib3==1.26.20 wcwidth==0.2.13 webencodings==0.5.1 websocket-client==1.3.1 widgetsnbextension==3.6.10 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - blas=1.0=openblas - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - cycler=0.11.0=pyhd3eb1b0_0 - dbus=1.13.18=hb2f20db_0 - expat=2.6.4=h6a678d5_0 - fontconfig=2.14.1=h52c9d5c_1 - freetype=2.12.1=h4a9f257_0 - giflib=5.2.2=h5eee18b_0 - glib=2.69.1=h4ff587b_1 - gst-plugins-base=1.14.1=h6a678d5_1 - gstreamer=1.14.1=h5eee18b_1 - icu=58.2=he6710b0_3 - jpeg=9e=h5eee18b_3 - kiwisolver=1.3.1=py36h2531618_0 - lcms2=2.16=hb9589c4_0 - ld_impl_linux-64=2.40=h12ee557_0 - lerc=4.0.0=h6a678d5_0 - libdeflate=1.22=h5eee18b_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgfortran-ng=7.5.0=ha8ba4b0_17 - libgfortran4=7.5.0=ha8ba4b0_17 - libgomp=11.2.0=h1234567_1 - libopenblas=0.3.18=hf726d26_0 - libpng=1.6.39=h5eee18b_0 - libstdcxx-ng=11.2.0=h1234567_1 - libtiff=4.5.1=hffd6297_1 - libuuid=1.41.5=h5eee18b_0 - libwebp=1.2.4=h11a3e52_1 - libwebp-base=1.2.4=h5eee18b_1 - libxcb=1.15=h7f8727e_0 - libxml2=2.9.14=h74e7548_0 - lz4-c=1.9.4=h6a678d5_1 - matplotlib=3.3.4=py36h06a4308_0 - matplotlib-base=3.3.4=py36h62a2d02_0 - ncurses=6.4=h6a678d5_0 - numpy=1.19.2=py36h6163131_0 - numpy-base=1.19.2=py36h75fe3a5_0 - olefile=0.46=pyhd3eb1b0_0 - openssl=1.1.1w=h7f8727e_0 - pcre=8.45=h295c915_0 - pillow=8.3.1=py36h5aabda8_0 - pip=21.2.2=py36h06a4308_0 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyqt=5.9.2=py36h05f1152_2 - python=3.6.13=h12debd9_1 - python-dateutil=2.8.2=pyhd3eb1b0_0 - pyyaml=5.4.1=py36h27cfd23_1 - qt=5.9.7=h5867ecd_1 - readline=8.2=h5eee18b_0 - scipy=1.5.2=py36habc2bb6_0 - setuptools=58.0.4=py36h06a4308_0 - sip=4.19.8=py36hf484d3e_0 - six=1.16.0=pyhd3eb1b0_1 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tornado=6.1=py36h27cfd23_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - yaml=0.2.5=h7b6447c_0 - zlib=1.2.13=h5eee18b_1 - zstd=1.5.6=hc292b87_0 - pip: - anyio==3.6.2 - argon2-cffi==21.3.0 - argon2-cffi-bindings==21.2.0 - async-generator==1.10 - attrs==22.2.0 - babel==2.11.0 - backcall==0.2.0 - bleach==4.1.0 - cffi==1.15.1 - charset-normalizer==2.0.12 - comm==0.1.4 - contextvars==2.4 - dataclasses==0.8 - decorator==5.1.1 - defusedxml==0.7.1 - entrypoints==0.4 - idna==3.10 - immutables==0.19 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - ipykernel==5.5.6 - ipython==7.16.3 - ipython-genutils==0.2.0 - ipywidgets==7.8.5 - jedi==0.17.2 - jinja2==3.0.3 - json5==0.9.16 - jsonschema==3.2.0 - jupyter==1.1.1 - jupyter-client==7.1.2 - jupyter-console==6.4.3 - jupyter-core==4.9.2 - jupyter-server==1.13.1 - jupyterlab==3.2.9 - jupyterlab-pygments==0.1.2 - jupyterlab-server==2.10.3 - jupyterlab-widgets==1.1.11 - markupsafe==2.0.1 - mistune==0.8.4 - nbclassic==0.3.5 - nbclient==0.5.9 - nbconvert==6.0.7 - nbformat==5.1.3 - nest-asyncio==1.6.0 - notebook==6.4.10 - packaging==21.3 - pandocfilters==1.5.1 - parso==0.7.1 - pexpect==4.9.0 - pickleshare==0.7.5 - pluggy==1.0.0 - prometheus-client==0.17.1 - prompt-toolkit==3.0.36 - ptyprocess==0.7.0 - py==1.11.0 - pycparser==2.21 - pygments==2.14.0 - pyrsistent==0.18.0 - pytest==7.0.1 - pytz==2025.2 - pyzmq==25.1.2 - requests==2.27.1 - send2trash==1.8.3 - sniffio==1.2.0 - terminado==0.12.1 - testpath==0.6.0 - tomli==1.2.3 - traitlets==4.3.3 - typing-extensions==4.1.1 - urllib3==1.26.20 - wcwidth==0.2.13 - webencodings==0.5.1 - websocket-client==1.3.1 - widgetsnbextension==3.6.10 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_branching.py::BranchTester::test_branchingUniverses", "serpentTools/tests/test_branching.py::BranchContainerTester::test_cannotAddBurnupDays", "serpentTools/tests/test_branching.py::BranchContainerTester::test_containerGetUniv", "serpentTools/tests/test_branching.py::BranchContainerTester::test_loadedUniv" ]	[]	[ "serpentTools/tests/test_branching.py::BranchTester::test_raiseError", "serpentTools/tests/test_branching.py::BranchTester::test_variables" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-141	d96de5b0f4bc4c93370cf508e0ea89701054ff41	2018-05-14 22:28:11	7c7da6012f509a2e71c3076ab510718585f75b11		diff --git a/docs/defaultSettings.rst b/docs/defaultSettings.rst index aa16202..8893098 100644 --- a/docs/defaultSettings.rst +++ b/docs/defaultSettings.rst @@ -206,6 +206,19 @@ If true, store the infinite medium cross sections. Type: bool +.. _xs-reshapeScatter: + +--------------------- +``xs.reshapeScatter`` +--------------------- + +If true, reshape the scattering matrices to square matrices. By default, these matrices are stored as vectors. +:: + + Default: False + Type: bool + + .. _xs-variableExtras: --------------------- diff --git a/docs/welcome/changelog.rst b/docs/welcome/changelog.rst index 8628472..62f0e1d 100644 --- a/docs/welcome/changelog.rst +++ b/docs/welcome/changelog.rst @@ -12,6 +12,9 @@ Next * :pull:`131` Updated variable groups between ``2.1.29`` and ``2.1.30`` - include poison cross section, kinetic parameters, six factor formula (2.1.30 exclusive), and minor differences +* :pull:`141` - Setting :ref:`xs-reshapeScatter` can be used to reshape scatter + matrices on :py:class:`~serpentTools.objects.containers.HomogUniv` + objects to square matrices .. _vDeprecated: diff --git a/serpentTools/objects/containers.py b/serpentTools/objects/containers.py index f9ec978..9626270 100644 --- a/serpentTools/objects/containers.py +++ b/serpentTools/objects/containers.py @@ -1,19 +1,22 @@ -""" Custom-built containers for storing data from serpent outputs +""" +Custom-built containers for storing data from serpent outputs Contents -------- -:py:class:`~serpentTools.objects.containers.HomogUniv` -:py:class:`~serpentTools.objects.containers.BranchContainer -:py:class:`~serpentTools.objects.containers.DetectorBase` -:py:class:`~serpentTools.objects.containers.Detector` +* :py:class:`~serpentTools.objects.containers.HomogUniv` +* :py:class:`~serpentTools.objects.containers.BranchContainer +* :py:class:`~serpentTools.objects.containers.DetectorBase` +* :py:class:`~serpentTools.objects.containers.Detector` """ from collections import OrderedDict +from itertools import product from matplotlib import pyplot +from numpy import (array, arange, unique, log, divide, ones_like, hstack, + ndarray) -from numpy import array, arange, unique, log, divide, ones_like, hstack - +from serpentTools.settings import rc from serpentTools.plot import cartMeshPlot, plot, magicPlotDocDecorator from serpentTools.objects import NamedObject, convertVariableName from serpentTools.messages import warning, SerpentToolsException, debug @@ -22,9 +25,16 @@ DET_COLS = ('value', 'energy', 'universe', 'cell', 'material', 'lattice', 'reaction', 'zmesh', 'ymesh', 'xmesh', 'tally', 'error', 'scores') """Name of the columns of the data""" +SCATTER_MATS = set() +SCATTER_ORDERS = 8 + +for xsSpectrum, xsType in product({'INF', 'B1'}, + {'S', 'SP'}): + SCATTER_MATS.update({'{}_{}{}'.format(xsSpectrum, xsType, xx) + for xx in range(SCATTER_ORDERS)}) __all__ = ('DET_COLS', 'HomogUniv', 'BranchContainer', 'Detector', - 'DetectorBase') + 'DetectorBase', 'SCATTER_MATS', 'SCATTER_ORDERS') def isNonNeg(value): @@ -69,6 +79,11 @@ class HomogUniv(NamedObject): Relative uncertainties for leakage-corrected group constants metadata: dict Other values that do not not conform to inf/b1 dictionaries + reshapedMats: bool + ``True`` if scattering matrices have been reshaped to square + matrices. Otherwise, these matrices are stored as vectors. + numGroups: None or int + Number of energy groups Raises ------ @@ -98,6 +113,12 @@ class HomogUniv(NamedObject): self.b1Unc = {} self.infUnc = {} self.metadata = {} + self.__reshaped = rc['xs.reshapeScatter'] + self.numGroups = None + + @property + def reshaped(self): + return self.__reshaped def __str__(self): extras = [] @@ -113,8 +134,14 @@ class HomogUniv(NamedObject): extras or '') def addData(self, variableName, variableValue, uncertainty=False): - """ - sets the value of the variable and, optionally, the associate s.d. + r""" + Sets the value of the variable and, optionally, the associate s.d. + + .. versionadded:: 0.5.0 + + Reshapes scattering matrices according to setting + `xs.reshapeScatter`. Matrices are of the form + :math:`S[i, j]=\Sigma_{s,i\rightarrow j}` .. warning:: @@ -127,8 +154,7 @@ class HomogUniv(NamedObject): variableValue: Variable Value uncertainty: bool - Set to ``True`` in order to retrieve the - uncertainty associated to the expected values + Set to ``True`` if this data is an uncertainty Raises ------ @@ -136,12 +162,31 @@ class HomogUniv(NamedObject): If the uncertainty flag is not boolean """ - - # 1. Check the input type - variableName = convertVariableName(variableName) if not isinstance(uncertainty, bool): raise TypeError('The variable uncertainty has type {}, ' 'should be boolean.'.format(type(uncertainty))) + if not isinstance(variableValue, ndarray): + debug("Converting {} from {} to array".format( + variableName, type(variableValue))) + variableValue = array(variableValue) + ng = self.numGroups + if self.__reshaped and variableName in SCATTER_MATS: + if ng is None: + warning("Number of groups is unknown at this time. " + "Will not reshape variable {}" + .format(variableName)) + else: + variableValue = variableValue.reshape(ng, ng) + incomingGroups = variableValue.shape[0] + if ng is None: + self.numGroups = incomingGroups + elif incomingGroups != ng and variableName not in SCATTER_MATS: + warning("Variable {} appears to have different group structure. " + "Current: {} vs. incoming: {}" + .format(variableName, ng, incomingGroups)) + + variableName = convertVariableName(variableName) + # 2. Pointer to the proper dictionary setter = self._lookup(variableName, uncertainty) # 3. Check if variable is already present. Then set the variable. @@ -166,7 +211,7 @@ class HomogUniv(NamedObject): x: Variable Value dx: - Associated uncertainty + Associated uncertainty if ``uncertainty`` Raises ------ diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index fa771cb..ffda4bb 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -134,6 +134,10 @@ class BranchingReader(XSReader): possibleEndOfFile=step == numVariables - 1) varName = splitList[0] varValues = [float(xx) for xx in splitList[2:]] + if not varValues: + debug("No data present for variable {}. Skipping" + .format(varName)) + continue if self._checkAddVariable(varName): if self.settings['areUncsPresent']: vals, uncs = splitItems(varValues) diff --git a/serpentTools/settings.py b/serpentTools/settings.py index a7ca265..745eea6 100644 --- a/serpentTools/settings.py +++ b/serpentTools/settings.py @@ -124,6 +124,12 @@ defaultSettings = { 'description': 'If true, store the critical leakage cross sections.', 'type': bool }, + 'xs.reshapeScatter': { + 'default': False, + 'description': 'If true, reshape the scattering matrices to square matrices. ' + 'By default, these matrices are stored as vectors.', + 'type': bool + }, 'xs.variableGroups': { 'default': [], 'description': ('Name of variable groups from variables.yaml to be ' @@ -254,10 +260,6 @@ class UserSettingsLoader(dict): self.__originals = {} dict.__init__(self, self._defaultLoader.retrieveDefaults()) - def __setitem__(self, key, value): - self._checkStoreOriginal(key) - self.setValue(key, value) - def __enter__(self): self.__inside= True return self @@ -268,10 +270,6 @@ class UserSettingsLoader(dict): self[key] = originalValue self.__originals= {} - def _checkStoreOriginal(self, key): - if self.__inside: - self.__originals[key] = self[key] - def setValue(self, name, value): """Set the value of a specific setting. @@ -290,6 +288,8 @@ class UserSettingsLoader(dict): If the value is not of the correct type """ + if self.__inside: + self.__originals[name] = self[name] if name not in self: raise KeyError('Setting {} does not exist'.format(name)) self._defaultLoader[name].validate(value) @@ -299,6 +299,8 @@ class UserSettingsLoader(dict): dict.__setitem__(self, name, value) messages.debug('Updated setting {} to {}'.format(name, value)) + __setitem__ = setValue + def getReaderSettings(self, settingsPreffix): """Get all module-wide and reader-specific settings.	[Feature] Auto-reshape scattering matrices for homogenized universes Implement a setting that will automatically reshape scattering matrices for homogenized universes. This should be done in such a way that both the BranchingReader and ResultsReader honor this request. Possibly a setting like `xs.reshapeScatter`?	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_container.py b/serpentTools/tests/test_container.py index fbed4dc..25a1188 100644 --- a/serpentTools/tests/test_container.py +++ b/serpentTools/tests/test_container.py @@ -1,43 +1,43 @@ """Test the container object. """ import unittest -from serpentTools.objects import containers + +from six import iteritems +from numpy import array, arange +from numpy.testing import assert_allclose + +from serpentTools.settings import rc +from serpentTools.objects.containers import HomogUniv from serpentTools.parsers import DepletionReader +NUM_GROUPS = 5 -class HomogenizedUniverseTester(unittest.TestCase): - """ Class to test the Homogenized Universe """ - @classmethod - def setUpClass(cls): - cls.univ = containers.HomogUniv('dummy', 0, 0, 0) - cls.Exp = {} - cls.Unc = {} - # Data definition - cls.Exp = {'B1_1': 1, 'B1_2': [1, 2], 'B1_3': [1, 2, 3], - 'INF_1': 3, 'INF_2': [4, 5], 'INF_3': [6, 7, 8], - 'MACRO_E': (.1, .2, .3, .1, .2, .3), 'Test_1': 'ciao'} - cls.Unc = {'B1_1': 1e-1, 'B1_2': [1e-1, 2e-1], 'B1_3': [1e-1, 2e-1, - 3e-1], - 'INF_1': 3e-1, 'INF_2': [4e-1, 5e-1], 'INF_3': [6e-1, 7e-1, - 8e-1], - 'MACRO_E': (.1e-1, .2e-1, .3e-1, .1e-1, .2e-1, .3e-1), - 'Test_1': 'addio'} +class _HomogUnivTestHelper(unittest.TestCase): + """Class that runs the tests for the two sub-classes + + Subclasses will differ in how the ``mat`` data + is arranged. For one case, the ``mat`` will be a + 2D matrix. + """ + def setUp(self): + self.univ, vec, mat = self.getParams() + # Data definition + rawData = {'B1_1': vec, 'B1_AS_LIST': list(range(NUM_GROUPS)), + 'INF_1': vec, 'INF_S0': mat} + meta = {'MACRO_E': vec} # Partial dictionaries - cls.b1Exp = {'b11': 1, 'b12': [1, 2], 'b13': [1, 2, 3]} - cls.b1Unc = {'b11': (1, 0.1), 'b12': ([1, 2], [.1, .2]), 'b13': - ([1, 2, 3], [.1, .2, .3])} - cls.infExp = {'inf1': 3, 'inf2': [4, 5], 'inf3': [6, 7, 8]} - cls.infUnc = {'inf1': (3, .3), 'inf2': ([4, 5], [.4, .5]), 'inf3': - ([6, 7, 8], [.6, .7, .8])} - cls.meta = {'macroE': (.1e-1, .2e-1, .3e-1, .1e-1, .2e-1, .3e-1), - 'test1': 'addio'} + self.b1Unc = self.b1Exp = {'b11': vec} + self.infUnc = self.infExp = {'inf1': vec, 'infS0': mat} + self.meta = {'macroE': vec} + # Use addData - for kk in cls.Exp: - cls.univ.addData(kk, cls.Exp[kk], False) - for kk in cls.Unc: - cls.univ.addData(kk, cls.Unc[kk], True) + for key, value in iteritems(rawData): + self.univ.addData(key, value, uncertainty=False) + self.univ.addData(key, value, uncertainty=True) + for key, value in iteritems(meta): + self.univ.addData(key, value) def test_getB1Exp(self): """ Get Expected vales from B1 dictionary""" @@ -45,16 +45,16 @@ class HomogenizedUniverseTester(unittest.TestCase): # Comparison for kk in self.univ.b1Exp: d[kk] = self.univ.get(kk, False) - self.assertDictEqual(self.b1Exp, d) - + compareDictOfArrays(self.b1Exp, d, 'b1 values') + def test_getB1Unc(self): """ Get Expected vales and associated uncertainties from B1 dictionary """ d = {} # Comparison for kk in self.univ.b1Exp: - d[kk] = self.univ.get(kk, True) - self.assertDictEqual(self.b1Unc, d) + d[kk] = self.univ.get(kk, True)[1] + compareDictOfArrays(self.b1Unc, d, 'b1 uncertainties') def test_getInfExp(self): """ Get Expected vales from Inf dictionary""" @@ -62,7 +62,7 @@ class HomogenizedUniverseTester(unittest.TestCase): # Comparison for kk in self.univ.infExp: d[kk] = self.univ.get(kk, False) - self.assertDictEqual(self.infExp, d) + compareDictOfArrays(self.infExp, d, 'infinite values') def test_getInfUnc(self): """ Get Expected vales and associated uncertainties from Inf dictionary @@ -70,8 +70,8 @@ class HomogenizedUniverseTester(unittest.TestCase): d = {} # Comparison for kk in self.univ.infUnc: - d[kk] = self.univ.get(kk, True) - self.assertDictEqual(self.infUnc, d) + d[kk] = self.univ.get(kk, True)[1] + compareDictOfArrays(self.infUnc, d, 'infinite uncertainties') def test_getMeta(self): """ Get metaData from corresponding dictionary""" @@ -79,8 +79,64 @@ class HomogenizedUniverseTester(unittest.TestCase): # Comparison for kk in self.univ.metadata: d[kk] = self.univ.get(kk, False) - self.assertDictEqual(self.meta, d) + compareDictOfArrays(self.meta, d, 'metadata') + + def test_getBothInf(self): + """ + Verify that the value and the uncertainty are returned if the + flag is passed. + """ + expected, uncertainties = {}, {} + for key in self.infExp.keys(): + value, unc = self.univ.get(key, True) + expected[key] = value + uncertainties[key] = unc + compareDictOfArrays(self.infExp, expected, 'infinite values') + compareDictOfArrays(self.infUnc, uncertainties, + 'infinite uncertainties') + + +class VectoredHomogUnivTester(_HomogUnivTestHelper): + """Class for testing HomogUniv that does not reshape scatter matrices""" + + def getParams(self): + univ, vec, mat = getParams() + self.assertFalse(univ.reshaped) + return univ, vec, mat + + +class ReshapedHomogUnivTester(_HomogUnivTestHelper): + """Class for testing HomogUniv that does reshape scatter matrices""" + + def getParams(self): + from serpentTools.settings import rc + with rc: + rc.setValue('xs.reshapeScatter', True) + univ, vec, mat = getParams() + univ.numGroups = NUM_GROUPS + self.assertTrue(univ.reshaped) + return univ, vec, mat.reshape(NUM_GROUPS, NUM_GROUPS) + + +def getParams(): + """Return the universe, vector, and matrix for testing.""" + univ = HomogUniv(300, 0, 0, 0) + vec = arange(NUM_GROUPS) + mat = arange(NUM_GROUPS ** 2) + return univ, vec, mat + + +def compareDictOfArrays(expected, actualDict, dataType): + for key, value in iteritems(expected): + actual = actualDict[key] + assert_allclose(value, actual, + err_msg="Error in {} dictionary: key={}" + .format(dataType, key)) +del _HomogUnivTestHelper if __name__ == '__main__': - unittest.main() + from serpentTools import rc + with rc: + rc['verbosity'] = 'debug' + unittest.main()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 5 }	0.4	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "numpy>=1.11.1 matplotlib>=1.5.0 pyyaml>=3.08 scipy six", "pip_packages": [ "pytest", "coverage" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 coverage==6.2 cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver @ file:///tmp/build/80754af9/kiwisolver_1612282412546/work matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1613407855456/work numpy @ file:///tmp/build/80754af9/numpy_and_numpy_base_1603483703303/work olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work packaging==21.3 Pillow @ file:///tmp/build/80754af9/pillow_1625670622947/work pluggy==1.0.0 py==1.11.0 pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work pytest==7.0.1 python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work PyYAML==5.4.1 scipy @ file:///tmp/build/80754af9/scipy_1597686635649/work -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@d96de5b0f4bc4c93370cf508e0ea89701054ff41#egg=serpentTools six @ file:///tmp/build/80754af9/six_1644875935023/work tomli==1.2.3 tornado @ file:///tmp/build/80754af9/tornado_1606942266872/work typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - blas=1.0=openblas - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - cycler=0.11.0=pyhd3eb1b0_0 - dbus=1.13.18=hb2f20db_0 - expat=2.6.4=h6a678d5_0 - fontconfig=2.14.1=h52c9d5c_1 - freetype=2.12.1=h4a9f257_0 - giflib=5.2.2=h5eee18b_0 - glib=2.69.1=h4ff587b_1 - gst-plugins-base=1.14.1=h6a678d5_1 - gstreamer=1.14.1=h5eee18b_1 - icu=58.2=he6710b0_3 - jpeg=9e=h5eee18b_3 - kiwisolver=1.3.1=py36h2531618_0 - lcms2=2.16=hb9589c4_0 - ld_impl_linux-64=2.40=h12ee557_0 - lerc=4.0.0=h6a678d5_0 - libdeflate=1.22=h5eee18b_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgfortran-ng=7.5.0=ha8ba4b0_17 - libgfortran4=7.5.0=ha8ba4b0_17 - libgomp=11.2.0=h1234567_1 - libopenblas=0.3.18=hf726d26_0 - libpng=1.6.39=h5eee18b_0 - libstdcxx-ng=11.2.0=h1234567_1 - libtiff=4.5.1=hffd6297_1 - libuuid=1.41.5=h5eee18b_0 - libwebp=1.2.4=h11a3e52_1 - libwebp-base=1.2.4=h5eee18b_1 - libxcb=1.15=h7f8727e_0 - libxml2=2.9.14=h74e7548_0 - lz4-c=1.9.4=h6a678d5_1 - matplotlib=3.3.4=py36h06a4308_0 - matplotlib-base=3.3.4=py36h62a2d02_0 - ncurses=6.4=h6a678d5_0 - numpy=1.19.2=py36h6163131_0 - numpy-base=1.19.2=py36h75fe3a5_0 - olefile=0.46=pyhd3eb1b0_0 - openssl=1.1.1w=h7f8727e_0 - pcre=8.45=h295c915_0 - pillow=8.3.1=py36h5aabda8_0 - pip=21.2.2=py36h06a4308_0 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyqt=5.9.2=py36h05f1152_2 - python=3.6.13=h12debd9_1 - python-dateutil=2.8.2=pyhd3eb1b0_0 - pyyaml=5.4.1=py36h27cfd23_1 - qt=5.9.7=h5867ecd_1 - readline=8.2=h5eee18b_0 - scipy=1.5.2=py36habc2bb6_0 - setuptools=58.0.4=py36h06a4308_0 - sip=4.19.8=py36hf484d3e_0 - six=1.16.0=pyhd3eb1b0_1 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tornado=6.1=py36h27cfd23_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - yaml=0.2.5=h7b6447c_0 - zlib=1.2.13=h5eee18b_1 - zstd=1.5.6=hc292b87_0 - pip: - attrs==22.2.0 - coverage==6.2 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - packaging==21.3 - pluggy==1.0.0 - py==1.11.0 - pytest==7.0.1 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getB1Exp", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getB1Unc", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getBothInf", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getInfExp", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getInfUnc", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getMeta", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getB1Exp", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getB1Unc", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getBothInf", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getInfExp", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getInfUnc", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getMeta" ]	[]	[]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-154	911894d67eb9677c4430a1aee91e9d1461ffc44b	2018-05-18 16:25:11	7c7da6012f509a2e71c3076ab510718585f75b11		diff --git a/serpentTools/objects/containers.py b/serpentTools/objects/containers.py index 8c30e1b..bcc9103 100644 --- a/serpentTools/objects/containers.py +++ b/serpentTools/objects/containers.py @@ -35,8 +35,8 @@ for xsSpectrum, xsType in product({'INF', 'B1'}, for xx in range(SCATTER_ORDERS)}) HOMOG_VAR_TO_ATTR = { - 'MICRO_E': 'microGroups', 'MICRO_NG': '_numMicroGroups', - 'MACRO_E': 'groups', 'MACRO_NG': '_numGroups'} + 'MICRO_E': 'microGroups', 'MICRO_NG': 'numMicroGroups', + 'MACRO_E': 'groups', 'MACRO_NG': 'numGroups'} __all__ = ('DET_COLS', 'HomogUniv', 'BranchContainer', 'Detector', 'DetectorBase', 'SCATTER_MATS', 'SCATTER_ORDERS') @@ -147,12 +147,22 @@ class HomogUniv(NamedObject): self._numGroups = self.groups.size - 1 return self._numGroups + @numGroups.setter + def numGroups(self, value): + value = value if isinstance(value, int) else int(value) + self._numGroups = value + @property def numMicroGroups(self): if self._numMicroGroups is None and self.microGroups is not None: self._numMicroGroups = self.microGroups.size - 1 return self._numMicroGroups + @numMicroGroups.setter + def numMicroGroups(self, value): + value = value if isinstance(value, int) else int(value) + self._numMicroGroups = value + def __str__(self): extras = [] if self.bu is not None:	[BUG] number of groups stored as a float; causes reshape scatter matrices to fail ## Summary of issue The `addData` routine stores the number of energy groups as a float. This causes numpy to fail during the reshaping of scattering matrices. ## Code for reproducing the issue ``` import serpentTools from serpentTools.settings import rc rc['xs.reshapeScatter'] = True r = serpentTools.read('bwr_res.m') ``` ## Actual outcome including console output and error traceback if applicable ``` ~/.local/lib/python3.5/site-packages/serpentTools-0.4.0+9.g277cb89-py3.5.egg/serpentTools/objects/containers.py in addData(self, variableName, variableValue, uncertainty) 200 'should be boolean.'.format(type(uncertainty))) 201 --> 202 value = self._cleanData(variableName, variableValue) 203 if variableName in HOMOG_VAR_TO_ATTR: 204 value = value if variableValue.size > 1 else value[0] ~/.local/lib/python3.5/site-packages/serpentTools-0.4.0+9.g277cb89-py3.5.egg/serpentTools/objects/containers.py in _cleanData(self, name, value) 233 .format(name)) 234 else: --> 235 value = value.reshape(ng, ng) 236 return value 237 TypeError: 'numpy.float64' object cannot be interpreted as an integer ``` ## Expected outcome No error and scattering matrices are reshaped properly ## Versions * Version from ``serpentTools.__version__`` `0.4.0+9.g277cb89` * Python version - ``python --version`` `3.5` * IPython or Jupyter version if applicable - `ipython 6.2.1`	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_container.py b/serpentTools/tests/test_container.py index 721dd1d..ded8988 100644 --- a/serpentTools/tests/test_container.py +++ b/serpentTools/tests/test_container.py @@ -4,7 +4,7 @@ import unittest from itertools import product from six import iteritems -from numpy import array, arange, ndarray +from numpy import array, arange, ndarray, float64 from numpy.testing import assert_array_equal from serpentTools.settings import rc @@ -171,6 +171,37 @@ class UnivTruthTester(unittest.TestCase): self.assertTrue(univ.hasData, msg=msg) +class HomogUnivIntGroupsTester(unittest.TestCase): + """Class that ensures number of groups is stored as ints.""" + + def setUp(self): + self.univ = HomogUniv('intGroups', 0, 0, 0) + self.numGroups = 2 + self.numMicroGroups = 4 + + def test_univGroupsFromFloats(self): + """Vefify integer groups are stored when passed as floats.""" + self.setAs(float) + self._tester() + + def test_univGroupsFromNPFloats(self): + """Vefify integer groups are stored when passed as numpy floats.""" + self.setAs(float64) + self._tester() + + def _tester(self): + for attr in {'numGroups', 'numMicroGroups'}: + actual = getattr(self.univ, attr) + msg ='Attribute: {}'.format(attr) + self.assertIsInstance(actual, int, msg=msg) + expected = getattr(self, attr) + self.assertEqual(expected, actual, msg=msg) + + def setAs(self, func): + """Set the number of groups to be as specific type.""" + for attr in {'numGroups', 'numMicroGroups'}: + expected = getattr(self, attr) + setattr(self.univ, attr, func(expected)) if __name__ == '__main__': unittest.main()	{ "commit_name": "merge_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 1 }	0.4	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "numpy>=1.11.1 matplotlib>=1.5.0 pyyaml>=3.08 scipy six", "pip_packages": [ "numpy>=1.11.1", "matplotlib>=1.5.0", "pyyaml>=3.08", "scipy", "six", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver @ file:///tmp/build/80754af9/kiwisolver_1612282412546/work matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1613407855456/work numpy @ file:///tmp/build/80754af9/numpy_and_numpy_base_1603483703303/work olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work packaging==21.3 Pillow @ file:///tmp/build/80754af9/pillow_1625670622947/work pluggy==1.0.0 py==1.11.0 pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work pytest==7.0.1 python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work PyYAML==5.4.1 scipy @ file:///tmp/build/80754af9/scipy_1597686635649/work -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@911894d67eb9677c4430a1aee91e9d1461ffc44b#egg=serpentTools six @ file:///tmp/build/80754af9/six_1644875935023/work tomli==1.2.3 tornado @ file:///tmp/build/80754af9/tornado_1606942266872/work typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - blas=1.0=openblas - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - cycler=0.11.0=pyhd3eb1b0_0 - dbus=1.13.18=hb2f20db_0 - expat=2.6.4=h6a678d5_0 - fontconfig=2.14.1=h52c9d5c_1 - freetype=2.12.1=h4a9f257_0 - giflib=5.2.2=h5eee18b_0 - glib=2.69.1=h4ff587b_1 - gst-plugins-base=1.14.1=h6a678d5_1 - gstreamer=1.14.1=h5eee18b_1 - icu=58.2=he6710b0_3 - jpeg=9e=h5eee18b_3 - kiwisolver=1.3.1=py36h2531618_0 - lcms2=2.16=hb9589c4_0 - ld_impl_linux-64=2.40=h12ee557_0 - lerc=4.0.0=h6a678d5_0 - libdeflate=1.22=h5eee18b_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgfortran-ng=7.5.0=ha8ba4b0_17 - libgfortran4=7.5.0=ha8ba4b0_17 - libgomp=11.2.0=h1234567_1 - libopenblas=0.3.18=hf726d26_0 - libpng=1.6.39=h5eee18b_0 - libstdcxx-ng=11.2.0=h1234567_1 - libtiff=4.5.1=hffd6297_1 - libuuid=1.41.5=h5eee18b_0 - libwebp=1.2.4=h11a3e52_1 - libwebp-base=1.2.4=h5eee18b_1 - libxcb=1.15=h7f8727e_0 - libxml2=2.9.14=h74e7548_0 - lz4-c=1.9.4=h6a678d5_1 - matplotlib=3.3.4=py36h06a4308_0 - matplotlib-base=3.3.4=py36h62a2d02_0 - ncurses=6.4=h6a678d5_0 - numpy=1.19.2=py36h6163131_0 - numpy-base=1.19.2=py36h75fe3a5_0 - olefile=0.46=pyhd3eb1b0_0 - openssl=1.1.1w=h7f8727e_0 - pcre=8.45=h295c915_0 - pillow=8.3.1=py36h5aabda8_0 - pip=21.2.2=py36h06a4308_0 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyqt=5.9.2=py36h05f1152_2 - python=3.6.13=h12debd9_1 - python-dateutil=2.8.2=pyhd3eb1b0_0 - pyyaml=5.4.1=py36h27cfd23_1 - qt=5.9.7=h5867ecd_1 - readline=8.2=h5eee18b_0 - scipy=1.5.2=py36habc2bb6_0 - setuptools=58.0.4=py36h06a4308_0 - sip=4.19.8=py36hf484d3e_0 - six=1.16.0=pyhd3eb1b0_1 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tornado=6.1=py36h27cfd23_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - yaml=0.2.5=h7b6447c_0 - zlib=1.2.13=h5eee18b_1 - zstd=1.5.6=hc292b87_0 - pip: - attrs==22.2.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - packaging==21.3 - pluggy==1.0.0 - py==1.11.0 - pytest==7.0.1 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_container.py::HomogUnivIntGroupsTester::test_univGroupsFromFloats", "serpentTools/tests/test_container.py::HomogUnivIntGroupsTester::test_univGroupsFromNPFloats" ]	[]	[ "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_attributes", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getB1Exp", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getB1Unc", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getBothInf", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getInfExp", "serpentTools/tests/test_container.py::VectoredHomogUnivTester::test_getInfUnc", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_attributes", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getB1Exp", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getB1Unc", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getBothInf", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getInfExp", "serpentTools/tests/test_container.py::ReshapedHomogUnivTester::test_getInfUnc", "serpentTools/tests/test_container.py::UnivTruthTester::test_loadedUnivTrue" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-21	b67f52bfd0b23baa3eae9f11bab1af14bd8b2798	2017-09-29 14:33:04	b67f52bfd0b23baa3eae9f11bab1af14bd8b2798		diff --git a/serpentTools/__init__.py b/serpentTools/__init__.py index 8e3f32c..3e8e8de 100644 --- a/serpentTools/__init__.py +++ b/serpentTools/__init__.py @@ -1,7 +1,7 @@ from serpentTools import settings from serpentTools import parsers -__version__ = '0.1.3' +__version__ = '0.1.4' # List TODOS/feature requests here for now # Messages/Errors diff --git a/serpentTools/objects/__init__.py b/serpentTools/objects/__init__.py index 8f2e2cd..2ef74d4 100644 --- a/serpentTools/objects/__init__.py +++ b/serpentTools/objects/__init__.py @@ -1,8 +1,5 @@ """Objects used to support the parsing.""" -import numpy -from matplotlib import pyplot - class _SupportingObject(object): """ @@ -17,14 +14,12 @@ class _SupportingObject(object): """ - def __init__(self, container, name): + def __init__(self, container): self._container = container - self.name = name self._filePath = container.filePath def __repr__(self): - return '<{} {} from {}>'.format(self.whatAmI(), - self.name, self._filePath) + return '<{} from {}>'.format(self.whatAmI(), self._filePath) def whatAmI(self): return type(self).__name__ @@ -47,194 +42,13 @@ class _SupportingObject(object): for item in lowerSplits[1:]]) -class DepletedMaterial(_SupportingObject): - """Class for storing material data from ``_dep.m`` files. - - Parameters - ---------- - parser: :py:class:`~serpentTools.parsers.depletion.DepletionReader` - Parser that found this material. - Used to obtain file metadata like isotope names and burnup - name: str - Name of this material - - Attributes - ---------- - zai: numpy.array - Isotope id's - names: numpy.array - Names of isotopes - days: numpy.array - Days overwhich the material was depleted - adens: numpy.array - Atomic density over time for each nuclide - - :note: - - These attributes only exist if the pasers was instructed to - read in this data. I.e. if ``readers.depletion.metadataKeys`` - does not contain ``ZAI``, then this object will not have - the ``zai`` data. - - """ - - def __init__(self, parser, name): - _SupportingObject.__init__(self, parser, name) - self._varData = {} - - def __getattr__(self, item): - """ - Allows the user to get items like ``zai`` and ``adens`` - with ``self.zai`` and ``self.adens``, respectively. - """ - if item in self._varData: - return self._varData[item] - return _SupportingObject.__getattr__(self, item) - - def __getitem__(self, item): - if item not in self._varData: - if item not in self._container.metadata: - raise KeyError('{} has no item {}'.format(self, item)) - return self._container.metadata[item] - return self._varData[item] - - def addData(self, variable, rawData): - """Add data straight from the file onto a variable. - - Parameters - ---------- - variable: str - Name of the variable directly from ``SERPENT`` - rawData: list - List of strings corresponding to the raw data from the file - """ - newName = self._convertVariableName(variable) - if isinstance(rawData, str): - scratch = [float(item) for item in rawData.split()] - else: - scratch = [] - for line in rawData: - if line: - scratch.append([float(item) for item in line.split()]) - self._varData[newName] = numpy.array(scratch) - - def getXY(self, xUnits, yUnits, timePoints=None, names=None): - """Return x values for given time, and corresponding isotope values. - - Parameters - ---------- - xUnits: str - name of x value to obtain, e.g. ``'days'``, ``'burnup'`` - yUnits: str - name of y value to return, e.g. ``'adens'``, ``'burnup'`` - timePoints: list or None - If given, select the time points according to those specified here. - Otherwise, select all points - names: list or None - If given, return y values corresponding to these isotope names. - Otherwise, return values for all isotopes. - - Returns - ------- - numpy.array - Array of values. - numpy.array - Vector of time points only if ``timePoints`` is ``None`` - - Raises - ------ - AttributeError - If the names of the isotopes have not been obtained and specific - isotopes have been requested - KeyError - If at least one of the days requested is not present - """ - if timePoints is not None: - returnX = False - timeCheck = self._checkTimePoints(xUnits, timePoints) - if any(timeCheck): - raise KeyError('The following times were not present in file {}' - '\n{}'.format(self._container.filePath, - ', '.join(timeCheck))) - else: - returnX = True - if names and 'names' not in self._container.metadata: - raise AttributeError('Parser {} has not stored the isotope names.' - .format(self._container)) - xVals, colIndices = self._getXSlice(xUnits, timePoints) - rowIndices = self._getIsoID(names) - allY = self[yUnits] - if allY.shape[0] == 1 or len(allY.shape) == 1: # vector - return xVals, allY[colIndices] if colIndices else allY - yVals = numpy.empty((len(rowIndices), len(xVals)), dtype=float) - for isoID, rowId in enumerate(rowIndices): - yVals[isoID, :] = (allY[rowId][colIndices] if colIndices - else allY[rowId][:]) - if returnX: - return yVals, xVals - return yVals - - def _checkTimePoints(self, xUnits, timePoints): - valid = self[xUnits] - badPoints = [str(time) for time in timePoints if time not in valid] - return badPoints - - - def _getXSlice(self, xUnits, timePoints): - allX = self[xUnits] - if timePoints is not None: - colIndices = [indx for indx, xx in enumerate(allX) - if xx in timePoints] - xVals = allX[colIndices] - else: - colIndices = None - xVals = allX - return xVals, colIndices - - def _getIsoID(self, isotopes): - """Return the row indices that correspond to specfic isotopes.""" - # TODO: List comprehension to make rowIDs then return array - if not isotopes: - return numpy.array(list(range(len(self.names))), dtype=int) - isoList = [isotopes] if isinstance(isotopes, (str, int)) else isotopes - rowIDs = numpy.empty_like(isoList, dtype=int) - for indx, isotope in enumerate(isoList): - rowIDs[indx] = self.names.index(isotope) - return rowIDs - - def plot(self, xUnits, yUnits, timePoints=None, names=None, ax=None): - """Plot some data as a function of time for some or all isotopes. - - Parameters - ---------- - xUnits: str - name of x value to obtain, e.g. ``'days'``, ``'burnup'`` - yUnits: str - name of y value to return, e.g. ``'adens'``, ``'burnup'`` - timePoints: list or None - If given, select the time points according to those - specified here. Otherwise, select all points - names: list or None - If given, return y values corresponding to these isotope - names. Otherwise, return values for all isotopes. - ax: None or ``matplotlib axes`` - If given, add the data to this plot. - Otherwise, create a new plot - - Returns - ------- - ``matplotlib axes`` - Axes corresponding to the figure that was plotted +class _NamedObject(_SupportingObject): + """Class for named objects like materials and detectors.""" - See Also - -------- - getXY - - """ - xVals, yVals = self.getXY(xUnits, yUnits, timePoints, names) - ax = ax or pyplot.subplots(1, 1)[1] - labels = names or [None] - for row in range(yVals.shape[0]): - ax.plot(xVals, yVals[row], label=labels[row]) + def __init__(self, container, name): + _SupportingObject.__init__(self, container) + self.name = name - return ax + def __repr__(self): + return '<{} {} from {}>'.format(self.whatAmI(), + self.name, self._filePath) \ No newline at end of file diff --git a/serpentTools/objects/materials.py b/serpentTools/objects/materials.py new file mode 100644 index 0000000..be2fe02 --- /dev/null +++ b/serpentTools/objects/materials.py @@ -0,0 +1,199 @@ +"""Classes for storing material data.""" + +import numpy +from matplotlib import pyplot + + +from serpentTools.objects import _NamedObject + + +class DepletedMaterial(_NamedObject): + """Class for storing material data from ``_dep.m`` files. + + Parameters + ---------- + parser: :py:class:`~serpentTools.parsers.depletion.DepletionReader` + Parser that found this material. + Used to obtain file metadata like isotope names and burnup + name: str + Name of this material + + Attributes + ---------- + zai: numpy.array + Isotope id's + names: numpy.array + Names of isotopes + days: numpy.array + Days overwhich the material was depleted + adens: numpy.array + Atomic density over time for each nuclide + + :note: + + These attributes only exist if the pasers was instructed to + read in this data. I.e. if ``readers.depletion.metadataKeys`` + does not contain ``ZAI``, then this object will not have + the ``zai`` data. + + """ + + def __init__(self, parser, name): + _NamedObject.__init__(self, parser, name) + self._varData = {} + + def __getattr__(self, item): + """ + Allows the user to get items like ``zai`` and ``adens`` + with ``self.zai`` and ``self.adens``, respectively. + """ + if item in self._varData: + return self._varData[item] + return _NamedObject.__getattr__(self, item) + + def __getitem__(self, item): + if item not in self._varData: + if item not in self._container.metadata: + raise KeyError('{} has no item {}'.format(self, item)) + return self._container.metadata[item] + return self._varData[item] + + def addData(self, variable, rawData): + """Add data straight from the file onto a variable. + + Parameters + ---------- + variable: str + Name of the variable directly from ``SERPENT`` + rawData: list + List of strings corresponding to the raw data from the file + """ + newName = self._convertVariableName(variable) + if isinstance(rawData, str): + scratch = [float(item) for item in rawData.split()] + else: + scratch = [] + for line in rawData: + if line: + scratch.append([float(item) for item in line.split()]) + self._varData[newName] = numpy.array(scratch) + + def getXY(self, xUnits, yUnits, timePoints=None, names=None): + """Return x values for given time, and corresponding isotope values. + + Parameters + ---------- + xUnits: str + name of x value to obtain, e.g. ``'days'``, ``'burnup'`` + yUnits: str + name of y value to return, e.g. ``'adens'``, ``'burnup'`` + timePoints: list or None + If given, select the time points according to those specified here. + Otherwise, select all points + names: list or None + If given, return y values corresponding to these isotope names. + Otherwise, return values for all isotopes. + + Returns + ------- + numpy.array + Array of values. + numpy.array + Vector of time points only if ``timePoints`` is ``None`` + + Raises + ------ + AttributeError + If the names of the isotopes have not been obtained and specific + isotopes have been requested + KeyError + If at least one of the days requested is not present + """ + if timePoints is not None: + returnX = False + timeCheck = self._checkTimePoints(xUnits, timePoints) + if any(timeCheck): + raise KeyError('The following times were not present in file {}' + '\n{}'.format(self._container.filePath, + ', '.join(timeCheck))) + else: + returnX = True + if names and 'names' not in self._container.metadata: + raise AttributeError('Parser {} has not stored the isotope names.' + .format(self._container)) + xVals, colIndices = self._getXSlice(xUnits, timePoints) + rowIndices = self._getIsoID(names) + allY = self[yUnits] + if allY.shape[0] == 1 or len(allY.shape) == 1: # vector + yVals = allY[colIndices] if colIndices else allY + else: + yVals = numpy.empty((len(rowIndices), len(xVals)), dtype=float) + for isoID, rowId in enumerate(rowIndices): + yVals[isoID, :] = (allY[rowId][colIndices] if colIndices + else allY[rowId][:]) + if returnX: + return yVals, xVals + return yVals + + def _checkTimePoints(self, xUnits, timePoints): + valid = self[xUnits] + badPoints = [str(time) for time in timePoints if time not in valid] + return badPoints + + def _getXSlice(self, xUnits, timePoints): + allX = self[xUnits] + if timePoints is not None: + colIndices = [indx for indx, xx in enumerate(allX) + if xx in timePoints] + xVals = allX[colIndices] + else: + colIndices = None + xVals = allX + return xVals, colIndices + + def _getIsoID(self, isotopes): + """Return the row indices that correspond to specfic isotopes.""" + # TODO: List comprehension to make rowIDs then return array + if not isotopes: + return numpy.array(list(range(len(self.names))), dtype=int) + isoList = [isotopes] if isinstance(isotopes, (str, int)) else isotopes + rowIDs = numpy.empty_like(isoList, dtype=int) + for indx, isotope in enumerate(isoList): + rowIDs[indx] = self.names.index(isotope) + return rowIDs + + def plot(self, xUnits, yUnits, timePoints=None, names=None, ax=None): + """Plot some data as a function of time for some or all isotopes. + + Parameters + ---------- + xUnits: str + name of x value to obtain, e.g. ``'days'``, ``'burnup'`` + yUnits: str + name of y value to return, e.g. ``'adens'``, ``'burnup'`` + timePoints: list or None + If given, select the time points according to those + specified here. Otherwise, select all points + names: list or None + If given, return y values corresponding to these isotope + names. Otherwise, return values for all isotopes. + ax: None or ``matplotlib axes`` + If given, add the data to this plot. + Otherwise, create a new plot + + Returns + ------- + ``matplotlib axes`` + Axes corresponding to the figure that was plotted + + See Also + -------- + getXY + + """ + xVals, yVals = self.getXY(xUnits, yUnits, timePoints, names) + ax = ax or pyplot.subplots(1, 1)[1] + labels = names or [None] + for row in range(yVals.shape[0]): + ax.plot(xVals, yVals[row], label=labels[row]) + return ax diff --git a/serpentTools/parsers/depletion.py b/serpentTools/parsers/depletion.py index 8a3b695..002f516 100644 --- a/serpentTools/parsers/depletion.py +++ b/serpentTools/parsers/depletion.py @@ -6,7 +6,7 @@ import numpy from drewtils.parsers import KeywordParser from serpentTools.objects.readers import MaterialReader -from serpentTools.objects import DepletedMaterial +from serpentTools.objects.materials import DepletedMaterial class DepletionReader(MaterialReader):	Days are still returned in time points are given for a vector quantity on materials, e.g. burnup Fix in #2 did not take in to account quantities like `burnup` and `volume` that do not return arrays for isotope quantities. If the user specifies one of these quantities from a depleted material, the time points are still returned. ``` if allY.shape[0] == 1 or len(allY.shape) == 1: # vector return xVals, allY[colIndices] if colIndices else allY ``` change to ``` if allY.shape[0] == 1 or len(allY.shape) == 1: # vector yVals = allY[colIndices] if colIndices else allY else: yVals = numpy.empty((len(rowIndices), len(xVals)), dtype=float) for isoID, rowId in enumerate(rowIndices): yVals[isoID, :] = (allY[rowId][colIndices] if colIndices else allY[rowId][:])` ``` and fix unit tests	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_depletion.py b/serpentTools/tests/test_depletion.py index d7f6d52..6cb911d 100644 --- a/serpentTools/tests/test_depletion.py +++ b/serpentTools/tests/test_depletion.py @@ -124,13 +124,12 @@ class DepletedMaterialTester(_DepletionTestHelper): """ Verify the material can produce the full burnup vector through getXY. """ - _days, actual = self.material.getXY('days', 'burnup', ) + actual, _days = self.material.getXY('days', 'burnup', ) numpy.testing.assert_equal(actual, self.fuelBU) def test_getXY_burnup_slice(self): """Verify depletedMaterial getXY correctly slices a vector.""" - _days, actual = self.material.getXY('days', 'burnup', - self.requestedDays) + actual = self.material.getXY('days', 'burnup', self.requestedDays) expected = [0.0E0, 1.90317E-2, 3.60163E-2, 1.74880E-1, 3.45353E-01, 8.49693E-01, 1.66071E0] numpy.testing.assert_equal(actual, expected)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 1 }, "num_modified_files": 3 }	unknown	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	contourpy==1.3.0 cycler==0.12.1 drewtils==0.1.9 exceptiongroup==1.2.2 fonttools==4.56.0 importlib_resources==6.5.2 iniconfig==2.1.0 kiwisolver==1.4.7 matplotlib==3.9.4 numpy==2.0.2 packaging==24.2 pillow==11.1.0 pluggy==1.5.0 pyparsing==3.2.3 pytest==8.3.5 python-dateutil==2.9.0.post0 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@b67f52bfd0b23baa3eae9f11bab1af14bd8b2798#egg=serpentTools six==1.17.0 tomli==2.2.1 zipp==3.21.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - contourpy==1.3.0 - cycler==0.12.1 - drewtils==0.1.9 - exceptiongroup==1.2.2 - fonttools==4.56.0 - importlib-resources==6.5.2 - iniconfig==2.1.0 - kiwisolver==1.4.7 - matplotlib==3.9.4 - numpy==2.0.2 - packaging==24.2 - pillow==11.1.0 - pluggy==1.5.0 - pyparsing==3.2.3 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - six==1.17.0 - tomli==2.2.1 - zipp==3.21.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_burnup_full", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_burnup_slice" ]	[]	[ "serpentTools/tests/test_depletion.py::DepletionTester::test_ReadMaterials", "serpentTools/tests/test_depletion.py::DepletionTester::test_metadata", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_fetchData", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_adens", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_adensAndTime", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_raisesError_badTime", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_materials" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-220	9429173b3a7a42bad6e4e0b791f8456f99eb606a	2018-07-27 20:20:45	03997bdce0a5adb75cf5796278ea61b799f7b6dc		diff --git a/docs/develop/logging.rst b/docs/develop/logging.rst index 8f36e03..c5c061c 100644 --- a/docs/develop/logging.rst +++ b/docs/develop/logging.rst @@ -31,3 +31,10 @@ or removed in the future. .. autofunction:: serpentTools.messages.willChange + +Custom Handlers +=============== + +.. autoclass:: serpentTools.messages.DictHandler + :show-inheritance: + :no-inherited-members: diff --git a/docs/develop/utils.rst b/docs/develop/utils.rst index 848929c..6d251ab 100644 --- a/docs/develop/utils.rst +++ b/docs/develop/utils.rst @@ -8,3 +8,16 @@ Utilities .. automodule:: serpentTools.utils :members: convertVariableName, linkToWiki, str2vec, splitValsUnc + +.. _dev-testUtils: + +================= +Testing Utilities +================= + +.. autoclass:: serpentTools.tests.utils.LoggerMixin + +.. autoclass:: serpentTools.tests.utils.TestCaseWithLogCapture + :show-inheritance: + :no-inherited-members: + :members: setUp, tearDown, assertMsgInLogs, assertMsgNotInLogs diff --git a/serpentTools/messages.py b/serpentTools/messages.py index b040939..8ef4f26 100644 --- a/serpentTools/messages.py +++ b/serpentTools/messages.py @@ -10,6 +10,7 @@ See Also import functools import warnings import logging +from logging import Handler from logging.config import dictConfig @@ -27,6 +28,10 @@ class MismatchedContainersError(SamplerError): """Attempting to sample from dissimilar containers""" pass +# +# Logger options +# + LOG_OPTS = ['critical', 'error', 'warning', 'info', 'debug'] @@ -46,9 +51,12 @@ loggingConfig = { 'stream': 'ext://sys.stdout' } }, - 'root': { - 'handlers': ['console'], - 'level': logging.WARNING + 'loggers': { + 'serpentTools': { + 'handlers': ['console'], + 'level': logging.WARNING, + 'propagate': False, + }, } } @@ -57,6 +65,35 @@ dictConfig(loggingConfig) __logger__ = logging.getLogger('serpentTools') +def addHandler(handler): + """ + Add a handler to the logger + + Parameters + ---------- + handler: :class:`python.logging.Handler` + Subclass to handle the formatting and emitting + of log messages + """ + if not issubclass(handler.__class__, Handler): + raise TypeError("Handler {} is of class {} and does not appear " + "to be a subclass of {}" + .format(handler, handler.__class__, Handler)) + return __logger__.addHandler(handler) + + +def removeHandler(handler): + """ + Remove a handler from the internal logger + + Parameters + ---------- + handler: :class:`python.logging.Handler` + Handler to be removed + """ + return __logger__.removeHandler(handler) + + def debug(message): """Log a debug message.""" __logger__.debug('%s', message) @@ -126,3 +163,44 @@ def _updateFilterAlert(msg, category): warnings.simplefilter('always', category) warnings.warn(msg, category=category, stacklevel=3) warnings.simplefilter('default', category) + + +class DictHandler(Handler): + """ + Handler that stores log messages in a dictionary + + Attributes + ---------- + logMessages: dict + Dictionary of lists where each key is a log level such + as ``'DEBUG'`` or ``'WARNING'``. The list associated + with each key contains all messages called under that + logging level + """ + def __init__(self, level=logging.NOTSET): + Handler.__init__(self, level) + self.logMessages = {} + + def flush(self): + """Clear the log messages dictionary""" + self.logMessages = {} + + def close(self): + """Tidy up before removing from list of handlers""" + self.logMessages = {} + Handler.close(self) + + def emit(self, record): + """ + Store the message in the log messages by level. + + Does no formatting to the record, simply stores + the message in :attr:`logMessages` dictionary + according to the records ``levelname`` + + Anticipates a :class:`logging.LogRecord` object + """ + level = record.levelname + if level not in self.logMessages: + self.logMessages[level] = [] + self.logMessages[level].append(record.getMessage())	Determine and implement a way to test our message/logging systems Working on the comparison feature, I realize we don't have a way to ensure that our logging system is actually logging what we want. This is more relevant for the testing the comparison utilities, and making sure that we are correctly notifying the user about the locations of differences. For example, if we take two identical readers, read from the same file, then the comparison routine should print no bad messages and return `True`. To test more of the internals, we would start tweaking values, both by shape and by contents. Including and/or removing extra values, in the `resdata` dictionary, would further test the internals. ## Possible suggestions 1. Have our loggers work as a LIFO list, where we can stack additional loggers (primary being our existing logger, secondary being this testable variant) and utilize only the last value in the list for logging 1. Swapping out loggers during testing, e.g. in `setUpModule`, switch the logging system to one that captures and stores messages 1. Utilize a custom [`Handler` object](https://docs.python.org/3.6/library/logging.html#handler-objects) object that places messages in dictionaries 1. Subclass Logger and overwrite the [`makeRecord`](https://docs.python.org/3.6/library/logging.html#logging.Logger.makeRecord) method to store data on a dictionary as well	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_depSampler.py b/serpentTools/tests/test_depSampler.py index 2ad166b..dcd2640 100644 --- a/serpentTools/tests/test_depSampler.py +++ b/serpentTools/tests/test_depSampler.py @@ -14,7 +14,7 @@ File Descriptions . ``bwr_missingT`` is missing the final burnup step """ -import unittest +from unittest import TestCase from six import iteritems from numpy import where, fabs, ndarray @@ -25,21 +25,24 @@ from serpentTools.data import getFile from serpentTools.parsers.depletion import DepletionReader from serpentTools.samplers.depletion import DepletionSampler from serpentTools.tests import computeMeansErrors +from serpentTools.tests.utils import TestCaseWithLogCapture _testFileNames = {'0', '1', 'badInventory', 'longT', 'missingT'} DEP_FILES = {key: getFile('bwr_{}_dep.m'.format(key)) for key in _testFileNames} -class DepletionSamplerFailTester(unittest.TestCase): +class DepletionSamplerFailTester(TestCaseWithLogCapture): def test_badInventory(self): """Verify an error is raised for files with dissimilar isotopics""" self._mismatchedFiles(DEP_FILES['badInventory']) + self.assertMsgInLogs("ERROR", DEP_FILES['badInventory'], partial=True) def test_missingTimeSteps(self): """Verify an error is raised if length of time steps are dissimilar""" self._mismatchedFiles(DEP_FILES['missingT']) + self.assertMsgInLogs("ERROR", DEP_FILES['missingT'], partial=True) def _mismatchedFiles(self, badFilePath, errorType=MismatchedContainersError): @@ -48,7 +51,7 @@ class DepletionSamplerFailTester(unittest.TestCase): DepletionSampler(files) -class DepletedSamplerTester(unittest.TestCase): +class DepletedSamplerTester(TestCase): """ Class that reads two similar files and validates the averaging and uncertainty propagation. @@ -107,4 +110,5 @@ class DepletedSamplerTester(unittest.TestCase): if __name__ == '__main__': - unittest.main() + from unittest import main + main() diff --git a/serpentTools/tests/test_detSampler.py b/serpentTools/tests/test_detSampler.py index a49a4fe..bc3daaa 100644 --- a/serpentTools/tests/test_detSampler.py +++ b/serpentTools/tests/test_detSampler.py @@ -21,8 +21,6 @@ File Descriptions tolerance can still be achieved. """ -import unittest - from six import iteritems from numpy import square, sqrt @@ -32,6 +30,7 @@ from serpentTools.messages import MismatchedContainersError from serpentTools.data import getFile from serpentTools.parsers.detector import DetectorReader from serpentTools.samplers.detector import DetectorSampler +from serpentTools.tests.utils import TestCaseWithLogCapture _DET_FILES = { 'bwr0': 'bwr_0', @@ -50,7 +49,7 @@ TOLERANCES = { } -class DetSamplerTester(unittest.TestCase): +class DetSamplerTester(TestCaseWithLogCapture): """ Tester that looks for errors in mismatched detector files and validates the averaging and uncertainty propagation @@ -70,6 +69,7 @@ class DetSamplerTester(unittest.TestCase): def setUp(self): self._checkContents() + TestCaseWithLogCapture.setUp(self) def test_properlyAveraged(self): """Validate the averaging for two unique detector files""" @@ -90,12 +90,16 @@ class DetSamplerTester(unittest.TestCase): files = [getFile(fp) for fp in ['bwr_0_det0.m', 'bwr_noxy_det0.m']] self._raisesMisMatchError(files) + self.assertMsgInLogs("ERROR", "detectors: Parser files", partial=True) def test_differentSizedDetectors(self): """Verify that an error is raised if detector shapes are different""" files = [getFile(fp) for fp in ['bwr_0_det0.m', 'bwr_smallxy_det0.m']] self._raisesMisMatchError(files) + self.assertMsgInLogs( + "ERROR", "shape: Parser files", + partial=True) def _raisesMisMatchError(self, files): with self.assertRaises(MismatchedContainersError): @@ -118,4 +122,5 @@ def _getExpectedAverages(d0, d1): if __name__ == '__main__': - unittest.main() + from unittest import main + main() diff --git a/serpentTools/tests/test_messages.py b/serpentTools/tests/test_messages.py new file mode 100644 index 0000000..fabd8e3 --- /dev/null +++ b/serpentTools/tests/test_messages.py @@ -0,0 +1,95 @@ +""" +Test the logging and messaging functions +""" + +from unittest import TestCase +from warnings import catch_warnings + +from serpentTools.messages import ( + deprecated, willChange, + addHandler, removeHandler, + __logger__, + debug, info, warning, error, critical, +) +from serpentTools.settings import rc +from serpentTools.tests.utils import TestCaseWithLogCapture, LoggerMixin + + +LOGGER_FUNCTIONS = [debug, info, warning, error, critical] + + +class DecoratorTester(TestCase): + """Class to test the decorators for warnings.""" + + def test_futureDecorator(self): + """Verify that the future decorator doesn't break""" + + @willChange('This function will be updated in the future, ' + 'but will still exist') + def demoFuture(x, val=5): + return x + val + + with catch_warnings(record=True) as record: + self.assertEqual(7, demoFuture(2)) + self.assertEqual(7, demoFuture(2, 5)) + self.assertEquals(len(record), 2, + 'Did not catch two warnings::willChange') + + def test_deprecatedDecorator(self): + """Verify that the deprecated decorator doesn't break things""" + + @deprecated('this nonexistent function') + def demoFunction(x, val=5): + return x + val + + with catch_warnings(record=True) as record: + self.assertEqual(7, demoFunction(2)) + self.assertEqual(7, demoFunction(2, 5)) + self.assertEquals(len(record), 2, + 'Did not catch two warnings::deprecation') + + +class LoggingTester(TestCaseWithLogCapture): + """ + Class for testing various logging capabilities + """ + + def test_logger(self): + """Test the basic logging functions.""" + searchMessage = "test_logger" + with rc: + rc['verbosity'] = 'debug' + for logFunc in LOGGER_FUNCTIONS: + funcLevel = logFunc.__name__.upper() + logFunc(searchMessage) + self.msgInLogs(funcLevel, searchMessage) + + def test_addRemoveHandlers(self): + """Test that the add/remove handler functions work.""" + with self.assertRaises(TypeError): + addHandler(1) + addHandler(self.handler) + self.assertIn(self.handler, __logger__.handlers, + msg="addHandler did not add the handler") + removeHandler(self.handler) + self.assertNotIn(self.handler, __logger__.handlers, + msg="removeHandler did not remove the handler") + + def test_keyInLogs(self): + """Verify the behavrior of LoggerMixin.msgInLogs""" + message = "look for me" + warning(message) + self.assertMsgInLogs("WARNING", message) + self.assertMsgInLogs("WARNING", message[:5], partial=True) + self.assertMsgNotInLogs("WARNING", "<none>") + self.assertMsgNotInLogs("WARNING", "<none>", partial=True) + with self.assertRaises(KeyError): + self.msgInLogs("DEBUG", message) + with self.assertRaises(AttributeError): + newM = LoggerMixin() + newM.msgInLogs("WARNING", message) + + +if __name__ == '__main__': + from unittest import main + main() diff --git a/serpentTools/tests/test_settings.py b/serpentTools/tests/test_settings.py index 00beffb..b4d60a2 100644 --- a/serpentTools/tests/test_settings.py +++ b/serpentTools/tests/test_settings.py @@ -1,16 +1,15 @@ """Tests for the settings loaders.""" from os import remove -import warnings -import unittest +from unittest import TestCase import yaml import six from serpentTools import settings -from serpentTools.messages import deprecated, willChange +from serpentTools.tests.utils import TestCaseWithLogCapture -class DefaultSettingsTester(unittest.TestCase): +class DefaultSettingsTester(TestCase): """Class to test the functionality of the master loader.""" @classmethod @@ -34,7 +33,7 @@ class DefaultSettingsTester(unittest.TestCase): return self.defaultLoader[setting].default -class RCTester(unittest.TestCase): +class RCTester(TestCase): """Class to test the functionality of the scriptable settings manager.""" @classmethod @@ -107,7 +106,7 @@ class RCTester(unittest.TestCase): self.assertSetEqual(expected, actual) -class ConfigLoaderTester(unittest.TestCase): +class ConfigLoaderTester(TestCaseWithLogCapture): """Class to test loading multiple setttings at once, i.e. config files""" @classmethod @@ -168,38 +167,9 @@ class ConfigLoaderTester(unittest.TestCase): badSettings.update(self.nestedSettings) self._writeTestRemoveConfFile(badSettings, self.files['nested'], self.configSettings, False) - - -class MessagingTester(unittest.TestCase): - """Class to test the messaging framework.""" - - def test_futureDecorator(self): - """Verify that the future decorator doesn't break""" - - @willChange('This function will be updated in the future, ' - 'but will still exist') - def demoFuture(x, val=5): - return x + val - - with warnings.catch_warnings(record=True) as record: - self.assertEqual(7, demoFuture(2)) - self.assertEqual(7, demoFuture(2, 5)) - self.assertEquals(len(record), 2, - 'Did not catch two warnings::willChange') - - def test_depreciatedDecorator(self): - """Verify that the depreciated decorator doesn't break things""" - - @deprecated('this nonexistent function') - def demoFunction(x, val=5): - return x + val - - with warnings.catch_warnings(record=True) as record: - self.assertEqual(7, demoFunction(2)) - self.assertEqual(7, demoFunction(2, 5)) - self.assertEquals(len(record), 2, - 'Did not catch two warnings::deprecation') + self.assertMsgInLogs("ERROR", "bad setting", partial=True) if __name__ == '__main__': - unittest.main() + from unittest import main + main() diff --git a/serpentTools/tests/utils.py b/serpentTools/tests/utils.py new file mode 100644 index 0000000..69ee4d5 --- /dev/null +++ b/serpentTools/tests/utils.py @@ -0,0 +1,165 @@ +""" +Utilities to make testing easier +""" + +from unittest import TestCase +from logging import NOTSET + +from serpentTools.messages import ( + DictHandler, __logger__, removeHandler, addHandler, +) + + +class LoggerMixin(object): + """ + Mixin class captures log messages + + Attributes + ---------- + handler: :class:`serpentTools.messages.DictHandler` + Logging handler that stores messages in a + :attr:`serpentTools.messages.DictHandler.logMessages` + dictionary according to level. + """ + def __init__(self): + self.__old = [] + self.handler = None + + def attach(self, level=NOTSET): + """ + Attach the :class:`serpentTools.messages.DictHandler` + + Removes all :class:`logging.Handler` objects from the + old logger, and puts them back when :class:`detach` is + called + + Parameters + ---------- + level: int + Initial level to apply to handler + """ + self.handler = DictHandler(level) + self.__old = __logger__.handlers + for handler in self.__old: + removeHandler(handler) + addHandler(self.handler) + + def detach(self): + """Restore the original handers to the main logger""" + if self.handler is None: + raise AttributeError("Handler not set. Possibly not attached.") + removeHandler(self.handler) + for handler in self.__old: + addHandler(handler) + self.handler = None + self.__old = [] + + def msgInLogs(self, level, msg, partial=False): + """ + Determine if the message is contained in the logs + + Parameters + ---------- + level: str + Level under which this message was posted. + Must be a key in the + :attr:`~serpentTools.messages.DictHandler.logMessages` + on the :attr:`handler` for this class + msg: str + Message to be found in the logs. + partial: bool + If this evaluates to true, then search through each + ``message`` in `logMessages` and return ``True`` if + ``msg in message``. Otherwise, look for exact matches + + Returns + ------- + bool: + If the message was found in the logs + + Raises + ------ + KeyError: + If the level was not found in the logs + AttributeError: + If the :attr:`handler` has not been created with :meth:`attach` + """ + if self.handler is None: + raise AttributeError("Handler has not been attached. Must run " + "<attach> first") + logs = self.handler.logMessages + if level not in logs: + raise KeyError("Level {} not found in logs. Existing levels:\n{}" + .format(level, list(sorted(logs.keys())))) + if not partial: + return msg in logs[level] + for message in logs[level]: + if msg in message: + return True + return False + + +class TestCaseWithLogCapture(TestCase, LoggerMixin): + """ + Lightly overwritten :class:`unittest.TestCase` that captures logs + + Mix in the :class:`LoggerMixin` to automatically + :meth:`~LoggerMixin.attach` during + :meth:`~unittest.TestCase.setUp` and :meth:`~LoggerMixin.detach` + during :meth:`~unittest.TestCase.tearDown` + + Intended to be subclassed for actual test methods + """ + + def __init__(self, args, *kwargs): + TestCase.__init__(self, args, **kwargs) + LoggerMixin.__init__(self) + + def setUp(self): + """ + Method to be called before every individual test. + + Call :meth:`~serpentTools.tests.utils.LoggerMixin.attach` + to capture any log messages that would be presented during testing. + Should be called during any subclassing. + """ + LoggerMixin.attach(self) + + def tearDown(self): + """ + Method to be called immediately after calling and recording test + + Call :meth:`~serpentTools.tests.utils.LoggerMixin.detach` + to reset the module logger to its original state. + Should be called during any subclassing. + """ + LoggerMixin.detach(self) + + def _concatLogs(self, level): + logs = self.handler.logMessages.get(level, []) + return "\n- ".join([str(item) for item in logs]) + + def assertMsgInLogs(self, level, msg, partial=False): + """ + Assert that the message was stored under a given level + + Combines :meth:`LoggerMixin.msgInLogs` with + :meth:`unittest.TestCase.assertTrue` + """ + matchType = "a partial" if partial else "an exact" + failMsg = "Could not find {} match for {} under {}\n{}".format( + matchType, msg, level, self._concatLogs(level)) + self.assertTrue(self.msgInLogs(level, msg, partial), + msg=failMsg) + + def assertMsgNotInLogs(self, level, msg, partial=False): + """ + Assert that the message was not stored under a given level + + Combines :meth:`LoggerMixin.msgInLogs` with + :meth:`unittest.TestCase.assertFalse` + """ + matchType = "a partial" if partial else "an exact" + failMsg = "Found {} match for {} under {} but should not have" + self.assertFalse(self.msgInLogs(level, msg, partial), + msg=failMsg.format(matchType, msg, level))	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 1 }, "num_modified_files": 3 }	0.5	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": null, "python": "3.6", "reqs_path": [ "requirements-test.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	anyio==3.6.2 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 async-generator==1.10 attrs==22.2.0 Babel==2.11.0 backcall==0.2.0 bleach==4.1.0 certifi==2021.5.30 cffi==1.15.1 charset-normalizer==2.0.12 comm==0.1.4 contextvars==2.4 coverage==6.2 cycler==0.11.0 dataclasses==0.8 decorator==5.1.1 defusedxml==0.7.1 entrypoints==0.4 execnet==1.9.0 flake8==5.0.4 idna==3.10 immutables==0.19 importlib-metadata==4.2.0 iniconfig==1.1.1 ipykernel==5.5.6 ipython==7.16.3 ipython-genutils==0.2.0 ipywidgets==7.8.5 jedi==0.17.2 Jinja2==3.0.3 json5==0.9.16 jsonschema==3.2.0 jupyter==1.1.1 jupyter-client==7.1.2 jupyter-console==6.4.3 jupyter-core==4.9.2 jupyter-server==1.13.1 jupyterlab==3.2.9 jupyterlab-pygments==0.1.2 jupyterlab-server==2.10.3 jupyterlab_widgets==1.1.11 kiwisolver==1.3.1 MarkupSafe==2.0.1 matplotlib==3.3.4 mccabe==0.7.0 mistune==0.8.4 nbclassic==0.3.5 nbclient==0.5.9 nbconvert==6.0.7 nbformat==5.1.3 nest-asyncio==1.6.0 notebook==6.4.10 numpy==1.19.5 packaging==21.3 pandas==1.1.5 pandocfilters==1.5.1 parso==0.7.1 pexpect==4.9.0 pickleshare==0.7.5 Pillow==8.4.0 pluggy==1.0.0 prometheus-client==0.17.1 prompt-toolkit==3.0.36 ptyprocess==0.7.0 py==1.11.0 pycodestyle==2.9.1 pycparser==2.21 pyflakes==2.5.0 Pygments==2.14.0 pyparsing==3.1.4 pyrsistent==0.18.0 pytest==7.0.1 pytest-asyncio==0.16.0 pytest-cov==4.0.0 pytest-mock==3.6.1 pytest-xdist==3.0.2 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.1 pyzmq==25.1.2 requests==2.27.1 scipy==1.5.4 Send2Trash==1.8.3 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@9429173b3a7a42bad6e4e0b791f8456f99eb606a#egg=serpentTools six==1.17.0 sniffio==1.2.0 terminado==0.12.1 testpath==0.6.0 tomli==1.2.3 tornado==6.1 traitlets==4.3.3 typing_extensions==4.1.1 urllib3==1.26.20 wcwidth==0.2.13 webencodings==0.5.1 websocket-client==1.3.1 widgetsnbextension==3.6.10 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - anyio==3.6.2 - argon2-cffi==21.3.0 - argon2-cffi-bindings==21.2.0 - async-generator==1.10 - attrs==22.2.0 - babel==2.11.0 - backcall==0.2.0 - bleach==4.1.0 - cffi==1.15.1 - charset-normalizer==2.0.12 - comm==0.1.4 - contextvars==2.4 - coverage==6.2 - cycler==0.11.0 - dataclasses==0.8 - decorator==5.1.1 - defusedxml==0.7.1 - entrypoints==0.4 - execnet==1.9.0 - flake8==5.0.4 - idna==3.10 - immutables==0.19 - importlib-metadata==4.2.0 - iniconfig==1.1.1 - ipykernel==5.5.6 - ipython==7.16.3 - ipython-genutils==0.2.0 - ipywidgets==7.8.5 - jedi==0.17.2 - jinja2==3.0.3 - json5==0.9.16 - jsonschema==3.2.0 - jupyter==1.1.1 - jupyter-client==7.1.2 - jupyter-console==6.4.3 - jupyter-core==4.9.2 - jupyter-server==1.13.1 - jupyterlab==3.2.9 - jupyterlab-pygments==0.1.2 - jupyterlab-server==2.10.3 - jupyterlab-widgets==1.1.11 - kiwisolver==1.3.1 - markupsafe==2.0.1 - matplotlib==3.3.4 - mccabe==0.7.0 - mistune==0.8.4 - nbclassic==0.3.5 - nbclient==0.5.9 - nbconvert==6.0.7 - nbformat==5.1.3 - nest-asyncio==1.6.0 - notebook==6.4.10 - numpy==1.19.5 - packaging==21.3 - pandas==1.1.5 - pandocfilters==1.5.1 - parso==0.7.1 - pexpect==4.9.0 - pickleshare==0.7.5 - pillow==8.4.0 - pluggy==1.0.0 - prometheus-client==0.17.1 - prompt-toolkit==3.0.36 - ptyprocess==0.7.0 - py==1.11.0 - pycodestyle==2.9.1 - pycparser==2.21 - pyflakes==2.5.0 - pygments==2.14.0 - pyparsing==3.1.4 - pyrsistent==0.18.0 - pytest==7.0.1 - pytest-asyncio==0.16.0 - pytest-cov==4.0.0 - pytest-mock==3.6.1 - pytest-xdist==3.0.2 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.1 - pyzmq==25.1.2 - requests==2.27.1 - scipy==1.5.4 - send2trash==1.8.3 - six==1.17.0 - sniffio==1.2.0 - terminado==0.12.1 - testpath==0.6.0 - tomli==1.2.3 - tornado==6.1 - traitlets==4.3.3 - typing-extensions==4.1.1 - urllib3==1.26.20 - wcwidth==0.2.13 - webencodings==0.5.1 - websocket-client==1.3.1 - widgetsnbextension==3.6.10 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_depSampler.py::DepletionSamplerFailTester::test_badInventory", "serpentTools/tests/test_depSampler.py::DepletionSamplerFailTester::test_missingTimeSteps", "serpentTools/tests/test_depSampler.py::DepletedSamplerTester::test_depSamplerValidCalcs", "serpentTools/tests/test_depSampler.py::DepletedSamplerTester::test_getitem", "serpentTools/tests/test_detSampler.py::DetSamplerTester::test_differentSizedDetectors", "serpentTools/tests/test_detSampler.py::DetSamplerTester::test_getitem", "serpentTools/tests/test_detSampler.py::DetSamplerTester::test_missingDetectors", "serpentTools/tests/test_detSampler.py::DetSamplerTester::test_properlyAveraged", "serpentTools/tests/test_messages.py::DecoratorTester::test_deprecatedDecorator", "serpentTools/tests/test_messages.py::DecoratorTester::test_futureDecorator", "serpentTools/tests/test_messages.py::LoggingTester::test_addRemoveHandlers", "serpentTools/tests/test_messages.py::LoggingTester::test_keyInLogs", "serpentTools/tests/test_messages.py::LoggingTester::test_logger", "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_cannotChangeDefaults", "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_getDefault", "serpentTools/tests/test_settings.py::RCTester::test_expandExtras", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSetting_options", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSettings_type", "serpentTools/tests/test_settings.py::RCTester::test_failAtNonexistentSetting", "serpentTools/tests/test_settings.py::RCTester::test_readerWithUpdatedSettings", "serpentTools/tests/test_settings.py::RCTester::test_returnReaderSettings", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedConfig", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedNonStrict", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadSingleLevelConfig" ]	[ "serpentTools/tests/test_settings.py::RCTester::test_fullExtend" ]	[]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-24	4b93cd86c6149b94960984892dad25de6fbbb41f	2017-10-05 20:12:21	224ef748f519903554f346d48071e58b43dcf902	drewejohnson: Reopened to squash and merge	diff --git a/serpentTools/__init__.py b/serpentTools/__init__.py index fdaf895..8d07887 100644 --- a/serpentTools/__init__.py +++ b/serpentTools/__init__.py @@ -3,10 +3,6 @@ from serpentTools import parsers # List TODOS/feature requests here for now -# Messages/Errors -# TODO: Add verbosity control -# TODO: Add specific exceptions and warnings -# TODO: Add logging module to help with warnings/exceptions/info # Compatability # TODO: Python 2 support # TODO: Test compatability with earlier numpy releases @@ -14,9 +10,10 @@ from serpentTools import parsers # TODO: Update rc with dictionary # TODO: Update rc with yaml file into dictionary # TODO: Capture materials with underscores for depletion -# TODO: Better version string management from ._version import get_versions __version__ = get_versions()['version'] del get_versions + +settings.messages.info('Using version {}'.format(__version__)) diff --git a/serpentTools/parsers/depletion.py b/serpentTools/parsers/depletion.py index 002f516..6c2ff82 100644 --- a/serpentTools/parsers/depletion.py +++ b/serpentTools/parsers/depletion.py @@ -8,6 +8,8 @@ from drewtils.parsers import KeywordParser from serpentTools.objects.readers import MaterialReader from serpentTools.objects.materials import DepletedMaterial +from serpentTools.settings import messages + class DepletionReader(MaterialReader): """Parser responsible for reading and working with depletion files. @@ -60,10 +62,13 @@ class DepletionReader(MaterialReader): """Return the patterns by which to find the requested materials.""" patterns = self.settings['materials'] or ['.'] # match all materials if nothing given + if any(['_' in pat for pat in patterns]): + messages.warning('Materials with underscores are not supported.') return [re.compile(mat) for mat in patterns] def read(self): """Read through the depletion file and store requested data.""" + messages.info('Preparing to read {}'.format(self.filePath)) keys = ['MAT', 'TOT'] if self.settings['processTotal'] else ['MAT'] keys.extend(self.settings['metadataKeys']) separators = ['\n', '];'] @@ -74,6 +79,8 @@ class DepletionReader(MaterialReader): elif (('TOT' in chunk[0] and self.settings['processTotal']) or 'MAT' in chunk[0]): self._addMaterial(chunk) + messages.info('Done reading depletion file') + messages.debug(' found {} materials'.format(len(self.materials))) def _addMetadata(self, chunk): options = {'ZAI': 'zai', 'NAMES': 'names', 'DAYS': 'days', diff --git a/serpentTools/settings/__init__.py b/serpentTools/settings/__init__.py index 2223fc0..09f5fdb 100644 --- a/serpentTools/settings/__init__.py +++ b/serpentTools/settings/__init__.py @@ -1,4 +1,5 @@ """Settings to yield control to the user.""" +from serpentTools.settings import messages defaultSettings = { 'depletion.metadataKeys': { @@ -23,6 +24,13 @@ defaultSettings = { 'default': True, 'description': 'Option to store the depletion data from the TOT block', 'type': bool + }, + 'verbosity': { + 'default': 'warning', + 'options': messages.LOG_OPTS, + 'type': str, + 'description': 'Set the level of errors to be shown.', + 'updater': messages.updateLevel } } @@ -30,12 +38,13 @@ defaultSettings = { class DefaultSetting(object): """Store a single setting.""" - def __init__(self, name, default, varType, description, options): + def __init__(self, name, default, varType, description, options, updater): self.name = name self.description = description self.default = default self.type = varType self.options = options + self.updater = updater def __repr__(self): return '<DefaultSetting {}: value: {}>'.format(self.name, self.default) @@ -82,7 +91,8 @@ class DefaultSettingsLoader(dict): dict.__init__(self, self._load()) self.__locked__ = True - def _load(self): + @staticmethod + def _load(): """Load the default setting objects.""" defaults = {} for name, value in defaultSettings.items(): @@ -95,7 +105,8 @@ class DefaultSettingsLoader(dict): 'default': value['default'], 'varType': value['type'], 'options': options, - 'description': value['description'] + 'description': value['description'], + 'updater': value.get('updater', None) } defaults[name] = DefaultSetting(*settingsOptions) return defaults @@ -163,14 +174,17 @@ class UserSettingsLoader(dict): raise KeyError('Setting {} does not exist'.format(name)) self._defaultLoader[name].validate(value) # if we've made it here, then the value is valid + if self._defaultLoader[name].updater is not None: + value = self._defaultLoader[name].updater(value) dict.__setitem__(self, name, value) + messages.debug('Updated setting {} to {}'.format(name, value)) def getReaderSettings(self, readerName): """Get all module-wide and reader-specific settings. Parameters ---------- - readerLevel: str + readerName: str Name of the specific reader. Will look for settings with lead with ``readerName``, e.g. ``depletion.metadataKeys`` diff --git a/serpentTools/settings/messages.py b/serpentTools/settings/messages.py new file mode 100644 index 0000000..a0fa0b0 --- /dev/null +++ b/serpentTools/settings/messages.py @@ -0,0 +1,84 @@ +""" +System-wide methods for producing status update and errors. + +See Also +-------- +https://docs.python.org/2/library/logging.html +https://www.python.org/dev/peps/pep-0391/ +http://docs.python-guide.org/en/latest/writing/logging/ +https://docs.python.org/2/howto/logging-cookbook.html#logging-cookbook +https://fangpenlin.com/posts/2012/08/26/good-logging-practice-in-python/ +""" + + +import logging +from logging.config import dictConfig + + +class SerpentToolsException(Exception): + """Base-class for all exceptions in this project""" + pass + + +LOG_OPTS = ['critical', 'error', 'warning', 'info', 'debug'] + + +loggingConfig = { + 'version': 1, + 'formatters': { + 'brief': {'format': '%(levelname)-8s: %(name)-15s: %(message)s'}, + 'precise': { + 'format': '%(asctime)s %(name)-12s %(levelname)-8s %(message)s' + } + }, + 'handlers': { + 'console': { + 'class': 'logging.StreamHandler', + 'formatter': 'brief', + 'level': logging.DEBUG, + 'stream': 'ext://sys.stdout' + } + }, + 'root': { + 'handlers': ['console'], + 'level': logging.INFO + } +} + +dictConfig(loggingConfig) + +__logger__ = logging.getLogger('serpentTools') + + +def debug(message): + """Log a debug message.""" + __logger__.debug('%s', message) + + +def info(message): + """Log an info message, e.g. status update.""" + __logger__.info('%s', message) + + +def warning(message): + """Log a warning that something that could go wrong or be avoided.""" + __logger__.warning('%s', message) + + +def error(message, fatal=True): + """Log that something went wrong.""" + if fatal: + __logger__.critical('%s', message, exc_info=True) + raise SerpentToolsException('%s', message) + __logger__.error('%s', message) + + +def updateLevel(level): + """Set the level of the logger.""" + if level.lower() not in LOG_OPTS: + __logger__.setLevel('INFO') + warning('Logger option {} not in options. Set to warning.') + return 'warning' + else: + __logger__.setLevel(level.upper()) + return level diff --git a/setup.py b/setup.py index fdb65d3..5dd24b4 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,8 @@ from setuptools import setup import versioneer +with open('README.md') as readme: + longDesc = readme.read() classifiers = [ 'License :: OSI Approved :: MIT License', @@ -23,6 +25,7 @@ setupArgs = { 'url': 'https://github.com/CORE-GATECH-GROUP/serpent-tools', 'description': ('A suite of parsers designed to make interacting with ' 'SERPENT output files simple, scriptable, and flawless'), + 'long_description': longDesc, 'test_suite': 'serpentTools.tests', 'author': 'Andrew Johnson', 'author_email': '[email protected]',	Feature: Implement a messaging and exception framework Implement a overarching data logger that controls warnings, errors, and debug statements that allows the user to set the verbosity through the `rc` system. Maybe piggyback off of the [logging module](https://docs.python.org/3.6/library/logging.html) Create a `SerpentToolsError` that is the base type for all critical errors thrown during operation. ## Usage ```from serpentTools.settings import rc rc['verbosity'] = 'debug' # print all status updates, errors, and debug statements rc['definitely not a setting'] = 'still not good' # raises SerpentToolsError or some subclass thereof rc['verbosity'] = 'quiet' # print only critical errors, same as `critical` ```	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_loaders.py b/serpentTools/tests/test_loaders.py index f6895e1..3bbd24e 100644 --- a/serpentTools/tests/test_loaders.py +++ b/serpentTools/tests/test_loaders.py @@ -57,7 +57,8 @@ class UserSettingsTester(unittest.TestCase): 'metadataKeys': ['ZAI', 'NAMES', 'DAYS', 'BU'], 'materialVariables': ['ADENS', 'MDENS', 'BURNUP'], 'materials': [], - 'processTotal': True + 'processTotal': True, + 'verbosity': 'warning' } actual = self.loader.getReaderSettings(readerName) self.assertDictEqual(expected, actual)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 4 }	1.00	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "drewtils>=0.1.5" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	contourpy==1.3.0 cycler==0.12.1 drewtils==0.1.9 exceptiongroup==1.2.2 fonttools==4.56.0 importlib_resources==6.5.2 iniconfig==2.1.0 kiwisolver==1.4.7 matplotlib==3.9.4 numpy==2.0.2 packaging==24.2 pillow==11.1.0 pluggy==1.5.0 pyparsing==3.2.3 pytest==8.3.5 python-dateutil==2.9.0.post0 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@4b93cd86c6149b94960984892dad25de6fbbb41f#egg=serpentTools six==1.17.0 tomli==2.2.1 versioneer==0.29 zipp==3.21.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - contourpy==1.3.0 - cycler==0.12.1 - drewtils==0.1.9 - exceptiongroup==1.2.2 - fonttools==4.56.0 - importlib-resources==6.5.2 - iniconfig==2.1.0 - kiwisolver==1.4.7 - matplotlib==3.9.4 - numpy==2.0.2 - packaging==24.2 - pillow==11.1.0 - pluggy==1.5.0 - pyparsing==3.2.3 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - six==1.17.0 - tomli==2.2.1 - versioneer==0.29 - zipp==3.21.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_loaders.py::UserSettingsTester::test_returnReaderSettings" ]	[]	[ "serpentTools/tests/test_loaders.py::DefaultSettingsTester::test_cannotChangeDefaults", "serpentTools/tests/test_loaders.py::DefaultSettingsTester::test_getDefault", "serpentTools/tests/test_loaders.py::UserSettingsTester::test_failAtBadSetting_options", "serpentTools/tests/test_loaders.py::UserSettingsTester::test_failAtBadSettings_type", "serpentTools/tests/test_loaders.py::UserSettingsTester::test_failAtNonexistentSetting", "serpentTools/tests/test_loaders.py::RCTester::test_readerWithUpdatedSettings" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-24
CORE-GATECH-GROUP__serpent-tools-240	03997bdce0a5adb75cf5796278ea61b799f7b6dc	2018-09-04 19:06:39	03997bdce0a5adb75cf5796278ea61b799f7b6dc		diff --git a/docs/changelog.rst b/docs/changelog.rst index 8b52e58..0c9561c 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -1,4 +1,8 @@ .. \|homogUniv\| replace:: :py:class:`~serpentTools.objects.containers.HomogUniv` +.. \|resultReader\| replace:: :class:`~serpentTools.parsers.results.ResultsReader` +.. \|detector\| replace:: :class:`~serpentTools.objects.detectors.Detector` +.. \|detectorReader\| replace:: :class:`~serpentTools.parsers.detector.DetectorReader` +.. \|depletionReader\| replace:: :class:`~serpentTools.parsers.depletion.DepletionReader` .. _changelog: @@ -6,9 +10,24 @@ Changelog ========= +.. _v0.6.0: + +0.6.0 +===== + +* :pull:`174` - Added parent object ``BaseObject`` with basic comparison + method from which all objects inherit. Comparison method contains + upper and lower bounds for values w/o uncertainties, :pull:`191` +* :pull:`196` - Add comparison methods for \|resultReader\| and + \|homogUniv\| objects +* :pull:`228` - Add comparison methods for \|detectorReader\| and + \|detector\| objects +* :pull:`236` - Add comparison methods for \|depletionReader\| and + :class:`~serpentTools.objects.materials.DepletedMaterial` objects + .. _v0.5.4: -:release-tag:`0.5.3` +:release-tag:`0.5.4` ==================== * :pull:`239` - Update python dependencies to continue use of python 2 @@ -24,7 +43,7 @@ Changelog files with unique random seeds - :mod:`serpentTools.seed` * :pull:`229` - :meth:`serpentTools.parsers.sensitivity.SensitivityReader.plot` now respects the option to not set x nor y labels. -* :pull:`231` - :class:`~serpentTools.parsers.results.ResultsReader` objects +* :pull:`231` - \|resultReader\| objects can now read files that do not contain group constant data. The setting :ref:`results-expectGcu` should be used to inform the reader that no group constant data is anticipated @@ -41,10 +60,10 @@ Changelog :func:`serpentTools.plot.cartMeshPlot` * :pull:`201` - Support for plotting hexagonal meshes with :meth:`serpentTools.objects.detectors.HexagonalDetector.hexPlot` -* :pull:`204` - Access :class:`serpentTools.objects.detectors.Detector` - objects directly from :class:`serpentTools.parsers.detector.DetectorReader` +* :pull:`204` - Access \|detector\| + objects directly from \|detectorReader\| with ``reader[detName]`` -* :pull:`205` - Access materials from :class:`serpentTools.readers.depletion.DepletionReader` +* :pull:`205` - Access materials from \|depletionReader\| and :class:`serpentTools.samplers.depletion.DepletionSampler` using key-like indexing, e.g. ``reader[matName] == reader.material[matName]`` * :pull:`213` - Better default x-axis labels for simple detector plots diff --git a/docs/develop/comparisons.rst b/docs/develop/comparisons.rst new file mode 100644 index 0000000..8ac3e90 --- /dev/null +++ b/docs/develop/comparisons.rst @@ -0,0 +1,132 @@ +.. \|baseObj\| replace:: ``BaseObject`` + +.. \|error\| replace:: :func:`~serpentTools.messages.error` + +.. \|warn\| replace:: :func:`~serpentTools.messages.warning` + +.. \|info\| replace:: :func:`~serpentTools.messages.info` + +.. \|debug\| replace:: :func:`~serpentTools.messages.debug` + +.. _dev-comparisons: + +================== +Comparison Methods +================== + +We are currently developing methods for our readers and containers to be able +for comparing between like objects. This could be used to compare the effect +of changing fuel enrichment on pin powers or criticality, or used to compare +the effect of different ``SERPENT`` settings. The ``BaseObject`` that +every object should inherit from contains the bulk of the input checking, +so each reader and object needs to implement a private ``_compare`` method with +the following structure:: + + def _compare(self, other, lower, upper, sigma): + return <boolean output of comparison> + +.. note:: + + While these methods will iterate over many quantities, and some quantities + may fail early on in the test, the comparison method should continue + until all quantities have been tested. + +The value ``sigma`` should be used to compare quantities with uncertainties +by constructing intervals bounded by :math:`x\pm S\sigma`, where +``sigma``:math:`=S`. Quantities that do not have overlapping confidence +windows will be considered too different and should result in a ``False`` +value being returned from the method. + +The ``lower`` and ``upper`` arguments should be used to compare values +that do not have uncertainties. Both will be ``float`` values, with +``lower`` less than or equal to ``upper``. This functionality is +implemented with the :func:`serpentTools.utils.directCompare` function, +while the result is reported with :func:`serpentTools.utils.logDirectCompare`. + +.. _dev-comp-message: + +Use of messaging module +======================= + +Below is a non-definitive nor comprehensive list of possible comparison cases +and the corresponding message that should be printed. Using a range of message +types allows the user to be able to easily focus on things that are really bad by +using our :ref:`verbosity` setting. + +* Two objects contain different data sets, e.g. different dictionary values + - \|warn\| displaying the missing items, and then apply test to items in both objects +* Two items are identically zero, or arrays of zeros - \|debug\| +* Two items are outside of the ``sigma`` confidence intervals - \|error\| +* Two items without uncertainties have relative difference + + * less than ``lower`` - \|debug\| + * greater than or equal to ``upper`` - \|error\| + * otherwise - \|warn\| + +* Two items are identical - \|debug\| +* Two arrays are not of similar size - \|error\| + + +.. _dev-comp-utils: + +High-level Logging and Comparison Utilities +=========================================== + +The :mod:`~serpentTools.utils` module contains a collection of functions +that can be used to compare quantities and automatically log results. +When possible, these routines should be favored over hand-writing +comparison routines. If the situation calls for custom comparison +functions, utilize or extend logging routines from :ref:`dev-comp-log` +appropriately. + +.. autofunction:: serpentTools.utils.compare.compareDictOfArrays + +.. autofunction:: serpentTools.utils.compare.getCommonKeys + +.. autofunction:: serpentTools.utils.compare.directCompare + +.. autofunction:: serpentTools.utils.compare.logDirectCompare + +.. autofunction:: serpentTools.utils.compare.splitdictByKeys + +.. autofunction:: serpentTools.utils.compare.getKeyMatchingShapes + +.. autofunction:: serpentTools.utils.compare.getOverlaps + +.. autofunction:: serpentTools.utils.compare.getLogOverlaps + +.. autofunction:: serpentTools.utils.docstrings.compareDocDecorator + +.. _dev-comp-log: + +Low-level Logging Utilities +=========================== + +The :mod:`~serpentTools.messages` module contains a collection of functions +that can be used to notify the user about the results of a comparison +routine. + +.. autofunction:: serpentTools.messages.logIdentical + +.. autofunction:: serpentTools.messages.logNotIdentical + +.. autofunction:: serpentTools.messages.logAcceptableLow + +.. autofunction:: serpentTools.messages.logAcceptableHigh + +.. autofunction:: serpentTools.messages.logOutsideTols + +.. autofunction:: serpentTools.messages.logIdenticalWithUncs + +.. autofunction:: serpentTools.messages.logInsideConfInt + +.. autofunction:: serpentTools.messages.logOutsideConfInt + +.. autofunction:: serpentTools.messages.logDifferentTypes + +.. autofunction:: serpentTools.messages.logMissingKeys + +.. autofunction:: serpentTools.messages.logBadTypes + +.. autofunction:: serpentTools.messages.logBadShapes + diff --git a/docs/develop/index.rst b/docs/develop/index.rst index 8845e23..e616b27 100644 --- a/docs/develop/index.rst +++ b/docs/develop/index.rst @@ -24,3 +24,4 @@ without any loss of comprehension. checklist.rst git.rst serpentVersions.rst + comparisons.rst diff --git a/docs/develop/utils.rst b/docs/develop/utils.rst index 6d251ab..726aa42 100644 --- a/docs/develop/utils.rst +++ b/docs/develop/utils.rst @@ -1,4 +1,4 @@ -.. _dev-utils: +.. _api-utils: ========= Utilities @@ -6,7 +6,6 @@ Utilities .. automodule:: serpentTools.utils - :members: convertVariableName, linkToWiki, str2vec, splitValsUnc .. _dev-testUtils: diff --git a/serpentTools/messages.py b/serpentTools/messages.py index 8ef4f26..119c7ce 100644 --- a/serpentTools/messages.py +++ b/serpentTools/messages.py @@ -12,6 +12,9 @@ import warnings import logging from logging import Handler from logging.config import dictConfig +from collections import Callable + +from numpy import ndarray class SerpentToolsException(Exception): @@ -164,6 +167,203 @@ def _updateFilterAlert(msg, category): warnings.warn(msg, category=category, stacklevel=3) warnings.simplefilter('default', category) +# ========================================================= +# Functions for notifying the user about comparison results +# ========================================================= + + +def _prefaceNotice(obj, leader): + msg = '\n\t{} '.format(leader) + ''.join(str(obj).split('\n')) + return msg + + +def _notify(func, quantity, header, obj0, obj1): + msg = header.format(quantity) + msg += _prefaceNotice(obj0, '>') + if obj1 is not None: + msg += _prefaceNotice(obj1, '<') + func(msg) + + +def logIdentical(obj0, obj1, quantity): + """Two objects are identical.""" + _notify(debug, quantity, 'Values for {} are identical', obj0, None) + + +def logNotIdentical(obj0, obj1, quantity): + """Values should be identical but aren't.""" + _notify(error, quantity, "Values for {} are not identical", + obj0, obj1) + + +def logAcceptableLow(obj0, obj1, quantity): + """Two values differ, but inside nominal and acceptable ranges.""" + _notify(info, quantity, "Values for {} are not identical, but close", + obj0, obj1) + + +def logAcceptableHigh(obj0, obj1, quantity): + """Two values differ, enough to merit a warning but not an error.""" + _notify(warning, quantity, + "Values for {} are different, but within tolerances", obj0, obj1) + + +def logOutsideTols(obj0, obj1, quantity): + """Two values differ outside acceptable tolerances.""" + _notify(error, quantity, + "Values for {} are outside acceptable tolerances.", obj0, obj1) + + +def _notifyWithUncs(func, quantity, msg, value0, unc0, value1, unc1): + logMsg = msg.format(quantity) + logMsg += _prefaceNotice(value0, '>V') + logMsg += _prefaceNotice(unc0, '>U') + if value1 is not None: + logMsg += _prefaceNotice(value1, '<V') + logMsg += _prefaceNotice(unc0, '<U') + func(logMsg) + + +def logIdenticalWithUncs(value, unc0, unc1, quantity): + """Notify that two values have identical expected values.""" + _notifyWithUncs(debug, quantity, + 'Expected values for {} are identical', + value, unc0, None, unc1) + + +def logInsideConfInt(value0, unc0, value1, unc1, quantity): + """Two values are within acceptable statistical limits.""" + _notifyWithUncs(debug, quantity, 'Confidence intervals for {} overlap', + value0, unc0, value1, unc1) + + +def logOutsideConfInt(value0, unc0, value1, unc1, quantity): + """Two values are outside acceptable statistical limits.""" + _notifyWithUncs(error, quantity, + "Values for {} are outside acceptable statistical limits", + value0, unc0, value1, unc1) + + +def logDifferentTypes(type0, type1, quantity): + """Two values are of different types.""" + _notify(error, quantity, "Types for {} are different.", + type0, type1) + + +def logBadShapes(obj0, obj1, quantity): + """ + Log an error message that two arrays are of different shapes. + + Parameters + ---------- + obj0: :class:`numpy.ndarray` + obj1: :class:`numpy.ndarray` + Arrays that have been compared and found to have different shapes + quantity: str + Descriptor of the quantity being compared, e.g. what these objects + represent + """ + shapes = [obj.shape if isinstance(obj, ndarray) + else len(obj) for obj in (obj0, obj1)] + _notify(error, quantity, "Shapes for {} are different.", + shapes[0], shapes[1]) + + +MISSING_MSG_HEADER = "{} from {} and {} contain different items" +MISSING_MSG_SUBJ = "\n\tItems present in {} but not in {}:\n\t\t{}" + + +def _checkHerald(herald): + if not isinstance(herald, Callable): + critical("Heralding object {} is not callable. Falling back to error." + .format(herald)) + return error + return herald + + +def logMissingKeys(quantity, desc0, desc1, in0, in1, herald=error): + """ + Log a warning message that two objects contain different items + + Parameters + ---------- + quantity: str + Indicator as to what is being compared, e.g. ``'metadata'`` + desc0: str + desc1: str + Descriptions of the two originators + in0: set or iterable + in1: set or iterable + Items that are unique to originators ``0`` and ``1``, respectively + herald: callable + Callable function that accepts a single string. This will be called + with the error message. If not given, defaults to :func:`error` + """ + if not any(in0) and not any(in1): + return + herald = _checkHerald(herald) + msg = MISSING_MSG_HEADER.format(quantity, desc0, desc1) + if any(in0): + msg += MISSING_MSG_SUBJ.format(desc0, desc1, + ', '.join([str(xx) for xx in in0])) + if any(in1): + msg += MISSING_MSG_SUBJ.format(desc1, desc0, + ', '.join([str(xx) for xx in in1])) + herald(msg) + + +BAD_TYPES_HEADER = "Items from {d0} and {d1} {q} have different types" +BAD_SHAPES_HEADER = "Items from {d0} and {d1} {q} have different shapes" +BAD_OBJ_SUBJ = "\n\t{key}: {t0} - {t1}" + + +def logBadTypes(quantity, desc0, desc1, types): + """ + Log an error message for containers with mismatched types + + Parameters + ---------- + quantity: str + Indicator as to what is being compared, e.g. ``'metadata'`` + desc0: str + desc1: str + Descriptions of the two originators + types: dict + Dictionary where the keys represent the locations of + items with mismatched types. Corresponding keys should + be a list or tuple of the types for objects from + ``desc0`` and ``desc1`` stored under ``key`` + """ + msg = BAD_TYPES_HEADER.format(q=quantity, d0=desc0, d1=desc1) + for key in sorted(list(types.keys())): + t0, t1 = types[key] + msg += BAD_OBJ_SUBJ.format(key=key, t0=t0, t1=t1) + error(msg) + + +def logMapOfBadShapes(quantity, desc0, desc1, shapes): + """ + Log an error message for containers with mismatched shapes + + Parameters + ---------- + quantity: str + Indicator as to what is being compared, e.g. ``'metadata'`` + desc0: str + desc1: str + Descriptions of the two originators + shapes: dict + Dictionary where the keys represent the locations of + items with mismatched shapes. Corresponding keys should + be a list or tuple of the shapes for objects from + ``desc0`` and ``desc1`` stored under ``key`` + """ + msg = BAD_SHAPES_HEADER.format(q=quantity, d0=desc0, d1=desc1) + for key in sorted(list(shapes.keys())): + t0, t1 = shapes[key] + msg += BAD_OBJ_SUBJ.format(key=key, t0=t0, t1=t1) + error(msg) + class DictHandler(Handler): """ diff --git a/serpentTools/objects/base.py b/serpentTools/objects/base.py index 8c961c4..eadb0fe 100644 --- a/serpentTools/objects/base.py +++ b/serpentTools/objects/base.py @@ -8,14 +8,138 @@ from six import add_metaclass from numpy import arange, hstack, log, divide from matplotlib.pyplot import axes -from serpentTools.messages import debug, warning, SerpentToolsException +from serpentTools.messages import ( + debug, warning, SerpentToolsException, info, + error, + BAD_OBJ_SUBJ, + +) from serpentTools.plot import plot, cartMeshPlot from serpentTools.utils import ( magicPlotDocDecorator, formatPlot, DETECTOR_PLOT_LABELS, + compareDocDecorator, DEF_COMP_LOWER, DEF_COMP_SIGMA, + DEF_COMP_UPPER, compareDictOfArrays, +) +from serpentTools.utils.compare import ( + getLogOverlaps, finalCompareMsg, ) +from serpentTools.settings import rc + + +class BaseObject(object): + """Most basic class shared by all other classes.""" + + @compareDocDecorator + def compare(self, other, lower=DEF_COMP_LOWER, upper=DEF_COMP_UPPER, + sigma=DEF_COMP_SIGMA, verbosity=None): + """ + Compare the results of this reader to another. + + For values without uncertainties, the upper and lower + arguments control what passes and what messages get + raised. If a quantity in ``other`` is less than + ``lower`` percent different that the same quantity + on this object, consider this allowable and make + no messages. + Quantities that are greater than ``upper`` percent + different will have a error messages printed and + the comparison will return ``False``, but continue. + Quantities with difference between these ranges will + have warning messages printed. + Parameters + ---------- + other: + Other reader instance against which to compare. + Must be a similar class as this one. + {compLimits} + {sigma} + verbosity: None or str + If given, update the verbosity just for this comparison. -class NamedObject(object): + Returns + ------- + bool: + ``True`` if the objects are in agreement with + each other according to the parameters specified + + Raises + ------ + {compTypeErr} + ValueError + If upper > lower, + If sigma, lower, or upper are negative + """ + upper = float(upper) + lower = float(lower) + sigma = int(sigma) + if upper < lower: + raise ValueError("Upper limit must be greater than lower. " + "{} is not greater than {}" + .format(upper, lower)) + for item, key in zip((upper, lower, sigma), + ('upper', 'lower', 'sigma')): + if item < 0: + raise ValueError("{} must be non-negative, is {}" + .format(key, item)) + + self._checkCompareObj(other) + + previousVerb = None + if verbosity is not None: + previousVerb = rc['verbosity'] + rc['verbosity'] = verbosity + + self._compareLogPreMsg(other, lower, upper, sigma) + + areSimilar = self._compare(other, lower, upper, sigma) + + if areSimilar: + herald = info + else: + herald = warning + herald(finalCompareMsg(self, other, areSimilar)) + if previousVerb is not None: + rc['verbosity'] = previousVerb + + return areSimilar + + def _compare(self, other, lower, upper, sigma): + """Actual comparison method for similar classes.""" + raise NotImplementedError + + def _checkCompareObj(self, other): + """Verify that the two objects are same class or subclasses.""" + if not (isinstance(other, self.__class__) or + issubclass(other.__class__, self.__class__)): + oName = other.__class__.__name__ + name = self.__class__.__name__ + raise TypeError( + "Cannot compare against {} - not instance nor subclass " + "of {}".format(oName, name)) + + def _compareLogPreMsg(self, other, lower=None, upper=None, sigma=None, + quantity=None): + """Log an INFO message about this specific comparison.""" + leader = "Comparing {}> against < with the following tolerances:" + tols = [leader.format((quantity + ' from ') if quantity else ''), ] + for leader, obj in zip(('>', '<'), (self, other)): + tols.append("{} {}".format(leader, obj)) + for title, val in zip(('Lower', 'Upper'), (lower, upper)): + if val is None: + continue + tols.append("{} tolerance: {:5.3F} [%]".format(title, val)) + if sigma is not None: + sigmaStr = ("Confidence interval for statistical values: {:d} " + "sigma or {} %") + sigmaDict = {1: 68, 2: 95} + tols.append( + sigmaStr.format(sigma, sigmaDict.get(sigma, '>= 99.7') + if sigma else 0)) + info('\n\t'.join(tols)) + + +class NamedObject(BaseObject): """Class for named objects like materials and detectors.""" def __init__(self, name): @@ -413,3 +537,34 @@ class DetectorBase(NamedObject): if qty in self.indexes: return self.indexes[qty], xlabel return fallbackX, xlabel + + def _compare(self, other, lower, upper, sigma): + myShape = self.tallies.shape + otherShape = other.tallies.shape + if myShape != otherShape: + error("Detector tallies do not have identical shapes" + + BAD_OBJ_SUBJ.format('tallies', myShape, otherShape)) + return False + similar = compareDictOfArrays(self.grids, other.grids, 'grids', + lower=lower, upper=upper) + + similar &= getLogOverlaps('tallies', self.tallies, other.tallies, + self.errors, other.errors, sigma, + relative=True) + hasScores = [obj.scores is not None for obj in (self, other)] + + similar &= hasScores[0] == hasScores[1] + + if not any(hasScores): + return similar + if all(hasScores): + similar &= getLogOverlaps('scores', self.scores, other.scores, + self.errors, other.errors, sigma, + relative=True) + return similar + firstK, secondK = "first", "second" + if hasScores[1]: + firstK, secondK = secondK, firstK + error("{} detector has scores while {} does not" + .format(firstK.capitalize(), secondK)) + return similar diff --git a/serpentTools/objects/containers.py b/serpentTools/objects/containers.py index 5b57bcd..e1c3fd2 100644 --- a/serpentTools/objects/containers.py +++ b/serpentTools/objects/containers.py @@ -15,10 +15,28 @@ from matplotlib import pyplot from numpy import array, arange, hstack, ndarray, zeros_like from serpentTools.settings import rc -from serpentTools.utils.plot import magicPlotDocDecorator, formatPlot -from serpentTools.objects.base import NamedObject -from serpentTools.utils import convertVariableName -from serpentTools.messages import warning, SerpentToolsException, debug, info +from serpentTools.objects.base import NamedObject, BaseObject +from serpentTools.utils import ( + convertVariableName, + getKeyMatchingShapes, + logDirectCompare, + getLogOverlaps, + compareDocReplacer, + compareDocDecorator, + magicPlotDocDecorator, + formatPlot, +) + +from serpentTools.objects.base import (DEF_COMP_LOWER, + DEF_COMP_UPPER, DEF_COMP_SIGMA) +from serpentTools.messages import ( + warning, + SerpentToolsException, + debug, + info, + critical, + error, +) SCATTER_MATS = set() SCATTER_ORDERS = 8 @@ -439,8 +457,132 @@ class HomogUniv(NamedObject): hasData = __bool__ + def _compare(self, other, lower, upper, sigma): + similar = self.compareAttributes(other, lower, upper, sigma) + similar &= self.compareInfData(other, sigma) + similar &= self.compareB1Data(other, sigma) + similar &= self.compareGCData(other, sigma) -class BranchContainer(object): + return similar + + @compareDocDecorator + def compareAttributes(self, other, lower=DEF_COMP_LOWER, + upper=DEF_COMP_UPPER, sigma=DEF_COMP_SIGMA): + """ + Compare attributes like group structure and burnup. Return the result + + Parameters + ---------- + other: :class:`HomogUniv` + Universe against which to compare + {compLimits} + {sigma} + + Returns + ------- + bools: + ``True`` if the attributes agree within specifications + + Raises + ------ + {compTypeErr} + """ + + self._checkCompareObj(other) + + myMeta = {} + otherMeta = {} + + for key in {'bu', 'step', 'groups', 'microGroups', 'reshaped'}: + for meta, obj in zip((myMeta, otherMeta), (self, other)): + try: + meta[key] = getattr(obj, key) + except AttributeError: + meta[key] = None + + matchingKeys = getKeyMatchingShapes(myMeta, otherMeta, 'metadata') + similar = len(matchingKeys) == len(myMeta) + + for key in sorted(matchingKeys): + similar &= logDirectCompare(myMeta[key], otherMeta[key], + lower, upper, key) + + return similar + + __docCompare = compareDocReplacer(""" + Return ``True`` if contents of ``{qty}Exp`` and ``{qty}Unc`` agree + + Parameters + ---------- + other: :class:`HomogUniv` + Object from which to grab group constant dictionaries + {sigma} + + Returns + bool + If the dictionaries contain identical values with uncertainties, + and if those values have overlapping confidence intervals + Raises + ------ + {compTypeErr} + """) + + def _helpCompareGCDict(self, other, attrBase, sigma): + """ + Method that actually compare group constant dictionaries. + + ``attrBase`` is used to find dictionaries by appending + ``'Exp'`` and ``'Unc'`` to ``attrBase`` + """ + self._checkCompareObj(other) + + valName = (attrBase + 'Exp') if attrBase != 'gc' else 'gc' + uncName = attrBase + 'Unc' + try: + myVals = getattr(self, valName) + myUncs = getattr(self, uncName) + otherVals = getattr(other, valName) + otherUncs = getattr(other, uncName) + except Exception as ee: + critical("The following error was raised extracting {} and " + "{} from universes {} and {}:\n\t{}" + .format(valName, uncName, self, other, ee)) + return False + + keys = getKeyMatchingShapes(myVals, otherVals, valName) + similar = len(keys) == len(myVals) == len(otherVals) + + for key in keys: + if key not in myUncs or key not in otherUncs: + loc = self if key in otherUncs else other + error("Uncertainty data for {} missing from {}" + .format(key, loc)) + similar = False + continue + myVal = myVals[key] + myUnc = myUncs[key] + otherVal = otherVals[key] + otherUnc = otherUncs[key] + + similar &= getLogOverlaps(key, myVal, otherVal, myUnc, otherUnc, + sigma, relative=True) + return similar + + def compareInfData(self, other, sigma): + return self._helpCompareGCDict(other, 'inf', sigma) + + def compareB1Data(self, other, sigma): + return self._helpCompareGCDict(other, 'b1', sigma) + + def compareGCData(self, other, sigma): + return self._helpCompareGCDict(other, 'gc', sigma) + + compareInfData.__doc__ = __docCompare.format(qty='inf') + compareB1Data.__doc__ = __docCompare.format(qty='b1') + compareGCData.__doc__ = __docCompare.format(qty='gc') + + +class BranchContainer(BaseObject): """ Class that stores data for a single branch. @@ -524,18 +666,6 @@ class BranchContainer(object): univID: int or str Identifier for this universe burnup: float or int - Value of burnup [MWd/kgU]. A negative value here indicates - the value is really in units of days. - burnIndex: int - Point in the depletion schedule - burnDays: int or float - Point in time - - Returns - ------- - serpentTools.objects.containers.HomogUniv - Empty new universe - """ if self.__hasDays is None and burnup: self.__hasDays = burnup < 0 diff --git a/serpentTools/objects/materials.py b/serpentTools/objects/materials.py index 294da7e..07d18a9 100644 --- a/serpentTools/objects/materials.py +++ b/serpentTools/objects/materials.py @@ -8,6 +8,10 @@ from serpentTools.utils import ( magicPlotDocDecorator, formatPlot, DEPLETION_PLOT_LABELS, convertVariableName, ) +from serpentTools.utils.compare import ( + logDirectCompare, + compareDictOfArrays, +) from serpentTools.objects.base import NamedObject @@ -235,6 +239,23 @@ class DepletedMaterialBase(NamedObject): return labels + def _compare(self, other, lower, upper, sigma): + # look for identical isotope names and + similar = logDirectCompare(self.names, other.names, 0, 0, + 'isotope names') + similar &= logDirectCompare(self.zai, other.zai, 0, 0, 'isotope ZAI') + + # test data dictionary + # if uncertianties exist, use those + myUncs = self.uncertainties if hasattr(self, 'uncertainties') else {} + otherUncs = (other.uncertainties if hasattr(other, 'uncertainties') + else {}) + similar &= compareDictOfArrays( + self.data, other.data, 'data', lower=lower, upper=upper, + sigma=sigma, u0=myUncs, u1=otherUncs, relative=False) + + return similar + class DepletedMaterial(DepletedMaterialBase): __doc__ = DepletedMaterialBase.__doc__ diff --git a/serpentTools/parsers/_collections.py b/serpentTools/parsers/_collections.py new file mode 100644 index 0000000..0b93156 --- /dev/null +++ b/serpentTools/parsers/_collections.py @@ -0,0 +1,97 @@ +""" +Collections of objects that are helpful for the parsers +""" + +RES_DATA_NO_UNCS = { + "burnMaterials", + "burnMode", + "burnStep", + "iniBurnFmass", + "totBurnFmass", + "resMemsize", + "totNuclides", + "fissionProductInhTox", + "ingestionToxicity", + "totSfRate", + "electronDecaySource", + "uresDiluCut", + "implNxn", + "neutronErgTol", + "useDbrc", + "actinideActivity", + "actinideInhTox", + "photonDecaySource", + "te132Activity", + "implCapt", + "alphaDecaySource", + "totActivity", + "fissionProductActivity", + "simulationCompleted", + "sourcePopulation", + "useUres", + "lostParticles", + "iniFmass", + "totPhotonNuclides", + "totTransmuRea", + "uresEmax", + "uresUsed", + "i132Activity", + "cpuUsage", + "xsMemsize", + "memsize", + "totDecayNuclides", + "tmsMode", + "actinideIngTox", + "totDosimetryNuclides", + "cs134Activity", + "uresEmin", + "totCells", + "neutronErgNe", + "fissionProductIngTox", + "sampleCapt", + "actinideDecayHeat", + "runningTime", + "uresAvail", + "cycleIdx", + "neutronEmin", + "neutronDecaySource", + "totDecayHeat", + "dopplerPreprocessor", + "matMemsize", + "inhalationToxicity", + "sampleFiss", + "totFmass", + "useDelnu", + "cs137Activity", + "availMem", + "neutronEmax", + "miscMemsize", + "sampleScatt", + "unusedMemsize", + "unionCells", + "sr90Activity", + "totCpuTime", + "implFiss", + "allocMemsize", + "unknownMemsize", + "ompParallelFrac", + "fissionProductDecayHeat", + "totReaChannels", + "totTransportNuclides", + "ifcMemsize", + "i131Activity", + "balaSrcNeutronTot", + "balaSrcNeutronFiss", + "balaSrcNeutronNxn", + "balaLossNeutronFiss", + "balaLossNeutronTot", + "balaLossNeutronCapt", + "balaLossNeutronLeak", + "transportCycleTime", + "processTime", + "initTime", +} +""" +Set containing keys for objects stored in :attr:`ResultsReader.resdata` +that do not contain uncertainties. +""" diff --git a/serpentTools/parsers/base.py b/serpentTools/parsers/base.py index 17519b9..457d706 100644 --- a/serpentTools/parsers/base.py +++ b/serpentTools/parsers/base.py @@ -11,10 +11,11 @@ from six import add_metaclass from serpentTools.messages import info from serpentTools.settings import rc +from serpentTools.objects.base import BaseObject @add_metaclass(ABCMeta) -class BaseReader(object): +class BaseReader(BaseObject): """Parent class from which all parsers will inherit. Parameters diff --git a/serpentTools/parsers/depletion.py b/serpentTools/parsers/depletion.py index 76e94e0..528929d 100644 --- a/serpentTools/parsers/depletion.py +++ b/serpentTools/parsers/depletion.py @@ -13,9 +13,17 @@ from serpentTools.engines import KeywordParser from serpentTools.parsers.base import MaterialReader from serpentTools.objects.materials import DepletedMaterial -from serpentTools.messages import (warning, debug, error, - SerpentToolsException) - +from serpentTools.messages import ( + warning, debug, error, SerpentToolsException, +) +from serpentTools.utils import ( + getKeyMatchingShapes, + logDirectCompare, + compareDocDecorator, + DEF_COMP_LOWER, + DEF_COMP_UPPER, + DEF_COMP_SIGMA, +) METADATA_KEYS = {'ZAI', 'NAMES', 'BU', 'DAYS'} @@ -263,3 +271,109 @@ class DepletionReader(DepPlotMixin, MaterialReader): if 'bu' in self.metadata: self.metadata['burnup'] = self.metadata.pop('bu') + + def _compare(self, other, lower, upper, _sigma): + + similar = self._compareMetadata(other, lower, upper, _sigma) + if not self._comparePrecheckMetadata(other): + return False + similar &= self._compareMaterials(other, lower, upper, _sigma) + return similar + + def _comparePrecheckMetadata(self, other): + for key, myVec in iteritems(self.metadata): + otherVec = other.metadata[key] + if len(myVec) != len(otherVec): + error("Stopping comparison early due to mismatched {} vectors" + "\n\t>{}\n\t<{}".format(key, myVec, otherVec)) + return False + return True + + @compareDocDecorator + def compareMaterials(self, other, lower=DEF_COMP_LOWER, + upper=DEF_COMP_UPPER, sigma=DEF_COMP_SIGMA): + """ + Return the result of comparing all materials on two readers + + Parameters + ---------- + other: :class:`DepletionReader` + Reader to compare against + {compLimits} + {sigma} + + Returns + ------- + bool: + ``True`` if all materials agree to the given tolerances + + Raises + ------ + {compTypeErr} + """ + self._checkCompareObj(other) + self._compareLogPreMsg(other, lower, upper, quantity='materials') + + if not self._comparePrecheckMetadata(other): + return False + + return self._compareMaterials(other, lower, upper, sigma) + + def _compareMaterials(self, other, lower, upper, sigma): + """Private method for going directly into the comparison.""" + commonMats = getKeyMatchingShapes( + self.materials, other.materials, 'materials') + similar = ( + len(self.materials) == len(other.materials) == len(commonMats)) + + for matName in sorted(commonMats): + myMat = self[matName] + otherMat = other[matName] + similar &= myMat.compare(otherMat, lower, upper, sigma) + return similar + + @compareDocDecorator + def compareMetadata(self, other, lower=DEF_COMP_LOWER, + upper=DEF_COMP_UPPER, sigma=DEF_COMP_SIGMA): + """ + Return the result of comparing metadata on two readers + + Parameters + ---------- + other: :class:`DepletionReader` + Object to compare against + {compLimits} + {header} + + Returns + ------- + bool + True if the metadata agree within the given tolerances + + Raises + ------ + {compTypeErr} + """ + + self._checkCompareObj(other) + + self._compareLogPreMsg(other, lower, upper, quantity='metadata') + + return self._compareMetadata(other, lower, upper, sigma) + + def _compareMetadata(self, other, lower, upper, _sigma): + """Private method for comparing metadata""" + + similar = logDirectCompare( + self.metadata['names'], other.metadata['names'], + 0, 0, 'names') + similar &= logDirectCompare( + self.metadata['zai'], other.metadata['zai'], + 0, 0, 'zai') + similar &= logDirectCompare( + self.metadata['days'], other.metadata['days'], + lower, upper, 'days') + similar &= logDirectCompare( + self.metadata['burnup'], other.metadata['burnup'], + lower, upper, 'burnup') + return similar diff --git a/serpentTools/parsers/detector.py b/serpentTools/parsers/detector.py index 9053962..e63bfaa 100644 --- a/serpentTools/parsers/detector.py +++ b/serpentTools/parsers/detector.py @@ -4,6 +4,7 @@ from six import iteritems from numpy import empty from serpentTools.utils import str2vec +from serpentTools.utils.compare import getKeyMatchingShapes from serpentTools.engines import KeywordParser from serpentTools.objects.detectors import detectorFactory from serpentTools.parsers.base import BaseReader @@ -95,6 +96,20 @@ class DetectorReader(BaseReader): if not self.detectors: warning("No detectors stored from file {}".format(self.filePath)) + def _compare(self, other, lower, upper, sigma): + """Compare two detector readers.""" + similar = len(self.detectors) == len(other.detectors) + + commonKeys = getKeyMatchingShapes(self.detectors, other.detectors, + 'detectors') + similar &= len(commonKeys) == len(self.detectors) + + for detName in sorted(commonKeys): + myDetector = self[detName] + otherDetector = other[detName] + similar &= myDetector.compare(otherDetector, lower, upper, sigma) + return similar + def cleanDetChunk(chunk): """ diff --git a/serpentTools/parsers/results.py b/serpentTools/parsers/results.py index ccf30cf..d46a7fd 100644 --- a/serpentTools/parsers/results.py +++ b/serpentTools/parsers/results.py @@ -7,11 +7,26 @@ from serpentTools.settings import rc from serpentTools.utils import convertVariableName from serpentTools.objects.containers import HomogUniv from serpentTools.parsers.base import XSReader +from serpentTools.parsers._collections import RES_DATA_NO_UNCS +from serpentTools.objects.base import (DEF_COMP_LOWER, + DEF_COMP_SIGMA, DEF_COMP_UPPER) from serpentTools.utils import ( - str2vec, splitValsUncs, - STR_REGEX, VEC_REGEX, SCALAR_REGEX, FIRST_WORD_REGEX, + str2vec, + splitValsUncs, + getCommonKeys, + logDirectCompare, + compareDocDecorator, + getKeyMatchingShapes, + getLogOverlaps, + STR_REGEX, + VEC_REGEX, + SCALAR_REGEX, + FIRST_WORD_REGEX, +) +from serpentTools.messages import ( + warning, debug, SerpentToolsException, + info, ) -from serpentTools.messages import (warning, debug, SerpentToolsException) MapStrVersions = { @@ -65,6 +80,9 @@ Convert items in metadata dictionary from arrays to these data types """ +__all__ = ['ResultsReader', ] + + class ResultsReader(XSReader): """ Parser responsible for reading and working with result files. @@ -102,6 +120,16 @@ class ResultsReader(XSReader): IOError: file is unexpectedly closes while reading """ + __METADATA_COMP_SKIPS = { + 'title', + 'inputFileName', + 'workingDirectory', + 'startDate', + 'completeDate', + 'seed', + } + """Metadata keys that will not be compared.""" + def __init__(self, filePath): XSReader.__init__(self, filePath, 'results') self.__serpentVersion = rc['serpentVersion'] @@ -345,6 +373,135 @@ class ResultsReader(XSReader): self._inspectData() self._cleanMetadata() + def _compare(self, other, lower, upper, sigma): + similar = self.compareMetadata(other) + similar &= self.compareResults(other, lower, upper, sigma) + similar &= self.compareUniverses(other, lower, upper, sigma) + return similar + + @compareDocDecorator + def compareMetadata(self, other, header=False): + """ + Return True if the metadata (settings) are identical. + + Parameters + ---------- + other: :class:`ResultsReader` + Class against which to compare + {header} + + Returns + ------- + bool: + If the metadata are identical + + Raises + ------ + {compTypeErr} + """ + + self._checkCompareObj(other) + if header: + self._compareLogPreMsg(other, quantity='metadata') + myKeys = set(self.metadata.keys()) + otherKeys = set(other.metadata.keys()) + similar = not any(myKeys.symmetric_difference(otherKeys)) + commonKeys = getCommonKeys(myKeys, otherKeys, 'metadata') + skips = commonKeys.intersection(self.__METADATA_COMP_SKIPS) + if any(skips): + info("The following items will be skipped in the comparison\n\t{}" + .format(', '.join(sorted(skips)))) + for key in sorted(commonKeys): + if key in self.__METADATA_COMP_SKIPS: + continue + selfV = self.metadata[key] + otherV = other.metadata[key] + similar &= logDirectCompare(selfV, otherV, 0., 0., key) + + return similar + + @compareDocDecorator + def compareResults(self, other, lower=DEF_COMP_LOWER, + upper=DEF_COMP_UPPER, sigma=DEF_COMP_SIGMA, + header=False): + """ + Compare the contents of the results dictionary + + Parameters + ---------- + other: :class:`ResultsReader` + Class against which to compare + {compLimits} + {sigma} + {header} + + Returns + ------- + bool: + If the results data agree to given tolerances + + Raises + ------ + {compTypeErr} + """ + self._checkCompareObj(other) + if header: + self._compareLogPreMsg(other, lower, upper, sigma, 'results') + myRes = self.resdata + otherR = other.resdata + + commonTypeKeys = getKeyMatchingShapes(myRes, otherR, 'results') + + similar = len(commonTypeKeys) == len(myRes) == len(otherR) + + for key in sorted(commonTypeKeys): + mine = myRes[key] + theirs = otherR[key] + if key in RES_DATA_NO_UNCS: + similar &= logDirectCompare(mine, theirs, lower, upper, key) + continue + myVals, myUncs = splitValsUncs(mine) + theirVals, theirUncs = splitValsUncs(theirs) + similar &= getLogOverlaps(key, myVals, theirVals, myUncs, + theirUncs, sigma, relative=True) + return similar + + @compareDocDecorator + def compareUniverses(self, other, lower=DEF_COMP_LOWER, + upper=DEF_COMP_UPPER, sigma=DEF_COMP_SIGMA): + """ + Compare the contents of the ``universes`` dictionary + + Parameters + ---------- + other: :class:`ResultsReader` + Reader by which to compare + {compLimits} + {sigma} + + Returns + ------- + bool: + If the contents of the universes agree to given tolerances + + Raises + ------ + {compTypeErr} + """ + self._checkCompareObj(other) + myUniverses = self.universes + otherUniverses = other.universes + keyGoodTypes = getKeyMatchingShapes(myUniverses, otherUniverses, + 'universes') + + similar = len(keyGoodTypes) == len(myUniverses) == len(otherUniverses) + + for univKey in keyGoodTypes: + myUniv = myUniverses[univKey] + otherUniv = otherUniverses[univKey] + similar &= myUniv.compare(otherUniv, lower, upper, sigma) + return similar + def _cleanMetadata(self): """Replace some items in metadata dictionary with easier data types.""" mdata = self.metadata diff --git a/serpentTools/samplers/depletion.py b/serpentTools/samplers/depletion.py index 96d7207..de226c3 100644 --- a/serpentTools/samplers/depletion.py +++ b/serpentTools/samplers/depletion.py @@ -163,7 +163,7 @@ class SampledDepletedMaterial(SampledContainer, DepletedMaterialBase): ---------- {depAttrs:s} uncertainties: dict - Uncertainties for all variables stored in ``data`` + Absolute uncertainties for all variables stored in ``data`` allData: dict Dictionary where key, value pairs correspond to names of variables stored on this object and arrays of data from all files. diff --git a/serpentTools/utils/__init__.py b/serpentTools/utils/__init__.py index e17bbd1..b9dec17 100644 --- a/serpentTools/utils/__init__.py +++ b/serpentTools/utils/__init__.py @@ -1,190 +1,7 @@ """ Commonly used functions and utilities """ -from re import compile - -from numpy import array, ndarray - +from serpentTools.utils.core import * # noqa from serpentTools.utils.docstrings import * # noqa +from serpentTools.utils.compare import * # noqa from serpentTools.utils.plot import * # noqa - -# Regular expressions - -STR_REGEX = compile(r'\'.+\'') # string -VEC_REGEX = compile(r'(?<==.)\[.+?\]') # vector -SCALAR_REGEX = compile(r'=.+;') # scalar -FIRST_WORD_REGEX = compile(r'^\w+') # first word in the line - - -def str2vec(iterable, of=float, out=array): - """ - Convert a string or other iterable to vector. - - Parameters - ---------- - iterable: str or iterable - If string, will be split with ``split(splitAt)`` - to create a list. Every item in this list, or original - iterable, will be iterated over and converted accoring - to the other arguments. - of: type - Convert each value in ``iterable`` to this data type. - out: type - Return data type. Will be passed the iterable of - converted items of data dtype ``of``. - - Returns - ------- - vector - Iterable of all values of ``iterable``, or split variant, - converted to type ``of``. - - Examples - -------- - :: - - >>> v = "1 2 3 4" - >>> str2vec(v) - array([1., 2., 3., 4.,]) - - >>> str2vec(v, int, list) - [1, 2, 3, 4] - - >>> x = [1, 2, 3, 4] - >>> str2vec(x) - array([1., 2., 3., 4.,]) - - """ - vec = (iterable.split() if isinstance(iterable, str) - else iterable) - return out([of(xx) for xx in vec]) - - -def splitValsUncs(iterable, copy=False): - """ - Return even and odd indexed values from iterable - - Designed to extract expected values and uncertainties from - SERPENT vectors/matrices of the form - ``[x1, u1, x2, u2, ...]`` - - Slices along the last axis present on ``iterable``, e.g. - columns in 2D matrix. - - Parameters - ---------- - iterable: :class:`numpy.ndarray`or iterable - Initial arguments to be processed. If not - :class:`numpy.ndarray`, then strings will be converted - by calling :func:`str2vec`. Lists and tuples - will be sent directly to arrays with - :func:`numpy.array`. - copy: bool - If true, return a unique instance of the values - and uncertainties. Otherwise, returns a view - per numpy slicing methods - - Returns - ------- - :class:`numpy.ndarray` - Even indexed values from ``iterable`` - :class:`numpy.ndarray` - Odd indexed values from ``iterable`` - - Examples - -------- - :: - - >>> v = [1, 2, 3, 4] - >>> splitValsUncs(v) - array([1, 3]), array([2, 4]) - - >>> line = "1 2 3 4" - >>> splitValsUnc(line) - array([1, 3]), array([2, 4]) - - >>> v = [[1, 2], [3, 4]] - >>> splitValsUncs(v) - array([[1], [3]]), array([[2], [4]]) - - """ - - if not isinstance(iterable, ndarray): - iterable = (str2vec(iterable) if isinstance(iterable, str) - else array(iterable)) - vals = iterable[..., 0::2] - uncs = iterable[..., 1::2] - if copy: - return vals.copy(), uncs.copy() - return vals, uncs - - -def convertVariableName(variable): - """ - Return the mixedCase version of a SERPENT variable. - - Parameters - ---------- - variable: str - ``SERPENT_STYLE`` variable name to be converted - - Returns - ------- - str: - Variable name that has been split at underscores and - converted to ``mixedCase`` - - Examples - -------- - :: - - >>> v = "INF_KINF" - >>> convertVariableName(v) - infKinf - - >>> v = "VERSION" - >>> convertVariableName(v) - version - - """ - lowerSplits = [item.lower() for item in variable.split('_')] - if len(lowerSplits) == 1: - return lowerSplits[0] - return lowerSplits[0] + ''.join([item.capitalize() - for item in lowerSplits[1:]]) - - -LEADER_TO_WIKI = "http://serpent.vtt.fi/mediawiki/index.php/" - - -def linkToWiki(subLink, text=None): - """ - Return a string that will render as a hyperlink to the SERPENT wiki. - - Parameters - ---------- - subLink: str - Desired path inside the SERPENT wiki - following the - ``index.php`` - text: None or str - If given, use this as the shown text for the full link. - - Returns - ------- - str: - String that can be used as an rst hyperlink to the - SERPENT wiki - - Examples - -------- - >>> linkToWiki('Input_syntax_manual') - http://serpent.vtt.fi/mediawiki/index.php/Input_syntax_manual - >>> linkToWiki('Description_of_output_files#Burnup_calculation_output', - ... "Depletion Output") - `Depletion Output <http://serpent.vtt.fi/mediawiki/index.php/ - Description_of_output_files#Burnup_calculation_output>`_ - """ - fullLink = LEADER_TO_WIKI + subLink - if not text: - return fullLink - return "`{} <{}>`_".format(text, fullLink) diff --git a/serpentTools/utils/compare.py b/serpentTools/utils/compare.py new file mode 100644 index 0000000..fee2a3a --- /dev/null +++ b/serpentTools/utils/compare.py @@ -0,0 +1,634 @@ +""" +Comparison utilities +""" + +from collections import Iterable + +from numpy.core.defchararray import equal as charEqual +from numpy import ( + fabs, zeros_like, ndarray, array, greater, multiply, subtract, + equal, +) + +from serpentTools.messages import ( + error, + logIdentical, + logNotIdentical, + logAcceptableLow, + logAcceptableHigh, + logOutsideTols, + logDifferentTypes, + logMissingKeys, + logBadTypes, + logBadShapes, + logMapOfBadShapes, + logIdenticalWithUncs, + logInsideConfInt, + logOutsideConfInt, +) + +from serpentTools.utils.docstrings import compareDocDecorator + +LOWER_LIM_DIVISION = 1E-8 +"""Lower limit for denominator for division""" + +# +# Defaults for comparison +# +DEF_COMP_LOWER = 0 +DEF_COMP_UPPER = 10 +DEF_COMP_SIGMA = 2 + + +@compareDocDecorator +def getCommonKeys(d0, d1, quantity, desc0='first', desc1='second', + herald=error): + """ + Return a set of common keys from two dictionaries + + Also supports printing warning messages for keys not + found on one collection. + + If ``d0`` and ``d1`` are :class:`dict`, then the + keys will be obtained with ``d1.keys()``. Otherwise, + assume we have an iterable of keys and convert to + :class:`set`. + + Parameters + ---------- + d0: dict or iterable + d1: dict or iterable + Dictionary of keys or iterable of keys to be compared + quantity: str + Indicator as to what is being compared, e.g. ``'metadata'`` + {desc} + {herald} + Returns + ------- + set: + Keys found in both ``d{{0, 1}}`` + """ + k0 = d0.keys() if isinstance(d0, dict) else d0 + k1 = d1.keys() if isinstance(d1, dict) else d1 + s0 = set(k0) + s1 = set(k1) + + common = s0.intersection(s1) + missing = s0.symmetric_difference(s1) + if missing: + in0 = s0.difference(s1) + in1 = s1.difference(s0) + logMissingKeys(quantity, desc0, desc1, in0, in1, herald) + return common + + +TPL_FLOAT_INT = float, int + +# Error codes for direct compare +DC_STAT_GOOD = 0 +"""Values are identical to FP precision, or by ``==`` operator.""" +DC_STAT_LE_LOWER = 1 +"""Values are not identical, but max diff <= lower tolerance.""" +DC_STAT_MID = 10 +"""Values differ with max difference between lower and upper tolerance.""" +DC_STAT_GE_UPPER = 100 +"""Values differ with max difference greater than or equal to upper tolerance""" # noqa +DC_STAT_NOT_IDENTICAL = 200 +"""Values should be identical but are not, e.g. strings or bools.""" +DC_STAT_DIFF_TYPES = 255 +"""Values are of different types""" +DC_STAT_NOT_IMPLEMENTED = -1 +"""Direct compare is not implemented for these types""" +DC_STAT_DIFF_SHAPES = 250 +"""Values are of different shapes.""" + +COMPARE_STATUS_CODES = { + DC_STAT_GOOD: (logIdentical, True), + DC_STAT_LE_LOWER: (logAcceptableLow, True), + DC_STAT_MID: (logAcceptableHigh, True), + DC_STAT_NOT_IDENTICAL: (logNotIdentical, False), + DC_STAT_GE_UPPER: (logOutsideTols, False), + DC_STAT_DIFF_TYPES: (logDifferentTypes, False), + DC_STAT_DIFF_SHAPES: (logBadShapes, False), +} +"""Keys of status codes with ``(caller, return)`` values.""" + + +@compareDocDecorator +def directCompare(obj0, obj1, lower, upper): + """ + Return True if values are close enough to each other. + + Wrapper around various comparision tests for strings, numeric, and + arrays. + + Parameters + ---------- + obj0: str or float or int or :class:`numpy.ndarray` + obj1: str or float or int or :class:`numpy.ndarray` + Objects to compare + {compLimits} + quantity: str + Description of the value being compared. Will be + used to notify the user about any differences + + Returns + ------- + int: + Status code of the comparison. + + * {good} - Values are identical to floating point precision or, + for strings/booleans, are identical with the ``==`` operator + * {leLower} - Values are not identical, but the max difference + is less than ``lower``. + * {mid} - Values differ, with the max difference greater + than ``lower`` but less than ``upper`` + * {geUpper} - Values differ by greater than or equal to ``upper`` + * {notIdentical} - Values should be identical (strings, booleans), + but are not + * {diffShapes} - Numeric data has different shapes + * {diffTypes} - Values are of different types + * {notImplemented} - Type comparison is not supported. This means that + developers should either implement a test for this + data type, or use a different function + + See Also + -------- + * :func:`logDirectCompare` - Function that utilizes this and logs + the results using the :mod:`serpentTools.messages` module + """ + type0 = type(obj0) + type1 = type(obj1) + + if type0 != type1: + # can still compare floats and ints easily + if type0 not in TPL_FLOAT_INT or type1 not in TPL_FLOAT_INT: + return DC_STAT_DIFF_TYPES + if type0 in (str, bool): + if obj0 != obj1: + return DC_STAT_NOT_IDENTICAL + return DC_STAT_GOOD + + # Convert all to numpy arrays + if not isinstance(obj0, Iterable): + obj0 = array([obj0]) + obj1 = array([obj1]) + else: + # convert to array, but return if data-type is object + # need some indexable structure so dicts and sets won't work + obj0 = array(obj0) + obj1 = array(obj1) + if obj0.dtype.name == 'object': + return DC_STAT_NOT_IMPLEMENTED + if obj0.shape != obj1.shape: + return DC_STAT_DIFF_SHAPES + + if not upper: + return _directCompareIdentical(obj0, obj1) + return _directCompareWithTols(obj0, obj1, lower, upper) + + +def _directCompareIdentical(obj0, obj1): + """Compare arrays that should be identical""" + # special case for strings + if obj0.dtype.name[:3] == 'str': + compArray = charEqual(obj0, obj1) + else: + compArray = equal(obj0, obj1) + if compArray.all(): + return DC_STAT_GOOD + return DC_STAT_NOT_IDENTICAL + + +def _directCompareWithTols(obj0, obj1, lower, upper): + """Compare arrays that have some allowable tolerances""" + diff = multiply( + fabs(subtract(obj0, obj1)), 100 + ) + nonZI = greater(fabs(obj0), LOWER_LIM_DIVISION) + diff[nonZI] /= obj0[nonZI] + maxDiff = diff.max() + if maxDiff < LOWER_LIM_DIVISION: + return DC_STAT_GOOD + if maxDiff <= lower: + return DC_STAT_LE_LOWER + if maxDiff >= upper: + return DC_STAT_GE_UPPER + return DC_STAT_MID + + +directCompare.__doc__ = directCompare.__doc__.format( + good=DC_STAT_GOOD, + leLower=DC_STAT_LE_LOWER, + mid=DC_STAT_MID, + geUpper=DC_STAT_GE_UPPER, + notIdentical=DC_STAT_NOT_IDENTICAL, + diffTypes=DC_STAT_DIFF_TYPES, + notImplemented=DC_STAT_NOT_IMPLEMENTED, + diffShapes=DC_STAT_DIFF_SHAPES, +) + + +@compareDocDecorator +def logDirectCompare(obj0, obj1, lower, upper, quantity): + """ + Compare objects using :func:`directCompare` and log the result + + Parameters + ---------- + obj0: str or float or int or :class:`numpy.ndarray` + obj1: str or float or int or :class:`numpy.ndarray` + Objects to compare + {compLimits} + quantity: str + Description of the value being compared. Will be + used to notify the user about any differences + + Returns + ------- + bool: + ``True`` if the objects agree according to tolerances, or numerics + differ less than ``upper``. ``False`` otherwise + + Raises + ------ + TypeError: + If the objects being compared are not supported by + :func:`directCompare`. Developers should either extend the + function or utilize a different comparison function + + See Also + -------- + * :func:`directCompare` - function that does the comparison + * :func:`getOverlaps` - function for evaluating values with uncertainties + * :func:`getLogOverlaps` - function that logs the result of stastistical + comparisions + """ + result = directCompare(obj0, obj1, lower, upper) + if result < 0: # failures + if result == DC_STAT_NOT_IMPLEMENTED: + raise TypeError( + "directCompare is not configured to make tests on objects " + "of type {tp}\n\tQuantity: {k}\n\tUsers: Create a issue on " + "GitHub to alert developers.\n\tDevelopers: Update this " + "function or create a compare function " + "for {tp} objects.".format(k=quantity, tp=type(obj0))) + noticeTuple = [obj0, obj1, quantity] + if result in COMPARE_STATUS_CODES: + func, returnV = COMPARE_STATUS_CODES[result] + func(noticeTuple) + return returnV + raise ValueError("Received value of {} from directCompare. Not sure " + "what this means.") + + +def splitDictByKeys(map0, map1, keySet=None): + """ + Return various sub-sets and dictionaries from two maps. + + Used to test the internal workings on :func:`getKeyMatchingShapes` + + Parameters + ---------- + map0: dict + map1: dict + Dictionaries to compare + keySet: set or None + Iterable collection of keys found in ``map0`` and ``map1``. + Missing keys will be returned from this function under + the ``missing0`` and ``missing1`` sets. If ``None``, take + to be the set of keys that exist in both maps + + Returns + ------- + missing0: set + Keys that exist in ``keySet`` but not in ``map0`` + missing1: set + Keys that exist in ``keySet`` but not in ``map1`` + differentTypes: dict + Dictionary with tuples ``{key: (t0, t1)}`` indicating the values + ``map0[key]`` and ``map1[key]`` are of different types + badShapes: dict + Dictionary with tuples ``{key: (t0, t1)}`` indicating the values + ``map0[key]`` and ``map1[key]`` are arrays of different shapes + goodKeys: set + Keys found in both ``map0`` and ``map1`` that are of the same type + or point to arrays of the same shape + """ + if keySet is None: + keySet = set(map1.keys()) + keySet.update(set(map0.keys())) + missing = {0: set(), 1: set()} + differentTypes = {} + badShapes = {} + goodKeys = set() + for key in keySet: + if key not in map0 or key not in map1: + for mapD, misK in zip((map0, map1), (0, 1)): + if key not in mapD: + missing[misK].add(key) + continue + v0 = map0[key] + v1 = map1[key] + t0 = type(v0) + t1 = type(v1) + if t0 != t1: + differentTypes[key] = (t0, t1) + continue + if t0 is ndarray: + if v0.shape != v1.shape: + badShapes[key] = (v0.shape, v1.shape) + continue + goodKeys.add(key) + + return missing[0], missing[1], differentTypes, badShapes, goodKeys + + +def getKeyMatchingShapes(map0, map1, quantity, keySet=None, desc0='first', + desc1='second'): + """ + Return a set of keys in map0/1 that point to arrays with identical shapes. + + Parameters + ---------- + keySet: set or list or tuple or iterable or None + Iterable container with keys that exist in map0 and map1. The contents + of ``map0/1`` under these keys will be compared. If ``None``, + will be determined by :func:`splitDictByKeys` + map0: dict + map1: dict + Two dictionaries containing at least all the keys in ``keySet``. + Objects under keys in ``keySet`` will have their sizes compared if + they are :class:`numpy.ndarray`. Non-arrays will be included only + if their types are identical + quantity: str + Indicator as to what is being compared, e.g. ``'metadata'`` + desc0: str + decs1: str + Descriptions of the two dictionaries being compared. Used to alert the + user to the shortcomings of the two dictionaries + + Returns + ------- + set: + Set of all keys that exist in both dictionaries and are either + identical types, or are arrays of identical shapes + + See Also + -------- + :func:`splitDictByKeys` + """ + missing0, missing1, differentTypes, badShapes, goodKeys = ( + splitDictByKeys(map0, map1, keySet)) + + # raise some messages + if any(missing0) or any(missing1): + logMissingKeys(quantity, desc0, desc1, missing0, missing1) + if differentTypes: + logBadTypes(quantity, desc0, desc1, differentTypes) + if badShapes: + logMapOfBadShapes(quantity, desc0, desc1, badShapes) + return goodKeys + + +@compareDocDecorator +def getOverlaps(arr0, arr1, unc0, unc1, sigma, relative=True): + r""" + Return the indicies of overlapping confidence intervals + + Parameters + ---------- + arr0: :class:`numpy.ndarray` + arr1: :class:`numpy.ndarray` + Arrays containing the expected values to be compared + unc0: :class:`numpy.ndarray` + unc1: :class:`numpy.ndarray` + Associated absolute uncertainties, :math:`1\sigma`, + corresponding to the values in ``arr0`` and ``arr1`` + {sigma} + relative: bool + True if uncertainties are relative and should be multiplied + by their respective values. Otherwise, assume values are + absolute + + Returns + ------- + :class:`numpy.ndarray` + Boolean array of equal shape to incoming arrays. + Every index with ``True`` as the value indicates that + the confidence intervals for the arrays overlap + at those indices. + + Examples + -------- + Using absolute uncertainties:: + + >>> from numpy import ones, zeros, array + >>> a0 = ones(4) + >>> a1 = ones(4) * 0.5 + >>> u0 = array([0, 0.2, 0.1, 0.2]) + >>> u1 = array([1, 0.55, 0.25, 0.4]) + + Here, the first point in the confidence interval for + ``a0`` is completely contained within that of ``a1``. + The upper limit of ``a1[1]`` is contained within the confidence + interval for ``a0``. + The confidence intervals for the third point do not overlap, + while the lower bound of ``a0[3]`` is within the confidence interval + of ``a1[3]``. + :: + + >>> getOverlaps(a0, a1, u0, u1, 1, relative=False) + array([True, True, False, True]) + + This function also works for multi-dimensional arrays as well. + :: + + >>> a2 = a0.reshape(2, 2) + >>> a3 = a1.reshape(2, 2) + >>> u2 = u0.reshape(2, 2) + >>> u3 = u1.reshape(2, 2) + >>> getOverlaps(a2, a3, u2, u3 1, relative=False) + array([[ True, True], + [False, False]) + + Raises + ------ + IndexError + If the shapes of incoming arrays do not agree + + See Also + -------- + * :func:`getLogOverlaps` - High-level function that + uses this to report if two values have overlapping + confidence intervals + """ + shapes = {arg.shape for arg in (arr0, arr1, unc1, unc0)} + if len(shapes) != 1: + shapes = [str(a.shape) for a in [arr0, arr1, unc1, unc0]] + raise IndexError("Array shapes do not agree:\n{}" + .format(', '.join(shapes))) + err0 = fabs(unc0 * sigma) + err1 = fabs(unc1 * sigma) + + if relative: + err0 = arr0 + err1 = arr1 + + min0 = arr0 - err0 + max0 = arr0 + err0 + min1 = arr1 - err1 + max1 = arr1 + err1 + + overlap = zeros_like(arr0, dtype=bool) + + # Where values are identical to numerical precision + overlap[arr0 == arr1] = True + + min0le1 = min0 <= min1 + max0ge1 = max0 >= max1 + min1le0 = min1 <= min0 + max1ge0 = max1 >= max0 + + # locations where condidence intervals are completely contained + # in the other set + cont0In1 = min0le1 * (min0le1 == max0ge1) + overlap[cont0In1] = True + cont1In0 = min1le0 * (min1le0 == max1ge0) + overlap[cont1In0] = True + + # locations where min of 0 is less than 1, but max 0 > min 1 + # and the opposite + overlap[min0le1 * (max0 >= min1)] = True + overlap[min1le0 * (max1 >= min0)] = True + + # locations where max 0 > max 1, but min 0 < max 1 + # and the opposite + overlap[max0ge1 * (min0 <= max1)] = True + overlap[max1ge0 * (min1 <= max0)] = True + + return overlap + + +@compareDocDecorator +def getLogOverlaps(quantity, arr0, arr1, unc0, unc1, sigma, relative=True): + """ + Wrapper around :func:`getOverlaps` that logs the result + + Parameters + ---------- + quantity: str + Name of the value being compared + arr0: :class:`numpy.ndarray` + arr1: :class:`numpy.ndarray` + unc0: :class:`numpy.ndarray` + unc1: :class:`numpy.ndarray` + Arrays and their uncertainties to evaluate + {sigma} + relative: bool + If uncertainties are relative. Otherwise, assume absolute + uncertainties. + + Returns + ------- + bool: + ``True`` if all locations ``arr0`` and ``arr1`` are either + identical or within allowable statistical variations. + + See Also + -------- + * :func:`getOverlaps` - This function performs all the comparisons + while this function simply reports the output using + :mod:`serpentTools.messages` + """ + + if (arr0 == arr1).all(): + logIdenticalWithUncs(arr0, unc0, unc1, quantity) + return True + overlaps = getOverlaps(arr0, arr1, unc0, unc1, sigma, relative) + if overlaps.all(): + logInsideConfInt(arr0, unc0, arr1, unc1, quantity) + return True + logOutsideConfInt(arr0, unc0, arr1, unc1, quantity) + return False + + +@compareDocDecorator +def compareDictOfArrays(d0, d1, desc, lower=DEF_COMP_LOWER, + upper=DEF_COMP_UPPER, sigma=DEF_COMP_SIGMA, + u0={}, u1={}, relative=True): + """ + High-level routine for evaluating the similarities of two dictionaries + + The following tests are performed + + 1. Find a set of keys that both exist in ``d0`` and ``d1`` + and point to arrays with identical shapes using + :meth:`getKeyMatchingShapes` + 2. For each key in this common set, compare the values + with :meth:`logDirectCompare` or :meth:`getLogOverlaps`. + The latter is used if the key exists in ``u0`` and + ``u1``, provided uncertainty arrays are of identical shapes. + + Parameters + ---------- + d0: dict + d1: dict + Dictionaries to be compared + desc: str + Descption of the two dictionaries. What data do they represent? + {compLimits} + {sigma} + u0: dict + u1: dict + If uncKeys is not ``None``, then find the uncertainties for data in + ``d0`` and ``d1`` under the same keys. + relative: bool + If this evaluates to ``true``, then uncertainties in ``u0`` and ``u1`` + are relative. + + Returns + ------- + bool + ``True`` If all comparisons pass + """ + similar = len(d0) == len(d1) + keysMatchingTypes = getKeyMatchingShapes(d0, d1, desc) + similar &= len(d0) == len(keysMatchingTypes) + + for key in sorted(keysMatchingTypes): + val0 = d0[key] + val1 = d1[key] + if key in u0 and key in u1: + unc0 = u0[key] + unc1 = u1[key] + similar &= getLogOverlaps(key, val0, val1, unc0, unc1, + sigma, relative) + continue + similar &= logDirectCompare(val0, val1, lower, upper, key) + return similar + + +FINAL_COMPARE_MSG = "Objects {} and {}{} agree within given tolerances" + + +def finalCompareMsg(obj0, obj1, similar): + """ + Return the string used to signify the conclusion of a comparison + + Mainly exposed to developers for testing purposes. + + Parameters + ---------- + obj0: + obj1: Subclass of :class:`~serpentTools.objects.base.BaseObject` + Objects that have been compared + similar: bool + Result of comparison + + Returns + ------- + str: + Concluding remark about the comparison. + """ + return FINAL_COMPARE_MSG.format(obj0, obj1, "" if similar else "do not") diff --git a/serpentTools/utils/core.py b/serpentTools/utils/core.py new file mode 100644 index 0000000..3a19ea7 --- /dev/null +++ b/serpentTools/utils/core.py @@ -0,0 +1,189 @@ +""" +Core utilities +""" + +from re import compile + +from numpy import array, ndarray + + +# Regular expressions + +STR_REGEX = compile(r'\'.+\'') # string +VEC_REGEX = compile(r'(?<==.)\[.+?\]') # vector +SCALAR_REGEX = compile(r'=.+;') # scalar +FIRST_WORD_REGEX = compile(r'^\w+') # first word in the line + + +def str2vec(iterable, of=float, out=array): + """ + Convert a string or other iterable to vector. + + Parameters + ---------- + iterable: str or iterable + If string, will be split with ``split(splitAt)`` + to create a list. Every item in this list, or original + iterable, will be iterated over and converted accoring + to the other arguments. + of: type + Convert each value in ``iterable`` to this data type. + out: type + Return data type. Will be passed the iterable of + converted items of data dtype ``of``. + + Returns + ------- + vector + Iterable of all values of ``iterable``, or split variant, + converted to type ``of``. + + Examples + -------- + :: + + >>> v = "1 2 3 4" + >>> str2vec(v) + array([1., 2., 3., 4.,]) + + >>> str2vec(v, int, list) + [1, 2, 3, 4] + + >>> x = [1, 2, 3, 4] + >>> str2vec(x) + array([1., 2., 3., 4.,]) + + """ + vec = (iterable.split() if isinstance(iterable, str) + else iterable) + return out([of(xx) for xx in vec]) + + +def splitValsUncs(iterable, copy=False): + """ + Return even and odd indexed values from iterable + + Designed to extract expected values and uncertainties from + SERPENT vectors/matrices of the form + ``[x1, u1, x2, u2, ...]`` + + Slices along the last axis present on ``iterable``, e.g. + columns in 2D matrix. + + Parameters + ---------- + iterable: :class:`numpy.ndarray`or iterable + Initial arguments to be processed. If not + :class:`numpy.ndarray`, then strings will be converted + by calling :func:`str2vec`. Lists and tuples + will be sent directly to arrays with + :func:`numpy.array`. + copy: bool + If true, return a unique instance of the values + and uncertainties. Otherwise, returns a view + per numpy slicing methods + + Returns + ------- + :class:`numpy.ndarray` + Even indexed values from ``iterable`` + :class:`numpy.ndarray` + Odd indexed values from ``iterable`` + + Examples + -------- + :: + + >>> v = [1, 2, 3, 4] + >>> splitValsUncs(v) + array([1, 3]), array([2, 4]) + + >>> line = "1 2 3 4" + >>> splitValsUnc(line) + array([1, 3]), array([2, 4]) + + >>> v = [[1, 2], [3, 4]] + >>> splitValsUncs(v) + array([[1], [3]]), array([[2], [4]]) + + """ + + if not isinstance(iterable, ndarray): + iterable = (str2vec(iterable) if isinstance(iterable, str) + else array(iterable)) + vals = iterable[..., 0::2] + uncs = iterable[..., 1::2] + if copy: + return vals.copy(), uncs.copy() + return vals, uncs + + +def convertVariableName(variable): + """ + Return the mixedCase version of a SERPENT variable. + + Parameters + ---------- + variable: str + ``SERPENT_STYLE`` variable name to be converted + + Returns + ------- + str: + Variable name that has been split at underscores and + converted to ``mixedCase`` + + Examples + -------- + :: + + >>> v = "INF_KINF" + >>> convertVariableName(v) + infKinf + + >>> v = "VERSION" + >>> convertVariableName(v) + version + + """ + lowerSplits = [item.lower() for item in variable.split('_')] + if len(lowerSplits) == 1: + return lowerSplits[0] + return lowerSplits[0] + ''.join([item.capitalize() + for item in lowerSplits[1:]]) + + +LEADER_TO_WIKI = "http://serpent.vtt.fi/mediawiki/index.php/" + + +def linkToWiki(subLink, text=None): + """ + Return a string that will render as a hyperlink to the SERPENT wiki. + + Parameters + ---------- + subLink: str + Desired path inside the SERPENT wiki - following the + ``index.php`` + text: None or str + If given, use this as the shown text for the full link. + + Returns + ------- + str: + String that can be used as an rst hyperlink to the + SERPENT wiki + + Examples + -------- + >>> linkToWiki('Input_syntax_manual') + http://serpent.vtt.fi/mediawiki/index.php/Input_syntax_manual + >>> linkToWiki('Description_of_output_files#Burnup_calculation_output', + ... "Depletion Output") + `Depletion Output <http://serpent.vtt.fi/mediawiki/index.php/ + Description_of_output_files#Burnup_calculation_output>`_ + """ + fullLink = LEADER_TO_WIKI + subLink + if not text: + return fullLink + return "`{} <{}>`_".format(text, fullLink) diff --git a/serpentTools/utils/docstrings.py b/serpentTools/utils/docstrings.py index cb32b74..499eb16 100644 --- a/serpentTools/utils/docstrings.py +++ b/serpentTools/utils/docstrings.py @@ -4,8 +4,12 @@ Utilities for modifying docstrings from functools import wraps from textwrap import dedent +from six import iteritems + __all__ = [ 'magicPlotDocDecorator', + 'compareDocDecorator', + 'compareDocReplacer', ] _MPL_AX = ':py:class:`matplotlib.axes.Axes`' @@ -102,3 +106,73 @@ def magicPlotDocDecorator(f): doc = doc.replace(lookF, replace) decorated.__doc__ = doc return decorated + + +COMPARE_DOC_DESC = """ + desc0: dict or None + desc1: dict or None + Description of the origin of each value set. Only needed + if ``quiet`` evalues to ``True``.""" +COMPARE_DOC_HERALD = """herald: callable + Function that accepts a single string argument used to + notify that differences were found. If + the function is not a callable object, a + :func:`serpentTools.messages.critical` message + will be printed and :func:`serpentTools.messages.error` + will be used.""" +COMPARE_DOC_LIMITS = """ + lower: float or int + Lower limit for relative tolerances in percent + Differences below this will be considered allowable + upper: float or int + Upper limit for relative tolerances in percent. Differences + above this will be considered failure and errors + messages will be raised""" +COMPARE_DOC_SIGMA = """sigma: int + Size of confidence interval to apply to + quantities with uncertainties. Quantities that do not + have overlapping confidence intervals will fail""" +COMPARE_DOC_TYPE_ERR = """TypeError + If ``other`` is not of the same class as this class + nor a subclass of this class""" +COMPARE_DOC_HEADER = """header: bool + Print/log an ``info`` message about this comparison.""" +COMPARE_DOC_MAPPING = { + 'herald': COMPARE_DOC_HERALD, + 'desc': COMPARE_DOC_DESC, + 'compLimits': COMPARE_DOC_LIMITS, + 'sigma': COMPARE_DOC_SIGMA, + 'compTypeErr': COMPARE_DOC_TYPE_ERR, + 'header': COMPARE_DOC_HEADER, +} + +COMPARE_FAIL_MSG = "Values {desc0} and {desc1} are not identical:\n\t" +COMPARE_WARN_MSG = ("Values {desc0} and {desc1} are not identical, but within " + "tolerances:\n\t") +COMPARE_PASS_MSG = "Values {desc0} and {desc0} are identical:\n\t" + + +def compareDocReplacer(doc): + """Make replacements for comparison docstrings.""" + if not doc: + return "" + doc = dedent(doc) + for magic, replace in iteritems(COMPARE_DOC_MAPPING): + lookF = '{' + magic + '}' + if lookF in doc: + doc = doc.replace(lookF, dedent(replace)) + return doc + + +def compareDocDecorator(f): + """Decorator that updates doc strings for comparison methods. + + Similar to :func:`serpentTools.plot.magicPlotDocDecorator` + but for comparison functions + """ + + @wraps(f) + def decorated(args, kwargs): + return f(args, **kwargs) + decorated.__doc__ = compareDocReplacer(f.__doc__) + return decorated	[ENH] Ability to compare readers and objects Implement a simple public method for readers and containers `.compare(other)` that would return `True` or `False` depending on the degree of difference between the two objects. This would be beneficial for comparing outputs across SERPENT versions, or sensitivity studies. ## Additional options 1. Allow tolerances (relative and/or absolute) to control the level of allowable difference 1. Take potential random behavior into account with an option to control confidence interval 1. Utilize the [`messages`](https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/develop/serpentTools/messages.py) module for displaying more information on what passes/fails comparisons ## Tasks This issue can be closed when the following comparisons are implemented - [x] ResultsReader - [x] HomogUniv - [ ] BranchingReader - [ ] DepletionReader - [ ] DepletedMaterial - [x] DetectorReader - [x] Detector Open to including other readers and containers on this list, but these are the big ones in my opinion ## Path forward 1. Start a new feature branch for these comparisons off of develop - [`enh-compare`](https://github.com/CORE-GATECH-GROUP/serpent-tools/tree/enh-compare) 1. Treat this branch as a develop-style branch, with review and CI protected pull requests 1. Add compare methods for readers and containers with branches off of this sub-branch to keep process clean and segmented 1. Once a sufficient number of comparisons have been added to this main feature branch, merge, not squash merge into develop	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/compare/__init__.py b/serpentTools/tests/compare/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/serpentTools/tests/compare/test_depletion.py b/serpentTools/tests/compare/test_depletion.py new file mode 100644 index 0000000..195b9ac --- /dev/null +++ b/serpentTools/tests/compare/test_depletion.py @@ -0,0 +1,110 @@ +""" +Test the comparisons of the depleted materials +""" + +import serpentTools +from serpentTools.tests.utils import TestCaseWithLogCapture + +DATA_FILE = 'ref_dep.m' +REF_MATERIAL = 'fuel' + + +class DepletionCompareHelper(TestCaseWithLogCapture): + """Read the reference files for creating the readers""" + + @classmethod + def setUpClass(cls): + cls.refReader = serpentTools.readDataFile(DATA_FILE) + cls.refMaterial = cls.refReader[REF_MATERIAL] + + def setUp(self): + self.otherReader = serpentTools.readDataFile(DATA_FILE) + self.otherMaterial = self.otherReader[REF_MATERIAL] + TestCaseWithLogCapture.setUp(self) + + def compare(self, lower=0, upper=0, sigma=0, verbosity='info'): + raise NotImplementedError + + def test_compareIdentical(self): + """Compare the objects against themselves for passage""" + self.assertTrue(self.compare(0, 0, 0)) + + def test_mishapenMetadata(self): + """Verify that changes in the metadata shape fail the comparison""" + numNames = len(self.refMaterial.names) + self.otherMaterial.names = self.refMaterial.names[:numNames - 1] + self.assertFalse(self.compare(0, 0, 0)) + + def test_missingData(self): + """Verify that the test fails if one object is missing data.""" + keys = list(self.refMaterial.data.keys()) + for key in keys: + data = self.otherMaterial.data.pop(key) + self.assertFalse(self.compare(0, 0, 0)) + self.assertMsgInLogs('ERROR', key, partial=True) + + # put things back in place + self.handler.logMessages = {} + self.otherMaterial.data[key] = data + + def test_minorTweakData(self): + """Verify that the test passes after minor tweaks to data""" + key = 'adens' + diffInPercent = 1. + self.otherMaterial.data[key] = (1 + diffInPercent / 100) + # Test by setting the lower tolerance to barely above perturbation + self.assertTrue(self.compare(diffInPercent + 1E-6, diffInPercent 2, + verbosity='info')) + self.assertMsgInLogs("INFO", key, partial=True) + for level in ["WARNING", "ERROR", "CRITICAL"]: + self.assertTrue(level not in self.handler.logMessages) + # Test again, with tighter lower tolerance and look for warning + self.assertTrue(self.compare(diffInPercent, diffInPercent * 2, + verbosity='info')) + self.assertMsgInLogs("WARNING", key, partial=True) + + +class DepletedMaterialComparisonTester(DepletionCompareHelper): + """TestCase that only compares depleted materials""" + + def compare(self, lower=0, upper=0, sigma=0, verbosity='info'): + return self.refMaterial.compare(self.otherMaterial, lower, upper, + sigma, verbosity=verbosity) + + +class DepletionReaderComparisonTester(DepletionCompareHelper): + """Class for comparing the reader compare method""" + + def compare(self, lower=0, upper=0, sigma=0, verbosity='info'): + return self.refReader.compare(self.otherReader, lower, upper, sigma, + verbosity=verbosity) + + def test_badMetadataShapes(self): + """ + Verify the comparison fails early for dissimilar metadata shapes. + """ + newVecKey = 'badMetadataKey' + self.refReader.metadata[newVecKey] = [0, 1, 2, 3] + self.otherReader.metadata[newVecKey] = [0, 1, 2] + self.assertFalse(self.compare(100, 100, 100)) + self.assertMsgInLogs('ERROR', newVecKey, partial=True) + self.refReader.metadata.pop(newVecKey) + + def test_diffMaterialDicts(self): + """Verify the test fails if different materials are stored.""" + for key in self.otherReader.materials: + self.otherReader.materials.pop(key) + break + newMaterialKey = "newMateria" + self.otherReader.materials[newMaterialKey] = None + self.assertFalse(self.compare()) + self.assertMsgInLogs("ERROR", key, partial=True) + self.assertMsgInLogs("ERROR", newMaterialKey, partial=True) + + +del DepletionCompareHelper + + +if __name__ == '__main__': + from unittest import main + main() diff --git a/serpentTools/tests/compare/test_detector.py b/serpentTools/tests/compare/test_detector.py new file mode 100644 index 0000000..61dd824 --- /dev/null +++ b/serpentTools/tests/compare/test_detector.py @@ -0,0 +1,202 @@ +""" +Test the comparison utilities for detectors +""" + +from serpentTools.tests.utils import TestCaseWithLogCapture +from serpentTools.utils.compare import finalCompareMsg +from serpentTools import readDataFile + +TEST_FILE = 'bwr_det0.m' +DET_TO_MODIFY = 'xymesh' +UNMODIFIED_DETS = [ + 'spectrum', +] +# All other detectors that shall be unmodified + + +def compareObjs(ref, other): + """Fixture to call identical comparisons across tests.""" + return ref.compare(other, verbosity='debug') + + +class DetCompareHelper(TestCaseWithLogCapture): + """ + Helper case for testing the detector compare methods + + setUpClass: + . Read the ``bwr_det0.m`` file + . Store this reader on the class + + setUp: + . Read the same file, but store this reader as the + one to mess up. + . Call setUp from TestCaseWithLogCapture to capture + log messages + """ + + @classmethod + def setUpClass(cls): + cls.refReader = readDataFile(TEST_FILE) + cls.refDetector = cls.refReader.detectors[DET_TO_MODIFY] + + def setUp(self): + TestCaseWithLogCapture.setUp(self) + self.otherReader = readDataFile(TEST_FILE) + self.otherDetector = self.otherReader.detectors[DET_TO_MODIFY] + + +class DetGridsCompareTester(DetCompareHelper): + """ + Tweak the grid structure on a detector and ensure that logs + capture this. + """ + + GRID_FMT = "Values for {} are {}" + + def setUp(self): + DetCompareHelper.setUp(self) + self.gridKeys = sorted(list(self.refDetector.grids.keys())) + + def test_identicalGrids(self): + """Verify that the inner grid compare works.""" + similar = compareObjs(self.refDetector, self.otherDetector) + self.assertTrue(similar, msg='output from comparison') + for gridKey in self.gridKeys: + self.assertMsgInLogs( + "DEBUG", self.GRID_FMT.format(gridKey, 'identical'), + partial=True) + + def test_modifiedGrids(self): + """Pick up on some errors due to modified grids.""" + # Changes + # X grid unchanged + # E grid slightly modified, but inside tolerances + # Y grid modified to be outside tolerances + # Z grid set to be of different shape + # Added a bad grid that should not exist in the reference + grids = self.otherDetector.grids + missingKey = 'NOT PRESENT' + grids['E'] = 1.05 + grids['Z'] = grids['X'] + grids['Y'] = 2 + grids[missingKey] = list(range(5)) + similar = compareObjs(self.refDetector, self.otherDetector) + self.assertFalse(similar, + msg="Mismatched grids did not induce failure.") + self.assertMsgInLogs( + "WARNING", + self.GRID_FMT.format('E', 'different, but within tolerances'), + partial=True) + self.assertMsgInLogs( + "ERROR", + self.GRID_FMT.format('Y', 'outside acceptable tolerances'), + partial=True) + self.assertMsgInLogs( + "ERROR", + "Z: {} - {}".format( + self.refDetector.grids['Z'].shape, + grids['Z'].shape), + partial=True) + self.assertMsgInLogs( + 'DEBUG', + self.GRID_FMT.format('X', 'identical'), + partial=True) + + +class TallyModifier(DetCompareHelper): + """Base class that modifies detectors and checks the comparisons.""" + + IDENTICAL_TALLY_MSG = "Expected values for tallies are identical" + INSIDE_CONF_MSG = "Confidence intervals for tallies overlap" + OUTISDE_CONF_MSG = ("Values for tallies are outside acceptable " + "statistical limits") + + def compare(self): + """Compare the two test objects and return the result.""" + return compareObjs(self.refObj, self.otherObj) + + @property + def refObj(self): + raise AttributeError + + @property + def otherObj(self): + raise AttributeError + + def checkUnmodifiedDetectors(self): + """Check all other detectors in the comparison.""" + pass + + def checkFinalStatus(self, obj0, obj1, status): + """Assert that the correct final status is logged.""" + expected = finalCompareMsg(obj0, obj1, status) + level = "INFO" if status else "WARNING" + self.assertMsgInLogs(level, expected) + + def test_unmodifiedCompare(self): + """Verify that w/o modifications the test passes""" + similar = self.compare() + self.assertTrue(similar) + self.assertMsgInLogs( + "DEBUG", self.IDENTICAL_TALLY_MSG, + partial=True) + self.checkFinalStatus(self.refObj, self.otherObj, True) + self.checkUnmodifiedDetectors() + + def test_withinConfIntervals(self): + """Verify that slight differences in tallies are logged.""" + self.otherDetector.tallies = 1.01 + similar = self.compare() + self.assertTrue(similar) + self.assertMsgInLogs( + "DEBUG", self.INSIDE_CONF_MSG, partial=True) + self.checkFinalStatus(self.refObj, self.otherObj, True) + self.checkUnmodifiedDetectors() + + def test_outsideConfIntervals(self): + """Verify that large differences in tallies are logged.""" + self.otherDetector.tallies = 2.0 + similar = self.compare() + self.assertFalse(similar) + self.assertMsgInLogs( + "ERROR", self.OUTISDE_CONF_MSG, partial=True) + self.checkFinalStatus(self.refObj, self.otherObj, False) + self.checkUnmodifiedDetectors() + + +class DetectorCompareTester(TallyModifier): + """Class that tests a compare across detectors""" + + @property + def refObj(self): + return self.refDetector + + @property + def otherObj(self): + return self.otherDetector + + +class DetectorReaderCompareTester(TallyModifier): + """Class that also tests the reader-level compare for completeness.""" + + @property + def refObj(self): + return self.refReader + + @property + def otherObj(self): + return self.otherReader + + def checkUnmodifiedDetectors(self): + for detName in UNMODIFIED_DETS: + myDet = self.refReader.detectors[detName] + otherDet = self.otherReader.detectors[detName] + finalMsg = finalCompareMsg(myDet, otherDet, True) + self.assertMsgInLogs("INFO", finalMsg) + + +del TallyModifier + +if __name__ == '__main__': + from unittest import main + main() diff --git a/serpentTools/tests/compare/test_results.py b/serpentTools/tests/compare/test_results.py new file mode 100644 index 0000000..32a1b6f --- /dev/null +++ b/serpentTools/tests/compare/test_results.py @@ -0,0 +1,80 @@ +""" +Test object-level compare methods +""" + +from serpentTools.tests.utils import TestCaseWithLogCapture +from serpentTools.data import readDataFile + + +RES_DATA_FILE = 'pwr_res.m' +# strings that should appear in specific logging messages after formatted +# with a single value key +IDENTICAL_KEY_FMT = "for {} are identical" +OVERLAPPING_KEY_FMT = "{} overlap" +WITHIN_TOLS_KEY_FMT = "{} are different, but within tolerance" +OUTSIDE_TOLS_KEY_FMT = "{} are outside acceptable tolerances" + + +class ResultsCompareTester(TestCaseWithLogCapture): + """ + Test the ResultsReader comparison methods + """ + + @classmethod + def setUpClass(cls): + cls.r0 = readDataFile(RES_DATA_FILE) + cls.resultsKeys = set(cls.r0.resdata.keys()) + cls.univKeys = set() + for univ in cls.r0.universes.values(): + cls.univKeys.update(set(univ.infExp.keys())) + cls.univKeys.update(set(univ.b1Exp.keys())) + cls.univKeys.update(set(univ.gc.keys())) + break + + def setUp(self): + self.r1 = readDataFile(RES_DATA_FILE) + TestCaseWithLogCapture.setUp(self) + + def _runCompare(self, verbosity): + return self.r0.compare(self.r1, verbosity=verbosity) + + def test_fullCompare(self): + """Test the primary comparison method with no modifications.""" + out = self._runCompare('debug') + self.assertTrue(out, msg="Result from comparison") + self.assertMsgInLogs('DEBUG', 'Updated setting verbosity to debug') + for resKey in self.resultsKeys: + self.assertMsgInLogs('DEBUG', IDENTICAL_KEY_FMT.format(resKey), + partial=True) + + def test_moddedResults(self): + """ + Verify that the results comparison logs errors in modified data. + """ + resd = self.r1.resdata + # drastically increase one value with uncertainties + # to ensure disagreement + resd['anaKeff'][:, ::2] = 2 + # slightly modify one value with uncertainties to force overlapping + # confidence intervals, but not identical quantities + resd['colKeff'][:, ::2] = 1.01 + resd['colKeff'][:, 1::2] = 1.05 + # modify a value w/o uncertainties slightly + resd['allocMemsize'] = 1.01 + # drastically modify a value w/o uncertainties + resd['uresAvail'] = -2 + out = self._runCompare('debug') + self.assertFalse(out) + self.assertMsgInLogs('ERROR', 'anaKeff', partial=True) + self.assertMsgInLogs( + 'DEBUG', OVERLAPPING_KEY_FMT.format('colKeff'), partial=True) + self.assertMsgInLogs( + 'WARNING', WITHIN_TOLS_KEY_FMT.format('allocMemsize'), + partial=True) + self.assertMsgInLogs( + 'ERROR', OUTSIDE_TOLS_KEY_FMT.format('uresAvail'), partial=True) + + +if __name__ == '__main__': + from unittest import main + main() diff --git a/serpentTools/tests/test_base_compare.py b/serpentTools/tests/test_base_compare.py new file mode 100644 index 0000000..82c5c19 --- /dev/null +++ b/serpentTools/tests/test_base_compare.py @@ -0,0 +1,56 @@ +"""Test the basic aspect of the comparison system.""" + +from unittest import TestCase + +from serpentTools.objects.base import BaseObject +from serpentTools.parsers.results import ResultsReader + + +class SafeCompare(BaseObject): + """Object that can use the comparison method.""" + + def _compare(self, args, *kwargs): + return True + + +class SafeSubclass(SafeCompare): + pass + + +class BaseCompareTester(TestCase): + """Class responsible for testing the basic compare system.""" + + def setUp(self): + self.obj = SafeCompare() + + def test_badCompType(self): + """Verify that comparisons against bad-types raise errors.""" + with self.assertRaises(TypeError): + self.obj.compare(ResultsReader(None)) + with self.assertRaises(TypeError): + self.obj.compare(list()) + + def test_safeCompare(self): + """ + Verify that the compare method doesn't fail when + comparing against similar objects or subclasses + """ + self.obj.compare(SafeCompare()) + self.obj.compare(SafeSubclass()) + + def test_badArgs(self): + """Verify that the compare method fails for bad arguments.""" + other = SafeCompare() + with self.assertRaises(ValueError): + self.obj.compare(other, lower=-1) + with self.assertRaises(ValueError): + self.obj.compare(other, upper=-1) + with self.assertRaises(ValueError): + self.obj.compare(other, sigma=-1) + with self.assertRaises(ValueError): + self.obj.compare(other, lower=100, upper=1) + + +if __name__ == '__main__': + from unittest import main + main() diff --git a/serpentTools/tests/test_utils.py b/serpentTools/tests/test_utils.py index 89301b2..ce80756 100644 --- a/serpentTools/tests/test_utils.py +++ b/serpentTools/tests/test_utils.py @@ -2,16 +2,32 @@ Test the various utilities in serpentTools/utils.py """ -import unittest +from unittest import TestCase -from numpy import arange, ndarray, array +from numpy import arange, ndarray, array, ones, ones_like, zeros_like from numpy.testing import assert_array_equal from six import iteritems -from serpentTools.utils import convertVariableName, splitValsUncs, str2vec +from serpentTools.utils import ( + convertVariableName, + splitValsUncs, + str2vec, + getCommonKeys, + directCompare, + getOverlaps, + splitDictByKeys, + DC_STAT_GOOD, + DC_STAT_LE_LOWER, + DC_STAT_MID, + DC_STAT_GE_UPPER, + DC_STAT_NOT_IDENTICAL, + DC_STAT_NOT_IMPLEMENTED, + DC_STAT_DIFF_TYPES, + DC_STAT_DIFF_SHAPES, +) -class VariableConverterTester(unittest.TestCase): +class VariableConverterTester(TestCase): """Class for testing our variable name conversion function.""" def test_variableConversion(self): @@ -26,7 +42,7 @@ class VariableConverterTester(unittest.TestCase): self.assertEqual(expected, actual, msg=serpentStyle) -class VectorConverterTester(unittest.TestCase): +class VectorConverterTester(TestCase): """Class for testing the str2vec function""" def setUp(self): @@ -60,7 +76,7 @@ class VectorConverterTester(unittest.TestCase): self.assertEqual(expected, actual, msg=case) -class SplitValsTester(unittest.TestCase): +class SplitValsTester(TestCase): """Class that tests splitValsUncs.""" def setUp(self): @@ -93,5 +109,274 @@ class SplitValsTester(unittest.TestCase): assert_array_equal(expectedU, actualU, err_msg="Uncertainties") +class CommonKeysTester(TestCase): + """Class that tests getCommonKeys""" + + def test_goodKeys_dict(self): + """Verify a complete set of keys is returned from getCommonKeys""" + d0 = {1: None, '2': None, (1, 2, 3): "tuple"} + expected = set(d0.keys()) + actual = getCommonKeys(d0, d0, 'identical dictionary') + self.assertSetEqual(expected, actual, + msg="Passed two dictionaries") + actual = getCommonKeys(d0, expected, 'dictionary and set') + self.assertSetEqual(expected, actual, + msg="Passed dictionary and set") + + def test_getKeys_missing(self): + """Verify that missing keys are properly notified.""" + log = [] + d0 = {1, 2, 3} + emptyS = set() + desc0 = "xObj0x" + desc1 = "xObj1x" + common = getCommonKeys(d0, emptyS, 'missing keys', desc0, desc1, + herald=log.append) + self.assertSetEqual(emptyS, common) + self.assertEqual(len(log), 1, msg="Failed to append warning message") + warnMsg = log[0] + self.assertIsInstance(warnMsg, str, msg="Log messages is not string") + for desc in (desc0, desc1): + errMsg = "Description {} missing from warning message\n{}" + self.assertIn(desc, warnMsg, msg=errMsg.format(desc, warnMsg)) + + +class DirectCompareTester(TestCase): + """Class for testing utils.directCompare.""" + + NUMERIC_ITEMS = ( + [0, 0.001], + [1E-8, 0], + [array([1, 1, ]), array([1, 1.0001])], + ) + + def checkStatus(self, expected, obj0, obj1, lower, upper, msg=None): + """Wrapper around directCompare with ``args``. Pass ``kwargs`` to + assertEqual.""" + actual = directCompare(obj0, obj1, lower, upper) + self.assertEqual(expected, actual, msg=msg) + + def test_badTypes(self): + """Verify that two objects of different types return -1.""" + status = DC_STAT_DIFF_TYPES + value = 1 + for otherType in (bool, str): + self.checkStatus(status, value, otherType(value), 0, 1, + msg=str(otherType)) + asIterable = [value, ] + for otherType in (list, set, tuple, array): + self.checkStatus(status, value, otherType(asIterable), 0, 1, + msg=str(otherType)) + + def test_identicalString(self): + """Verify that identical strings return 0.""" + msg = obj = 'identicalStrings' + status = DC_STAT_GOOD + self.checkStatus(status, obj, obj, 0, 1, msg=msg) + + def test_dissimilarString(self): + """Verify returns the proper code for dissimilar strings.""" + msg = "dissimilarStrings" + status = DC_STAT_NOT_IDENTICAL + self.checkStatus(status, 'item0', 'item1', 0, 1, msg=msg) + + def _testNumericsForItems(self, status, lower, upper): + msg = "lower: {lower}, upper: {upper}\n{}\n{}" + for (obj0, obj1) in self.NUMERIC_ITEMS: + self.checkStatus(status, obj0, obj1, lower, upper, + msg=msg.format(obj0, obj1, lower=lower, + upper=upper)) + + def test_acceptableLow(self): + """Verify returns the proper code for close numerics.""" + lower = 5 + upper = 1E4 + self._testNumericsForItems(DC_STAT_LE_LOWER, lower, upper) + + def test_acceptableHigh(self): + """Verify returns the proper code for close but not quite values.""" + lower = 0 + upper = 1E4 + self._testNumericsForItems(DC_STAT_MID, lower, upper) + + def test_outsideTols(self): + """Verify returns the proper code for values outside tolerances.""" + lower = 1E-8 + upper = 1E-8 + self._testNumericsForItems(DC_STAT_GE_UPPER, lower, upper) + + def test_notImplemented(self): + """Check the not-implemented cases.""" + self.checkStatus(DC_STAT_NOT_IMPLEMENTED, {'hello': 'world'}, + {'hello': 'world'}, 0, 0, + msg="testing on dictionaries") + + def test_stringArrays(self): + """Verify the function handles string arrays properly.""" + stringList = ['hello', 'world', '1', '-1'] + vec0 = array(stringList) + self.checkStatus(DC_STAT_GOOD, vec0, vec0, 0, 0, + msg='Identical string arrays') + vec1 = array(stringList) + vec1[-1] = 'foobar' + self.checkStatus(DC_STAT_NOT_IDENTICAL, vec0, vec1, 0, 0, + msg='Dissimilar string arrays') + + def test_diffShapes(self): + """ + Verify that that directCompare fails for arrays of different shape. + """ + vec0 = [0, 1, 2, 3, 4] + vec1 = [0, 1] + mat0 = [[0, 1], [1, 2]] + mat1 = [[0, 1, 2], [3, 4, 5], [6, 7, 8]] + self.checkStatus(DC_STAT_DIFF_SHAPES, vec0, vec1, 0, 0, + msg="Compare two vectors.") + self.checkStatus(DC_STAT_DIFF_SHAPES, vec0, mat0, 0, 0, + msg="Compare vector and array.") + self.checkStatus(DC_STAT_DIFF_SHAPES, mat0, mat1, 0, 0, + msg="Compare vector and array.") + + +class OverlapTester(TestCase): + """Class for testing the Overlapping uncertainties function.""" + + a0 = ones(4) + a1 = ones(4) 0.5 + u0 = array([0, 0.2, 0.1, 0.2]) + u1 = array([1, 0.55, 0.25, 0.4]) + expected = array([True, True, False, True]) + sigma = 1 + relative = False + + _errMsg = "Sigma:{}\na0:\n{}\nu0:\n{}\na1:\n{}\nu1:\n{}" + + def _test(self, expected, a0, a1, u0, u1, sigma, relative): + """Symmetric test on the data by switching the order of arguments.""" + assert_array_equal(expected, getOverlaps(a0, a1, u0, u1, sigma, + relative), + err_msg=self._errMsg.format(a0, u0, a1, u1, sigma)) + assert_array_equal(expected, getOverlaps(a1, a0, u1, u0, sigma, + relative), + err_msg=self._errMsg.format(sigma, a1, u1, a0, u0)) + + def _testWithReshapes(self, expected, a0, a1, u0, u1, sigma, shape, + relative): + """Call symmetric test twice, using reshaped arrays the second time.""" + self._test(expected, a0, a1, u0, u1, sigma, relative) + ra0, ra1, ru0, ru1 = [arg.reshape(shape) for arg in [a0, a1, u0, u1]] + self._test(expected.reshape(shape), ra0, ra1, ru0, ru1, sigma, + relative) + + def test_overlap_absolute(self): + """Verify the getOverlaps works using absolute uncertainties.""" + self._testWithReshapes(self.expected, self.a0, self.a1, self.u0, + self.u1, self.sigma, (2, 2), False) + + def test_overlap_relative(self): + """Verify the getOverlaps works using relative uncertainties.""" + u0 = self.u0 / self.a0 + u1 = self.u1 / self.a1 + self._testWithReshapes(self.expected, self.a0, self.a1, u0, + u1, self.sigma, (2, 2), True) + + @staticmethod + def _setupIdentical(nItems, shape=None): + arr = arange(nItems) + if shape is not None: + arr = arr.reshape(*shape) + unc = zeros_like(arr) + expected = ones_like(arr, dtype=bool) + return arr, unc, expected + + def test_overlap_identical_1D(self): + """ + Verify that all positions are found to overlap for identical arrays. + """ + vec, unc, expected = self._setupIdentical(8) + self._test(expected, vec, vec, unc, unc, 1, False) + + def test_overlap_identical_2D(self): + """ + Verify that all positions are found to overlap for identical arrays. + """ + vec, unc, expected = self._setupIdentical(8, (2, 4)) + self._test(expected, vec, vec, unc, unc, 1, False) + + def test_overlap_identical_3D(self): + """ + Verify that all positions are found to overlap for identical arrays. + """ + vec, unc, expected = self._setupIdentical(8, (2, 2, 2)) + self._test(expected, vec, vec, unc, unc, 1, False) + + def test_overlap_badshapes(self): + """Verify IndexError is raised for bad shapes.""" + vec = arange(4) + mat = vec.reshape(2, 2) + with self.assertRaises(IndexError): + getOverlaps(vec, mat, vec, mat, 1) + + +class SplitDictionaryTester(TestCase): + """Class for testing utils.splitDictByKeys.""" + + def setUp(self): + self.map0 = { + 'hello': 'world', + 'missingFrom1': True, + 'infKeff': arange(2), + 'float': 0.24, + 'absKeff': arange(2), + 'anaKeff': arange(6), + 'notBool': 1, + } + self.map1 = { + 'hello': 'world', + 'infKeff': arange(2), + 'float': 0.24, + 'missingFrom0': True, + 'notBool': False, + 'anaKeff': arange(2), + 'absKeff': arange(2), + } + self.badTypes = {'notBool': (int, bool)} + self.badShapes = {'anaKeff': ((6, ), (2, )), } + self.goodKeys = {'hello', 'absKeff', 'float', 'infKeff', } + + def callTest(self, keySet=None): + return splitDictByKeys(self.map0, self.map1, keySet) + + def test_noKeys(self): + """Verify that splitDictByKeys works when keySet is None.""" + m0, m1, badTypes, badShapes, goodKeys = self.callTest(None) + self.assertSetEqual({'missingFrom0', }, m0) + self.assertSetEqual({'missingFrom1', }, m1) + self.assertDictEqual(self.badTypes, badTypes) + self.assertDictEqual(self.badShapes, badShapes) + self.assertSetEqual(self.goodKeys, goodKeys) + + def test_keySet_all(self): + """Verify that splitDictByKeys works when keySet is all keys.""" + keys = set(self.map0.keys()) + keys.update(set(self.map1.keys())) + keys.add('NOT IN ANY MAP') + missing0 = set() + missing1 = set() + for key in keys: + if key not in self.map0: + missing0.add(key) + if key not in self.map1: + missing1.add(key) + + m0, m1, badTypes, badShapes, goodKeys = self.callTest(keys) + self.assertSetEqual(missing0, m0) + self.assertSetEqual(missing1, m1) + self.assertDictEqual(self.badTypes, badTypes) + self.assertDictEqual(self.badShapes, badShapes) + self.assertSetEqual(self.goodKeys, goodKeys) + + if __name__ == '__main__': - unittest.main() + from unittest import main + main()	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_added_files", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 3, "issue_text_score": 2, "test_score": 0 }, "num_modified_files": 14 }	0.5	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": null, "python": "3.6", "reqs_path": [ "requirements-test.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	anyio==3.6.2 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 async-generator==1.10 attrs==22.2.0 Babel==2.11.0 backcall==0.2.0 bleach==4.1.0 certifi==2021.5.30 cffi==1.15.1 charset-normalizer==2.0.12 comm==0.1.4 contextvars==2.4 coverage==6.2 cycler==0.11.0 dataclasses==0.8 decorator==5.1.1 defusedxml==0.7.1 entrypoints==0.4 execnet==1.9.0 flake8==5.0.4 idna==3.10 immutables==0.19 importlib-metadata==4.2.0 iniconfig==1.1.1 ipykernel==5.5.6 ipython==7.16.3 ipython-genutils==0.2.0 ipywidgets==7.8.5 jedi==0.17.2 Jinja2==3.0.3 json5==0.9.16 jsonschema==3.2.0 jupyter==1.1.1 jupyter-client==7.1.2 jupyter-console==6.4.3 jupyter-core==4.9.2 jupyter-server==1.13.1 jupyterlab==3.2.9 jupyterlab-pygments==0.1.2 jupyterlab-server==2.10.3 jupyterlab_widgets==1.1.11 kiwisolver==1.3.1 MarkupSafe==2.0.1 matplotlib==2.2.3 mccabe==0.7.0 mistune==0.8.4 nbclassic==0.3.5 nbclient==0.5.9 nbconvert==6.0.7 nbformat==5.1.3 nest-asyncio==1.6.0 notebook==6.4.10 numpy==1.19.5 packaging==21.3 pandas==1.1.5 pandocfilters==1.5.1 parso==0.7.1 pexpect==4.9.0 pickleshare==0.7.5 pluggy==1.0.0 prometheus-client==0.17.1 prompt-toolkit==3.0.36 ptyprocess==0.7.0 py==1.11.0 pycodestyle==2.9.1 pycparser==2.21 pyflakes==2.5.0 Pygments==2.14.0 pyparsing==3.1.4 pyrsistent==0.18.0 pytest==7.0.1 pytest-asyncio==0.16.0 pytest-cov==4.0.0 pytest-mock==3.6.1 pytest-xdist==3.0.2 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.1 pyzmq==25.1.2 requests==2.27.1 scipy==1.5.4 Send2Trash==1.8.3 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@03997bdce0a5adb75cf5796278ea61b799f7b6dc#egg=serpentTools six==1.17.0 sniffio==1.2.0 terminado==0.12.1 testpath==0.6.0 tomli==1.2.3 tornado==6.1 traitlets==4.3.3 typing_extensions==4.1.1 urllib3==1.26.20 wcwidth==0.2.13 webencodings==0.5.1 websocket-client==1.3.1 widgetsnbextension==3.6.10 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - anyio==3.6.2 - argon2-cffi==21.3.0 - argon2-cffi-bindings==21.2.0 - async-generator==1.10 - attrs==22.2.0 - babel==2.11.0 - backcall==0.2.0 - bleach==4.1.0 - cffi==1.15.1 - charset-normalizer==2.0.12 - comm==0.1.4 - contextvars==2.4 - coverage==6.2 - cycler==0.11.0 - dataclasses==0.8 - decorator==5.1.1 - defusedxml==0.7.1 - entrypoints==0.4 - execnet==1.9.0 - flake8==5.0.4 - idna==3.10 - immutables==0.19 - importlib-metadata==4.2.0 - iniconfig==1.1.1 - ipykernel==5.5.6 - ipython==7.16.3 - ipython-genutils==0.2.0 - ipywidgets==7.8.5 - jedi==0.17.2 - jinja2==3.0.3 - json5==0.9.16 - jsonschema==3.2.0 - jupyter==1.1.1 - jupyter-client==7.1.2 - jupyter-console==6.4.3 - jupyter-core==4.9.2 - jupyter-server==1.13.1 - jupyterlab==3.2.9 - jupyterlab-pygments==0.1.2 - jupyterlab-server==2.10.3 - jupyterlab-widgets==1.1.11 - kiwisolver==1.3.1 - markupsafe==2.0.1 - matplotlib==2.2.3 - mccabe==0.7.0 - mistune==0.8.4 - nbclassic==0.3.5 - nbclient==0.5.9 - nbconvert==6.0.7 - nbformat==5.1.3 - nest-asyncio==1.6.0 - notebook==6.4.10 - numpy==1.19.5 - packaging==21.3 - pandas==1.1.5 - pandocfilters==1.5.1 - parso==0.7.1 - pexpect==4.9.0 - pickleshare==0.7.5 - pluggy==1.0.0 - prometheus-client==0.17.1 - prompt-toolkit==3.0.36 - ptyprocess==0.7.0 - py==1.11.0 - pycodestyle==2.9.1 - pycparser==2.21 - pyflakes==2.5.0 - pygments==2.14.0 - pyparsing==3.1.4 - pyrsistent==0.18.0 - pytest==7.0.1 - pytest-asyncio==0.16.0 - pytest-cov==4.0.0 - pytest-mock==3.6.1 - pytest-xdist==3.0.2 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.1 - pyzmq==25.1.2 - requests==2.27.1 - scipy==1.5.4 - send2trash==1.8.3 - six==1.17.0 - sniffio==1.2.0 - terminado==0.12.1 - testpath==0.6.0 - tomli==1.2.3 - tornado==6.1 - traitlets==4.3.3 - typing-extensions==4.1.1 - urllib3==1.26.20 - wcwidth==0.2.13 - webencodings==0.5.1 - websocket-client==1.3.1 - widgetsnbextension==3.6.10 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/compare/test_depletion.py::DepletedMaterialComparisonTester::test_compareIdentical", "serpentTools/tests/compare/test_depletion.py::DepletedMaterialComparisonTester::test_minorTweakData", "serpentTools/tests/compare/test_depletion.py::DepletedMaterialComparisonTester::test_mishapenMetadata", "serpentTools/tests/compare/test_depletion.py::DepletedMaterialComparisonTester::test_missingData", "serpentTools/tests/compare/test_depletion.py::DepletionReaderComparisonTester::test_badMetadataShapes", "serpentTools/tests/compare/test_depletion.py::DepletionReaderComparisonTester::test_compareIdentical", "serpentTools/tests/compare/test_depletion.py::DepletionReaderComparisonTester::test_diffMaterialDicts", "serpentTools/tests/compare/test_depletion.py::DepletionReaderComparisonTester::test_minorTweakData", "serpentTools/tests/compare/test_depletion.py::DepletionReaderComparisonTester::test_mishapenMetadata", "serpentTools/tests/compare/test_depletion.py::DepletionReaderComparisonTester::test_missingData", "serpentTools/tests/compare/test_detector.py::DetGridsCompareTester::test_identicalGrids", "serpentTools/tests/compare/test_detector.py::DetGridsCompareTester::test_modifiedGrids", "serpentTools/tests/compare/test_detector.py::DetectorCompareTester::test_outsideConfIntervals", "serpentTools/tests/compare/test_detector.py::DetectorCompareTester::test_unmodifiedCompare", "serpentTools/tests/compare/test_detector.py::DetectorCompareTester::test_withinConfIntervals", "serpentTools/tests/compare/test_detector.py::DetectorReaderCompareTester::test_outsideConfIntervals", "serpentTools/tests/compare/test_detector.py::DetectorReaderCompareTester::test_unmodifiedCompare", "serpentTools/tests/compare/test_detector.py::DetectorReaderCompareTester::test_withinConfIntervals", "serpentTools/tests/compare/test_results.py::ResultsCompareTester::test_fullCompare", "serpentTools/tests/compare/test_results.py::ResultsCompareTester::test_moddedResults", "serpentTools/tests/test_base_compare.py::BaseCompareTester::test_badArgs", "serpentTools/tests/test_base_compare.py::BaseCompareTester::test_badCompType", "serpentTools/tests/test_base_compare.py::BaseCompareTester::test_safeCompare", "serpentTools/tests/test_utils.py::VariableConverterTester::test_variableConversion", "serpentTools/tests/test_utils.py::VectorConverterTester::test_listOfInts", "serpentTools/tests/test_utils.py::VectorConverterTester::test_str2Arrays", "serpentTools/tests/test_utils.py::SplitValsTester::test_splitAtCols", "serpentTools/tests/test_utils.py::SplitValsTester::test_splitCopy", "serpentTools/tests/test_utils.py::SplitValsTester::test_splitVals", "serpentTools/tests/test_utils.py::CommonKeysTester::test_getKeys_missing", "serpentTools/tests/test_utils.py::CommonKeysTester::test_goodKeys_dict", "serpentTools/tests/test_utils.py::DirectCompareTester::test_acceptableHigh", "serpentTools/tests/test_utils.py::DirectCompareTester::test_acceptableLow", "serpentTools/tests/test_utils.py::DirectCompareTester::test_badTypes", "serpentTools/tests/test_utils.py::DirectCompareTester::test_diffShapes", "serpentTools/tests/test_utils.py::DirectCompareTester::test_dissimilarString", "serpentTools/tests/test_utils.py::DirectCompareTester::test_identicalString", "serpentTools/tests/test_utils.py::DirectCompareTester::test_notImplemented", "serpentTools/tests/test_utils.py::DirectCompareTester::test_outsideTols", "serpentTools/tests/test_utils.py::DirectCompareTester::test_stringArrays", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_absolute", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_badshapes", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_identical_1D", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_identical_2D", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_identical_3D", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_relative", "serpentTools/tests/test_utils.py::SplitDictionaryTester::test_keySet_all", "serpentTools/tests/test_utils.py::SplitDictionaryTester::test_noKeys" ]	[]	[]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-243	67846baca60f959ac92bdf208e6f1c6744688890	2018-09-15 13:51:41	13961f3712a08e069c1ac96aaecf07ea7e7e5524		diff --git a/serpentTools/parsers/results.py b/serpentTools/parsers/results.py index d46a7fd..095cb6c 100644 --- a/serpentTools/parsers/results.py +++ b/serpentTools/parsers/results.py @@ -183,9 +183,10 @@ class ResultsReader(XSReader): """Process universes' data""" brState = self._getBUstate() # obtain the branching tuple values = str2vec(varVals) # convert the string to float numbers - if not self.universes or brState not in self.universes.keys(): + if brState not in self.universes: self.universes[brState] = \ HomogUniv(brState[0], brState[1], brState[2], brState[3]) + if varNameSer == self._keysVersion['univ']: return if varNameSer not in self._keysVersion['varsUnc']: vals, uncs = splitValsUncs(values)	[BUG] ResultsReader fails when only group constants are requested ## Summary of issue When restricting the scope of the variables extracted from the results file to only group constants, the reader fails. It is also worth noting that, when non-group constant data are requested as well, 'ABS_KEFF' and 'INF_FLX', for example, the reader will not fail, but the universes produced will not have group constant data ## Code for reproducing the issue ``` import serpentTools serpentTools.settings.rc['xs.variableExtras'] = ['INF_FLX', ] serpentTools.readDataFile('pwr_res.m') ``` Alternative case ``` import serpentTools serpentTools.settings.rc['xs.variableExtras'] = ['INF_FLX', 'ABS_KEFF'] r = serpentTools.readDataFile('pwr_res.m') u = r.getUniv('0', index=1) u.infExp # returns {} ``` ## Actual outcome including console output and error traceback if applicable ``` Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/ajohnson400/.local/lib/python3.6/site-packages/serpentTools-0.5.4-py3.6.egg/serpentTools/data/__init__.py", line 70, in readDataFile return read(filePath, *kwargs) File "/home/ajohnson400/.local/lib/python3.6/site-packages/serpentTools-0.5.4-py3.6.egg/serpentTools/parsers/__init__.py", line 149, in read returnedFromLoader.read() File "/home/ajohnson400/.local/lib/python3.6/site-packages/serpentTools-0.5.4-py3.6.egg/serpentTools/parsers/base.py", line 49, in read self._postcheck() File "/home/ajohnson400/.local/lib/python3.6/site-packages/serpentTools-0.5.4-py3.6.egg/serpentTools/parsers/results.py", line 345, in _postcheck self._inspectData() File "/home/ajohnson400/.local/lib/python3.6/site-packages/serpentTools-0.5.4-py3.6.egg/serpentTools/parsers/results.py", line 342, in _inspectData .format(self.filePath)) serpentTools.messages.SerpentToolsException: metadata, resdata and universes are all empty from /home/ajohnson400/.local/lib/python3.6/site-packages/serpentTools-0.5.4-py3.6.egg/serpentTools/data/pwr_res.m and <results.expectGcu> is True ``` ## Expected outcome The parser should respect the settings passed, and properly store all the requested data on the homogenized universes ## Versions Version from ``serpentTools.__version__`` `0.5.4` * Python version - ``python --version`` `3.6`	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_ResultsReader.py b/serpentTools/tests/test_ResultsReader.py index 03e9d28..1384c4d 100644 --- a/serpentTools/tests/test_ResultsReader.py +++ b/serpentTools/tests/test_ResultsReader.py @@ -8,7 +8,7 @@ from numpy.testing import assert_equal from six import iteritems from serpentTools.settings import rc -from serpentTools.data import getFile +from serpentTools.data import getFile, readDataFile from serpentTools.parsers import ResultsReader from serpentTools.messages import SerpentToolsException @@ -597,6 +597,40 @@ class TestResultsNoBurnNoGcu(TestFilterResultsNoBurnup): self.reader.read() +class RestrictedResultsReader(unittest.TestCase): + """Class that restricts the variables read from the results file""" + + expectedInfFlux_bu0 = TestReadAllResults.expectedInfVals + expectedAbsKeff = TestReadAllResults.expectedKeff + dataFile = "pwr_res.m" + + def _testUnivFlux(self, reader): + univ = reader.getUniv('0', index=1) + assert_equal(self.expectedInfFlux_bu0, univ.get("infFlx")) + + def test_justFlux(self): + """Restrict the variables to gcu inf flux and verify their values""" + with rc: + rc['xs.variableExtras'] = ["INF_FLX", ] + r = readDataFile(self.dataFile) + self._testUnivFlux(r) + + def test_xsGroups(self): + """Restrict the variables groups to gc-meta to obtain flux and test.""" + with rc: + rc['xs.variableGroups'] = ['gc-meta', ] + r = readDataFile(self.dataFile) + self._testUnivFlux(r) + + def test_fluxAndKeff(self): + """Restrict to two unique parameters and verify their contents.""" + with rc: + rc['xs.variableExtras'] = ['ABS_KEFF', 'INF_FLX'] + r = readDataFile(self.dataFile) + self._testUnivFlux(r) + assert_equal(self.expectedAbsKeff, r.resdata['absKeff']) + + del TesterCommonResultsReader if __name__ == '__main__':	{ "commit_name": "merge_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 0, "issue_text_score": 2, "test_score": 0 }, "num_modified_files": 1 }	0.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest", "flake8>=3.1.0", "pandas>=0.21.0", "jupyter>=1.0", "coverage==4.5.1" ], "pre_install": null, "python": "3.6", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	anyio==3.6.2 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 async-generator==1.10 attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work Babel==2.11.0 backcall==0.2.0 bleach==4.1.0 certifi==2021.5.30 cffi==1.15.1 charset-normalizer==2.0.12 comm==0.1.4 contextvars==2.4 coverage==4.5.1 cycler==0.11.0 dataclasses==0.8 decorator==5.1.1 defusedxml==0.7.1 entrypoints==0.4 flake8==5.0.4 idna==3.10 immutables==0.19 importlib-metadata==4.2.0 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work ipykernel==5.5.6 ipython==7.16.3 ipython-genutils==0.2.0 ipywidgets==7.8.5 jedi==0.17.2 Jinja2==3.0.3 json5==0.9.16 jsonschema==3.2.0 jupyter==1.1.1 jupyter-client==7.1.2 jupyter-console==6.4.3 jupyter-core==4.9.2 jupyter-server==1.13.1 jupyterlab==3.2.9 jupyterlab-pygments==0.1.2 jupyterlab-server==2.10.3 jupyterlab_widgets==1.1.11 kiwisolver==1.3.1 MarkupSafe==2.0.1 matplotlib==2.2.3 mccabe==0.7.0 mistune==0.8.4 more-itertools @ file:///tmp/build/80754af9/more-itertools_1637733554872/work nbclassic==0.3.5 nbclient==0.5.9 nbconvert==6.0.7 nbformat==5.1.3 nest-asyncio==1.6.0 notebook==6.4.10 numpy==1.19.5 packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work pandas==1.1.5 pandocfilters==1.5.1 parso==0.7.1 pexpect==4.9.0 pickleshare==0.7.5 pluggy @ file:///tmp/build/80754af9/pluggy_1615976315926/work prometheus-client==0.17.1 prompt-toolkit==3.0.36 ptyprocess==0.7.0 py @ file:///opt/conda/conda-bld/py_1644396412707/work pycodestyle==2.9.1 pycparser==2.21 pyflakes==2.5.0 Pygments==2.14.0 pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work pyrsistent==0.18.0 pytest==6.2.4 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.1 pyzmq==25.1.2 requests==2.27.1 scipy==1.5.4 Send2Trash==1.8.3 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@67846baca60f959ac92bdf208e6f1c6744688890#egg=serpentTools six==1.17.0 sniffio==1.2.0 terminado==0.12.1 testpath==0.6.0 toml @ file:///tmp/build/80754af9/toml_1616166611790/work tornado==6.1 traitlets==4.3.3 typing_extensions @ file:///opt/conda/conda-bld/typing_extensions_1647553014482/work urllib3==1.26.20 wcwidth==0.2.13 webencodings==0.5.1 websocket-client==1.3.1 widgetsnbextension==3.6.10 zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - attrs=21.4.0=pyhd3eb1b0_0 - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - importlib_metadata=4.8.1=hd3eb1b0_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - more-itertools=8.12.0=pyhd3eb1b0_0 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - packaging=21.3=pyhd3eb1b0_0 - pip=21.2.2=py36h06a4308_0 - pluggy=0.13.1=py36h06a4308_0 - py=1.11.0=pyhd3eb1b0_0 - pyparsing=3.0.4=pyhd3eb1b0_0 - pytest=6.2.4=py36h06a4308_2 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - toml=0.10.2=pyhd3eb1b0_0 - typing_extensions=4.1.1=pyh06a4308_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zipp=3.6.0=pyhd3eb1b0_0 - zlib=1.2.13=h5eee18b_1 - pip: - anyio==3.6.2 - argon2-cffi==21.3.0 - argon2-cffi-bindings==21.2.0 - async-generator==1.10 - babel==2.11.0 - backcall==0.2.0 - bleach==4.1.0 - cffi==1.15.1 - charset-normalizer==2.0.12 - comm==0.1.4 - contextvars==2.4 - coverage==4.5.1 - cycler==0.11.0 - dataclasses==0.8 - decorator==5.1.1 - defusedxml==0.7.1 - entrypoints==0.4 - flake8==5.0.4 - idna==3.10 - immutables==0.19 - importlib-metadata==4.2.0 - ipykernel==5.5.6 - ipython==7.16.3 - ipython-genutils==0.2.0 - ipywidgets==7.8.5 - jedi==0.17.2 - jinja2==3.0.3 - json5==0.9.16 - jsonschema==3.2.0 - jupyter==1.1.1 - jupyter-client==7.1.2 - jupyter-console==6.4.3 - jupyter-core==4.9.2 - jupyter-server==1.13.1 - jupyterlab==3.2.9 - jupyterlab-pygments==0.1.2 - jupyterlab-server==2.10.3 - jupyterlab-widgets==1.1.11 - kiwisolver==1.3.1 - markupsafe==2.0.1 - matplotlib==2.2.3 - mccabe==0.7.0 - mistune==0.8.4 - nbclassic==0.3.5 - nbclient==0.5.9 - nbconvert==6.0.7 - nbformat==5.1.3 - nest-asyncio==1.6.0 - notebook==6.4.10 - numpy==1.19.5 - pandas==1.1.5 - pandocfilters==1.5.1 - parso==0.7.1 - pexpect==4.9.0 - pickleshare==0.7.5 - prometheus-client==0.17.1 - prompt-toolkit==3.0.36 - ptyprocess==0.7.0 - pycodestyle==2.9.1 - pycparser==2.21 - pyflakes==2.5.0 - pygments==2.14.0 - pyrsistent==0.18.0 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.1 - pyzmq==25.1.2 - requests==2.27.1 - scipy==1.5.4 - send2trash==1.8.3 - six==1.17.0 - sniffio==1.2.0 - terminado==0.12.1 - testpath==0.6.0 - tornado==6.1 - traitlets==4.3.3 - urllib3==1.26.20 - wcwidth==0.2.13 - webencodings==0.5.1 - websocket-client==1.3.1 - widgetsnbextension==3.6.10 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_fluxAndKeff", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_justFlux" ]	[ "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_allVarsNone", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_noUnivState", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_nonPostiveIndex", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_validUniv", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_universes", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_universes", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_xsGroups" ]	[ "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_emptyFile_noGcu", "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_noResults", "serpentTools/tests/test_ResultsReader.py::TestEmptyAttributes::test_emptyAttributes", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_varsMatchSettings" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-243
CORE-GATECH-GROUP__serpent-tools-272	0e61685f19f644b9bb7a1c2a2b3d8aec30ffcaf5	2019-02-08 13:07:01	13961f3712a08e069c1ac96aaecf07ea7e7e5524		diff --git a/docs/changelog.rst b/docs/changelog.rst index 8301113..062242e 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -31,6 +31,8 @@ Next to perturbations, universes, and burnup. * Plotting routines now use attach to the active plot or generate a new plot figure if ``ax`` argument not given - :issue:`267` +* :class:`~serpentTools.parsers.branching.BranchingReader` can + read coefficient files with uncertainties - :issue:`270` .. warning:: diff --git a/docs/defaultSettings.rst b/docs/defaultSettings.rst index f3664fd..c7102ec 100644 --- a/docs/defaultSettings.rst +++ b/docs/defaultSettings.rst @@ -1,16 +1,3 @@ -.. _branching-areUncsPresent: - ----------------------------- -``branching.areUncsPresent`` ----------------------------- - -True if the values in the .coe file contain uncertainties -:: - - Default: False - Type: bool - - .. _branching-floatVariables: ---------------------------- diff --git a/docs/examples/Settings.rst b/docs/examples/Settings.rst index 852ea39..66e3aba 100644 --- a/docs/examples/Settings.rst +++ b/docs/examples/Settings.rst @@ -35,7 +35,7 @@ with the ``.keys`` method. dict_keys(['depletion.processTotal', 'verbosity', 'xs.getInfXS', - 'branching.intVariables', 'branching.areUncsPresent', 'serpentVersion', + 'branching.intVariables', 'serpentVersion', 'sampler.raiseErrors', 'xs.getB1XS', 'xs.variableGroups', 'xs.variableExtras', 'depletion.materialVariables', 'depletion.metadataKeys', 'sampler.freeAll', 'sampler.allExist', 'depletion.materials', 'sampler.skipPrecheck', @@ -206,7 +206,6 @@ However, the loader can also expand a nested dictionary structure, as :: branching: - areUncsPresent: False floatVariables: [Fhi, Blo] depletion: materials: [fuel] @@ -226,7 +225,6 @@ However, the loader can also expand a nested dictionary structure, as xs.variableGroups: [gc-meta, kinetics, xs] branching: - areUncsPresent: False floatVariables: [Fhi, Blo] depletion: materials: [fuel] @@ -240,7 +238,7 @@ However, the loader can also expand a nested dictionary structure, as >>> myConf = 'myConfig.yaml' >>> rc.loadYaml(myConf) - >>> rc['branching.areUncsPresent'] + >>> rc['xs.getInfXS'] .. parsed-literal:: diff --git a/examples/Branching.ipynb b/examples/Branching.ipynb index 9967883..5b1a687 100644 --- a/examples/Branching.ipynb +++ b/examples/Branching.ipynb @@ -570,7 +570,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "* [`branching.areUncsPresent`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#branching-areuncspresent)\n", "* [`branching.floatVariables`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#branching-floatvariables)\n", "* [`branching.intVariables`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#branching-intvariables)\n", "* [`xs.getB1XS`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#xs-getb1xs)\n", diff --git a/examples/Settings.ipynb b/examples/Settings.ipynb index 385c525..27951fb 100644 --- a/examples/Settings.ipynb +++ b/examples/Settings.ipynb @@ -50,7 +50,7 @@ { "data": { "text/plain": [ - "dict_keys(['depletion.processTotal', 'verbosity', 'xs.getInfXS', 'branching.intVariables', 'branching.areUncsPresent', 'serpentVersion', 'sampler.raiseErrors', 'xs.getB1XS', 'xs.variableGroups', 'xs.variableExtras', 'depletion.materialVariables', 'depletion.metadataKeys', 'sampler.freeAll', 'sampler.allExist', 'depletion.materials', 'sampler.skipPrecheck', 'xs.reshapeScatter', 'branching.floatVariables', 'detector.names'])" + "dict_keys(['depletion.processTotal', 'verbosity', 'xs.getInfXS', 'branching.intVariables', 'serpentVersion', 'sampler.raiseErrors', 'xs.getB1XS', 'xs.variableGroups', 'xs.variableExtras', 'depletion.materialVariables', 'depletion.metadataKeys', 'sampler.freeAll', 'sampler.allExist', 'depletion.materials', 'sampler.skipPrecheck', 'xs.reshapeScatter', 'branching.floatVariables', 'detector.names'])" ] }, "execution_count": 13, @@ -264,7 +264,6 @@ "However, the loader can also expand a nested dictionary structure, as\n", "```\n", "branching:\n", - " areUncsPresent: False\n", " floatVariables: [Fhi, Blo]\n", "depletion:\n", " materials: [fuel]\n", @@ -286,7 +285,6 @@ "xs.getB1XS: True\r\n", "xs.variableGroups: [gc-meta, kinetics, xs]\r\n", "branching:\r\n", - " areUncsPresent: False\r\n", " floatVariables: [Fhi, Blo]\r\n", "depletion:\r\n", " materials: [fuel]\r\n", @@ -327,7 +325,7 @@ "source": [ "myConf = 'myConfig.yaml'\n", "rc.loadYaml(myConf)\n", - "rc['branching.areUncsPresent']" + "rc['xs.getInfXS']" ] } ], diff --git a/examples/myConfig.yaml b/examples/myConfig.yaml index 8953d66..523fa61 100644 --- a/examples/myConfig.yaml +++ b/examples/myConfig.yaml @@ -2,7 +2,6 @@ xs.getInfXS: False xs.getB1XS: True xs.variableGroups: [gc-meta, kinetics, xs] branching: - areUncsPresent: False floatVariables: [Fhi, Blo] depletion: materials: [fuel*] diff --git a/serpentTools/data/demo_uncs.coe b/serpentTools/data/demo_uncs.coe new file mode 100644 index 0000000..256c7d0 --- /dev/null +++ b/serpentTools/data/demo_uncs.coe @@ -0,0 +1,14 @@ +1 2 1 2 1 +1 nom +3 VERSION 2.1.30 DATE 19/02/07 TIME 15:27:13 +0 1 1 +0 2 +INF_TOT 2 4.92499E-01 0.00074 1.01767E+00 0.00072 +INF_S0 4 4.66391E-01 0.00072 1.55562E-02 0.00148 1.28821E-03 0.01793 9.21487E-01 0.00091 +2 2 2 2 1 +1 hot +3 VERSION 2.1.30 DATE 19/02/07 TIME 15:27:55 +0 1 1 +0 2 +INF_TOT 2 4.92806E-01 0.00063 1.01680E+00 0.00104 +INF_S0 4 4.66639E-01 0.00062 1.53978E-02 0.00214 1.38727E-03 0.03073 9.20270E-01 0.00112 diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index daaa8f3..8ba6fb4 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -33,6 +33,12 @@ class BranchingReader(XSReader): self._whereAmI = {key: None for key in {'runIndx', 'coefIndx', 'buIndx', 'universe'}} self._totalBranches = None + self._hasUncs = None + + @property + def hasUncs(self): + """boolean if uncertainties are present in the file""" + return self._hasUncs def _read(self): """Read the branching file and store the coefficients.""" @@ -116,10 +122,10 @@ class BranchingReader(XSReader): .format(varName)) continue if self._checkAddVariable(varName): - if self.settings['areUncsPresent']: + if self._hasUncs: vals, uncs = splitValsUncs(varValues) - univ.addData(varName, array(vals), uncertaity=False) - univ.addData(varName, array(uncs), unertainty=True) + univ.addData(varName, array(vals), uncertainty=False) + univ.addData(varName, array(uncs), uncertainty=True) else: univ.addData(varName, array(varValues), uncertainty=False) @@ -129,13 +135,19 @@ class BranchingReader(XSReader): yield bID, b def _precheck(self): - """Currently, just grabs total number of coeff calcs.""" + """Total number of branches and check for uncertainties""" with open(self.filePath) as fObj: try: self._totalBranches = int(fObj.readline().split()[1]) except Exception as ee: error("COE output at {} likely malformatted or misnamed\n{}" .format(self.filePath, str(ee))) + # skip first few lines with integer until first named value + for _x in range(4): + fObj.readline() + lsplt = fObj.readline().split() + nvals = int(lsplt[1]) + self._hasUncs = not nvals == len(lsplt) - 2 def _postcheck(self): """Make sure Serpent finished printing output.""" diff --git a/serpentTools/settings.py b/serpentTools/settings.py index 4a2b414..7954ee5 100644 --- a/serpentTools/settings.py +++ b/serpentTools/settings.py @@ -28,12 +28,6 @@ SETTING_DOC_FMTR = """.. _{tag}: SETTING_OPTIONS_FMTR = "Options: [{}]" defaultSettings = { - 'branching.areUncsPresent': { - 'default': False, - 'type': bool, - 'description': "True if the values in the .coe file contain " - "uncertainties" - }, 'branching.intVariables': { 'default': [], 'description': 'Name of state data variables to convert to integers '	BUG Branching Reader fails with uncertainties ## Summary of issue When reading a file with uncertainties, and the `branching.areUncsPresent` setting is `True`, the code errors. Also, I think we can eliminate this setting entirely, because when a file has uncertainties, the value N still indicates the number of groups/values present without uncertainties. https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/0e61685f19f644b9bb7a1c2a2b3d8aec30ffcaf5/serpentTools/data/demo.coe#L6-L13 From the [SERPENT wiki](http://serpent.vtt.fi/mediawiki/index.php/Automated_burnup_sequence#Output): ``` The relative statistical errors are not printed by default, but they can be included with an option in the set coefpara card, which also defines list of parameters included in the output. In such case the data is provided in mean value - statistical error pairs and N gives the number of pairs. ``` ## Code for reproducing the issue ``` >>> import serpentTools >>> serpentTools.settings.rc['branching.areUncsPresent'] = True >>> serpentTools.readDataFile('demo.coe') ``` ## Actual outcome including console output and error traceback if applicable ``` Traceback (most recent call last): File "test.py", line 3, in <module> serpentTools.readDataFile('demo.coe') File "/home/ajohnson400/github/my-serpent-tools/serpentTools/data/__init__.py", line 76, in readDataFile return read(filePath, *kwargs) File "/home/ajohnson400/github/my-serpent-tools/serpentTools/parsers/__init__.py", line 155, in read returnedFromLoader.read() File "/home/ajohnson400/github/my-serpent-tools/serpentTools/parsers/base.py", line 52, in read self._read() File "/home/ajohnson400/github/my-serpent-tools/serpentTools/parsers/branching.py", line 42, in _read self._processBranchBlock() File "/home/ajohnson400/github/my-serpent-tools/serpentTools/parsers/branching.py", line 69, in _processBranchBlock self._whereAmI['buIndx']) File "/home/ajohnson400/github/my-serpent-tools/serpentTools/parsers/branching.py", line 121, in _processBranchUniverses univ.addData(varName, array(vals), uncertaity=False) TypeError: addData() got an unexpected keyword argument 'uncertaity' ``` ## Expected outcome Code does not fail. Even better, code can infer when the output file has uncertainties or not ## Versions Version from ``serpentTools.__version__`` `0.6.1+55.g0e6185` (develop) and `0.6.1` (release) * Python version - ``python --version`` `3.7.1`	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_branching.py b/serpentTools/tests/test_branching.py index 8f60fb0..9780cd4 100644 --- a/serpentTools/tests/test_branching.py +++ b/serpentTools/tests/test_branching.py @@ -50,12 +50,13 @@ class BranchTester(_BranchTesterHelper): 'CMM_TRANSPXS_Z', 'CMM_DIFFCOEF', 'CMM_DIFFCOEF_X', 'CMM_DIFFCOEF_Y', 'CMM_DIFFCOEF_Z'} self.assertSetEqual(expected, self.reader.settings['variables']) + self.assertFalse(self.reader.hasUncs) def test_raiseError(self): """Verify that the reader raises an error for unexpected EOF""" badFile = 'bad_branching.coe' with open(self.file) as fObj, open(badFile, 'w') as badObj: - for _line in range(5): + for _line in range(10): badObj.write(fObj.readline()) badReader = BranchingReader(badFile) with self.assertRaises(EOFError): @@ -123,5 +124,53 @@ class BranchContainerTester(_BranchTesterHelper): containerWithDays.addUniverse(101, 10, 1) +class BranchWithUncsTester(unittest.TestCase): + """Tester that just tests a small file with uncertainties""" + + BRANCH_DATA = { + 'nom': { + 'infTot': [ + [4.92499E-01, 1.01767E+00], # value + [0.00074, 0.00072], # uncertainty + ], + 'infS0': [ + [0.466391, 0.0155562, 0.00128821, 0.921487], + [0.00072, 0.00148, 0.01793, 0.00091], + ], + }, + 'hot': { + 'infTot': [ + [4.92806E-1, 1.0168E00], # value + [0.00063, 0.00104], # uncertainty + ], + 'infS0': [ + [4.66639E-1, 1.53978E-02, 1.38727E-03, 9.20270E-01], + [0.00062, 0.00214, 0.03073, 0.00112], + ], + }, + } + BRANCH_UNIVKEY = (0, 0, 0) + + def setUp(self): + fp = getFile('demo_uncs.coe') + self.reader = BranchingReader(fp) + self.reader.read() + + def test_valsWithUncs(self): + """Test that the branches and the uncertainties are present""" + self.assertTrue(self.reader.hasUncs) + for expKey, expSubData in iteritems(self.BRANCH_DATA): + self.assertTrue(expKey in self.reader.branches, msg=expKey) + actBranch = self.reader.branches[expKey] + self.assertTrue(self.BRANCH_UNIVKEY in actBranch.universes, + msg=self.BRANCH_UNIVKEY) + univ = actBranch.universes[self.BRANCH_UNIVKEY] + for gcKey, gcVals in iteritems(expSubData): + actData = univ.get(gcKey, True) + for act, exp, what in zip(actData, gcVals, ['val', 'unc']): + assert_allclose(act, exp, err_msg='{} {} {}'.format( + expKey, gcKey, what)) + + if __name__ == '__main__': unittest.main() diff --git a/serpentTools/tests/test_settings.py b/serpentTools/tests/test_settings.py index b4d60a2..22671b9 100644 --- a/serpentTools/tests/test_settings.py +++ b/serpentTools/tests/test_settings.py @@ -112,7 +112,6 @@ class ConfigLoaderTester(TestCaseWithLogCapture): @classmethod def setUpClass(cls): cls.configSettings = { - 'branching.areUncsPresent': True, 'branching.floatVariables': ['Bhi', 'Tlo'], 'verbosity': 'warning', 'depletion.materials': ['fuel*'], @@ -120,8 +119,6 @@ class ConfigLoaderTester(TestCaseWithLogCapture): } cls.nestedSettings = { 'branching': { - 'areUncsPresent': - cls.configSettings['branching.areUncsPresent'], 'floatVariables': cls.configSettings['branching.floatVariables'] },	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_added_files", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 3 }, "num_modified_files": 8 }	0.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.16.0 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==3.13 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@0e61685f19f644b9bb7a1c2a2b3d8aec30ffcaf5#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.16.0 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==3.13 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_branching.py::BranchTester::test_variables", "serpentTools/tests/test_branching.py::BranchWithUncsTester::test_valsWithUncs" ]	[]	[ "serpentTools/tests/test_branching.py::BranchTester::test_branchingUniverses", "serpentTools/tests/test_branching.py::BranchTester::test_raiseError", "serpentTools/tests/test_branching.py::BranchContainerTester::test_cannotAddBurnupDays", "serpentTools/tests/test_branching.py::BranchContainerTester::test_containerGetUniv", "serpentTools/tests/test_branching.py::BranchContainerTester::test_loadedUniv", "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_cannotChangeDefaults", "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_getDefault", "serpentTools/tests/test_settings.py::RCTester::test_expandExtras", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSetting_options", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSettings_type", "serpentTools/tests/test_settings.py::RCTester::test_failAtNonexistentSetting", "serpentTools/tests/test_settings.py::RCTester::test_fullExtend", "serpentTools/tests/test_settings.py::RCTester::test_readerWithUpdatedSettings", "serpentTools/tests/test_settings.py::RCTester::test_returnReaderSettings", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedConfig", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedNonStrict", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadSingleLevelConfig" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-273	aa332f9a1f9c3fcb2424d3d2a8b3a949ddb49714	2019-02-08 15:25:51	13961f3712a08e069c1ac96aaecf07ea7e7e5524		diff --git a/.travis.yml b/.travis.yml index f370223..b0f456d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -3,11 +3,19 @@ python: - "3.6" - "3.5" - "2.7" +env: + - ST_INSTALL=setup + - ST_INSTALL=sdist + - ST_INSTALL=bdist_wheel + - ST_INSTALL=bdist_egg -# install -install: +before_install: - pip install -r requirements.txt -r requirements-test.txt - - python setup.py install + - rm -rf dist +# install matrix +# build some distributions (source and wheel) and install from those +install: + - bash scripts/travis/install.sh # use xvfb to give the plot functions something to use # PR 70 diff --git a/MANIFEST.in b/MANIFEST.in index 334ac1c..bc97e1a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -3,3 +3,4 @@ include serpentTools/_version.py include serpentTools/variables.yaml include serpentTools/data/.m include serpentTools/data/.coe +include requirements.txt diff --git a/docs/changelog.rst b/docs/changelog.rst index 062242e..8301113 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -31,8 +31,6 @@ Next to perturbations, universes, and burnup. * Plotting routines now use attach to the active plot or generate a new plot figure if ``ax`` argument not given - :issue:`267` -* :class:`~serpentTools.parsers.branching.BranchingReader` can - read coefficient files with uncertainties - :issue:`270` .. warning:: diff --git a/docs/defaultSettings.rst b/docs/defaultSettings.rst index c7102ec..f3664fd 100644 --- a/docs/defaultSettings.rst +++ b/docs/defaultSettings.rst @@ -1,3 +1,16 @@ +.. _branching-areUncsPresent: + +---------------------------- +``branching.areUncsPresent`` +---------------------------- + +True if the values in the .coe file contain uncertainties +:: + + Default: False + Type: bool + + .. _branching-floatVariables: ---------------------------- diff --git a/docs/examples/Settings.rst b/docs/examples/Settings.rst index 66e3aba..852ea39 100644 --- a/docs/examples/Settings.rst +++ b/docs/examples/Settings.rst @@ -35,7 +35,7 @@ with the ``.keys`` method. dict_keys(['depletion.processTotal', 'verbosity', 'xs.getInfXS', - 'branching.intVariables', 'serpentVersion', + 'branching.intVariables', 'branching.areUncsPresent', 'serpentVersion', 'sampler.raiseErrors', 'xs.getB1XS', 'xs.variableGroups', 'xs.variableExtras', 'depletion.materialVariables', 'depletion.metadataKeys', 'sampler.freeAll', 'sampler.allExist', 'depletion.materials', 'sampler.skipPrecheck', @@ -206,6 +206,7 @@ However, the loader can also expand a nested dictionary structure, as :: branching: + areUncsPresent: False floatVariables: [Fhi, Blo] depletion: materials: [fuel] @@ -225,6 +226,7 @@ However, the loader can also expand a nested dictionary structure, as xs.variableGroups: [gc-meta, kinetics, xs] branching: + areUncsPresent: False floatVariables: [Fhi, Blo] depletion: materials: [fuel] @@ -238,7 +240,7 @@ However, the loader can also expand a nested dictionary structure, as >>> myConf = 'myConfig.yaml' >>> rc.loadYaml(myConf) - >>> rc['xs.getInfXS'] + >>> rc['branching.areUncsPresent'] .. parsed-literal:: diff --git a/examples/Branching.ipynb b/examples/Branching.ipynb index 5b1a687..9967883 100644 --- a/examples/Branching.ipynb +++ b/examples/Branching.ipynb @@ -570,6 +570,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "* [`branching.areUncsPresent`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#branching-areuncspresent)\n", "* [`branching.floatVariables`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#branching-floatvariables)\n", "* [`branching.intVariables`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#branching-intvariables)\n", "* [`xs.getB1XS`](http://serpent-tools.readthedocs.io/en/latest/settingsTop.html#xs-getb1xs)\n", diff --git a/examples/Settings.ipynb b/examples/Settings.ipynb index 27951fb..385c525 100644 --- a/examples/Settings.ipynb +++ b/examples/Settings.ipynb @@ -50,7 +50,7 @@ { "data": { "text/plain": [ - "dict_keys(['depletion.processTotal', 'verbosity', 'xs.getInfXS', 'branching.intVariables', 'serpentVersion', 'sampler.raiseErrors', 'xs.getB1XS', 'xs.variableGroups', 'xs.variableExtras', 'depletion.materialVariables', 'depletion.metadataKeys', 'sampler.freeAll', 'sampler.allExist', 'depletion.materials', 'sampler.skipPrecheck', 'xs.reshapeScatter', 'branching.floatVariables', 'detector.names'])" + "dict_keys(['depletion.processTotal', 'verbosity', 'xs.getInfXS', 'branching.intVariables', 'branching.areUncsPresent', 'serpentVersion', 'sampler.raiseErrors', 'xs.getB1XS', 'xs.variableGroups', 'xs.variableExtras', 'depletion.materialVariables', 'depletion.metadataKeys', 'sampler.freeAll', 'sampler.allExist', 'depletion.materials', 'sampler.skipPrecheck', 'xs.reshapeScatter', 'branching.floatVariables', 'detector.names'])" ] }, "execution_count": 13, @@ -264,6 +264,7 @@ "However, the loader can also expand a nested dictionary structure, as\n", "```\n", "branching:\n", + " areUncsPresent: False\n", " floatVariables: [Fhi, Blo]\n", "depletion:\n", " materials: [fuel]\n", @@ -285,6 +286,7 @@ "xs.getB1XS: True\r\n", "xs.variableGroups: [gc-meta, kinetics, xs]\r\n", "branching:\r\n", + " areUncsPresent: False\r\n", " floatVariables: [Fhi, Blo]\r\n", "depletion:\r\n", " materials: [fuel]\r\n", @@ -325,7 +327,7 @@ "source": [ "myConf = 'myConfig.yaml'\n", "rc.loadYaml(myConf)\n", - "rc['xs.getInfXS']" + "rc['branching.areUncsPresent']" ] } ], diff --git a/examples/myConfig.yaml b/examples/myConfig.yaml index 523fa61..8953d66 100644 --- a/examples/myConfig.yaml +++ b/examples/myConfig.yaml @@ -2,6 +2,7 @@ xs.getInfXS: False xs.getB1XS: True xs.variableGroups: [gc-meta, kinetics, xs] branching: + areUncsPresent: False floatVariables: [Fhi, Blo] depletion: materials: [fuel] diff --git a/scripts/travis/install.sh b/scripts/travis/install.sh new file mode 100644 index 0000000..e0960a5 --- /dev/null +++ b/scripts/travis/install.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# Install script that depends on $INSTALL variable +# Options: +# setup: install with setup.py +# sdist: create a source distribution and install from that +# bdist_wheel: create a wheel and install from that + +if [[ -z $ST_INSTALL ]]; then exit; fi + +set -ev +set -o pipefail + +ST_VERSION=$(python setup.py version \| grep Version \| cut -d" " -f 2) +echo $ST_VERSION + +case $ST_INSTALL in + 'setup') + python setup.py install + ;; + 'sdist') + python setup.py sdist + pip install dist/serpentTools-${ST_VERSION}.tar.gz + ;; + 'bdist_wheel') + python setup.py bdist_wheel + easy_install dist/serpentTools-${ST_VERSION}whl + ;; + 'bdist_egg') + python setup.py bdist_egg + easy_install dist/serpentTools-${ST_VERSION}egg + ;; + ) + echo No install option + exit 1 +esac + diff --git a/serpentTools/data/demo_uncs.coe b/serpentTools/data/demo_uncs.coe deleted file mode 100644 index 256c7d0..0000000 --- a/serpentTools/data/demo_uncs.coe +++ /dev/null @@ -1,14 +0,0 @@ -1 2 1 2 1 -1 nom -3 VERSION 2.1.30 DATE 19/02/07 TIME 15:27:13 -0 1 1 -0 2 -INF_TOT 2 4.92499E-01 0.00074 1.01767E+00 0.00072 -INF_S0 4 4.66391E-01 0.00072 1.55562E-02 0.00148 1.28821E-03 0.01793 9.21487E-01 0.00091 -2 2 2 2 1 -1 hot -3 VERSION 2.1.30 DATE 19/02/07 TIME 15:27:55 -0 1 1 -0 2 -INF_TOT 2 4.92806E-01 0.00063 1.01680E+00 0.00104 -INF_S0 4 4.66639E-01 0.00062 1.53978E-02 0.00214 1.38727E-03 0.03073 9.20270E-01 0.00112 diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index 8ba6fb4..daaa8f3 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -33,12 +33,6 @@ class BranchingReader(XSReader): self._whereAmI = {key: None for key in {'runIndx', 'coefIndx', 'buIndx', 'universe'}} self._totalBranches = None - self._hasUncs = None - - @property - def hasUncs(self): - """boolean if uncertainties are present in the file""" - return self._hasUncs def _read(self): """Read the branching file and store the coefficients.""" @@ -122,10 +116,10 @@ class BranchingReader(XSReader): .format(varName)) continue if self._checkAddVariable(varName): - if self._hasUncs: + if self.settings['areUncsPresent']: vals, uncs = splitValsUncs(varValues) - univ.addData(varName, array(vals), uncertainty=False) - univ.addData(varName, array(uncs), uncertainty=True) + univ.addData(varName, array(vals), uncertaity=False) + univ.addData(varName, array(uncs), unertainty=True) else: univ.addData(varName, array(varValues), uncertainty=False) @@ -135,19 +129,13 @@ class BranchingReader(XSReader): yield bID, b def _precheck(self): - """Total number of branches and check for uncertainties""" + """Currently, just grabs total number of coeff calcs.""" with open(self.filePath) as fObj: try: self._totalBranches = int(fObj.readline().split()[1]) except Exception as ee: error("COE output at {} likely malformatted or misnamed\n{}" .format(self.filePath, str(ee))) - # skip first few lines with integer until first named value - for _x in range(4): - fObj.readline() - lsplt = fObj.readline().split() - nvals = int(lsplt[1]) - self._hasUncs = not nvals == len(lsplt) - 2 def _postcheck(self): """Make sure Serpent finished printing output.""" diff --git a/serpentTools/settings.py b/serpentTools/settings.py index 7954ee5..4a2b414 100644 --- a/serpentTools/settings.py +++ b/serpentTools/settings.py @@ -28,6 +28,12 @@ SETTING_DOC_FMTR = """.. _{tag}: SETTING_OPTIONS_FMTR = "Options: [{}]" defaultSettings = { + 'branching.areUncsPresent': { + 'default': False, + 'type': bool, + 'description': "True if the values in the .coe file contain " + "uncertainties" + }, 'branching.intVariables': { 'default': [], 'description': 'Name of state data variables to convert to integers ' diff --git a/setup.py b/setup.py index e671d87..9e29d5c 100644 --- a/setup.py +++ b/setup.py @@ -63,6 +63,7 @@ setupArgs = { 'package_data': { 'serpentTools.data': ['data/{}'.format(ext) for ext in DATA_EXTS], }, + 'include_package_data': True, 'cmdclass': versioneer.get_cmdclass(), 'data_files': [ ('serpentTools', ['serpentTools/variables.yaml', ]),	BUG Data files not installed through pip ## Summary of issue When installing with pip, the data files used in examples are not present. ## Code for reproducing the issue From a fresh install using python [`venv`](https://docs.python.org/3.5/library/venv.html): ``` $ python -m venv serpentTools $ source serpentTools/bin/activate (serpentTools) python -m pip install serpentTools (serpentTools) python -c "import serpentTools; serpentTools.readDataFile('demo.coe') ``` ## Actual outcome including console output and error traceback if applicable ```OSError: File matching demo.coe does not exist``` ## Expected outcome This doesn't fail ## Versions * Version from ``serpentTools.__version__`` ``0.6.1`` * Python version - ``python --version`` ``3.7.2``	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_branching.py b/serpentTools/tests/test_branching.py index 9780cd4..8f60fb0 100644 --- a/serpentTools/tests/test_branching.py +++ b/serpentTools/tests/test_branching.py @@ -50,13 +50,12 @@ class BranchTester(_BranchTesterHelper): 'CMM_TRANSPXS_Z', 'CMM_DIFFCOEF', 'CMM_DIFFCOEF_X', 'CMM_DIFFCOEF_Y', 'CMM_DIFFCOEF_Z'} self.assertSetEqual(expected, self.reader.settings['variables']) - self.assertFalse(self.reader.hasUncs) def test_raiseError(self): """Verify that the reader raises an error for unexpected EOF""" badFile = 'bad_branching.coe' with open(self.file) as fObj, open(badFile, 'w') as badObj: - for _line in range(10): + for _line in range(5): badObj.write(fObj.readline()) badReader = BranchingReader(badFile) with self.assertRaises(EOFError): @@ -124,53 +123,5 @@ class BranchContainerTester(_BranchTesterHelper): containerWithDays.addUniverse(101, 10, 1) -class BranchWithUncsTester(unittest.TestCase): - """Tester that just tests a small file with uncertainties""" - - BRANCH_DATA = { - 'nom': { - 'infTot': [ - [4.92499E-01, 1.01767E+00], # value - [0.00074, 0.00072], # uncertainty - ], - 'infS0': [ - [0.466391, 0.0155562, 0.00128821, 0.921487], - [0.00072, 0.00148, 0.01793, 0.00091], - ], - }, - 'hot': { - 'infTot': [ - [4.92806E-1, 1.0168E00], # value - [0.00063, 0.00104], # uncertainty - ], - 'infS0': [ - [4.66639E-1, 1.53978E-02, 1.38727E-03, 9.20270E-01], - [0.00062, 0.00214, 0.03073, 0.00112], - ], - }, - } - BRANCH_UNIVKEY = (0, 0, 0) - - def setUp(self): - fp = getFile('demo_uncs.coe') - self.reader = BranchingReader(fp) - self.reader.read() - - def test_valsWithUncs(self): - """Test that the branches and the uncertainties are present""" - self.assertTrue(self.reader.hasUncs) - for expKey, expSubData in iteritems(self.BRANCH_DATA): - self.assertTrue(expKey in self.reader.branches, msg=expKey) - actBranch = self.reader.branches[expKey] - self.assertTrue(self.BRANCH_UNIVKEY in actBranch.universes, - msg=self.BRANCH_UNIVKEY) - univ = actBranch.universes[self.BRANCH_UNIVKEY] - for gcKey, gcVals in iteritems(expSubData): - actData = univ.get(gcKey, True) - for act, exp, what in zip(actData, gcVals, ['val', 'unc']): - assert_allclose(act, exp, err_msg='{} {} {}'.format( - expKey, gcKey, what)) - - if __name__ == '__main__': unittest.main() diff --git a/serpentTools/tests/test_settings.py b/serpentTools/tests/test_settings.py index 22671b9..b4d60a2 100644 --- a/serpentTools/tests/test_settings.py +++ b/serpentTools/tests/test_settings.py @@ -112,6 +112,7 @@ class ConfigLoaderTester(TestCaseWithLogCapture): @classmethod def setUpClass(cls): cls.configSettings = { + 'branching.areUncsPresent': True, 'branching.floatVariables': ['Bhi', 'Tlo'], 'verbosity': 'warning', 'depletion.materials': ['fuel*'], @@ -119,6 +120,8 @@ class ConfigLoaderTester(TestCaseWithLogCapture): } cls.nestedSettings = { 'branching': { + 'areUncsPresent': + cls.configSettings['branching.areUncsPresent'], 'floatVariables': cls.configSettings['branching.floatVariables'] },	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_added_files", "has_removed_files", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 11 }	0.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.16.0 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==3.13 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@aa332f9a1f9c3fcb2424d3d2a8b3a949ddb49714#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.16.0 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==3.13 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_branching.py::BranchTester::test_raiseError", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedConfig", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedNonStrict", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadSingleLevelConfig" ]	[]	[ "serpentTools/tests/test_branching.py::BranchTester::test_branchingUniverses", "serpentTools/tests/test_branching.py::BranchTester::test_variables", "serpentTools/tests/test_branching.py::BranchContainerTester::test_cannotAddBurnupDays", "serpentTools/tests/test_branching.py::BranchContainerTester::test_containerGetUniv", "serpentTools/tests/test_branching.py::BranchContainerTester::test_loadedUniv", "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_cannotChangeDefaults", "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_getDefault", "serpentTools/tests/test_settings.py::RCTester::test_expandExtras", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSetting_options", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSettings_type", "serpentTools/tests/test_settings.py::RCTester::test_failAtNonexistentSetting", "serpentTools/tests/test_settings.py::RCTester::test_fullExtend", "serpentTools/tests/test_settings.py::RCTester::test_readerWithUpdatedSettings", "serpentTools/tests/test_settings.py::RCTester::test_returnReaderSettings" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-288	1c730ef70c6f9ce98247e1d8b935d3bb98366e4a	2019-03-05 03:45:05	13961f3712a08e069c1ac96aaecf07ea7e7e5524		diff --git a/docs/changelog.rst b/docs/changelog.rst index dd57dbc..90d13c3 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -39,6 +39,15 @@ Next The API for the \|branchCollector\| may be subject to change through revisions until ``0.7.0`` +Incompatible API Changes +------------------------ + +* \|homogUniv\| objects are now stored on \|resultReader\| with + zero-based indexing for burnup. The previous first value of + burnup step was one. All burnup indices are now decreased by + one. Similarly, if no burnup was present in the file, the + values of burnup and days for all universes is zero. + Pending Deprecations -------------------- diff --git a/docs/examples/ResultsReader.rst b/docs/examples/ResultsReader.rst index 3065fba..733c602 100644 --- a/docs/examples/ResultsReader.rst +++ b/docs/examples/ResultsReader.rst @@ -301,27 +301,27 @@ Universe data is stored for each state point, i.e. .. parsed-literal:: - dict_keys([('3101', 0.0, 1, 0.0), ('3102', 0.0, 1, 0.0), ('0', 0.0, 1, 0.0), - ('3101', 0.10000000000000001, 2, 1.20048), ('3102', 0.10000000000000001, 2, - 1.20048), ('0', 0.10000000000000001, 2, 1.20048), ('3101', 1.0, 3, - 12.004799999999999), ('3102', 1.0, 3, 12.004799999999999), ('0', 1.0, 3, - 12.004799999999999), ('3101', 2.0, 4, 24.009599999999999), ('3102', 2.0, 4, - 24.009599999999999), ('0', 2.0, 4, 24.009599999999999), ('3101', 3.0, 5, - 36.014400000000002), ('3102', 3.0, 5, 36.014400000000002), ('0', 3.0, 5, - 36.014400000000002), ('3101', 4.0, 6, 48.019199999999998), ('3102', 4.0, 6, - 48.019199999999998), ('0', 4.0, 6, 48.019199999999998)]) + dict_keys([('3101', 0.0, 0, 0.0), ('3102', 0.0, 0, 0.0), ('0', 0.0, 0, 0.0), + ('3101', 0.10000000000000001, 1, 1.20048), ('3102', 0.10000000000000001, 1, + 1.20048), ('0', 0.10000000000000001, 1, 1.20048), ('3101', 1.0, 2, + 12.004799999999999), ('3102', 1.0, 2, 12.004799999999999), ('0', 1.0, 2, + 12.004799999999999), ('3101', 2.0, 3, 24.009599999999999), ('3102', 2.0, 3, + 24.009599999999999), ('0', 2.0, 3, 24.009599999999999), ('3101', 3.0, 4, + 36.014400000000002), ('3102', 3.0, 4, 36.014400000000002), ('0', 3.0, 4, + 36.014400000000002), ('3101', 4.0, 5, 48.019199999999998), ('3102', 4.0, 5, + 48.019199999999998), ('0', 4.0, 5, 48.019199999999998)]) .. code:: >>> # Let's use the following unique state - >>> print(res.universes[('3102', 0.0, 1, 0.0)]) + >>> print(res.universes[('3102', 0.0, 2, 0.0)]) .. parsed-literal:: - <HomogUniv 3102: burnup: 0.000 MWd/kgu, step: 1, 0.000 days> + <HomogUniv 3102: burnup: 0.000 MWd/kgu, step: 0, 0.000 days> Each state contains the same data fields, which can be obtained by using @@ -355,7 +355,7 @@ interest. In order to obtain the data, the user needs to pass the ``univ`` must be a string ``burnup`` is a float or int with the units MWd/kgU ``time`` is a float or int with the units Days ``index`` is a -positive integer (i.e. 1, 2, ...) +non-negative integer (i.e. 0, 1, 2, ...) The method requires to insert the universe and burnup or time or index (only one of these is actually used to retrieve the data). If more than @@ -365,7 +365,7 @@ priority), burnup, time (lowest priority) .. code:: >>> # Examples to use various time entries - >>> univ3101 = res.getUniv('3101', index=4) # obtain the results for universe=3101 and index=4 + >>> univ3101 = res.getUniv('3101', index=3) # obtain the results for universe=3101 and index=3 >>> univ3102 = res.getUniv('3102', burnup=0.1) # obtain the results for universe=3102 and index=0.1 MWd/kgU >>> univ0 = res.getUniv('0', timeDays=24.0096) # obtain the results for universe=0 and index=24.0096 days @@ -379,22 +379,22 @@ priority), burnup, time (lowest priority) .. parsed-literal:: - <HomogUniv 3101: burnup: 2.000 MWd/kgu, step: 4, 24.010 days> + <HomogUniv 3101: burnup: 2.000 MWd/kgu, step: 3, 24.010 days> <HomogUniv 3102: - burnup: 0.100 MWd/kgu, step: 2, 1.200 days> + burnup: 0.100 MWd/kgu, step: 1, 1.200 days> <HomogUniv 0: burnup: 2.000 - MWd/kgu, step: 4, 24.010 days> + MWd/kgu, step: 3, 24.010 days> .. code:: - >>> # obtain the results for universe=0 and index=1 (burnup and timeDays are inserted but not used) - >>> univ0 = res.getUniv('0', burnup=0.0, index=1, timeDays=0.0) + >>> # obtain the results for universe=0 and index=0 (burnup and timeDays are inserted but not used) + >>> univ0 = res.getUniv('0', burnup=0.0, index=0, timeDays=0.0) >>> print(univ0) .. parsed-literal:: - <HomogUniv 0: burnup: 0.000 MWd/kgu, step: 1, 0.000 days> + <HomogUniv 0: burnup: 0.000 MWd/kgu, step: 0, 0.000 days> .. code:: @@ -422,7 +422,7 @@ priority), burnup, time (lowest priority) .. code:: >>> # The values are all energy dependent - >>> univ0.infExp['infAbs'] # obtain the infinite macroscopic xs for ('0', 0.0, 1, 0.0) + >>> univ0.infExp['infAbs'] # obtain the infinite macroscopic xs for ('0', 0.0, 0, 0.0) .. parsed-literal:: @@ -435,7 +435,7 @@ priority), burnup, time (lowest priority) .. code:: - >>> # Obtain the infinite flux for ('0', 0.0, 1, 0.0) + >>> # Obtain the infinite flux for ('0', 0.0, 0, 0.0) >>> univ0.infExp['infFlx'] .. parsed-literal:: @@ -516,7 +516,7 @@ be zeros. .. code:: - >>> # Obtain the b1 fluxes for ('3101', 0.0, 1, 0.0) + >>> # Obtain the b1 fluxes for ('3101', 0.0, 0, 0.0) >>> univ3101.b1Exp['b1Flx'] .. parsed-literal:: @@ -532,7 +532,7 @@ be zeros. .. code:: - >>> # Obtain the b1 fluxes for ('3101', 0.0, 1, 0.0) + >>> # Obtain the b1 absorption cross section for ('3101', 0.0, 0, 0.0) >>> univ3101.b1Exp['b1Abs'] .. parsed-literal:: diff --git a/examples/ResultsReader.ipynb b/examples/ResultsReader.ipynb index 02dd9fc..10be322 100644 --- a/examples/ResultsReader.ipynb +++ b/examples/ResultsReader.ipynb @@ -518,7 +518,7 @@ { "data": { "text/plain": [ - "dict_keys([('3101', 0.0, 1, 0.0), ('3102', 0.0, 1, 0.0), ('0', 0.0, 1, 0.0), ('3101', 0.1, 2, 1.20048), ('3102', 0.1, 2, 1.20048), ('0', 0.1, 2, 1.20048), ('3101', 1.0, 3, 12.0048), ('3102', 1.0, 3, 12.0048), ('0', 1.0, 3, 12.0048), ('3101', 2.0, 4, 24.0096), ('3102', 2.0, 4, 24.0096), ('0', 2.0, 4, 24.0096), ('3101', 3.0, 5, 36.0144), ('3102', 3.0, 5, 36.0144), ('0', 3.0, 5, 36.0144), ('3101', 4.0, 6, 48.0192), ('3102', 4.0, 6, 48.0192), ('0', 4.0, 6, 48.0192)])" + "dict_keys([('3101', 0.0, 0, 0.0), ('3102', 0.0, 0, 0.0), ('0', 0.0, 0, 0.0), ('3101', 0.1, 1, 1.20048), ('3102', 0.1, 1, 1.20048), ('0', 0.1, 1, 1.20048), ('3101', 1.0, 2, 12.0048), ('3102', 1.0, 2, 12.0048), ('0', 1.0, 3, 12.0048), ('3101', 2.0, 3, 24.0096), ('3102', 2.0, 3, 24.0096), ('0', 2.0, 3, 24.0096), ('3101', 3.0, 4, 36.0144), ('3102', 3.0, 4, 36.0144), ('0', 3.0, 4, 36.0144), ('3101', 4.0, 5, 48.0192), ('3102', 4.0, 5, 48.0192), ('0', 4.0, 5, 48.0192)])" ] }, "execution_count": 18, @@ -541,13 +541,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "<HomogUniv 3102: burnup: 0.000 MWd/kgu, step: 1, 0.000 days>\n" + "<HomogUniv 3102: burnup: 0.000 MWd/kgu, step: 0, 0.000 days>\n" ] } ], "source": [ "# Let's use the following unique state\n", - "print(res.universes[('3102', 0.0, 1, 0.0)])" + "print(res.universes[('3102', 0.0, 0, 0.0)])" ] }, { @@ -592,7 +592,7 @@ "`univ` must be a string\n", "`burnup` is a float or int with the units MWd/kgU\n", "`time` is a float or int with the units Days\n", - "`index` is a positive integer (i.e. 1, 2, ...)\n", + "`index` is a non-negative integer (i.e. 0, 1, 2, ...)\n", "\n", "The method requires to insert the universe and burnup or time or index (only one of these is actually used to retrieve the data). \n", "If more than one time parameter is given, the hierarchy of search is:\n", @@ -606,7 +606,7 @@ "outputs": [], "source": [ "# Examples to use various time entries\n", - "univ3101 = res.getUniv('3101', index=4) # obtain the results for universe=3101 and index=4 \n", + "univ3101 = res.getUniv('3101', index=3) # obtain the results for universe=3101 and index=3 \n", "univ3102 = res.getUniv('3102', burnup=0.1) # obtain the results for universe=3102 and index=0.1 MWd/kgU\n", "univ0 = res.getUniv('0', timeDays=24.0096) # obtain the results for universe=0 and index=24.0096 days" ] @@ -620,9 +620,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "<HomogUniv 3101: burnup: 2.000 MWd/kgu, step: 4, 24.010 days>\n", - "<HomogUniv 3102: burnup: 0.100 MWd/kgu, step: 2, 1.200 days>\n", - "<HomogUniv 0: burnup: 2.000 MWd/kgu, step: 4, 24.010 days>\n" + "<HomogUniv 3101: burnup: 2.000 MWd/kgu, step: 3, 24.010 days>\n", + "<HomogUniv 3102: burnup: 0.100 MWd/kgu, step: 1, 1.200 days>\n", + "<HomogUniv 0: burnup: 2.000 MWd/kgu, step: 3, 24.010 days>\n" ] } ], @@ -642,13 +642,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "<HomogUniv 0: burnup: 0.000 MWd/kgu, step: 1, 0.000 days>\n" + "<HomogUniv 0: burnup: 0.000 MWd/kgu, step: 0, 0.000 days>\n" ] } ], "source": [ - "# obtain the results for universe=0 and index=1 (burnup and timeDays are inserted but not used)\n", - "univ0 = res.getUniv('0', burnup=0.0, index=1, timeDays=0.0) \n", + "# obtain the results for universe=0 and index=0 (burnup and timeDays are inserted but not used)\n", + "univ0 = res.getUniv('0', burnup=0.0, index=0, timeDays=0.0) \n", "print(univ0)" ] }, @@ -802,7 +802,7 @@ } ], "source": [ - "# Obtain the b1 fluxes for ('3101', 0.0, 1, 0.0)\n", + "# Obtain the b1 fluxes for ('3101', 0.0, 0, 0.0)\n", "univ3101.b1Exp['b1Flx']" ] }, @@ -827,7 +827,7 @@ } ], "source": [ - "# Obtain the b1 fluxes for ('3101', 0.0, 1, 0.0)\n", + "# Obtain the b1 absorption cross section for ('3101', 0.0, 0, 0.0)\n", "univ3101.b1Exp['b1Abs']" ] }, diff --git a/serpentTools/parsers/results.py b/serpentTools/parsers/results.py index a1f0aec..366959a 100644 --- a/serpentTools/parsers/results.py +++ b/serpentTools/parsers/results.py @@ -31,7 +31,7 @@ from serpentTools.utils import ( magicPlotDocDecorator, ) from serpentTools.messages import ( - warning, debug, SerpentToolsException, + warning, SerpentToolsException, info, ) @@ -111,7 +111,9 @@ class ResultsReader(XSReader): :py:class:`~serpentTools.objects.containers.HomogUniv` objects. The keys describe a unique state: 'universe', burnup (MWd/kg), burnup index, time (days) - ('0', 0.0, 1, 0.0) + ('0', 0.0, 0, 0.0). + Burnup indexes are zero-indexed, meaning the first + step is index 0. Parameters ---------- @@ -209,9 +211,10 @@ class ResultsReader(XSReader): vals = str2vec(varVals) # convert the string to float numbers if varNamePy in self.resdata.keys(): # extend existing matrix currVar = self.resdata[varNamePy] - ndim = 1 if len(currVar.shape) == 2: ndim = currVar.shape[0] + else: + ndim = 1 if ndim < self._counter['rslt']: # append this data only once! try: @@ -234,21 +237,23 @@ class ResultsReader(XSReader): def _getBUstate(self): """Define unique branch state""" - days = self._counter['meta'] - burnup = self._counter['meta'] - burnIdx = self._counter['rslt'] # assign indices + burnIdx = self._counter['rslt'] - 1 varPyDays = convertVariableName(self._keysVersion['days']) # Py style varPyBU = convertVariableName(self._keysVersion['burn']) if varPyDays in self.resdata.keys(): - if burnIdx > 1: + if burnIdx > 0: days = self.resdata[varPyDays][-1, 0] else: days = self.resdata[varPyDays][-1] + else: + days = self._counter['meta'] - 1 if varPyBU in self.resdata.keys(): - if burnIdx > 1: + if burnIdx > 0: burnup = self.resdata[varPyBU][-1, 0] else: burnup = self.resdata[varPyBU][-1] + else: + burnup = self._counter['meta'] - 1 return self._univlist[-1], burnup, burnIdx, days def _getVarName(self, tline): @@ -312,14 +317,8 @@ class ResultsReader(XSReader): else 3) searchValue = (index if index is not None else burnup if burnup is not None else timeDays) - if searchIndex == 2 and searchValue < 1: # index must be non-zero - raise KeyError( - 'Index read is {}, however only integers above zero are ' - 'allowed'.format(searchValue)) for key, dictUniv in iteritems(self.universes): if key[0] == univ and key[searchIndex] == searchValue: - debug('Found universe that matches with keys {}' - .format(key)) return self.universes[key] searchName = ('index ' if index else 'burnup ' if burnup else 'timeDays ')	Universes in results reader are not zero-indexed ## Summary of issue The first index for universes is 1, likely taken directly from the result file burnup index. This misconstrues some of the style and pythonicness of the layout, as python arrays start at 0. ## Code for reproducing the issue Read a result file with burnup and view the `universes` attribute ## Expected outcome Universe burnup indexes start at 0 ## Versions * Version from ``serpentTools.__version__`` 0.6.1 * Python version - ``python --version`` N/A	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_ResultsReader.py b/serpentTools/tests/test_ResultsReader.py index 1384c4d..50209a5 100644 --- a/serpentTools/tests/test_ResultsReader.py +++ b/serpentTools/tests/test_ResultsReader.py @@ -122,11 +122,6 @@ class TestGetUniv(unittest.TestCase): with self.assertRaises(SerpentToolsException): self.reader.getUniv('0', burnup=None, index=None, timeDays=None) - def test_nonPostiveIndex(self): - """Verify that the reader raises error when the time index is not positive""" # noqa - with self.assertRaises(KeyError): - self.reader.getUniv('0', burnup=None, index=0, timeDays=None) - def test_noUnivState(self): """Verify that the reader raises error when the state tuple does not exist""" # noqa with self.assertRaises(KeyError): @@ -134,7 +129,7 @@ class TestGetUniv(unittest.TestCase): def test_validUniv(self): """Verify that getUniv returns the correct universe""" - xsDict = self.reader.getUniv('0', burnup=0.0, index=1, timeDays=0.0) + xsDict = self.reader.getUniv('0', burnup=0.0, index=0, timeDays=0.0) assert_equal(xsDict.infExp['infAbs'], self.expectedinfValAbs) @@ -337,7 +332,7 @@ class TestFilterResults(TesterCommonResultsReader): expectedInfVals = array([2.46724E+18, 2.98999E+17]) expectedInfUnc = array([0.00115, 0.00311]) - expectedStates = (('0', 0.0, 1, 0.0), ('0', 500, 2, 5.0)) + expectedStates = (('0', 0.0, 0, 0.0), ('0', 500, 1, 5.0)) def setUp(self): self.file = getFile('pwr_res.m') @@ -447,7 +442,7 @@ class TestReadAllResults(TesterCommonResultsReader): [1.00000E+37, 6.25000E-07, 0.00000E+00]) expectedInfVals = array([2.46724E+18, 2.98999E+17]) expectedInfUnc = array([0.00115, 0.00311]) - expectedStates = (('0', 0.0, 1, 0.0), ('0', 500, 2, 5.0)) + expectedStates = (('0', 0.0, 0, 0.0), ('0', 500, 1, 5.0)) def setUp(self): self.file = getFile('pwr_filter_res.m') @@ -564,7 +559,7 @@ class TestFilterResultsNoBurnup(TesterCommonResultsReader): [1.00000E+37, 6.25000E-07, 0.00000E+00]) expectedInfVals = array([8.71807E+14, 4.80974E+13]) expectedInfUnc = array([0.00097, 0.00121]) - expectedStates = (('0', 1, 1, 1), ('0', 1, 1, 1)) + expectedStates = (('0', 0, 0, 0), ('0', 0, 0, 0)) def setUp(self): self.file = getFile('pwr_noBU_res.m') @@ -605,7 +600,7 @@ class RestrictedResultsReader(unittest.TestCase): dataFile = "pwr_res.m" def _testUnivFlux(self, reader): - univ = reader.getUniv('0', index=1) + univ = reader.getUniv('0', index=0) assert_equal(self.expectedInfFlux_bu0, univ.get("infFlx")) def test_justFlux(self):	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 4 }	0.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 coverage==6.2 cycler==0.11.0 execnet==1.9.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.16.0 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 pytest-asyncio==0.16.0 pytest-cov==4.0.0 pytest-mock==3.6.1 pytest-xdist==3.0.2 python-dateutil==2.9.0.post0 PyYAML==3.13 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@1c730ef70c6f9ce98247e1d8b935d3bb98366e4a#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - coverage==6.2 - cycler==0.11.0 - execnet==1.9.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.16.0 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - pytest-asyncio==0.16.0 - pytest-cov==4.0.0 - pytest-mock==3.6.1 - pytest-xdist==3.0.2 - python-dateutil==2.9.0.post0 - pyyaml==3.13 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_validUniv", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_universes", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_universes", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_fluxAndKeff", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_justFlux", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_xsGroups" ]	[]	[ "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_emptyFile_noGcu", "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_noResults", "serpentTools/tests/test_ResultsReader.py::TestEmptyAttributes::test_emptyAttributes", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_allVarsNone", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_noUnivState", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_varsMatchSettings" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-288
CORE-GATECH-GROUP__serpent-tools-289	85f47bd559fdc8b1bef3bdea7d64db4a1c5b6271	2019-03-05 04:11:09	13961f3712a08e069c1ac96aaecf07ea7e7e5524		diff --git a/docs/changelog.rst b/docs/changelog.rst index 90d13c3..f7dec84 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -47,6 +47,9 @@ Incompatible API Changes burnup step was one. All burnup indices are now decreased by one. Similarly, if no burnup was present in the file, the values of burnup and days for all universes is zero. +* When reading detectors with a single tally, the value of ``tallies``, + ``errors``, and ``scores`` are stored as floats, rather than + :term:`numpy` arrays. Pending Deprecations -------------------- diff --git a/serpentTools/objects/base.py b/serpentTools/objects/base.py index ac48432..a478d5e 100644 --- a/serpentTools/objects/base.py +++ b/serpentTools/objects/base.py @@ -173,11 +173,11 @@ class DetectorBase(NamedObject): Name of this detector""" baseAttrs = """grids: dict Dictionary with additional data describing energy grids or mesh points - tallies: None or {np} + tallies: None, float, or {np} Reshaped tally data to correspond to the bins used - errors: None or {np} + errors: None, float, or {np} Reshaped relative error data corresponding to bins used - scores: None or {np} + scores: None, float, or {np} Reshaped array of tally scores. SERPENT 1 only indexes: None or :class:`collections.OrderedDict` Collection of unique indexes for each requested bin diff --git a/serpentTools/objects/detectors.py b/serpentTools/objects/detectors.py index acb07c0..378c117 100644 --- a/serpentTools/objects/detectors.py +++ b/serpentTools/objects/detectors.py @@ -31,7 +31,7 @@ from matplotlib.patches import RegularPolygon from matplotlib.collections import PatchCollection from matplotlib.pyplot import gca -from serpentTools.messages import warning, debug, SerpentToolsException +from serpentTools.messages import warning, SerpentToolsException from serpentTools.objects.base import DetectorBase from serpentTools.utils import ( magicPlotDocDecorator, formatPlot, setAx_xlims, setAx_ylims, @@ -108,22 +108,27 @@ class Detector(DetectorBase): if self.__reshaped: warning('Data has already been reshaped') return - debug('Starting to sort tally data...') shape = [] self.indexes = OrderedDict() - for index in range(1, 10): - uniqueVals = unique(self.bins[:, index]) - if len(uniqueVals) > 1: - indexName = self._indexName(index) - self.indexes[indexName] = array(uniqueVals, dtype=int) - 1 - shape.append(len(uniqueVals)) - self.tallies = self.bins[:, 10].reshape(shape) - self.errors = self.bins[:, 11].reshape(shape) - if self.bins.shape[1] == 13: - self.scores = self.bins[:, 12].reshape(shape) + hasScores = self.bins.shape[1] == 13 + if self.bins.shape[0] == 1: + self.tallies = self.bins[0, 10] + self.errors = self.bins[0, 11] + if hasScores: + self.scores = self.bins[0, 12] + else: + for index in range(1, 10): + uniqueVals = unique(self.bins[:, index]) + if len(uniqueVals) > 1: + indexName = self._indexName(index) + self.indexes[indexName] = array(uniqueVals, dtype=int) - 1 + shape.append(len(uniqueVals)) + self.tallies = self.bins[:, 10].reshape(shape) + self.errors = self.bins[:, 11].reshape(shape) + if hasScores: + self.scores = self.bins[:, 12].reshape(shape) self._map = {'tallies': self.tallies, 'errors': self.errors, 'scores': self.scores} - debug('Done') self.__reshaped = True return shape	[API] Detector tallies of a single bin are difficult to work with ## Summary of issue When using a detector w/ a single bin, e.g. one group flux, the bin data in the output file is a single bin. Rather than a proper array, these are floats cast into array form. Indexing doesn't work, but you can get the "values" by converting the arrays to `float`. ## Code for reproducing the issue [tally_det0.txt](https://github.com/CORE-GATECH-GROUP/serpent-tools/files/2340714/tally_det0.txt) ``` import serpentTools r = serpentTools.read('tally_det0.txt', 'det') one = r['one'] one.tallies # array(8.19E17) r['two'].tallies # array([8.19E17, 7.18E17]) r['two'].tallies[0] # 8.19E17 one.tallies[0] ``` ## Actual outcome including console output and error traceback if applicable This raises an `IndexError` ## Expected outcome Better way to index tally data of a single point. Plotting also doesn't work for this as well, as the tally data has no shape, but is of size 1. Converting these to floats, or an array of a single float, e.g. array([tally]), would help with plotting and slicing down the road. Labeling as `API-Incompatible` because, depending on the implementation, this could require changes from the user. If this is the case, then this should be included with a release, like 0.6.0 ## Versions * Version from ``serpentTools.__version__`` `0.5.3`	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_detector.py b/serpentTools/tests/test_detector.py index 4b34ad3..6a79aee 100644 --- a/serpentTools/tests/test_detector.py +++ b/serpentTools/tests/test_detector.py @@ -9,25 +9,19 @@ from six import iteritems from numpy import arange, array from numpy.testing import assert_equal -from serpentTools.parsers import DetectorReader +from serpentTools.parsers import read from serpentTools.data import getFile from serpentTools.objects.detectors import ( CartesianDetector, HexagonalDetector, CylindricalDetector) from serpentTools.tests import compareDictOfArrays -def read(fileP): - reader = DetectorReader(fileP) - reader.read() - return reader - - class DetectorHelper(TestCase): """ Class that assists setting up and testing readers""" @classmethod def setUpClass(cls): - cls.reader = read(cls.FILE_PATH) + cls.reader = read(cls.FILE_PATH, 'det') cls.detectors = cls.reader.detectors def test_loadedDetectors(self): @@ -278,6 +272,7 @@ TEST_SUB_CLASSES = {CartesianDetectorTester, HexagonalDetectorTester, COMBINED_OUTPUT_FILE = 'combinedDets_det0.m' +SINGLE_TALLY_FILE = "single_det0.m" def setUpModule(): @@ -292,11 +287,17 @@ def setUpModule(): for subCls in TEST_SUB_CLASSES: with open(subCls.FILE_PATH) as subFile: out.write(subFile.read()) + with open(SINGLE_TALLY_FILE, 'w') as out: + out.write(""" +DETone = [ + 1 1 1 1 1 1 1 1 1 1 8.19312E+17 0.05187 +];""") def tearDownModule(): - """Remove any test fixtures created for this module.""" + """Remove any test files created for this module.""" remove(COMBINED_OUTPUT_FILE) + remove(SINGLE_TALLY_FILE) class CombinedDetTester(DetectorHelper): @@ -318,6 +319,21 @@ class CombinedDetTester(DetectorHelper): del DetectorHelper + +class SingleTallyTester(TestCase): + """Test storing detector data with a single tally""" + + def setUp(self): + self.detector = read(SINGLE_TALLY_FILE, 'det').detectors['one'] + + def test_singleTally(self): + """Test the conversion of a single tally to floats, not arrays""" + self.assertTrue(isinstance(self.detector.tallies, float)) + self.assertTrue(isinstance(self.detector.errors, float)) + self.assertEqual(self.detector.tallies, 8.19312E17) + self.assertEqual(self.detector.errors, 0.05187) + + if __name__ == '__main__': from unittest import main main()	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 3 }	0.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.16.0 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==3.13 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@85f47bd559fdc8b1bef3bdea7d64db4a1c5b6271#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.16.0 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==3.13 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_detector.py::SingleTallyTester::test_singleTally" ]	[]	[ "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_detectorGrids", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_detectorIndex", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_detectorSlice", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_getitem", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_iterDets", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_loadedDetectors", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_sharedPlot", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_detectorGrids", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_detectorIndex", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_detectorSlice", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_getitem", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_iterDets", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_loadedDetectors", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_detectorGrids", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_detectorIndex", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_detectorSlice", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_getitem", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_iterDets", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_loadedDetectors", "serpentTools/tests/test_detector.py::CombinedDetTester::test_detectorGrids", "serpentTools/tests/test_detector.py::CombinedDetTester::test_detectorIndex", "serpentTools/tests/test_detector.py::CombinedDetTester::test_detectorSlice", "serpentTools/tests/test_detector.py::CombinedDetTester::test_getitem", "serpentTools/tests/test_detector.py::CombinedDetTester::test_iterDets", "serpentTools/tests/test_detector.py::CombinedDetTester::test_loadedDetectors" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-289
CORE-GATECH-GROUP__serpent-tools-306	fb197b44d97ef3ae24d95662dcbdeb1cd9e9694e	2019-04-12 19:19:33	09a5feab5b95f98aef4f7dc7b07edbf8e458f955		diff --git a/serpentTools/utils/core.py b/serpentTools/utils/core.py index e56468e..c1b35ad 100644 --- a/serpentTools/utils/core.py +++ b/serpentTools/utils/core.py @@ -4,7 +4,7 @@ Core utilities from re import compile -from numpy import array, ndarray +from numpy import array, ndarray, fromiter # Regular expressions @@ -15,28 +15,33 @@ SCALAR_REGEX = compile(r'=.+;') # scalar FIRST_WORD_REGEX = compile(r'^\w+') # first word in the line -def str2vec(iterable, of=float, out=array): +def str2vec(iterable, dtype=float, out=array): """ Convert a string or other iterable to vector. Parameters ---------- iterable: str or iterable - If string, will be split with ``split(splitAt)`` - to create a list. Every item in this list, or original + If a string containing spaces, will be split using + ```iterable.split()``. If no spaces are found, the + outgoing type is filled with a single string, e.g. + a list with a single string as the first and only + entry. This is returned directly, avoiding conversion + with ``dtype``. + Every item in this split list, or original iterable, will be iterated over and converted accoring to the other arguments. - of: type + dtype: type Convert each value in ``iterable`` to this data type. out: type Return data type. Will be passed the iterable of - converted items of data dtype ``of``. + converted items of data dtype ``dtype``. Returns ------- vector Iterable of all values of ``iterable``, or split variant, - converted to type ``of``. + converted to type ``dtype``. Examples -------- @@ -53,10 +58,19 @@ def str2vec(iterable, of=float, out=array): >>> str2vec(x) array([1., 2., 3., 4.,]) + >>> str2vec("ADF") + array(['ADF', dtype='<U3') + """ - vec = (iterable.split() if isinstance(iterable, str) - else iterable) - return out([of(xx) for xx in vec]) + if isinstance(iterable, str): + if ' ' in iterable: + iterable = iterable.split() + else: + return out([iterable]) + cmap = map(dtype, iterable) + if out is array: + return fromiter(cmap, dtype) + return out(cmap) def splitValsUncs(iterable, copy=False):	BUG ADFs in results file fail in numeric conversion ## Summary of issue When reading a file with assembly discontinuity factors (ADFs), there is a string that serpentTools attempts to convert to a vector. This causes a ValueError ## Code for reproducing the issue Add the following code to a section of a result file ``` % Assembly discontinuity factors (order: W-S-E-N / NW-NE-SE-SW): DF_SURFACE (idx, [1: 3]) = 'ADF' ; DF_SYM (idx, 1) = 1 ; DF_N_SURF (idx, 1) = 4 ; DF_N_CORN (idx, 1) = 4 ; ``` ``` $ python -c "import serpentTools; serpentTools.read(\"adf_res.m\")" ``` ## Actual outcome including console output and error traceback if applicable ``` Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/ajohnson400/.local/lib/python3.7/site-packages/serpentTools/parsers/__init__.py", line 155, in read returnedFromLoader.read() File "/home/ajohnson400/.local/lib/python3.7/site-packages/serpentTools/parsers/base.py", line 52, in read self._read() File "/home/ajohnson400/.local/lib/python3.7/site-packages/serpentTools/parsers/results.py", line 158, in _read self._processResults(tline) File "/home/ajohnson400/.local/lib/python3.7/site-packages/serpentTools/parsers/results.py", line 176, in _processResults self._storeUnivData(varNameSer, varVals) File "/home/ajohnson400/.local/lib/python3.7/site-packages/serpentTools/parsers/results.py", line 198, in _storeUnivData values = str2vec(varVals) # convert the string to float numbers File "/home/ajohnson400/.local/lib/python3.7/site-packages/serpentTools/utils/core.py", line 59, in str2vec return out([of(xx) for xx in vec]) File "/home/ajohnson400/.local/lib/python3.7/site-packages/serpentTools/utils/core.py", line 59, in <listcomp> return out([of(xx) for xx in vec]) ValueError: could not convert string to float: 'ADF' ``` ## Expected outcome Doesn't fail ## Versions * Version from ``serpentTools.__version__`` `0.7.0rc0` * Python version - ``python --version`` `3.7`	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_ResultsReader.py b/serpentTools/tests/test_ResultsReader.py index 44c9514..001408b 100644 --- a/serpentTools/tests/test_ResultsReader.py +++ b/serpentTools/tests/test_ResultsReader.py @@ -1,10 +1,11 @@ """Test the results reader.""" from os import remove +from shutil import copy from unittest import TestCase from numpy import array -from numpy.testing import assert_equal +from numpy.testing import assert_array_equal from six import iteritems from serpentTools.settings import rc @@ -15,21 +16,33 @@ from serpentTools.messages import SerpentToolsException GCU_START_STR = "GCU_UNIVERSE_NAME" NO_GCU_FILE = "./pwr_noGcu_res.m" -GOOD_FILE = getFile("pwr_noBU_res.m") +ADF_FILE = "./pwr_adf_res.m" +RES_NO_BU = getFile("pwr_noBU_res.m") def setUpModule(): """Write the result file with no group constant data.""" - with open(NO_GCU_FILE, 'w') as noGcu, open(GOOD_FILE) as good: + with open(NO_GCU_FILE, 'w') as noGcu, open(RES_NO_BU) as good: for line in good: if GCU_START_STR in line: break noGcu.write(line) + copy(RES_NO_BU, ADF_FILE) + with open(ADF_FILE, 'a') as stream: + stream.write(""" +% Assembly discontinuity factors (order: W-S-E-N / NW-NE-SE-SW): + +DF_SURFACE (idx, [1: 3]) = 'ADF' ; +DF_SYM (idx, 1) = 1 ; +DF_N_SURF (idx, 1) = 4 ; +DF_N_CORN (idx, 1) = 4 ; +""") def tearDownModule(): """Remove the noGcu file.""" remove(NO_GCU_FILE) + remove(ADF_FILE) class Serp2129Helper(TestCase): @@ -130,7 +143,7 @@ class TestGetUniv(TestCase): def test_validUniv(self): """Verify that getUniv returns the correct universe""" xsDict = self.reader.getUniv('0', burnup=0.0, index=0, timeDays=0.0) - assert_equal(xsDict.infExp['infAbs'], self.expectedinfValAbs) + assert_array_equal(xsDict.infExp['infAbs'], self.expectedinfValAbs) class TesterCommonResultsReader(TestCase): @@ -179,14 +192,14 @@ class TesterCommonResultsReader(TestCase): self.assertSetEqual(set(self.reader.metadata[key]), set(expectedValue)) else: - assert_equal(self.reader.metadata[key], expectedValue) + assert_array_equal(self.reader.metadata[key], expectedValue) def test_resdata(self): """Verify that results data is properly stored.""" expectedKeys = self.expectedResdata actualKeys = set(self.reader.resdata.keys()) self.assertSetEqual(expectedKeys, actualKeys) - assert_equal(self.reader.resdata['absKeff'], self.expectedKeff) + assert_array_equal(self.reader.resdata['absKeff'], self.expectedKeff) def test_burnup(self): """Verify the burnup vector is properly stored.""" @@ -197,7 +210,7 @@ class TesterCommonResultsReader(TestCase): "{} should have burnup, but does not".format(self)) raise self.skipTest( "{} does not, and should not, have burnup".format(self)) - assert_equal(actualBurnDays, self.expectedDays) + assert_array_equal(actualBurnDays, self.expectedDays) def test_universes(self): """Verify that results for all states (univ, bu, buIdx, days) exist. @@ -216,12 +229,12 @@ class TesterCommonResultsReader(TestCase): expUniv = self.reader.universes[expSt0] self.assertSetEqual(set(expUniv.infExp.keys()), self.expectedInfExp) self.assertSetEqual(set(expUniv.gc.keys()), self.expectedUnivgcData) - assert_equal(expUniv.infExp['infFlx'], self.expectedInfVals) - assert_equal(expUniv.infUnc['infFlx'], self.expectedInfUnc) - assert_equal(expUniv.gc['cmmTranspxs'], self.expectedCMM) - assert_equal(expUniv.gcUnc['cmmTranspxs'], self.expectedCMMunc) - assert_equal(expUniv.groups, self.expectedGroups) - assert_equal(expUniv.microGroups, self.expectedMicroGroups) + assert_array_equal(expUniv.infExp['infFlx'], self.expectedInfVals) + assert_array_equal(expUniv.infUnc['infFlx'], self.expectedInfUnc) + assert_array_equal(expUniv.gc['cmmTranspxs'], self.expectedCMM) + assert_array_equal(expUniv.gcUnc['cmmTranspxs'], self.expectedCMMunc) + assert_array_equal(expUniv.groups, self.expectedGroups) + assert_array_equal(expUniv.microGroups, self.expectedMicroGroups) class TestFilterResults(TesterCommonResultsReader): @@ -599,7 +612,7 @@ class RestrictedResultsReader(Serp2129Helper): def _testUnivFlux(self, reader): univ = reader.getUniv('0', index=0) - assert_equal(self.expectedInfFlux_bu0, univ.get("infFlx")) + assert_array_equal(self.expectedInfFlux_bu0, univ.get("infFlx")) def test_justFlux(self): """Restrict the variables to gcu inf flux and verify their values""" @@ -621,11 +634,25 @@ class RestrictedResultsReader(Serp2129Helper): rc['xs.variableExtras'] = ['ABS_KEFF', 'INF_FLX'] r = readDataFile(self.dataFile) self._testUnivFlux(r) - assert_equal(self.expectedAbsKeff, r.resdata['absKeff']) + assert_array_equal(self.expectedAbsKeff, r.resdata['absKeff']) del TesterCommonResultsReader + +class ResADFTester(TestCase): + + @classmethod + def setUpClass(cls): + cls.reader = ResultsReader(ADF_FILE) + cls.reader.read() + + def test_adf(self): + univ = self.reader.getUniv('0', index=0) + expected = array('ADF') + assert_array_equal(expected, univ.gc['dfSurface']) + + if __name__ == '__main__': from unittest import main main() diff --git a/serpentTools/tests/test_utils.py b/serpentTools/tests/test_utils.py index ce80756..ebb8e6e 100644 --- a/serpentTools/tests/test_utils.py +++ b/serpentTools/tests/test_utils.py @@ -45,8 +45,9 @@ class VariableConverterTester(TestCase): class VectorConverterTester(TestCase): """Class for testing the str2vec function""" - def setUp(self): - self.testCases = ("0 1 2 3", [0, 1, 2, 3], (0, 1, 2, 3), arange(4)) + @classmethod + def setUpClass(cls): + cls.testCases = ("0 1 2 3", [0, 1, 2, 3], (0, 1, 2, 3), arange(4)) def test_str2Arrays(self): """Verify that the str2vec converts to arrays.""" @@ -60,6 +61,13 @@ class VectorConverterTester(TestCase): expected = [0, 1, 2, 3] self._runConversionTest(int, expected, list) + def test_vecOfStr(self): + """Verify a single word can be converted with str2vec""" + key = 'ADF' + expected = array('ADF') + actual = str2vec(key) + assert_array_equal(expected, actual) + def _runConversionTest(self, valType, expected, outType=None): if outType is None: outType = array @@ -67,7 +75,7 @@ class VectorConverterTester(TestCase): else: compareType = outType for case in self.testCases: - actual = str2vec(case, of=valType, out=outType) + actual = str2vec(case, dtype=valType, out=outType) self.assertIsInstance(actual, compareType, msg=case) ofRightType = [isinstance(xx, valType) for xx in actual] self.assertTrue(all(ofRightType), @@ -79,8 +87,9 @@ class VectorConverterTester(TestCase): class SplitValsTester(TestCase): """Class that tests splitValsUncs.""" - def setUp(self): - self.input = arange(4) + @classmethod + def setUp(cls): + cls.input = arange(4) def test_splitVals(self): """Verify the basic functionality.""" @@ -321,8 +330,9 @@ class OverlapTester(TestCase): class SplitDictionaryTester(TestCase): """Class for testing utils.splitDictByKeys.""" - def setUp(self): - self.map0 = { + @classmethod + def setUpClass(cls): + cls.map0 = { 'hello': 'world', 'missingFrom1': True, 'infKeff': arange(2), @@ -331,7 +341,7 @@ class SplitDictionaryTester(TestCase): 'anaKeff': arange(6), 'notBool': 1, } - self.map1 = { + cls.map1 = { 'hello': 'world', 'infKeff': arange(2), 'float': 0.24, @@ -340,9 +350,9 @@ class SplitDictionaryTester(TestCase): 'anaKeff': arange(2), 'absKeff': arange(2), } - self.badTypes = {'notBool': (int, bool)} - self.badShapes = {'anaKeff': ((6, ), (2, )), } - self.goodKeys = {'hello', 'absKeff', 'float', 'infKeff', } + cls.badTypes = {'notBool': (int, bool)} + cls.badShapes = {'anaKeff': ((6, ), (2, )), } + cls.goodKeys = {'hello', 'absKeff', 'float', 'infKeff', } def callTest(self, keySet=None): return splitDictByKeys(self.map0, self.map1, keySet)	{ "commit_name": "head_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 1 }	0.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.19.5 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==3.13 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@fb197b44d97ef3ae24d95662dcbdeb1cd9e9694e#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.19.5 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==3.13 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_ResultsReader.py::ResADFTester::test_adf", "serpentTools/tests/test_utils.py::VectorConverterTester::test_listOfInts", "serpentTools/tests/test_utils.py::VectorConverterTester::test_vecOfStr" ]	[]	[ "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_emptyAttributes", "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_emptyFile_noGcu", "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_noResults", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_allVarsNone", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_noUnivState", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_validUniv", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_universes", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_universes", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_fluxAndKeff", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_justFlux", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_xsGroups", "serpentTools/tests/test_utils.py::VariableConverterTester::test_variableConversion", "serpentTools/tests/test_utils.py::VectorConverterTester::test_str2Arrays", "serpentTools/tests/test_utils.py::SplitValsTester::test_splitAtCols", "serpentTools/tests/test_utils.py::SplitValsTester::test_splitCopy", "serpentTools/tests/test_utils.py::SplitValsTester::test_splitVals", "serpentTools/tests/test_utils.py::CommonKeysTester::test_getKeys_missing", "serpentTools/tests/test_utils.py::CommonKeysTester::test_goodKeys_dict", "serpentTools/tests/test_utils.py::DirectCompareTester::test_acceptableHigh", "serpentTools/tests/test_utils.py::DirectCompareTester::test_acceptableLow", "serpentTools/tests/test_utils.py::DirectCompareTester::test_badTypes", "serpentTools/tests/test_utils.py::DirectCompareTester::test_diffShapes", "serpentTools/tests/test_utils.py::DirectCompareTester::test_dissimilarString", "serpentTools/tests/test_utils.py::DirectCompareTester::test_identicalString", "serpentTools/tests/test_utils.py::DirectCompareTester::test_notImplemented", "serpentTools/tests/test_utils.py::DirectCompareTester::test_outsideTols", "serpentTools/tests/test_utils.py::DirectCompareTester::test_stringArrays", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_absolute", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_badshapes", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_identical_1D", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_identical_2D", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_identical_3D", "serpentTools/tests/test_utils.py::OverlapTester::test_overlap_relative", "serpentTools/tests/test_utils.py::SplitDictionaryTester::test_keySet_all", "serpentTools/tests/test_utils.py::SplitDictionaryTester::test_noKeys" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-306
CORE-GATECH-GROUP__serpent-tools-315	7ae29a333288609664c41979e8093923cbc95f50	2019-05-29 17:36:45	09a5feab5b95f98aef4f7dc7b07edbf8e458f955		diff --git a/docs/changelog.rst b/docs/changelog.rst index baa3628..2d7fdb2 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -16,6 +16,11 @@ Next :meth:`~serpentTools.objects.CartesianDetector.meshPlot`, where only data greater than ``thresh`` is plotted. +Bug fixes +--------- + +* Tally data for detectors with time-bins are properly handled - :issue:`312` + .. _v0.7.0: :release-tag:`0.7.0` diff --git a/serpentTools/data/time_det0.m b/serpentTools/data/time_det0.m new file mode 100644 index 0000000..7692115 --- /dev/null +++ b/serpentTools/data/time_det0.m @@ -0,0 +1,11 @@ + +DETFP = [ + 1 1 1 1 1 1 1 1 1 1 1 9.99978E-01 0.00002 + 2 2 1 1 1 1 1 1 1 1 1 2.24379E-05 1.00000 +]; + + +DETFPT = [ + 0.00000E+00 2.50000E-05 1.25000E-05 + 2.50000E-05 5.00000E-05 3.75000E-05 +]; diff --git a/serpentTools/objects/detectors.py b/serpentTools/objects/detectors.py index 98b8fc9..3f3eccc 100644 --- a/serpentTools/objects/detectors.py +++ b/serpentTools/objects/detectors.py @@ -43,8 +43,12 @@ __all__ = ['Detector', 'CartesianDetector', 'HexagonalDetector', 'CylindricalDetector', 'SphericalDetector'] -DET_COLS = ('value', 'energy', 'universe', 'cell', 'material', 'lattice', - 'reaction', 'zmesh', 'ymesh', 'xmesh', 'tally', 'error', 'scores') +DET_COLS = ( + 'value', 'time', 'energy', 'universe', + 'cell', 'material', 'lattice', + 'reaction', 'zmesh', 'ymesh', 'xmesh', + 'tally', 'error', 'scores', +) """Name of the columns of the data""" @@ -74,13 +78,13 @@ class Detector(DetectorBase): def _isReshaped(self): return self.__reshaped - def addTallyData(self, bins): + def addTallyData(self, bins, hasTime=False): """Add tally data to this detector""" self.__reshaped = False self.bins = bins - self.reshape() + self.reshape(hasTime) - def reshape(self): + def reshape(self, hasTime=False): """ Reshape the tally data into a multidimensional array @@ -110,24 +114,35 @@ class Detector(DetectorBase): warning('Data has already been reshaped') return shape = [] + tallyCol = 10 + errorCol = 11 + scoreCol = 12 + if hasTime: + tallyCol += 1 + errorCol += 1 + scoreCol += 1 self.indexes = OrderedDict() - hasScores = self.bins.shape[1] == 13 + hasScores = self.bins.shape[1] == (scoreCol + 1) + if self.bins.shape[0] == 1: - self.tallies = self.bins[0, 10] - self.errors = self.bins[0, 11] + # single tally value + self.tallies = self.bins[0, tallyCol] + self.errors = self.bins[0, errorCol] if hasScores: - self.scores = self.bins[0, 12] + self.scores = self.bins[0, scoreCol] else: - for index in range(1, 10): - uniqueVals = unique(self.bins[:, index]) + for tallyIx, colIx in enumerate( + range(1, tallyCol), + start=1 if hasTime else 2): + uniqueVals = unique(self.bins[:, colIx]) if len(uniqueVals) > 1: - indexName = self._indexName(index) + indexName = self._indexName(tallyIx) self.indexes[indexName] = array(uniqueVals, dtype=int) - 1 shape.append(len(uniqueVals)) - self.tallies = self.bins[:, 10].reshape(shape) - self.errors = self.bins[:, 11].reshape(shape) + self.tallies = self.bins[:, tallyCol].reshape(shape) + self.errors = self.bins[:, errorCol].reshape(shape) if hasScores: - self.scores = self.bins[:, 12].reshape(shape) + self.scores = self.bins[:, scoreCol].reshape(shape) self._map = {'tallies': self.tallies, 'errors': self.errors, 'scores': self.scores} self.__reshaped = True @@ -214,9 +229,10 @@ class HexagonalDetector(Detector): detAttrs=Detector.docAttrs, baseAttrs=DetectorBase.baseAttrs) + # column indicies in full (time-bin included) bins matrix _INDEX_MAP = { - 8: 'ycoord', - 9: 'xcoord', + 9: 'ycoord', + 10: 'xcoord', } _NON_CART = _INDEX_MAP.values() @@ -405,10 +421,10 @@ class CylindricalDetector(Detector): {seeAlso:s}""".format( params=DetectorBase.baseParams, detAttrs=Detector.docAttrs, baseAttrs=DetectorBase.baseAttrs, seeAlso=docSeeAlso) - + # column indices in full (time-bin included) bins matrix _INDEX_MAP = { - 8: 'phi', - 9: 'rmesh' + 9: 'phi', + 10: 'rmesh' } _NON_CART = _INDEX_MAP.values() @@ -456,10 +472,12 @@ class SphericalDetector(Detector): params=DetectorBase.baseParams, detAttrs=Detector.docAttrs, baseAttrs=DetectorBase.baseAttrs, seeAlso=CylindricalDetector.docSeeAlso) + + # column indices in full (time-bin included) bins matrix _INDEX_MAP = { - 7: 'theta', - 8: 'phi', - 9: 'rmesh', + 8: 'theta', + 9: 'phi', + 10: 'rmesh', } _NON_CART = _INDEX_MAP.values() @@ -528,7 +546,7 @@ def detectorFactory(name, dataDict): tallyD = dataDict.pop('tally') detCls = _getDetectorType(dataDict) det = detCls(name) - det.addTallyData(tallyD) + det.addTallyData(tallyD, 'T' in dataDict) for gridK, value in iteritems(dataDict): det.grids[gridK] = value return det diff --git a/serpentTools/utils/plot.py b/serpentTools/utils/plot.py index e4cfb54..dc574e9 100644 --- a/serpentTools/utils/plot.py +++ b/serpentTools/utils/plot.py @@ -48,6 +48,7 @@ DEPLETION_PLOT_LABELS = { DETECTOR_PLOT_LABELS = { 'energy': 'Energy [MeV]', + 'time': 'Time [s]', } for dim in ['x', 'y', 'z']: DETECTOR_PLOT_LABELS[dim] = "{} Position [cm]".format(dim.capitalize())	Detectors with time binning ## Summary of issue Detectors with [time binning](http://serpent.vtt.fi/mediawiki/index.php/Input_syntax_manual#det_di) have 13 columns in the detector output file (an additional column indicating the time bin between the normal 1st and 2nd column) and thus get mixed up with the Serpent 1 style detector output with the number of scores as the 13th column. The Detector Reader reads the tally values from column 11 and error values from column 12 as usual. The problem is that in detectors with time binning, the tally values are actually at column 12 and error values at column 13. I'm not sure what's the best approach for this at serpentTools side, but the best way to figure out whether the detector that has 13 columns has time binning is to check for the `DET<name>T` array that is printed in the detector file and gives the time binning associated with the detector. ## Code for reproducing the issue Serpent input: set title "Homogeneous case" % --- Disable group constant calculation set gcu -1 % --- Geometry is just a cube surf 2 cuboid -100.0 100.0 -100.0 100.0 -100.0 100.0 % --- Cell definitions: cell 3 0 fuel -2 cell 99 0 outside 2 % Outside world % --- Fuel material: mat fuel sum vol 8.0000E+06 8016.03c 0.316667 1001.03c 0.633333 92235.03c 0.0015 92238.03c 0.0485 5010.03c 0.000115797 % --- Reflective boundary conditions: set bc 2 2 2 % --- Neutron population: set pop 1000 10 10 % --- Normalization set power 1.0 % --- Specify a time binnig (10 bins between 0 and 50 microseconds) tme dettime 2 10 0 50e-6 % --- Detector with time binning det FP dr -8 void di dettime Produced input_det0.m: DETFP = [ 1 1 1 1 1 1 1 1 1 1 1 8.68766E-01 0.00612 2 2 1 1 1 1 1 1 1 1 1 1.14554E-01 0.03156 3 3 1 1 1 1 1 1 1 1 1 1.49875E-02 0.10890 4 4 1 1 1 1 1 1 1 1 1 1.51392E-03 0.28029 5 5 1 1 1 1 1 1 1 1 1 1.60274E-04 1.00000 6 6 1 1 1 1 1 1 1 1 1 1.89739E-05 1.00000 7 7 1 1 1 1 1 1 1 1 1 0.00000E+00 0.00000 8 8 1 1 1 1 1 1 1 1 1 0.00000E+00 0.00000 9 9 1 1 1 1 1 1 1 1 1 0.00000E+00 0.00000 10 10 1 1 1 1 1 1 1 1 1 0.00000E+00 0.00000 ]; DETFPT = [ 0.00000E+00 5.00000E-06 2.50000E-06 5.00000E-06 1.00000E-05 7.50000E-06 1.00000E-05 1.50000E-05 1.25000E-05 1.50000E-05 2.00000E-05 1.75000E-05 2.00000E-05 2.50000E-05 2.25000E-05 2.50000E-05 3.00000E-05 2.75000E-05 3.00000E-05 3.50000E-05 3.25000E-05 3.50000E-05 4.00000E-05 3.75000E-05 4.00000E-05 4.50000E-05 4.25000E-05 4.50000E-05 5.00000E-05 4.75000E-05 ]; Postprocessing with serpentTools: #!/usr/bin/env python # -- coding: utf-8 -- import serpentTools as sT dr = sT.DetectorReader("./input_det0.m") dr.read() det = dr.detectors["FP"] print(det.tallies) print(det.errors) ## Actual outcome including console output and error traceback if applicable [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [8.68766e-01 1.14554e-01 1.49875e-02 1.51392e-03 1.60274e-04 1.89739e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00] ## Expected outcome [8.68766e-01 1.14554e-01 1.49875e-02 1.51392e-03 1.60274e-04 1.89739e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00] [0.00612 0.03156 0.1089 0.28029 1. 1. 0. 0. 0. 0. ] ## Versions - Serpent 2.1.31 - serpentTools 0.7.0+17.g7ae29a3.dirty - Python 3.6.7	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_detector.py b/serpentTools/tests/test_detector.py index 6a79aee..a369455 100644 --- a/serpentTools/tests/test_detector.py +++ b/serpentTools/tests/test_detector.py @@ -334,6 +334,29 @@ class SingleTallyTester(TestCase): self.assertEqual(self.detector.errors, 0.05187) +class TimeBinnedDetectorTester(TestCase): + + @classmethod + def setUpClass(cls): + cls.reader = read(getFile('time_det0.m'), 'det') + cls.timeDet = cls.reader['FP'] + + def test_timeDetector(self): + """Verify a simple time-binned detector is processed properly""" + self.assertEqual(1, len(self.timeDet.grids), + msg=', '.join(self.timeDet.grids.keys())) + self.assertTrue("T" in self.timeDet.grids) + expTallies = array([9.99978E-01, 2.24379E-05]) + expErrors = array([0.00002, 1.0]) + expTimeGrid = array([ + [0.00000E+00, 2.50000E-05, 1.25000E-05], + [2.50000E-05, 5.00000E-05, 3.75000E-05], + ]) + assert_equal(self.timeDet.tallies, expTallies) + assert_equal(self.timeDet.errors, expErrors) + assert_equal(self.timeDet.grids['T'], expTimeGrid) + + if __name__ == '__main__': from unittest import main main()	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 3 }	0.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.19.5 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==3.13 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@7ae29a333288609664c41979e8093923cbc95f50#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.19.5 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==3.13 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_detector.py::TimeBinnedDetectorTester::test_timeDetector" ]	[]	[ "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_detectorGrids", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_detectorIndex", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_detectorSlice", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_getitem", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_iterDets", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_loadedDetectors", "serpentTools/tests/test_detector.py::CartesianDetectorTester::test_sharedPlot", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_detectorGrids", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_detectorIndex", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_detectorSlice", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_getitem", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_iterDets", "serpentTools/tests/test_detector.py::HexagonalDetectorTester::test_loadedDetectors", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_detectorGrids", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_detectorIndex", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_detectorSlice", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_getitem", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_iterDets", "serpentTools/tests/test_detector.py::CylindricalDetectorTester::test_loadedDetectors", "serpentTools/tests/test_detector.py::CombinedDetTester::test_detectorGrids", "serpentTools/tests/test_detector.py::CombinedDetTester::test_detectorIndex", "serpentTools/tests/test_detector.py::CombinedDetTester::test_detectorSlice", "serpentTools/tests/test_detector.py::CombinedDetTester::test_getitem", "serpentTools/tests/test_detector.py::CombinedDetTester::test_iterDets", "serpentTools/tests/test_detector.py::CombinedDetTester::test_loadedDetectors", "serpentTools/tests/test_detector.py::SingleTallyTester::test_singleTally" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-315
CORE-GATECH-GROUP__serpent-tools-321	b3f3c39609319985f7047dd6fed52654e285cd0b	2019-07-16 02:30:22	09a5feab5b95f98aef4f7dc7b07edbf8e458f955		diff --git a/docs/changelog.rst b/docs/changelog.rst index 706620d..97aee8a 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -21,6 +21,16 @@ Bug fixes --------- * Tally data for detectors with time-bins are properly handled - :issue:`312` +* Support for generic string universe names for \|BranchingReader\| and + \|BranchCollector\| - :issue:`318` + +Pending Deprecations +-------------------- + +* Keys to \|BranchedUniv\| objects stored in + :attr:`serpentTools.xs.BranchCollector.universes` are stored as strings, + rather than integers, e.g. ``0`` is replaced with ``"0"``. A workaround + is in-place, but will be removed in future versions. .. _v0.7.0: diff --git a/docs/examples/BranchToNodalDiffusion.rst b/docs/examples/BranchToNodalDiffusion.rst index 131a53d..ef159ae 100644 --- a/docs/examples/BranchToNodalDiffusion.rst +++ b/docs/examples/BranchToNodalDiffusion.rst @@ -155,12 +155,12 @@ constants for specific universes with the \|BranchCol-universes\| dictionary. .. code:: >>> collector.universes - {0: <serpentTools.BranchedUniv at 0x7fb62f749a98>, 10: + {"0": <serpentTools.BranchedUniv at 0x7fb62f749a98>, 10: <serpentTools.BranchedUniv at 0x7fb62f731b88>, 20: <serpentTools.BranchedUniv at 0x7fb62f749e08>, 30: <serpentTools.BranchedUniv at 0x7fb62f749e58>, 40: <serpentTools.BranchedUniv at 0x7fb62f749ea8>} - >>> u0 = collector.universes[0] + >>> u0 = collector.universes["0"] These \|BranchedUniv\| objects store views into the underlying collectors \|BranchedUniv-tables\| data corresponding to a single universe. The diff --git a/docs/examples/Branching.rst b/docs/examples/Branching.rst index 6f9b5a5..28dff8d 100644 --- a/docs/examples/Branching.rst +++ b/docs/examples/Branching.rst @@ -101,21 +101,21 @@ The \|BranchContainer\| stores group constant data in \|HomogUniv\| objects in the >>> for key in b0.universes: ... print(key) - (0, 1.0, 1) - (10, 1.0, 1) - (20, 1.0, 1) - (30, 1.0, 1) - (20, 0, 0) - (40, 0, 0) - (20, 10.0, 2) - (10, 10.0, 2) - (0, 0, 0) - (10, 0, 0) - (0, 10.0, 2) - (30, 0, 0) - (40, 10.0, 2) - (40, 1.0, 1) - (30, 10.0, 2) + ('0", 1.0, 1) + ('10", 1.0, 1) + ('20", 1.0, 1) + ('30", 1.0, 1) + ('20", 0, 0) + ('40", 0, 0) + ('20", 10.0, 2) + ('10", 10.0, 2) + ('0", 0, 0) + ('10", 0, 0) + ('0", 10.0, 2) + ('30", 0, 0) + ('40", 10.0, 2) + ('40", 1.0, 1) + ('30", 10.0, 2) The keys here are vectors indicating the universe ID, burnup, and burnup index corresponding to the point in the burnup schedule. ``SERPENT`` @@ -129,7 +129,7 @@ the :meth:`~serpentTools.objects.BranchContainer.getUniv` method .. code:: - >>> univ0 = b0.universes[0, 1, 1] + >>> univ0 = b0.universes["0", 1, 1] >>> print(univ0) <HomogUniv 0: burnup: 1.000 MWd/kgu, step: 1> >>> print(univ0.name) @@ -295,7 +295,7 @@ variables explicitly requested are present .. code:: - >>> univ4 = b1.getUniv(0, 0) + >>> univ4 = b1.getUniv("0", 0) >>> univ4.infExp {'infTot': array([ 0.313338, 0.54515 ])} >>> univ4.b1Exp diff --git a/examples/BranchToNodalDiffusion.ipynb b/examples/BranchToNodalDiffusion.ipynb index adb329e..fbf45fd 100644 --- a/examples/BranchToNodalDiffusion.ipynb +++ b/examples/BranchToNodalDiffusion.ipynb @@ -87,16 +87,7 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ajohnson400/github/my-serpent-tools/.venv/develop/lib/python3.7/site-packages/serpentTools-0.6.1+36.g1facac0.dirty-py3.7.egg/serpentTools/xs.py:227: UserWarning: This is an experimental feature, subject to change.\n", - " UserWarning)\n" - ] - } - ], + "outputs": [], "source": [ "collector = BranchCollector(coe)" ] @@ -244,7 +235,7 @@ { "data": { "text/plain": [ - "(0, 10, 20, 30, 40)" + "('0', '10', '20', '30', '40')" ] }, "execution_count": 11, @@ -392,11 +383,11 @@ { "data": { "text/plain": [ - "{0: <serpentTools.xs.BranchedUniv at 0x7fb62f749a98>,\n", - " 10: <serpentTools.xs.BranchedUniv at 0x7fb62f731b88>,\n", - " 20: <serpentTools.xs.BranchedUniv at 0x7fb62f749e08>,\n", - " 30: <serpentTools.xs.BranchedUniv at 0x7fb62f749e58>,\n", - " 40: <serpentTools.xs.BranchedUniv at 0x7fb62f749ea8>}" + "{'0': <serpentTools.xs.BranchedUniv at 0x7f322a0458b8>,\n", + " '10': <serpentTools.xs.BranchedUniv at 0x7f322a0538b8>,\n", + " '20': <serpentTools.xs.BranchedUniv at 0x7f322a053908>,\n", + " '30': <serpentTools.xs.BranchedUniv at 0x7f322a053958>,\n", + " '40': <serpentTools.xs.BranchedUniv at 0x7f322a0539a8>}" ] }, "execution_count": 17, @@ -412,9 +403,33 @@ "cell_type": "code", "execution_count": 18, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'0': <serpentTools.xs.BranchedUniv at 0x7f322a0458b8>,\n", + " '10': <serpentTools.xs.BranchedUniv at 0x7f322a0538b8>,\n", + " '20': <serpentTools.xs.BranchedUniv at 0x7f322a053908>,\n", + " '30': <serpentTools.xs.BranchedUniv at 0x7f322a053958>,\n", + " '40': <serpentTools.xs.BranchedUniv at 0x7f322a0539a8>}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "collector.universes" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ - "u0 = collector.universes[0]" + "u0 = collector.universes['0']" ] }, { @@ -426,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -435,7 +450,7 @@ "('BOR', 'TFU')" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -446,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -455,7 +470,7 @@ "('BOR', 'TFU', 'Burnup', 'Group')" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -475,7 +490,7 @@ "(('B1000', 'B750', 'nom'), ('FT1200', 'FT600', 'nom'))" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -493,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -511,7 +526,7 @@ " 'b1Diffcoef']" ] }, - "execution_count": 37, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -531,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -540,7 +555,7 @@ "(3, 3, 3, 2)" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -551,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "scrolled": true }, @@ -598,7 +613,7 @@ " [0.206532, 0. ]]]])" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -618,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -628,7 +643,7 @@ " [1200., 600., 900.]])" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -650,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -680,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -689,7 +704,7 @@ "['boron conc', 'fuel temperature']" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -701,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -732,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -741,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -780,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -789,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -819,17 +834,17 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# write to a file \"in memory\"\n", - "out = writer.write(0)" + "out = writer.write(\"0\")" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -861,6 +876,13 @@ "source": [ "print(out[:1000])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -879,7 +901,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/Branching.ipynb b/examples/Branching.ipynb index 5b1a687..af548e6 100644 --- a/examples/Branching.ipynb +++ b/examples/Branching.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -75,33 +75,33 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{('B1000',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff79783ff28>,\n", - " ('B1000',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff79789c048>,\n", - " ('B1000',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff797820e10>,\n", - " ('B750',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff797839eb8>,\n", + "{('nom',\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7e6664c88>,\n", " ('B750',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff797853f98>,\n", - " ('B750',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff797852e10>,\n", - " ('nom',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff797832e48>,\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7b6f28>,\n", + " ('B1000',\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7c8208>,\n", " ('nom',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff79784cf28>,\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7d34e0>,\n", + " ('B750',\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7df668>,\n", + " ('B1000',\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd76a978>,\n", " ('nom',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7977c7e10>}" + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd776c18>,\n", + " ('B750',\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd780f28>,\n", + " ('B1000',\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd793278>}" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +126,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "<BranchContainer for B1000, FT600 from demo.coe>\n" + "<BranchContainer for B1000, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n" ] } ], @@ -203,21 +203,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "(0, 0, 0)\n", - "(10, 0, 0)\n", - "(20, 0, 0)\n", - "(30, 0, 0)\n", - "(40, 0, 0)\n", - "(0, 1.0, 1)\n", - "(10, 1.0, 1)\n", - "(20, 1.0, 1)\n", - "(30, 1.0, 1)\n", - "(40, 1.0, 1)\n", - "(0, 10.0, 2)\n", - "(10, 10.0, 2)\n", - "(20, 10.0, 2)\n", - "(30, 10.0, 2)\n", - "(40, 10.0, 2)\n" + "('0', 0, 0)\n", + "('10', 0, 0)\n", + "('20', 0, 0)\n", + "('30', 0, 0)\n", + "('40', 0, 0)\n", + "('0', 1.0, 1)\n", + "('10', 1.0, 1)\n", + "('20', 1.0, 1)\n", + "('30', 1.0, 1)\n", + "('40', 1.0, 1)\n", + "('0', 10.0, 2)\n", + "('10', 10.0, 2)\n", + "('20', 10.0, 2)\n", + "('30', 10.0, 2)\n", + "('40', 10.0, 2)\n" ] } ], @@ -254,7 +254,7 @@ } ], "source": [ - "univ0 = b0.universes[0, 1, 1]\n", + "univ0 = b0.universes['0', 1, 1]\n", "print(univ0)\n", "print(univ0.name)\n", "print(univ0.bu)\n", @@ -276,8 +276,8 @@ "metadata": {}, "outputs": [], "source": [ - "univ1 = b0.getUniv(0, burnup=1)\n", - "univ2 = b0.getUniv(0, index=1)\n", + "univ1 = b0.getUniv('0', burnup=1)\n", + "univ2 = b0.getUniv('0', index=1)\n", "assert univ0 is univ1 is univ2" ] }, @@ -468,22 +468,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.axes._subplots.AxesSubplot at 0x7f769a7a0da0>" + "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7bd7a3cf8>" ] }, - "execution_count": 26, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEOCAYAAABM5Pr8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZ0ZJpKkzgwTSVJnhokkqbP/A15lRo2uMdPxAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -498,12 +498,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAH7VJREFUeJzt3X2YVWW9//H3R3zAlEqF0hxoUDlH\nEcahM0IKPzLNxDToGJlPJZRZnYi6+ul16LKs6HiO5nV68IiZB0U7h1S0PKJSWqn9IoLDoAQCkkCo\nE1aAj2lE6Pf3x1qj283M7LVm9hObz+u69jXr4d73+rJms79zr3ut+1ZEYGZm1pM9ah2AmZnVPycL\nMzMrycnCzMxKcrIwM7OSnCzMzKwkJwszMyvJycLMzEpysjAzs5KcLMzMrKQ9ax1AuQwcODCam5tr\nHYaZ2S5j2bJlWyJiUJayDZMsmpubaW9vr3UYZma7DEmPZy3ry1BmZlaSk4WZmZXkZGFmZiU1TJ9F\nV/72t7/R0dHBtm3bah1KzfXv35+mpib22muvWodiZrughk4WHR0dDBgwgObmZiTVOpyaiQi2bt1K\nR0cHQ4cOrXU4ZrYLaujLUNu2beOggw7arRMFgCQOOuggt7DMrNcaOlkAu32i6OTzYGZ90dCXoXrj\nw9/7NQC3fvK4GkdiDaF9Dqy8vdZRWCM7eCScennFD9PwLYtaO/7440uW+eUvf8nRRx9Na2sra9as\nYd9996W1tfXV1/bt25k/fz6XX175D4SV2crb4Q8rax2FWZ+5ZVFhixYtKllm7ty5XHTRRUydOpWN\nGzdy+OGHs3z58teVmThxIhMnTqxUmFZJB4+EqffUOgqzPql6y0LSBElrJa2TNKObMmdKWi1plaQf\nVDvGctp///0BePDBBznhhBOYPHkyRx55JOeeey4RwezZs5k3bx4zZ87k3HPP7baeG2+8kWnTpgFw\n2223MWLECI455hjGjx8PwKpVqxg9ejStra20tLTw2GOPVf4fZ2a7jaq2LCT1A2YBJwMdwFJJ8yNi\ndUGZYcAXgbER8Yykt5Tj2F+7axWrNz1fstzqp5IynX0XPRn+tjfylfcfnTmGhx9+mFWrVvG2t72N\nsWPH8qtf/YoLLriAhQsXcvrppzN58mQ2btzI+vXraW1tBWDs2LHMmjXrdfXMnDmTe++9l0MPPZRn\nn30WgGuvvZbPfe5znHvuuWzfvp2XX345c1xmZqVU+zLUaGBdRGwAkHQLMAlYXVDmE8CsiHgGICL+\nVOUYK2b06NE0NTUB0NraysaNGxk3btxO5bq6DFVo7NixTJkyhTPPPJMzzjgDgOOOO47LLruMjo4O\nzjjjDIYNG1aZf4SZ7ZaqnSwOBZ4sWO8AxhSV+TsASb8C+gFfjYif9PXAWVsAlbwbap999nl1uV+/\nfuzYsaNX9Vx77bUsWbKEe+65h9bWVpYvX84555zDmDFjuOeeezjllFOYPXs2J554YrlCN7PdXLWT\nRVc3+0fR+p7AMOAEoAn4paQREfHsTpVJFwIXAgwZMqS8kdax9evXM2bMGMaMGcNdd93Fk08+yXPP\nPcdhhx3G9OnT2bBhAytWrHCyMLOyqXYHdwcwuGC9CdjURZk7I+JvEfE7YC1J8thJRFwXEW0R0TZo\nUKb5OxrCxRdfzMiRIxkxYgTjx4/nmGOO4dZbb2XEiBG0trby6KOP8tGPfrTWYZpZA1FE8R/2FTyY\ntCfwW+Ak4PfAUuCciFhVUGYCcHZEnC9pIPAw0BoRW3uqu62tLYonP1qzZg1HHXVUrhgb+aG83pwP\n66M5pyU/feus1SFJyyKiLUvZql6GiogdkqYB95L0R9wQEaskzQTaI2J+uu+9klYDLwMXl0oU5dSI\nScLMrK+q/lBeRCwAFhRtu7RgOYAvpC8zM6sDHu7DzMxKcrIwM7OSnCzMzKwkJ4tic0577Q4WMzMD\nnCwqbuPGjYwYMWKn7VdffTVHHHEEktiyZQsAc+bMeXVY8r333puRI0fS2trKjBldjrcIwP3338/i\nxYsrFr+ZGXiI8poZO3Ysp59+OieccMKr26ZOncrUqVMBaG5u5oEHHmDgwIE91nP//fczcOBA3vnO\nd1YyXDPbzbllUQU7duzg/PPPp6WlhcmTJ/PSSy8xatQompubM9exZcsWJk6cSEtLC8cffzyPPPII\n69evZ/bs2Vx55ZW0trZmmjvDzKw3dp+WxY9nZJux7A8rkp9Z+i0yTme4du1arr/+esaOHcvHPvYx\nrrnmGi666KLS9Rf48pe/zJgxY5g/fz733XcfU6ZMob29nQsuuICBAwfy+c9/Pld9ZmZ5uGVRBYMH\nD2bs2LEAnHfeeSxcuDB3HQsXLuQjH/kIAO9973vZtGkTL774YlnjNDPrzu7Tssg6oXkFxvKR1ON6\nFsVjeFVzTC8zM7csquCJJ57g179OBii8+eabu5zwqJTx48czd+5cAH72s5/R1NTEfvvtx4ABA3jh\nhRfKGq+ZWTEniyo46qijuOmmm2hpaeHpp5/m05/+NFdddRVNTU10dHTQ0tLCBRdc0GMdM2fOZNGi\nRbS0tHDppZcyZ84cACZNmsS8efMYNWqUO7jNrGJ2n8tQNdLc3Mzq1at32j59+nSmT5/e7fs2btz4\nuvWBAwdy11137VTuyCOPZOXKDB33ZmZ94GRRzPMOmJntxJehzMyspIZPFr5rKOHzYGZ90dDJon//\n/mzdunW3/6KMCLZu3Ur//v1rHYqZ7aIaus+i826jzZs31zqUmuvfvz9NTU21DsPMdlENnSz22msv\nhg4dWuswzMx2eQ19GcrMzMrDycLMzEpysjAzs5KcLMzMrKRMHdySDsxQ7JWIeLaP8ZiZWR3KejfU\npvTV09ja/YAhpSqSNAH4Tlp+dkRcXrR/CnAl8Pt009URMTtjnGZmVgFZk8WaiBjVUwFJD5eqRFI/\nYBZwMtABLJU0PyKKR9q7NSKmZYzNzMwqLGufxXFlKjMaWBcRGyJiO3ALMCljDGZmViOZkkVEbCtH\nGeBQ4MmC9Y50W7EPSloh6XZJg7urTNKFktoltfspbTOzysl9N5Skf+7D8brq8ygeuOkuoDkiWoCf\nATd1V1lEXBcRbRHRNmjQoD6EZWZmPSnZZyFpXuEq0Apc0cvjdQCFLYUmko7zV0XE1oLV/+zDsczM\nrEyydHA/HxGvzvkp6bt9ON5SYJikoSR3O50FnFNYQNIhEfFUujoRWNOH45mZWRlkSRaXFa1f0tuD\nRcQOSdOAe0lunb0hIlZJmgm0R8R8YLqkicAO4GlgSm+PZ2Zm5VEyWUTE7wAkDYyILRHxdF8OGBEL\ngAVF2y4tWP4i8MW+HMPMzMorTwf3DRWLwszM6lqeZNHT09tmZtbA8iSL3XtuUjOz3ZhbFmZmVlKe\nZOFOZzOz3VTmZBERj1QyEDMzq19ZR50FQFIbyXMWb0/fKyDSoTnMzKxB5UoWwFzgYmAl8Er5wzEz\ns3qUN1lsTp+yNjOz3UjeZPEVSbOBnwN/7dwYET8qa1RmZlZX8iaLqcCRwF68dhkqACcLM7MGljdZ\nHBMRIysSiZmZ1a28kx8tljS8IpGYmVndytuyGAecL+l3JH0WvnXWzGw3kDdZTKhIFGZmVtfyXoaa\nCTwXEY9HxOPA88BXyh+WmZnVk7zJoiUinu1ciYhngFHlDcnMzOpN3mSxh6QDOlckHUj+S1lmZraL\nyftF/+/AIkm3kzxfcSY7z9FtZmYNJleyiIjvS2oHTiS5E+qMiFhdkcjMzKxu5L6ElCYHJwgzs91I\npj4LSQ+Vo4yZme2asrYsjpK0oof9At5UhnjMzKwOZU0WR2Yo83LWg0qaAHwH6AfMjojLuyk3GbgN\nODYi2rPWb2Zm5ZUpWaQP4JWFpH7ALOBkoANYKml+cUe5pAHAdGBJuY5tZma9k/c5i3IYDayLiA0R\nsR24BZjURbmvA98AtlUzODMz21muZCGpHEN7HAo8WbDekW4rPM4oYHBE3F0ingsltUtq37x5cxlC\nMzOzrvRmprw3AAcCDwG3pEN+5KEutsWrO6U9gG8BU0pVFBHXAdcBtLW1RYniZmbWS3kvQwXJZaF7\ngcEkT3Mfk7OOjvS9nZqATQXrA4ARwIOSNgLvBOZLast5HDMzK5O8LYtHI6LzUtTtkm4EriV5ojur\npcAwSUOB3wNnAed07oyI54CBneuSHgQu8t1QZma1k7dlsUXSP3SuRMRvgUF5KoiIHcA0ktbJGmBe\nRKySNFPSxJzxmJlZFeRtWUwHbpG0DFgJtAC/y3vQiFgALCjadmk3ZU/IW7+ZmZVXrpZFRPwGaAVu\nTjc9AJxd7qDMzKy+9GYgwb8C96QvMzPbDWROFpJGAxERSyUNJ5mP+9H0kpKZmTWwTMkifRjvVGBP\nST8FxgAPAjMkjYoIT4BkZtbAsrYsJpP0VewD/AFoiojnJV1JMnaTk4WZWQPL2sG9IyJejoiXgPUR\n8TxARPwFeKVi0ZmZWV3Imiy2p8N8ALz6nIWkN+FkYWbW8LJehhqf3gVFRBQmh72A88selZmZ1ZVM\nLYvORAEg6eSC7VuAgysQl5mZ1ZHezGdxRYl1MzNrMOWY/KirIcfNzKyB5Hkobw7JEOVDJN0AEBEf\nq1RgZmZWP/IM93Fj+vP/ADeVPxQzM6tXmZNFRPwCQNILncudu8oelZmZ1ZXe9FlsL7FuZmYNJney\niIh39rRuZmaNpxx3Q5mZWYNzsjAzs5KcLMzMrCQnCzMzK8nJwszMSso9B3fD+fEM+MPKWkdhjeoP\nK+HgkbWOwqzP3LIwq6SDR8LIybWOwqzPcrUsJO0DfBBoLnxvRMzMWc8E4DtAP2B2RFxetP9TwGeA\nl4E/AxdGxOo8x8js1MtLlzEz283lbVncCUwCdgAvFrwyk9QPmAWcCgwHzpY0vKjYDyJiZES0At8A\nvpkzTjMzK6O8fRZNETGhj8ccDayLiA0Akm4hSUCvthw65/hO7YfHnzIzq6m8LYtFkvraW3co8GTB\neke67XUkfUbSepKWxfSuKpJ0oaR2Se2bN2/uY1hmZtadvMliHLBM0lpJKyStlLQiZx1dTZa0U8sh\nImZFxOHAPwNf6qqiiLguItoiom3QoEE5wzAzs6zyXoY6tQzH7AAGF6w3AZt6KH8L8N0yHNfMzHop\nV8siIh4H3gy8P329Od2Wx1JgmKShkvYGzgLmFxaQNKxg9TTgsZzHMDOzMsqVLCR9DpgLvCV9/bek\nz+apIyJ2ANOAe4E1wLyIWCVppqSJabFpklZJWg58ATg/zzHMzKy8FJH9RqO0f+K4iHgxXd8P+HVE\ntFQovsza2tqivb291mGYme0yJC2LiLYsZfN2cIvkQblOL9N1h7WZmTWQvB3cc4Alku5I1z8AXF/e\nkMzMrN7kShYR8U1JvwDGkrQopkbEwxWJzMzM6kbuUWcjYhmwrAKxmJlZncqULCQtjIhxkl7g9Q/Q\nCYiIeGNFojMzs7qQKVlExLj054DKhmNmZvUo73MWV2TZZmZmjSXvrbMnd7GtHEOAmJlZHcvaZ/Fp\n4J+Aw4sGDhwALKpEYGZmVj+y3g31A+DHwL8BMwq2vxART5c9KjMzqyuZLkNFxHMRsRHYDjwXEY+n\nAwiGpBsqGaCZmdVe3j6Lloh4tnMlIp4BRpU3JDMzqzd5k8Uekg7oXJF0IL14sM/MzHYteb/o/x34\ntaTbSB7OOxO4rOxRmZlZXck7NtT3JbUDJ5I8vX1GRKyuSGRmZlY38j6UJ+AdwIER8R/AnyWNrkhk\nZmZWN/L2WVwDHAecna6/AMwqa0RmZlZ38vZZjImId0h6GJK7odJ5tM3MrIHlbVn8TVI/0pFnJQ0C\nXil7VGZmVlfyJourgDuAt0i6DFgI/GvZozIzs7qS926ouZKWASeR3A31gYhYU5HIzMysbvRmprxH\ngUcrEIuZmdWpTJehJB0r6eCC9Y9KulPSVelT3GZm1sCy9ll8j2QQQSSNBy4Hvg88B1yX54CSJkha\nK2mdpBld7P+CpNWSVkj6uaS356nfzMzKL2uy6FcwFPmHgesi4ocR8WXgiKwHS++kmkUyYdJw4GxJ\nw4uKPQy0RUQLcDvwjaz1m5lZZWROFpI6+zdOAu4v2Jen32M0sC4iNkTEduAWYFJhgYh4ICJeSlcX\nA0056jczswrI+kV/M/ALSVuAvwC/BJB0BMmlqKwOBZ4sWO8AxvRQ/uMkky6ZmVkNZUoWEXGZpJ8D\nhwD3RUSku/YAPpvjeOqq+i4LSucBbcC7uq1MuhC4EGDIkCE5wjAzszwyX0KKiMVdbPttzuN1AIML\n1puATcWFJL0HuAR4V0T8tYeYriPtYG9ra+sy6ZiZWd/lfYK7r5YCwyQNTceUOguYX1hA0iiSu68m\nRsSfqhyfmZl1oarJIiJ2ANOAe4E1wLyIWCVppqSJabErgf2B2yQtlzS/m+rMzKxKcj3BLelDwE8i\n4gVJXyKZ2+JfIuKhrHVExAJgQdG2SwuW35MnJjMzq7y8LYsvp4liHHAKcBPw3fKHZWZm9SRvsng5\n/Xka8N2IuBPwfBZmZg0ub7L4vaTvAWcCCyTt04s6zMxsF5P3i/5Mks7pCRHxLHAAcHHZozIzs7qS\nN1mcBvw0Ih5LO7ivAbaUPywzM6sn7uA2M7OS3MFtZmYluYPbzMxK6msH94G4g9vMrOHlShbpPBPr\ngVMkTQPeEhH3VSQyMzOrG7mShaTPAXOBt6Sv/5aUZ4hyMzPbBeUaG4pkMqIxEfEigKQrgF8D/1Hu\nwMzMrH7k7bMQr90RRbrc1YRGZmbWQPK2LOYASyTdka5/ALi+vCGZmVm9yZUsIuKbkh4ExpG0KKZG\nxMOVCMzMzOpH5mQhSUBTOndF5vkrzMxs15e5zyIiAvifCsZiZmZ1Km8H92JJx1YkEjMzq1t5O7jf\nDXxS0uPAi50bI6KlrFGZmVldyZQsJB0BvBU4tWjX24FN5Q7KzMzqS9bLUN8GXoiIxwtfwEvAtyoX\nnpmZ1YOsyaI5IlYUb4yIdqC5rBGZmVndyZos+vewb99yBGJmZvUra7JYKukTxRslfRxYlueAkiZI\nWitpnaQZXewfL+khSTskTc5Tt5mZVUbWu6E+D9wh6VxeSw5tJLPk/WPWg0nqB8wCTgY6SJLQ/IhY\nXVDsCWAKcFHWes3MrLIyJYuI+CNwvKR3AyPSzfdExP05jzcaWBcRGwAk3QJMAl5NFhGxMd33Ss66\nzcysQvKODfUA8EAfjnco8GTBegcwpg/1mZlZFVR7/uyuhjOPXlcmXSipXVL75s2b+xCWmZn1pNrJ\nogMYXLDeRB8e6ouI6yKiLSLaBg0a1OfgzMysa9VOFkuBYZKGStobOAuYX+UYzMwsp6omi4jYAUwD\n7gXWAPMiYpWkmZImAkg6VlIH8CHge5JWVTNGMzPbWd6BBPssIhYAC4q2XVqwvJTk8pSZmdWJal+G\nMjOzXZCThZmZleRkYWZmJTlZmJlZSU4WZmZWkpOFmZmV5GRhZmYlVf05C7PdyQ+WPMGdy39f6zCs\ngQ1/2xv5yvuPrvhx3LIwq6A7l/+e1U89X+swzPrMLQuzCht+yBu59ZPH1ToMsz5xy8LMzEpysjAz\ns5KcLMzMrCQnCzMzK8nJwszMSnKyMDOzkpwszMysJCcLMzMrycnCzMxKcrIwM7OSnCzMzKwkJwsz\nMyvJycLMzEqqSbKQNEHSWknrJM3oYv8+km5N9y+R1Fz9KM3MrFPVk4WkfsAs4FRgOHC2pOFFxT4O\nPBMRRwDfAq6obpRmZlaoFvNZjAbWRcQGAEm3AJOA1QVlJgFfTZdvB66WpIiIcgfztbtWsXqTJ6ex\nylj91PMMP+SNtQ7DrM9qcRnqUODJgvWOdFuXZSJiB/AccFBVojMro+GHvJFJrcUfb7NdTy1aFupi\nW3GLIUsZJF0IXAgwZMiQXgVTjblrzcx2dbVoWXQAgwvWm4BN3ZWRtCfwJuDp4ooi4rqIaIuItkGD\nBlUoXDMzq0WyWAoMkzRU0t7AWcD8ojLzgfPT5cnA/ZXorzAzs2yqfhkqInZImgbcC/QDboiIVZJm\nAu0RMR+4HvgvSetIWhRnVTtOMzN7TS36LIiIBcCCom2XFixvAz5U7bjMzKxrfoLbzMxKcrIwM7OS\nnCzMzKwkJwszMytJjXJHqqTNwOO9fPtAYEsZwykXx5WP48rHceXTiHG9PSIyPaTWMMmiLyS1R0Rb\nreMo5rjycVz5OK58dve4fBnKzMxKcrIwM7OSnCwS19U6gG44rnwcVz6OK5/dOi73WZiZWUluWZiZ\nWUkNnyz6Mt+3pC+m29dKOqWKMX1B0mpJKyT9XNLbC/a9LGl5+ioerbcasU2RtLkghgsK9p0v6bH0\ndX7xeysc17cKYvqtpGcL9lXknEm6QdKfJD3SzX5JuiqNeYWkdxTsq+S5KhXXuWk8KyQtknRMwb6N\nklam56q9ynGdIOm5gt/VpQX7evz9VziuiwtieiT9PB2Y7qvk+Ros6QFJayStkvS5LspU7zMWEQ37\nIhnVdj1wGLA38BtgeFGZfwKuTZfPAm5Nl4en5fcBhqb19KtSTO8G3pAuf7ozpnT9zzU+X1OAq7t4\n74HAhvTnAenyAdWKq6j8Z0lGM67oOQPGA+8AHulm//uAH5NM5vVOYEmlz1XGuI7vPB5wamdc6fpG\nYGCNztcJwN19/f2XO66isu8nmTKhGufrEOAd6fIA4Ldd/H+s2mes0VsWr873HRHbgc75vgtNAm5K\nl28HTpKkdPstEfHXiPgdsC6tr+IxRcQDEfFSurqYZIKoashyvrpzCvDTiHg6Ip4BfgpMqFFcZwM3\nl+nY3YqI/0cXk3IVmAR8PxKLgTdLOoTKnquScUXEovS4UMXPV4bz1Z2+fC7LHVdVPlsAEfFURDyU\nLr8ArGHnKair9hlr9GTRl/m+s7y3UjEV+jjJXw6d+ktql7RY0gfKEE9vYvtg2uS9XVLnrIeVOl+5\n6k4v2Q0F7i/YXMlz1pPu4q7kucqr+PMVwH2SlimZtrjajpP0G0k/ltQ553FdnC9JbyD5wv1hweaq\nnC8ll8dHAUuKdlXtM1aT+SyqqC/zfWeaB7wXMtcr6TygDXhXweYhEbFJ0mHA/ZJWRsT6MsSVNba7\ngJsj4q+SPkXSKjsx43srGVens4DbI+Llgm2VPGc9qfZnKxdJ7yZJFuMKNo9Nz9VbgJ9KejT9y7sa\nHiIZfuLPkt4H/A8wjDo5XySXoH4VEYWtkIqfL0n7kySoz0fE88W7u3hLRT5jjd6y6Mt831neW6mY\nkPQe4BJgYkT8tXN7RGxKf24AHiT5a6NcSsYWEVsL4vlP4B+yvreScRU4i6LLBBU+Zz3pLu5KnqtM\nJLUAs4FJEbG1c3vBufoTcAflufSaSUQ8HxF/TpcXAHtJGkgdnK9UT5+tipwvSXuRJIq5EfGjLopU\n7zNWiY6ZenmRtJw2kFyW6OwYO7qozGd4fQf3vHT5aF7fwb2B8nRwZ4lpFEmH3rCi7QcA+6TLA4HH\nKG9HX5bYDilY/kdgcbzWofa7NMYD0uUDqxVXWu7vSTocVcVz1kz3Hban8frOx/+t9LnKGNcQkj64\n44u27wcMKFheBEyoYlwHd/7uSL50n0jPXabff6XiSvd3/hG5X7XOV/pv/z7w7R7KVO0zVraTXa8v\nkrsFfkvy5XtJum0myV/sAP2B29L/PP8LHFbw3kvS960FTq1iTD8D/ggsT1/z0+3HAyvT/ywrgY/X\n4Hz9G7AqjeEB4MiC934sPY/rgKnVjCtd/ypwedH7KnbOSP7KfAr4G8lfch8HPgV8Kt0vYFYa80qg\nrUrnqlRcs4FnCj5f7en2w9Lz9Jv0d3xJleOaVvDZWkxBMuvq91+tuNIyU0hueCl8X6XP1ziSS0cr\nCn5X76vVZ8xPcJuZWUmN3mdhZmZl4GRhZmYlOVmYmVlJThZmZlaSk4WZmZXkZGENrWjE2eXlHrG0\ntyTtKelf0xFBO2O7pNZxmXWn0Yf7MPtLRLSWs0JJe0Yyjlhf/AvJQ2gjI2KbpAHA/+3iWCJ5UO2V\nPh7PrE/csrDdUjoPwdckPZTOR3Bkun2/dH6DpZIeljQp3T5F0m2S7iIZOG4PSdek8wzcLWmBpMmS\nTpJ0R8FxTpb0o6JjvwH4BPDZiNgGyaiiEfHVdH9zOofBNSTjJQ2WdHYa5yOSriio688Fy5Ml3Zgu\n3yjpWkm/VDK/x+kVOZG223CysEa3b9FlqA8X7NsSEe8AvgtclG67hGS+gmNJ5hW5UtJ+6b7jgPMj\n4kTgDJIhIkYCF6T7IBnt9ihJg9L1qcCcopiOAJ6IZNjp7vw9ydDTo0ieLL6CZMDGVuDYjKPnNpMM\nQnkacK2k/hneY9YlJwtrdH+JiNaC160F+zr/4l9G8sUK8F5ghqTlJIMO9icZSwnS+QHS5XHAbRHx\nSkT8gWToEyIZEuG/gPMkvZkkiRQOAb4TSVPTRPZkwZDvj0cyPwHAscCDEbE5vfw1l2TCnlLmpfE9\nRjK20pEZ3mPWJfdZ2O6sc/Tcl3nt/4KAD0bE2sKCksYALxZu6qHeOSRDuW8jSSjF/RvrgCGSBqSX\nn+YAc5RM69kvLZP1WIXj9RS3HIrH8vHYPtZrblmYvd69wGfTjmUkdTec+UKSSaD2kPRWkilBgVeH\nrd4EfAm4sfiNkcyCeD1wdeelIUn9SEZU7coS4F2SBqblzgZ+ke77o6SjJO1BMgpwoQ+l8R1OMujd\nWsx6yS0La3T7ppeUOv0kInq6ffbrwLeBFWnC2Ah01Tn8Q+Ak4BGS0VCXkMyy2GkuMCgiVndznEvS\nYz0i6QXgLyQTSW0C3lZYMCKekvRFkktdAhZExJ3p7hnA3SSzoj0C7F/w1rUkSeWtJKOUbuvh323W\nI486a9ZLkvaPZFa3g0iGtx+b9l8g6Wrg4Yi4vkax3QjcHRG31+L41njcsjDrvbvTTuy9ga8XJIpl\nJH0OOz03YbarcsvCzMxKcge3mZmV5GRhZmYlOVmYmVlJThZmZlaSk4WZmZXkZGFmZiX9f4MVG8yF\nwTyCAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -533,22 +533,22 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "('nom', 'nom') <BranchContainer for nom, nom from demo.coe>\n", - "('B750', 'nom') <BranchContainer for B750, nom from demo.coe>\n", - "('B1000', 'nom') <BranchContainer for B1000, nom from demo.coe>\n", - "('nom', 'FT1200') <BranchContainer for nom, FT1200 from demo.coe>\n", - "('B750', 'FT1200') <BranchContainer for B750, FT1200 from demo.coe>\n", - "('B1000', 'FT1200') <BranchContainer for B1000, FT1200 from demo.coe>\n", - "('nom', 'FT600') <BranchContainer for nom, FT600 from demo.coe>\n", - "('B750', 'FT600') <BranchContainer for B750, FT600 from demo.coe>\n", - "('B1000', 'FT600') <BranchContainer for B1000, FT600 from demo.coe>\n" + "('nom', 'nom') <BranchContainer for nom, nom from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('B750', 'nom') <BranchContainer for B750, nom from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('B1000', 'nom') <BranchContainer for B1000, nom from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('nom', 'FT1200') <BranchContainer for nom, FT1200 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('B750', 'FT1200') <BranchContainer for B750, FT1200 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('B1000', 'FT1200') <BranchContainer for B1000, FT1200 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('nom', 'FT600') <BranchContainer for nom, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('B750', 'FT600') <BranchContainer for B750, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", + "('B1000', 'FT600') <BranchContainer for B1000, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n" ] } ], @@ -588,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -618,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -640,7 +640,7 @@ " 'TFU': 600}" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -668,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -677,19 +677,19 @@ "{'infTot': array([0.313338, 0.54515 ])}" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "univ4 = b1.getUniv(0, 0)\n", + "univ4 = b1.getUniv('0', 0)\n", "univ4.infExp" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -698,7 +698,7 @@ "{}" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -732,7 +732,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/serpentTools/objects/containers.py b/serpentTools/objects/containers.py index 23bd7e4..8a8984c 100644 --- a/serpentTools/objects/containers.py +++ b/serpentTools/objects/containers.py @@ -9,8 +9,10 @@ Contents """ from re import compile from itertools import product +from warnings import warn +from numbers import Real -from six import iteritems +from six import iteritems, PY2 from matplotlib import pyplot from numpy import array, arange, hstack, ndarray, zeros_like @@ -39,6 +41,11 @@ from serpentTools.messages import ( error, ) +if PY2: + from collections import Iterable +else: + from collections.abc import Iterable + SCATTER_MATS = set() SCATTER_ORDERS = 8 @@ -614,6 +621,33 @@ class HomogUniv(NamedObject): compareGCData.__doc__ = __docCompare.format(qty='gc') +# remove for versions >= 0.8.0 + +class _BranchContainerUnivDict(dict): + """ + Workaround for supporting integer and string universe ids + + Keys are of the form ``univID, index, burnup`` + """ + + def __getitem__(self, key): + if not isinstance(key, Iterable): + raise KeyError(key) + if isinstance(key[0], Real) and key not in self: + warn("Universe ids are stored as unconverted strings, not int. " + "Support for previous integer-access will be removed in " + "future versions.", + FutureWarning) + key = (str(key[0]), ) + key[1:] + return dict.__getitem__(self, key) + + def get(self, key, default=None): + try: + return self[key] + except KeyError: + return default + + class BranchContainer(BaseObject): """ Class that stores data for a single branch. @@ -652,7 +686,8 @@ class BranchContainer(BaseObject): self.filePath = filePath self.branchID = branchID self.stateData = stateData - self.universes = {} + # Revert to dict for version >= 0.8.0 + self.universes = _BranchContainerUnivDict() self.branchNames = branchNames self.__orderedUniverses = None self.__keys = set() @@ -733,9 +768,15 @@ class BranchContainer(BaseObject): If burnup and index are given, burnup is used to search + ..warning:: + + Future versions will store read and store universes from + coefficient files as generic strings, without integer + conversion + Parameters ---------- - univID: int + univID: str Unique ID for the desired universe burnup: float or int Burnup [MWd/kgU] of the desired universe @@ -744,7 +785,7 @@ class BranchContainer(BaseObject): Returns ------- - :py:class:`~serpentTools.objects.containers.HomogUniv` + :class:`~serpentTools.objects.containers.HomogUniv` Requested universe Raises diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index 8ba6fb4..d2a491a 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -110,11 +110,12 @@ class BranchingReader(XSReader): def _processBranchUniverses(self, branch, burnup, burnupIndex): """Add universe data to this branch at this burnup.""" - unvID, numVariables = [int(xx) for xx in self._advance()] + unvID, numVariables = self._advance() + numVariables = int(numVariables) univ = branch.addUniverse(unvID, burnup, burnupIndex - 1) for step in range(numVariables): splitList = self._advance( - possibleEndOfFile=step == numVariables - 1) + possibleEndOfFile=(step == numVariables - 1)) varName = splitList[0] varValues = [float(xx) for xx in splitList[2:]] if not varValues: diff --git a/serpentTools/xs.py b/serpentTools/xs.py index 6ca006c..44edf12 100644 --- a/serpentTools/xs.py +++ b/serpentTools/xs.py @@ -5,6 +5,8 @@ branching file from itertools import product from warnings import warn +from numbers import Real + from six import iteritems from six.moves import range from numpy import empty, nan, array, ndarray @@ -18,6 +20,35 @@ __all__ = [ ] +# remove for versions >= 0.8.0 + + +class IntToStringDict(dict): + """Dictionary that allows accessing string keys with Reals + + Used to mitigate API changes in how universe keys are stored + in BranchingReader and BranchCollector objects. + """ + + def __getitem__(self, key): + if key in self: + return dict.__getitem__(self, key) + if isinstance(key, Real) and float(key) in self: + warn("Universes will be stored as unconverted strings in future " + "versions", FutureWarning) + return dict.__getitem__(self, str(key)) + raise KeyError(key) + + def get(self, key, default=None): + if key in self: + return dict.__getitem__(self, key) + if isinstance(key, Real) and float(key) in self: + warn("Universes will be stored as unconverted strings in future " + "versions", FutureWarning) + return dict.__getitem__(self, str(key)) + return dict.get(self, key, default) + + class BranchedUniv(object): """ Class for storing cross sections for a single universe across branches @@ -224,7 +255,8 @@ class BranchCollector(object): self.filePath = reader.filePath self._branches = reader.branches self.xsTables = {} - self.universes = {} + # Revert do dict for version >= 0.8.0 + self.universes = IntToStringDict() self._perturbations = None self._states = None self._axis = None	Support for non-integer named universes for branching reader ## Summary of issue Serpent 2 allows universe names to be quite generic string literals. However, the branching reader of serpentTools only seems to support universe names that are integers. This seems to be related to the int conversion on line 113 of serpentTools/parsers/branching.py (commit dd886ff): `unvID, numVariables = [int(xx) for xx in self._advance()]` Simply limiting the int conversion to numVariables reads the universe in without problems. ## Code for reproducing the issue ``` #!/usr/bin/env python3 # -- coding: utf-8 -- import serpentTools # --- Try to read in a .coe file with a non-integer universe name serpentTools.read("./problematic.coe") ``` problematic.coe: ``` 1 1 1 1 1 1 nom 3 VERSION 2.1.31 DATE 19/07/10 TIME 14:27:27 0 1 1 -u_SI_RR01 66 INF_FLX 2 5.74041E+14 3.00746E+13 INF_KINF 1 0.00000E+00 INF_REP_TIME 0 INF_PROMPT_LIFE 0 INF_TOT 2 5.78347E-01 1.28879E+00 INF_CAPT 2 3.99876E-03 9.30813E-02 INF_FISS 2 0.00000E+00 0.00000E+00 INF_NSF 2 0.00000E+00 0.00000E+00 INF_KAPPA 2 0.00000E+00 0.00000E+00 INF_INVV 2 5.83143E-08 2.27285E-06 INF_NUBAR 2 0.00000E+00 0.00000E+00 INF_ABS 2 3.99876E-03 9.30813E-02 INF_REMXS 2 7.45100E-03 1.03549E-01 INF_RABSXS 2 3.99876E-03 9.30813E-02 INF_CHIT 2 0.00000E+00 0.00000E+00 INF_CHIP 2 0.00000E+00 0.00000E+00 INF_CHID 2 0.00000E+00 0.00000E+00 INF_I135_YIELD 2 0.00000E+00 0.00000E+00 INF_XE135_YIELD 2 0.00000E+00 0.00000E+00 INF_XE135M_YIELD 2 0.00000E+00 0.00000E+00 INF_PM149_YIELD 2 0.00000E+00 0.00000E+00 INF_SM149_YIELD 2 0.00000E+00 0.00000E+00 INF_I135_MICRO_ABS 2 0.00000E+00 0.00000E+00 INF_XE135_MICRO_ABS 2 0.00000E+00 0.00000E+00 INF_XE135M_MICRO_ABS 2 0.00000E+00 0.00000E+00 INF_PM149_MICRO_ABS 2 0.00000E+00 0.00000E+00 INF_SM149_MICRO_ABS 2 0.00000E+00 0.00000E+00 INF_I135_MACRO_ABS 0 INF_XE135_MACRO_ABS 2 0.00000E+00 0.00000E+00 INF_XE135M_MACRO_ABS 2 0.00000E+00 0.00000E+00 INF_PM149_MACRO_ABS 0 INF_SM149_MACRO_ABS 2 0.00000E+00 0.00000E+00 INF_S0 4 5.70896E-01 3.51081E-03 4.01709E-03 1.18524E+00 INF_S1 4 6.95375E-02 4.69803E-04 9.90811E-05 1.36274E-01 INF_S2 4 2.99465E-02 -3.00374E-04 5.04317E-04 1.47021E-02 INF_S3 4 5.67487E-03 -1.63405E-04 -9.41070E-04 2.33707E-02 INF_S4 4 -6.29423E-04 1.48058E-05 -3.04844E-04 9.08625E-03 INF_S5 4 -8.77400E-04 5.21018E-05 -7.86868E-04 1.47220E-02 INF_S6 4 -1.00766E-03 -3.48433E-05 7.28146E-04 8.25812E-03 INF_S7 4 3.55102E-04 9.54221E-05 -1.89967E-04 3.26062E-03 INF_SP0 4 5.70896E-01 3.51081E-03 4.01709E-03 1.18524E+00 INF_SP1 4 6.95375E-02 4.69803E-04 9.90811E-05 1.36274E-01 INF_SP2 4 2.99465E-02 -3.00374E-04 5.04317E-04 1.47021E-02 INF_SP3 4 5.67487E-03 -1.63405E-04 -9.41070E-04 2.33707E-02 INF_SP4 4 -6.29423E-04 1.48058E-05 -3.04844E-04 9.08625E-03 INF_SP5 4 -8.77400E-04 5.21018E-05 -7.86868E-04 1.47220E-02 INF_SP6 4 -1.00766E-03 -3.48433E-05 7.28146E-04 8.25812E-03 INF_SP7 4 3.55102E-04 9.54221E-05 -1.89967E-04 3.26062E-03 INF_SCATT0 2 5.74407E-01 1.18926E+00 INF_SCATT1 2 7.00073E-02 1.36373E-01 INF_SCATT2 2 2.96461E-02 1.52065E-02 INF_SCATT3 2 5.51146E-03 2.24297E-02 INF_SCATT4 2 -6.14618E-04 8.78141E-03 INF_SCATT5 2 -8.25298E-04 1.39351E-02 INF_SCATT6 2 -1.04250E-03 8.98627E-03 INF_SCATT7 2 4.50524E-04 3.07065E-03 INF_SCATTP0 2 5.``` 74407E-01 1.18926E+00 INF_SCATTP1 2 7.00073E-02 1.36373E-01 INF_SCATTP2 2 2.96461E-02 1.52065E-02 INF_SCATTP3 2 5.51146E-03 2.24297E-02 INF_SCATTP4 2 -6.14618E-04 8.78141E-03 INF_SCATTP5 2 -8.25298E-04 1.39351E-02 INF_SCATTP6 2 -1.04250E-03 8.98627E-03 INF_SCATTP7 2 4.50524E-04 3.07065E-03 INF_TRANSPXS 2 3.32000E-01 1.06608E+00 INF_DIFFCOEF 2 1.00402E+00 3.12672E-01 ``` ## Actual outcome including console output and error traceback if applicable ``` Traceback (most recent call last): File "test.py", line 8, in <module> serpentTools.read("./problematic.coe") File "/usr/local/lib/python3.6/dist-packages/serpentTools-0.7.0-py3.6.egg/serpentTools/parsers/__init__.py", line 155, in read returnedFromLoader.read() File "/usr/local/lib/python3.6/dist-packages/serpentTools-0.7.0-py3.6.egg/serpentTools/parsers/base.py", line 52, in read self._read() File "/usr/local/lib/python3.6/dist-packages/serpentTools-0.7.0-py3.6.egg/serpentTools/parsers/branching.py", line 48, in _read self._processBranchBlock() File "/usr/local/lib/python3.6/dist-packages/serpentTools-0.7.0-py3.6.egg/serpentTools/parsers/branching.py", line 75, in _processBranchBlock self._whereAmI['buIndx']) File "/usr/local/lib/python3.6/dist-packages/serpentTools-0.7.0-py3.6.egg/serpentTools/parsers/branching.py", line 113, in _processBranchUniverses unvID, numVariables = [int(xx) for xx in self._advance()] File "/usr/local/lib/python3.6/dist-packages/serpentTools-0.7.0-py3.6.egg/serpentTools/parsers/branching.py", line 113, in <listcomp> unvID, numVariables = [int(xx) for xx in self._advance()] ValueError: invalid literal for int() with base 10: '-u_SI_RR01' ``` ## Expected outcome ## Versions * Version from ``serpentTools.__version__``: 0.7.0 (commit dd886ff)	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_branching.py b/serpentTools/tests/test_branching.py index 9780cd4..7be45e2 100644 --- a/serpentTools/tests/test_branching.py +++ b/serpentTools/tests/test_branching.py @@ -23,7 +23,7 @@ class _BranchTesterHelper(unittest.TestCase): cls.expectedBranches = {('nom', 'nom', 'nom')} cls.expectedUniverses = { # universe id, burnup, step - (0, 0, 0), + ("0", 0, 0), } with rc: rc['serpentVersion'] = '2.1.29' @@ -78,10 +78,16 @@ class BranchContainerTester(_BranchTesterHelper): def setUpClass(cls): _BranchTesterHelper.setUpClass() cls.refBranchID = ('nom', 'nom', 'nom') - cls.refUnivKey = (0, 0, 0) + cls.refUnivKey = ("0", 0, 0) cls.refBranch = cls.reader.branches[cls.refBranchID] cls.refUniv = cls.refBranch.universes[cls.refUnivKey] + def test_universeIDWorkaround(self): + """Test that, for now, integer universe ids work""" + # Remove this test for versions >= 0.8.0 + key = (int(self.refUnivKey[0]), ) + self.refUnivKey[1:] + self.assertIs(self.refUniv, self.refBranch.universes[key]) + def test_loadedUniv(self): """Verify the reference universe has the correct data loaded""" assortedExpected = { @@ -149,7 +155,7 @@ class BranchWithUncsTester(unittest.TestCase): ], }, } - BRANCH_UNIVKEY = (0, 0, 0) + BRANCH_UNIVKEY = ("0", 0, 0) def setUp(self): fp = getFile('demo_uncs.coe') diff --git a/serpentTools/tests/test_xs.py b/serpentTools/tests/test_xs.py index ee3e31c..91dc047 100644 --- a/serpentTools/tests/test_xs.py +++ b/serpentTools/tests/test_xs.py @@ -27,36 +27,36 @@ class CollectedHelper(_BranchTestHelper): ('nom', 'nom'): { # branch 'infTot': { # group constant 0: { # burnup - 0: array([2.10873E-01, 2.23528E-01]), # univ: value - 20: array([2.10868E-01, 1.22701E-01]), + "0": array([2.10873E-01, 2.23528E-01]), # univ: value + "20": array([2.10868E-01, 1.22701E-01]), }, 1: { - 0: array([2.08646E-01, 0.00000E+00]), - 20: array([2.08083E-01, 0.00000E+00]), + "0": array([2.08646E-01, 0.00000E+00]), + "20": array([2.08083E-01, 0.00000E+00]), }, }, 'b1Diffcoef': { 0: { - 0: array([1.80449E+00, 1.97809E+00]), - 20: array([1.80519E+00, 0.00000E+00]), + "0": array([1.80449E+00, 1.97809E+00]), + "20": array([1.80519E+00, 0.00000E+00]), }, }, }, ('B750', 'FT1200'): { 'infTot': { 0: { - 0: array([3.13772E-01, 5.41505E-01]), - 20: array([3.13711E-01, 5.41453E-01]), + "0": array([3.13772E-01, 5.41505E-01]), + "20": array([3.13711E-01, 5.41453E-01]), }, 1: { - 0: array([3.11335E-01, 6.09197E-01]), - 20: array([3.11729E-01, 8.37580E-01]), + "0": array([3.11335E-01, 6.09197E-01]), + "20": array([3.11729E-01, 8.37580E-01]), }, }, 'b1Diffcoef': { 0: { - 0: array([1.75127E+00, 8.63097E-01]), - 20: array([1.75206E+00, 8.62754E-01]), + "0": array([1.75127E+00, 8.63097E-01]), + "20": array([1.75206E+00, 8.62754E-01]), }, }, },	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 1, "test_score": 1 }, "num_modified_files": 8 }	0.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.19.5 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==3.13 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@b3f3c39609319985f7047dd6fed52654e285cd0b#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.19.5 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==3.13 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_branching.py::BranchTester::test_branchingUniverses", "serpentTools/tests/test_branching.py::BranchContainerTester::test_cannotAddBurnupDays", "serpentTools/tests/test_branching.py::BranchContainerTester::test_containerGetUniv", "serpentTools/tests/test_branching.py::BranchContainerTester::test_loadedUniv", "serpentTools/tests/test_branching.py::BranchContainerTester::test_universeIDWorkaround", "serpentTools/tests/test_branching.py::BranchWithUncsTester::test_valsWithUncs", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_xsTables", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_xsTables", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_xsTables" ]	[]	[ "serpentTools/tests/test_branching.py::BranchTester::test_raiseError", "serpentTools/tests/test_branching.py::BranchTester::test_variables", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setAxis", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setBurnup", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setPert", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setPertStates", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setAxis", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setBurnup", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setPert", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setPertStates", "serpentTools/tests/test_xs.py::BranchUnivTester::test_xsTables", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setAxis", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setBurnup", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setPert", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setPertStates", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setAxis", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setBurnup", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setPert", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setPertStates", "serpentTools/tests/test_xs.py::BareBranchContainerTester::test_bareBranchedContainer" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-324	7d9899c1c7ee03797d109211a9416d55ca5c5064	2019-07-23 23:15:49	09a5feab5b95f98aef4f7dc7b07edbf8e458f955		diff --git a/docs/changelog.rst b/docs/changelog.rst index 606a4f7..706620d 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -16,22 +16,11 @@ Next :meth:`~serpentTools.objects.CartesianDetector.meshPlot`, where only data greater than ``thresh`` is plotted. * Mitigate pending deprecated imports from ``collections`` - :issue:`313` -* Increase required version of :term:`yaml` to ``5.1.1`` Bug fixes --------- * Tally data for detectors with time-bins are properly handled - :issue:`312` -* Support for generic string universe names for \|BranchingReader\| and - \|BranchCollector\| - :issue:`318` - -Pending Deprecations --------------------- - -* Keys to \|BranchedUniv\| objects stored in - :attr:`serpentTools.xs.BranchCollector.universes` are stored as strings, - rather than integers, e.g. ``0`` is replaced with ``"0"``. A workaround - is in-place, but will be removed in future versions. .. _v0.7.0: diff --git a/docs/defaultSettings.rst b/docs/defaultSettings.rst index c7102ec..7f470d7 100644 --- a/docs/defaultSettings.rst +++ b/docs/defaultSettings.rst @@ -128,19 +128,6 @@ If true, store the micro-group cross sections. Type: bool -.. _results-expectGcu: - ---------------------- -``results.expectGcu`` ---------------------- - -Set this to False if no homogenized group contants are present in the output, as if ``set gcu -1`` is preset in the input file -:: - - Default: True - Type: bool - - .. _sampler-allExist: -------------------- diff --git a/docs/examples/BranchToNodalDiffusion.rst b/docs/examples/BranchToNodalDiffusion.rst index ef159ae..131a53d 100644 --- a/docs/examples/BranchToNodalDiffusion.rst +++ b/docs/examples/BranchToNodalDiffusion.rst @@ -155,12 +155,12 @@ constants for specific universes with the \|BranchCol-universes\| dictionary. .. code:: >>> collector.universes - {"0": <serpentTools.BranchedUniv at 0x7fb62f749a98>, 10: + {0: <serpentTools.BranchedUniv at 0x7fb62f749a98>, 10: <serpentTools.BranchedUniv at 0x7fb62f731b88>, 20: <serpentTools.BranchedUniv at 0x7fb62f749e08>, 30: <serpentTools.BranchedUniv at 0x7fb62f749e58>, 40: <serpentTools.BranchedUniv at 0x7fb62f749ea8>} - >>> u0 = collector.universes["0"] + >>> u0 = collector.universes[0] These \|BranchedUniv\| objects store views into the underlying collectors \|BranchedUniv-tables\| data corresponding to a single universe. The diff --git a/docs/examples/Branching.rst b/docs/examples/Branching.rst index 28dff8d..6f9b5a5 100644 --- a/docs/examples/Branching.rst +++ b/docs/examples/Branching.rst @@ -101,21 +101,21 @@ The \|BranchContainer\| stores group constant data in \|HomogUniv\| objects in the >>> for key in b0.universes: ... print(key) - ('0", 1.0, 1) - ('10", 1.0, 1) - ('20", 1.0, 1) - ('30", 1.0, 1) - ('20", 0, 0) - ('40", 0, 0) - ('20", 10.0, 2) - ('10", 10.0, 2) - ('0", 0, 0) - ('10", 0, 0) - ('0", 10.0, 2) - ('30", 0, 0) - ('40", 10.0, 2) - ('40", 1.0, 1) - ('30", 10.0, 2) + (0, 1.0, 1) + (10, 1.0, 1) + (20, 1.0, 1) + (30, 1.0, 1) + (20, 0, 0) + (40, 0, 0) + (20, 10.0, 2) + (10, 10.0, 2) + (0, 0, 0) + (10, 0, 0) + (0, 10.0, 2) + (30, 0, 0) + (40, 10.0, 2) + (40, 1.0, 1) + (30, 10.0, 2) The keys here are vectors indicating the universe ID, burnup, and burnup index corresponding to the point in the burnup schedule. ``SERPENT`` @@ -129,7 +129,7 @@ the :meth:`~serpentTools.objects.BranchContainer.getUniv` method .. code:: - >>> univ0 = b0.universes["0", 1, 1] + >>> univ0 = b0.universes[0, 1, 1] >>> print(univ0) <HomogUniv 0: burnup: 1.000 MWd/kgu, step: 1> >>> print(univ0.name) @@ -295,7 +295,7 @@ variables explicitly requested are present .. code:: - >>> univ4 = b1.getUniv("0", 0) + >>> univ4 = b1.getUniv(0, 0) >>> univ4.infExp {'infTot': array([ 0.313338, 0.54515 ])} >>> univ4.b1Exp diff --git a/docs/glossary.rst b/docs/glossary.rst index 16c42f5..1bdb243 100644 --- a/docs/glossary.rst +++ b/docs/glossary.rst @@ -82,8 +82,3 @@ Glossary that can be parsed by this project. More information, including distribution and licensing of ``SERPENT`` can be found at `<http://montecarlo.vtt.fi>`_. - - yaml - Human-readable format used for configuration files in this - project. For more information, see - `<https://pyyaml.org/wiki/PyYAMLDocumentation>`_ diff --git a/examples/BranchToNodalDiffusion.ipynb b/examples/BranchToNodalDiffusion.ipynb index fbf45fd..adb329e 100644 --- a/examples/BranchToNodalDiffusion.ipynb +++ b/examples/BranchToNodalDiffusion.ipynb @@ -87,7 +87,16 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ajohnson400/github/my-serpent-tools/.venv/develop/lib/python3.7/site-packages/serpentTools-0.6.1+36.g1facac0.dirty-py3.7.egg/serpentTools/xs.py:227: UserWarning: This is an experimental feature, subject to change.\n", + " UserWarning)\n" + ] + } + ], "source": [ "collector = BranchCollector(coe)" ] @@ -235,7 +244,7 @@ { "data": { "text/plain": [ - "('0', '10', '20', '30', '40')" + "(0, 10, 20, 30, 40)" ] }, "execution_count": 11, @@ -383,11 +392,11 @@ { "data": { "text/plain": [ - "{'0': <serpentTools.xs.BranchedUniv at 0x7f322a0458b8>,\n", - " '10': <serpentTools.xs.BranchedUniv at 0x7f322a0538b8>,\n", - " '20': <serpentTools.xs.BranchedUniv at 0x7f322a053908>,\n", - " '30': <serpentTools.xs.BranchedUniv at 0x7f322a053958>,\n", - " '40': <serpentTools.xs.BranchedUniv at 0x7f322a0539a8>}" + "{0: <serpentTools.xs.BranchedUniv at 0x7fb62f749a98>,\n", + " 10: <serpentTools.xs.BranchedUniv at 0x7fb62f731b88>,\n", + " 20: <serpentTools.xs.BranchedUniv at 0x7fb62f749e08>,\n", + " 30: <serpentTools.xs.BranchedUniv at 0x7fb62f749e58>,\n", + " 40: <serpentTools.xs.BranchedUniv at 0x7fb62f749ea8>}" ] }, "execution_count": 17, @@ -403,33 +412,9 @@ "cell_type": "code", "execution_count": 18, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'0': <serpentTools.xs.BranchedUniv at 0x7f322a0458b8>,\n", - " '10': <serpentTools.xs.BranchedUniv at 0x7f322a0538b8>,\n", - " '20': <serpentTools.xs.BranchedUniv at 0x7f322a053908>,\n", - " '30': <serpentTools.xs.BranchedUniv at 0x7f322a053958>,\n", - " '40': <serpentTools.xs.BranchedUniv at 0x7f322a0539a8>}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "collector.universes" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, "outputs": [], "source": [ - "u0 = collector.universes['0']" + "u0 = collector.universes[0]" ] }, { @@ -441,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -450,7 +435,7 @@ "('BOR', 'TFU')" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -461,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -470,7 +455,7 @@ "('BOR', 'TFU', 'Burnup', 'Group')" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -481,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -490,7 +475,7 @@ "(('B1000', 'B750', 'nom'), ('FT1200', 'FT600', 'nom'))" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -508,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -526,7 +511,7 @@ " 'b1Diffcoef']" ] }, - "execution_count": 23, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -537,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -546,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -555,7 +540,7 @@ "(3, 3, 3, 2)" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -566,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -613,7 +598,7 @@ " [0.206532, 0. ]]]])" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -633,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -643,7 +628,7 @@ " [1200., 600., 900.]])" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -665,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -695,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -704,7 +689,7 @@ "['boron conc', 'fuel temperature']" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -716,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -747,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -756,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -795,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -804,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -834,17 +819,17 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# write to a file \"in memory\"\n", - "out = writer.write(\"0\")" + "out = writer.write(0)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -876,13 +861,6 @@ "source": [ "print(out[:1000])" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -901,7 +879,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.2" } }, "nbformat": 4, diff --git a/examples/Branching.ipynb b/examples/Branching.ipynb index af548e6..5b1a687 100644 --- a/examples/Branching.ipynb +++ b/examples/Branching.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -75,33 +75,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{('nom',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7e6664c88>,\n", - " ('B750',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7b6f28>,\n", + "{('B1000',\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff79783ff28>,\n", " ('B1000',\n", - " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7c8208>,\n", - " ('nom',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7d34e0>,\n", - " ('B750',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd7df668>,\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff79789c048>,\n", " ('B1000',\n", - " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd76a978>,\n", - " ('nom',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd776c18>,\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff797820e10>,\n", " ('B750',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd780f28>,\n", - " ('B1000',\n", - " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff7bd793278>}" + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff797839eb8>,\n", + " ('B750',\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff797853f98>,\n", + " ('B750',\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff797852e10>,\n", + " ('nom',\n", + " 'FT1200'): <serpentTools.objects.containers.BranchContainer at 0x7ff797832e48>,\n", + " ('nom',\n", + " 'FT600'): <serpentTools.objects.containers.BranchContainer at 0x7ff79784cf28>,\n", + " ('nom',\n", + " 'nom'): <serpentTools.objects.containers.BranchContainer at 0x7ff7977c7e10>}" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +126,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "<BranchContainer for B1000, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n" + "<BranchContainer for B1000, FT600 from demo.coe>\n" ] } ], @@ -203,21 +203,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "('0', 0, 0)\n", - "('10', 0, 0)\n", - "('20', 0, 0)\n", - "('30', 0, 0)\n", - "('40', 0, 0)\n", - "('0', 1.0, 1)\n", - "('10', 1.0, 1)\n", - "('20', 1.0, 1)\n", - "('30', 1.0, 1)\n", - "('40', 1.0, 1)\n", - "('0', 10.0, 2)\n", - "('10', 10.0, 2)\n", - "('20', 10.0, 2)\n", - "('30', 10.0, 2)\n", - "('40', 10.0, 2)\n" + "(0, 0, 0)\n", + "(10, 0, 0)\n", + "(20, 0, 0)\n", + "(30, 0, 0)\n", + "(40, 0, 0)\n", + "(0, 1.0, 1)\n", + "(10, 1.0, 1)\n", + "(20, 1.0, 1)\n", + "(30, 1.0, 1)\n", + "(40, 1.0, 1)\n", + "(0, 10.0, 2)\n", + "(10, 10.0, 2)\n", + "(20, 10.0, 2)\n", + "(30, 10.0, 2)\n", + "(40, 10.0, 2)\n" ] } ], @@ -254,7 +254,7 @@ } ], "source": [ - "univ0 = b0.universes['0', 1, 1]\n", + "univ0 = b0.universes[0, 1, 1]\n", "print(univ0)\n", "print(univ0.name)\n", "print(univ0.bu)\n", @@ -276,8 +276,8 @@ "metadata": {}, "outputs": [], "source": [ - "univ1 = b0.getUniv('0', burnup=1)\n", - "univ2 = b0.getUniv('0', index=1)\n", + "univ1 = b0.getUniv(0, burnup=1)\n", + "univ2 = b0.getUniv(0, index=1)\n", "assert univ0 is univ1 is univ2" ] }, @@ -468,22 +468,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7bd7a3cf8>" + "<matplotlib.axes._subplots.AxesSubplot at 0x7f769a7a0da0>" ] }, - "execution_count": 16, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEOCAYAAABM5Pr8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZ0ZJpKkzgwTSVJnhokkqbP/A15lRo2uMdPxAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -498,12 +498,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwTyCAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -533,22 +533,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "('nom', 'nom') <BranchContainer for nom, nom from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('B750', 'nom') <BranchContainer for B750, nom from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('B1000', 'nom') <BranchContainer for B1000, nom from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('nom', 'FT1200') <BranchContainer for nom, FT1200 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('B750', 'FT1200') <BranchContainer for B750, FT1200 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('B1000', 'FT1200') <BranchContainer for B1000, FT1200 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('nom', 'FT600') <BranchContainer for nom, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('B750', 'FT600') <BranchContainer for B750, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n", - "('B1000', 'FT600') <BranchContainer for B1000, FT600 from /home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+30.g3066be7.dirty-py3.7.egg/serpentTools/data/demo.coe>\n" + "('nom', 'nom') <BranchContainer for nom, nom from demo.coe>\n", + "('B750', 'nom') <BranchContainer for B750, nom from demo.coe>\n", + "('B1000', 'nom') <BranchContainer for B1000, nom from demo.coe>\n", + "('nom', 'FT1200') <BranchContainer for nom, FT1200 from demo.coe>\n", + "('B750', 'FT1200') <BranchContainer for B750, FT1200 from demo.coe>\n", + "('B1000', 'FT1200') <BranchContainer for B1000, FT1200 from demo.coe>\n", + "('nom', 'FT600') <BranchContainer for nom, FT600 from demo.coe>\n", + "('B750', 'FT600') <BranchContainer for B750, FT600 from demo.coe>\n", + "('B1000', 'FT600') <BranchContainer for B1000, FT600 from demo.coe>\n" ] } ], @@ -588,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -618,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -640,7 +640,7 @@ " 'TFU': 600}" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -668,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -677,19 +677,19 @@ "{'infTot': array([0.313338, 0.54515 ])}" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "univ4 = b1.getUniv('0', 0)\n", + "univ4 = b1.getUniv(0, 0)\n", "univ4.infExp" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -698,7 +698,7 @@ "{}" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -732,7 +732,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt index dd3c44d..10e1d56 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ six==1.12.0 numpy>=1.16.0 matplotlib>=2.2.3 -pyyaml==5.1.1 +pyyaml==3.13 diff --git a/serpentTools/data/pwr_noUniv_res.m b/serpentTools/data/pwr_noUniv_res.m new file mode 100644 index 0000000..0afefbf --- /dev/null +++ b/serpentTools/data/pwr_noUniv_res.m @@ -0,0 +1,210 @@ + + +% Increase counter: + +if (exist('idx', 'var')); + idx = idx + 1; +else; + idx = 1; +end; + +% Version, title and date: + +VERSION (idx, [1: 14]) = 'Serpent 2.1.29' ; +COMPILE_DATE (idx, [1: 20]) = 'Jan 4 2018 17:22:46' ; +DEBUG (idx, 1) = 0 ; +TITLE (idx, [1: 7]) = 'pwr pin' ; +CONFIDENTIAL_DATA (idx, 1) = 0 ; +INPUT_FILE_NAME (idx, [1: 6]) = 'pwrPin' ; +WORKING_DIRECTORY (idx, [1: 49]) = '/home/ajohnson400/research/gpt-dep/testing/depmtx' ; +HOSTNAME (idx, [1: 14]) = 'ME04L0358GRD04' ; +CPU_TYPE (idx, [1: 40]) = 'Intel(R) Core(TM) i7-6700T CPU @ 2.80GHz' ; +CPU_MHZ (idx, 1) = 194.0 ; +START_DATE (idx, [1: 24]) = 'Mon Feb 19 15:39:23 2018' ; +COMPLETE_DATE (idx, [1: 24]) = 'Mon Feb 19 15:39:40 2018' ; + +% Run parameters: + +POP (idx, 1) = 5000 ; +CYCLES (idx, 1) = 100 ; +SKIP (idx, 1) = 10 ; +BATCH_INTERVAL (idx, 1) = 1 ; +SRC_NORM_MODE (idx, 1) = 2 ; +SEED (idx, 1) = 1526311206 ; +UFS_MODE (idx, 1) = 0 ; +UFS_ORDER (idx, 1) = 1.00000; +NEUTRON_TRANSPORT_MODE (idx, 1) = 1 ; +PHOTON_TRANSPORT_MODE (idx, 1) = 0 ; +GROUP_CONSTANT_GENERATION (idx, 1) = 1 ; +B1_CALCULATION (idx, [1: 3]) = [ 0 0 0 ]; +B1_BURNUP_CORRECTION (idx, 1) = 0 ; +IMPLICIT_REACTION_RATES (idx, 1) = 1 ; + +% Optimization: + +OPTIMIZATION_MODE (idx, 1) = 4 ; +RECONSTRUCT_MICROXS (idx, 1) = 1 ; +RECONSTRUCT_MACROXS (idx, 1) = 1 ; +DOUBLE_INDEXING (idx, 1) = 0 ; +MG_MAJORANT_MODE (idx, 1) = 0 ; + +% Parallelization: + +MPI_TASKS (idx, 1) = 1 ; +OMP_THREADS (idx, 1) = 8 ; +MPI_REPRODUCIBILITY (idx, 1) = 0 ; +OMP_REPRODUCIBILITY (idx, 1) = 1 ; +OMP_HISTORY_PROFILE (idx, [1: 8]) = [ 9.87692E-01 9.98407E-01 9.99815E-01 1.00577E+00 1.00453E+00 1.00540E+00 1.00143E+00 9.96955E-01 ]; +SHARE_BUF_ARRAY (idx, 1) = 0 ; +SHARE_RES2_ARRAY (idx, 1) = 1 ; + +% File paths: + +XS_DATA_FILE_PATH (idx, [1: 53]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7u.xsdata' ; +DECAY_DATA_FILE_PATH (idx, [1: 49]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7.dec' ; +SFY_DATA_FILE_PATH (idx, [1: 49]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7.nfy' ; +NFY_DATA_FILE_PATH (idx, [1: 49]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7.nfy' ; +BRA_DATA_FILE_PATH (idx, [1: 3]) = 'N/A' ; + +% Collision and reaction sampling (neutrons/photons): + +MIN_MACROXS (idx, [1: 4]) = [ 5.00000E-02 0.0E+00 0.00000E+00 0.0E+00 ]; +DT_THRESH (idx, [1: 2]) = [ 9.00000E-01 9.00000E-01 ]; +ST_FRAC (idx, [1: 4]) = [ 1.62904E-02 0.00343 0.00000E+00 0.0E+00 ]; +DT_FRAC (idx, [1: 4]) = [ 9.83710E-01 5.7E-05 0.00000E+00 0.0E+00 ]; +DT_EFF (idx, [1: 4]) = [ 7.06733E-01 0.00021 0.00000E+00 0.0E+00 ]; +REA_SAMPLING_EFF (idx, [1: 4]) = [ 1.00000E+00 0.0E+00 0.00000E+00 0.0E+00 ]; +REA_SAMPLING_FAIL (idx, [1: 4]) = [ 0.00000E+00 0.0E+00 0.00000E+00 0.0E+00 ]; +TOT_COL_EFF (idx, [1: 4]) = [ 7.07379E-01 0.00021 0.00000E+00 0.0E+00 ]; +AVG_TRACKING_LOOPS (idx, [1: 8]) = [ 2.44970E+00 0.00074 0.00000E+00 0.0E+00 0.00000E+00 0.0E+00 0.00000E+00 0.0E+00 ]; +AVG_TRACKS (idx, [1: 4]) = [ 2.63979E+01 0.00064 0.00000E+00 0.0E+00 ]; +AVG_REAL_COL (idx, [1: 4]) = [ 2.63979E+01 0.00064 0.00000E+00 0.0E+00 ]; +AVG_VIRT_COL (idx, [1: 4]) = [ 1.09200E+01 0.00087 0.00000E+00 0.0E+00 ]; +AVG_SURF_CROSS (idx, [1: 4]) = [ 5.34339E-01 0.00367 0.00000E+00 0.0E+00 ]; +LOST_PARTICLES (idx, 1) = 0 ; + +% Parameters for burnup calculation: + +BURN_MATERIALS (idx, 1) = 1 ; +BURN_MODE (idx, 1) = 2 ; +BURN_STEP (idx, 1) = 0 ; +BURNUP (idx, [1: 2]) = [ 0.00000E+00 0.00000E+00 ]; +BURN_DAYS (idx, 1) = 0.00000E+00 ; + +% Fission neutron and energy production: + +NUBAR (idx, [1: 2]) = [ 2.46293E+00 7.8E-05 ]; + +% Criticality eigenvalues: + +ANA_KEFF (idx, [1: 6]) = [ 9.95003E-01 0.00411 9.88678E-01 0.00423 7.21837E-03 0.05562 ]; +IMP_KEFF (idx, [1: 2]) = [ 9.91938E-01 0.00145 ]; +COL_KEFF (idx, [1: 2]) = [ 9.93385E-01 0.00279 ]; +ABS_KEFF (idx, [1: 2]) = [ 9.91938E-01 0.00145 ]; +ABS_KINF (idx, [1: 2]) = [ 9.91938E-01 0.00145 ]; +GEOM_ALBEDO (idx, [1: 6]) = [ 1.00000E+00 0.0E+00 1.00000E+00 0.0E+00 1.00000E+00 0.0E+00 ]; + +% Increase counter: + +if (exist('idx', 'var')); + idx = idx + 1; +else; + idx = 1; +end; + +% Version, title and date: + +VERSION (idx, [1: 14]) = 'Serpent 2.1.29' ; +COMPILE_DATE (idx, [1: 20]) = 'Jan 4 2018 17:22:46' ; +DEBUG (idx, 1) = 0 ; +TITLE (idx, [1: 7]) = 'pwr pin' ; +CONFIDENTIAL_DATA (idx, 1) = 0 ; +INPUT_FILE_NAME (idx, [1: 6]) = 'pwrPin' ; +WORKING_DIRECTORY (idx, [1: 49]) = '/home/ajohnson400/research/gpt-dep/testing/depmtx' ; +HOSTNAME (idx, [1: 14]) = 'ME04L0358GRD04' ; +CPU_TYPE (idx, [1: 40]) = 'Intel(R) Core(TM) i7-6700T CPU @ 2.80GHz' ; +CPU_MHZ (idx, 1) = 194.0 ; +START_DATE (idx, [1: 24]) = 'Mon Feb 19 15:39:23 2018' ; +COMPLETE_DATE (idx, [1: 24]) = 'Mon Feb 19 15:39:53 2018' ; + +% Run parameters: + +POP (idx, 1) = 5000 ; +CYCLES (idx, 1) = 100 ; +SKIP (idx, 1) = 10 ; +BATCH_INTERVAL (idx, 1) = 1 ; +SRC_NORM_MODE (idx, 1) = 2 ; +SEED (idx, 1) = 1526311206 ; +UFS_MODE (idx, 1) = 0 ; +UFS_ORDER (idx, 1) = 1.00000; +NEUTRON_TRANSPORT_MODE (idx, 1) = 1 ; +PHOTON_TRANSPORT_MODE (idx, 1) = 0 ; +GROUP_CONSTANT_GENERATION (idx, 1) = 1 ; +B1_CALCULATION (idx, [1: 3]) = [ 0 0 0 ]; +B1_BURNUP_CORRECTION (idx, 1) = 0 ; +IMPLICIT_REACTION_RATES (idx, 1) = 1 ; + +% Optimization: + +OPTIMIZATION_MODE (idx, 1) = 4 ; +RECONSTRUCT_MICROXS (idx, 1) = 1 ; +RECONSTRUCT_MACROXS (idx, 1) = 1 ; +DOUBLE_INDEXING (idx, 1) = 0 ; +MG_MAJORANT_MODE (idx, 1) = 0 ; + +% Parallelization: + +MPI_TASKS (idx, 1) = 1 ; +OMP_THREADS (idx, 1) = 8 ; +MPI_REPRODUCIBILITY (idx, 1) = 0 ; +OMP_REPRODUCIBILITY (idx, 1) = 1 ; +OMP_HISTORY_PROFILE (idx, [1: 8]) = [ 9.87692E-01 9.98407E-01 9.99815E-01 1.00577E+00 1.00453E+00 1.00540E+00 1.00143E+00 9.96955E-01 ]; +SHARE_BUF_ARRAY (idx, 1) = 0 ; +SHARE_RES2_ARRAY (idx, 1) = 1 ; + +% File paths: + +XS_DATA_FILE_PATH (idx, [1: 53]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7u.xsdata' ; +DECAY_DATA_FILE_PATH (idx, [1: 49]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7.dec' ; +SFY_DATA_FILE_PATH (idx, [1: 49]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7.nfy' ; +NFY_DATA_FILE_PATH (idx, [1: 49]) = '/nv/hp22/dkotlyar6/data/Codes/DATA/sss_endfb7.nfy' ; +BRA_DATA_FILE_PATH (idx, [1: 3]) = 'N/A' ; + +% Collision and reaction sampling (neutrons/photons): + +MIN_MACROXS (idx, [1: 4]) = [ 5.00000E-02 0.0E+00 0.00000E+00 0.0E+00 ]; +DT_THRESH (idx, [1: 2]) = [ 9.00000E-01 9.00000E-01 ]; +ST_FRAC (idx, [1: 4]) = [ 1.62904E-02 0.00343 0.00000E+00 0.0E+00 ]; +DT_FRAC (idx, [1: 4]) = [ 9.83710E-01 5.7E-05 0.00000E+00 0.0E+00 ]; +DT_EFF (idx, [1: 4]) = [ 7.06733E-01 0.00021 0.00000E+00 0.0E+00 ]; +REA_SAMPLING_EFF (idx, [1: 4]) = [ 1.00000E+00 0.0E+00 0.00000E+00 0.0E+00 ]; +REA_SAMPLING_FAIL (idx, [1: 4]) = [ 0.00000E+00 0.0E+00 0.00000E+00 0.0E+00 ]; +TOT_COL_EFF (idx, [1: 4]) = [ 7.07379E-01 0.00021 0.00000E+00 0.0E+00 ]; +AVG_TRACKING_LOOPS (idx, [1: 8]) = [ 2.44970E+00 0.00074 0.00000E+00 0.0E+00 0.00000E+00 0.0E+00 0.00000E+00 0.0E+00 ]; +AVG_TRACKS (idx, [1: 4]) = [ 2.63979E+01 0.00064 0.00000E+00 0.0E+00 ]; +AVG_REAL_COL (idx, [1: 4]) = [ 2.63979E+01 0.00064 0.00000E+00 0.0E+00 ]; +AVG_VIRT_COL (idx, [1: 4]) = [ 1.09200E+01 0.00087 0.00000E+00 0.0E+00 ]; +AVG_SURF_CROSS (idx, [1: 4]) = [ 5.34339E-01 0.00367 0.00000E+00 0.0E+00 ]; +LOST_PARTICLES (idx, 1) = 0 ; + +% Parameters for burnup calculation: + +BURN_MATERIALS (idx, 1) = 1 ; +BURN_MODE (idx, 1) = 2 ; +BURN_STEP (idx, 1) = 1 ; +BURNUP (idx, [1: 2]) = [ 5.00000E+02 5.00260E+02 ]; +BURN_DAYS (idx, 1) = 5.00000E+00 ; + +% Fission neutron and energy production: + +NUBAR (idx, [1: 2]) = [ 2.44555E+00 0.00035 ]; + +% Criticality eigenvalues: + +ANA_KEFF (idx, [1: 6]) = [ 1.83604E-01 0.01097 1.81980E-01 0.01105 1.56566E-03 0.10949 ]; +IMP_KEFF (idx, [1: 2]) = [ 1.81729E-01 0.00240 ]; +COL_KEFF (idx, [1: 2]) = [ 1.81451E-01 0.00331 ]; +ABS_KEFF (idx, [1: 2]) = [ 1.81729E-01 0.00240 ]; +ABS_KINF (idx, [1: 2]) = [ 1.81729E-01 0.00240 ]; +GEOM_ALBEDO (idx, [1: 6]) = [ 1.00000E+00 0.0E+00 1.00000E+00 0.0E+00 1.00000E+00 0.0E+00 ]; + diff --git a/serpentTools/objects/containers.py b/serpentTools/objects/containers.py index 8a8984c..23bd7e4 100644 --- a/serpentTools/objects/containers.py +++ b/serpentTools/objects/containers.py @@ -9,10 +9,8 @@ Contents """ from re import compile from itertools import product -from warnings import warn -from numbers import Real -from six import iteritems, PY2 +from six import iteritems from matplotlib import pyplot from numpy import array, arange, hstack, ndarray, zeros_like @@ -41,11 +39,6 @@ from serpentTools.messages import ( error, ) -if PY2: - from collections import Iterable -else: - from collections.abc import Iterable - SCATTER_MATS = set() SCATTER_ORDERS = 8 @@ -621,33 +614,6 @@ class HomogUniv(NamedObject): compareGCData.__doc__ = __docCompare.format(qty='gc') -# remove for versions >= 0.8.0 - -class _BranchContainerUnivDict(dict): - """ - Workaround for supporting integer and string universe ids - - Keys are of the form ``univID, index, burnup`` - """ - - def __getitem__(self, key): - if not isinstance(key, Iterable): - raise KeyError(key) - if isinstance(key[0], Real) and key not in self: - warn("Universe ids are stored as unconverted strings, not int. " - "Support for previous integer-access will be removed in " - "future versions.", - FutureWarning) - key = (str(key[0]), ) + key[1:] - return dict.__getitem__(self, key) - - def get(self, key, default=None): - try: - return self[key] - except KeyError: - return default - - class BranchContainer(BaseObject): """ Class that stores data for a single branch. @@ -686,8 +652,7 @@ class BranchContainer(BaseObject): self.filePath = filePath self.branchID = branchID self.stateData = stateData - # Revert to dict for version >= 0.8.0 - self.universes = _BranchContainerUnivDict() + self.universes = {} self.branchNames = branchNames self.__orderedUniverses = None self.__keys = set() @@ -768,15 +733,9 @@ class BranchContainer(BaseObject): If burnup and index are given, burnup is used to search - ..warning:: - - Future versions will store read and store universes from - coefficient files as generic strings, without integer - conversion - Parameters ---------- - univID: str + univID: int Unique ID for the desired universe burnup: float or int Burnup [MWd/kgU] of the desired universe @@ -785,7 +744,7 @@ class BranchContainer(BaseObject): Returns ------- - :class:`~serpentTools.objects.containers.HomogUniv` + :py:class:`~serpentTools.objects.containers.HomogUniv` Requested universe Raises diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index d2a491a..8ba6fb4 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -110,12 +110,11 @@ class BranchingReader(XSReader): def _processBranchUniverses(self, branch, burnup, burnupIndex): """Add universe data to this branch at this burnup.""" - unvID, numVariables = self._advance() - numVariables = int(numVariables) + unvID, numVariables = [int(xx) for xx in self._advance()] univ = branch.addUniverse(unvID, burnup, burnupIndex - 1) for step in range(numVariables): splitList = self._advance( - possibleEndOfFile=(step == numVariables - 1)) + possibleEndOfFile=step == numVariables - 1) varName = splitList[0] varValues = [float(xx) for xx in splitList[2:]] if not varValues: diff --git a/serpentTools/parsers/results.py b/serpentTools/parsers/results.py index e0ecfd6..2e2d2e4 100644 --- a/serpentTools/parsers/results.py +++ b/serpentTools/parsers/results.py @@ -187,7 +187,7 @@ class ResultsReader(XSReader): self._counter['rslt'] = ( (self._counter['meta'] - 1) // self._numUniv + 1) return - self._counter['rslt'] = self._counter['meta'] + self._counter['rslt'] = self._counter['meta'] elif self._keysVersion['univ'] in tline: self._posFile = 'univ' varType, varVals = self._getVarValues(tline) # universe name @@ -374,10 +374,6 @@ class ResultsReader(XSReader): .format(self.filePath, self._numUniv, self._counter['rslt'], self._counter['meta'])) if not self.resdata and not self.metadata: - if not self.settings['expectGcu']: - raise SerpentToolsException( - "Metadata and results data were not found in {}" - .format(self.filePath)) for keys, dictUniv in iteritems(self.universes): if dictUniv.hasData(): return diff --git a/serpentTools/settings.py b/serpentTools/settings.py index 0c7e6f7..734d0f7 100644 --- a/serpentTools/settings.py +++ b/serpentTools/settings.py @@ -1,5 +1,6 @@ """Settings to yield control to the user.""" import os +from warnings import warn from six import iteritems from yaml import safe_load @@ -26,6 +27,8 @@ SETTING_DOC_FMTR = """.. _{tag}: """ +_DEPRECATED = {'results.expectGcu'} + SETTING_OPTIONS_FMTR = "Options: [{}]" defaultSettings = { 'branching.intVariables': { @@ -70,13 +73,6 @@ defaultSettings = { 'detectors', 'type': list }, - 'results.expectGcu': { - 'default': True, - 'description': 'Set this to False if no homogenized group contants ' - 'are present in the output, as if ``set gcu -1`` is ' - 'preset in the input file', - 'type': bool, - }, 'verbosity': { 'default': 'warning', 'options': messages.LOG_OPTS, @@ -305,12 +301,15 @@ class UserSettingsLoader(dict): If the value is not of the correct type """ - if self.__inside: - self.__originals[name] = self[name] + if name in _DEPRECATED: + warn("Setting {} has been removed.".format(name)) + return if name not in self: raise KeyError('Setting {} does not exist'.format(name)) self._defaultLoader[name].validate(value) # if we've made it here, then the value is valid + if self.__inside: + self.__originals[name] = self[name] if self._defaultLoader[name].updater is not None: value = self._defaultLoader[name].updater(value) dict.__setitem__(self, name, value) diff --git a/serpentTools/xs.py b/serpentTools/xs.py index 44edf12..6ca006c 100644 --- a/serpentTools/xs.py +++ b/serpentTools/xs.py @@ -5,8 +5,6 @@ branching file from itertools import product from warnings import warn -from numbers import Real - from six import iteritems from six.moves import range from numpy import empty, nan, array, ndarray @@ -20,35 +18,6 @@ __all__ = [ ] -# remove for versions >= 0.8.0 - - -class IntToStringDict(dict): - """Dictionary that allows accessing string keys with Reals - - Used to mitigate API changes in how universe keys are stored - in BranchingReader and BranchCollector objects. - """ - - def __getitem__(self, key): - if key in self: - return dict.__getitem__(self, key) - if isinstance(key, Real) and float(key) in self: - warn("Universes will be stored as unconverted strings in future " - "versions", FutureWarning) - return dict.__getitem__(self, str(key)) - raise KeyError(key) - - def get(self, key, default=None): - if key in self: - return dict.__getitem__(self, key) - if isinstance(key, Real) and float(key) in self: - warn("Universes will be stored as unconverted strings in future " - "versions", FutureWarning) - return dict.__getitem__(self, str(key)) - return dict.get(self, key, default) - - class BranchedUniv(object): """ Class for storing cross sections for a single universe across branches @@ -255,8 +224,7 @@ class BranchCollector(object): self.filePath = reader.filePath self._branches = reader.branches self.xsTables = {} - # Revert do dict for version >= 0.8.0 - self.universes = IntToStringDict() + self.universes = {} self._perturbations = None self._states = None self._axis = None	BUG Failure to read files with burnup and no universes ## Summary of issue If there are no universes in the file, then the result counter does not get incremented due to an unreachable line https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/b3f3c39609319985f7047dd6fed52654e285cd0b/serpentTools/parsers/results.py#L186-L190 This causes the `_postread` method to fail, thinking the file is incomplete when it actually isn't. This should have been fixed with #231 and the inclusion of the settings `results.expectGcu`. ## Code for reproducing the issue Read the [now deleted](https://github.com/CORE-GATECH-GROUP/serpent-tools/pull/231/commits/264901eb1157f8dd672fef9c873312425300792a) [result file without universes](https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/f106d735fb31fc6a8a04d5904f69d1bd45b21e1c/serpentTools/data/pwr_noUniv_res.m) ## Actual outcome including console output and error traceback if applicable ``` Traceback (most recent call last): File "test.py", line 3, in <module> r = serpentTools.read("leqi_res.m", "results") File "/home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+26.gb3f3c39-py3.7.egg/serpentTools/parsers/__init__.py", line 155, in read returnedFromLoader.read() File "/home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+26.gb3f3c39-py3.7.egg/serpentTools/parsers/base.py", line 54, in read self._postcheck() File "/home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+26.gb3f3c39-py3.7.egg/serpentTools/parsers/results.py", line 390, in _postcheck self._inspectData() File "/home/drew/.local/lib/python3.7/site-packages/serpentTools-0.7.0+26.gb3f3c39-py3.7.egg/serpentTools/parsers/results.py", line 375, in _inspectData self._counter['rslt'], self._counter['meta'])) serpentTools.messages.SerpentToolsException: The file leqi_res.m is not complete. The reader found 0 universes, 0 time-points, and 2 overall result points ``` ## Expected outcome Reader can read the file without universes, preferably without the need to set `results.expectGcu` ## Versions * Version from ``serpentTools.__version__`` develop version b3f3c39 * Python version - ``python --version`` `3.7`	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_ResultsReader.py b/serpentTools/tests/test_ResultsReader.py index 9f4e678..2a29d02 100644 --- a/serpentTools/tests/test_ResultsReader.py +++ b/serpentTools/tests/test_ResultsReader.py @@ -20,14 +20,14 @@ from serpentTools.tests.utils import ( GCU_START_STR = "GCU_UNIVERSE_NAME" -NO_GCU_FILE = "./pwr_noGcu_res.m" +NO_BU_GCU_FILE = "./pwr_noGcu_res.m" ADF_FILE = "./pwr_adf_res.m" RES_NO_BU = getFile("pwr_noBU_res.m") def setUpModule(): """Write the result file with no group constant data.""" - with open(NO_GCU_FILE, 'w') as noGcu, open(RES_NO_BU) as good: + with open(NO_BU_GCU_FILE, 'w') as noGcu, open(RES_NO_BU) as good: for line in good: if GCU_START_STR in line: break @@ -46,7 +46,7 @@ DF_N_CORN (idx, 1) = 4 ; def tearDownModule(): """Remove the noGcu file.""" - remove(NO_GCU_FILE) + remove(NO_BU_GCU_FILE) remove(ADF_FILE) @@ -597,7 +597,7 @@ class TestResultsNoBurnNoGcu(TestFilterResultsNoBurnup): HAS_UNIV = False def setUp(self): - self.file = NO_GCU_FILE + self.file = NO_BU_GCU_FILE with rc: rc['xs.variableGroups'] = ['versions', 'gc-meta', 'xs', 'diffusion', 'eig', 'burnup-coeff'] @@ -608,6 +608,26 @@ class TestResultsNoBurnNoGcu(TestFilterResultsNoBurnup): self.reader.read() +class NoUniverseTester(Serp2129Helper): + """Read a file ith burnup but no universes""" + + def setUp(self): + filep = getFile("pwr_noUniv_res.m") + self.reader = ResultsReader(filep) + self.reader.read() + + def test_noUniverse(self): + expectedBurnup = array([ + [0.00000E+00, 0.00000E+00], + [5.00000E+02, 5.00260E+02]]) + expectedAbsKeff = array([ + [9.91938E-01, 0.00145], + [1.81729E-01, 0.00240]]) + self.assertEqual(0, len(self.reader.universes)) + assert_array_equal(expectedBurnup, self.reader.resdata['burnup']) + assert_array_equal(expectedAbsKeff, self.reader.resdata['absKeff']) + + class RestrictedResultsReader(Serp2129Helper): """Class that restricts the variables read from the results file""" @@ -642,7 +662,7 @@ class RestrictedResultsReader(Serp2129Helper): assert_array_equal(self.expectedAbsKeff, r.resdata['absKeff']) -del TesterCommonResultsReader +del TesterCommonResultsReader, Serp2129Helper class ResPlotTester(TestCase): diff --git a/serpentTools/tests/test_branching.py b/serpentTools/tests/test_branching.py index 7be45e2..9780cd4 100644 --- a/serpentTools/tests/test_branching.py +++ b/serpentTools/tests/test_branching.py @@ -23,7 +23,7 @@ class _BranchTesterHelper(unittest.TestCase): cls.expectedBranches = {('nom', 'nom', 'nom')} cls.expectedUniverses = { # universe id, burnup, step - ("0", 0, 0), + (0, 0, 0), } with rc: rc['serpentVersion'] = '2.1.29' @@ -78,16 +78,10 @@ class BranchContainerTester(_BranchTesterHelper): def setUpClass(cls): _BranchTesterHelper.setUpClass() cls.refBranchID = ('nom', 'nom', 'nom') - cls.refUnivKey = ("0", 0, 0) + cls.refUnivKey = (0, 0, 0) cls.refBranch = cls.reader.branches[cls.refBranchID] cls.refUniv = cls.refBranch.universes[cls.refUnivKey] - def test_universeIDWorkaround(self): - """Test that, for now, integer universe ids work""" - # Remove this test for versions >= 0.8.0 - key = (int(self.refUnivKey[0]), ) + self.refUnivKey[1:] - self.assertIs(self.refUniv, self.refBranch.universes[key]) - def test_loadedUniv(self): """Verify the reference universe has the correct data loaded""" assortedExpected = { @@ -155,7 +149,7 @@ class BranchWithUncsTester(unittest.TestCase): ], }, } - BRANCH_UNIVKEY = ("0", 0, 0) + BRANCH_UNIVKEY = (0, 0, 0) def setUp(self): fp = getFile('demo_uncs.coe') diff --git a/serpentTools/tests/test_xs.py b/serpentTools/tests/test_xs.py index 91dc047..ee3e31c 100644 --- a/serpentTools/tests/test_xs.py +++ b/serpentTools/tests/test_xs.py @@ -27,36 +27,36 @@ class CollectedHelper(_BranchTestHelper): ('nom', 'nom'): { # branch 'infTot': { # group constant 0: { # burnup - "0": array([2.10873E-01, 2.23528E-01]), # univ: value - "20": array([2.10868E-01, 1.22701E-01]), + 0: array([2.10873E-01, 2.23528E-01]), # univ: value + 20: array([2.10868E-01, 1.22701E-01]), }, 1: { - "0": array([2.08646E-01, 0.00000E+00]), - "20": array([2.08083E-01, 0.00000E+00]), + 0: array([2.08646E-01, 0.00000E+00]), + 20: array([2.08083E-01, 0.00000E+00]), }, }, 'b1Diffcoef': { 0: { - "0": array([1.80449E+00, 1.97809E+00]), - "20": array([1.80519E+00, 0.00000E+00]), + 0: array([1.80449E+00, 1.97809E+00]), + 20: array([1.80519E+00, 0.00000E+00]), }, }, }, ('B750', 'FT1200'): { 'infTot': { 0: { - "0": array([3.13772E-01, 5.41505E-01]), - "20": array([3.13711E-01, 5.41453E-01]), + 0: array([3.13772E-01, 5.41505E-01]), + 20: array([3.13711E-01, 5.41453E-01]), }, 1: { - "0": array([3.11335E-01, 6.09197E-01]), - "20": array([3.11729E-01, 8.37580E-01]), + 0: array([3.11335E-01, 6.09197E-01]), + 20: array([3.11729E-01, 8.37580E-01]), }, }, 'b1Diffcoef': { 0: { - "0": array([1.75127E+00, 8.63097E-01]), - "20": array([1.75206E+00, 8.62754E-01]), + 0: array([1.75127E+00, 8.63097E-01]), + 20: array([1.75206E+00, 8.62754E-01]), }, }, },	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 3 }, "num_modified_files": 13 }	0.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.19.5 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==5.1.1 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@7d9899c1c7ee03797d109211a9416d55ca5c5064#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.19.5 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==5.1.1 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_ResultsReader.py::NoUniverseTester::test_noUniverse", "serpentTools/tests/test_branching.py::BranchTester::test_branchingUniverses", "serpentTools/tests/test_branching.py::BranchContainerTester::test_containerGetUniv", "serpentTools/tests/test_branching.py::BranchWithUncsTester::test_valsWithUncs", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_xsTables", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_xsTables", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_xsTables" ]	[]	[ "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_emptyAttributes", "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_emptyFile_noGcu", "serpentTools/tests/test_ResultsReader.py::TestBadFiles::test_noResults", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_allVarsNone", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_noUnivState", "serpentTools/tests/test_ResultsReader.py::TestGetUniv::test_validUniv", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestFilterResults::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_burnup", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_universes", "serpentTools/tests/test_ResultsReader.py::TestReadAllResults::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_universes", "serpentTools/tests/test_ResultsReader.py::TestFilterResultsNoBurnup::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_metadata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_resdata", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_universes", "serpentTools/tests/test_ResultsReader.py::TestResultsNoBurnNoGcu::test_varsMatchSettings", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_fluxAndKeff", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_justFlux", "serpentTools/tests/test_ResultsReader.py::RestrictedResultsReader::test_xsGroups", "serpentTools/tests/test_ResultsReader.py::ResPlotTester::test_rightPlot", "serpentTools/tests/test_ResultsReader.py::ResPlotTester::test_singlePlot", "serpentTools/tests/test_ResultsReader.py::ResADFTester::test_adf", "serpentTools/tests/test_branching.py::BranchTester::test_raiseError", "serpentTools/tests/test_branching.py::BranchTester::test_variables", "serpentTools/tests/test_branching.py::BranchContainerTester::test_cannotAddBurnupDays", "serpentTools/tests/test_branching.py::BranchContainerTester::test_loadedUniv", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setAxis", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setBurnup", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setPert", "serpentTools/tests/test_xs.py::BranchCollectorTester::test_setPertStates", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setAxis", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setBurnup", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setPert", "serpentTools/tests/test_xs.py::BranchUnivTester::test_setPertStates", "serpentTools/tests/test_xs.py::BranchUnivTester::test_xsTables", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setAxis", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setBurnup", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setPert", "serpentTools/tests/test_xs.py::UnknownPertCollectorTester::test_setPertStates", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setAxis", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setBurnup", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setPert", "serpentTools/tests/test_xs.py::CollectorFromFileTester::test_setPertStates", "serpentTools/tests/test_xs.py::BareBranchContainerTester::test_bareBranchedContainer" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-341	88b5707ebb49f9fe3d4755999c0659fa6b5ee131	2019-10-03 19:30:03	09a5feab5b95f98aef4f7dc7b07edbf8e458f955		diff --git a/.gitignore b/.gitignore index 924c6e3..b27f41f 100644 --- a/.gitignore +++ b/.gitignore @@ -63,8 +63,4 @@ ENV/ # pycharm .idea/* -# vim -..swp - -# Ctag file -tags +/docs//generated/ diff --git a/docs/_templates/mydetector.rst b/docs/_templates/mydetector.rst new file mode 100644 index 0000000..8b20c98 --- /dev/null +++ b/docs/_templates/mydetector.rst @@ -0,0 +1,18 @@ +{{ fullname }} +{{ underline }} + +.. currentmodule:: {{ module }} + + +.. warning:: + + Serpent 1 detectors are not supported as of 0.8.0 + +.. autoclass:: {{ objname }} + :members: + + .. automethod:: plot + + .. automethod:: spectrumPlot + + .. automethod:: meshPlot diff --git a/docs/conf.py b/docs/conf.py index c5babe4..c63d716 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -33,14 +33,14 @@ from _version import get_versions # extensions coming with Sphinx (named 'sphinx.ext.') or your custom # ones. extensions = [ - 'matplotlib.sphinxext.plot_directive', 'sphinx.ext.autodoc', - 'sphinx.ext.doctest', - 'sphinx.ext.coverage', 'sphinx.ext.mathjax', + 'sphinx.ext.autosummary', 'sphinx.ext.napoleon', 'sphinx.ext.intersphinx', - 'sphinx.ext.extlinks'] + 'sphinx.ext.extlinks', + 'matplotlib.sphinxext.plot_directive', +] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -188,9 +188,12 @@ texinfo_documents = [ ] # -- Options for auto documentation -------------------------------------- -autodoc_default_options = { - 'members': 'inherited-members', -} + +# Autogenerate stub files for autosummary +autosummary_generate = True + +autodoc_mock_imports = ["serpentTools"] + # -- Links to external documentation intersphinx_mapping = { @@ -211,7 +214,7 @@ rst_prolog = """ .. \|HomogUniv-gc\| replace:: :attr:`~serpentTools.objects.HomogUniv.gc` .. \|HomogUniv-gcUnc\| replace:: :attr:`~serpentTools.objects.HomogUniv.gcUnc` .. \|ResultsReader\| replace:: :class:`~serpentTools.ResultsReader` -.. \|Detector\| replace:: :class:`~serpentTools.objects.Detector` +.. \|Detector\| replace:: :class:`~serpentTools.Detector` .. \|DetectorReader\| replace:: :class:`~serpentTools.DetectorReader` .. \|DepletionReader\| replace:: :class:`~serpentTools.DepletionReader` .. \|DepmtxReader\| replace:: :class:`~serpentTools.DepmtxReader` diff --git a/docs/containers/detectors.rst b/docs/containers/detectors.rst index 84db174..424caf7 100644 --- a/docs/containers/detectors.rst +++ b/docs/containers/detectors.rst @@ -1,9 +1,16 @@ .. _api-detectors: +.. currentmodule:: serpentTools + Detectors --------- +.. warning:: + + Serpent 1 detectors are not supported as of 0.8.0 + + .. note:: Energy grids are stored in order of increasing @@ -11,12 +18,14 @@ Detectors columns of this array correspond to lower bound, upper bound, and mid-point of that energy group -.. autoclass:: serpentTools.objects.Detector - -.. autoclass:: serpentTools.objects.CartesianDetector - -.. autoclass:: serpentTools.objects.HexagonalDetector -.. autoclass:: serpentTools.objects.CylindricalDetector +.. autosummary:: + :toctree: generated + :nosignatures: + :template: mydetector.rst -.. autoclass:: serpentTools.objects.SphericalDetector + Detector + CartesianDetector + HexagonalDetector + CylindricalDetector + SphericalDetector diff --git a/docs/examples/Detector.rst b/docs/examples/Detector.rst index 97abb10..f5314a3 100644 --- a/docs/examples/Detector.rst +++ b/docs/examples/Detector.rst @@ -1,13 +1,13 @@ -.. \|DetBins\| replace:: :attr:`~serpentTools.objects.Detector.bins` -.. \|DetIndx\| replace:: :attr:`~serpentTools.objects.Detector.indexes` -.. \|DetTallies\| replace:: :attr:`~serpentTools.objects.Detector.tallies` -.. \|DetErrors\| replace:: :attr:`~serpentTools.objects.Detector.errors` -.. \|DetGrids\| replace:: :attr:`~serpentTools.objects.Detector.grids` -.. \|DetSlice\| replace:: :meth:`~serpentTools.objects.Detector.slice` -.. \|plot\| replace:: :meth:`~serpentTools.objects.Detector.plot` -.. \|mesh\| replace:: :meth:`~serpentTools.objects.Detector.meshPlot` -.. \|spectrum\| replace:: :meth:`~serpentTools.objects.Detector.spectrumPlot` -.. \|hexDet\| replace:: :class:`~serpentTools.objects.HexagonalDetector` +.. \|DetBins\| replace:: :attr:`~serpentTools.Detector.bins` +.. \|DetIndx\| replace:: :attr:`~serpentTools.Detector.indexes` +.. \|DetTallies\| replace:: :attr:`~serpentTools.Detector.tallies` +.. \|DetErrors\| replace:: :attr:`~serpentTools.Detector.errors` +.. \|DetGrids\| replace:: :attr:`~serpentTools.Detector.grids` +.. \|DetSlice\| replace:: :meth:`~serpentTools.Detector.slice` +.. \|plot\| replace:: :meth:`~serpentTools.Detector.plot` +.. \|mesh\| replace:: :meth:`~serpentTools.Detector.meshPlot` +.. \|spectrum\| replace:: :meth:`~serpentTools.Detector.spectrumPlot` +.. \|hexDet\| replace:: :class:`~serpentTools.HexagonalDetector` .. _detector-example: @@ -46,10 +46,10 @@ lattice bins. >>> pin = serpentTools.readDataFile(pinFile) >>> bwr = serpentTools.readDataFile(bwrFile) >>> print(pin.detectors) - {'nodeFlx': <serpentTools.objects.Detector object at 0x7f6df2162b70>} + {'nodeFlx': <serpentTools.Detector object at 0x7f6df2162b70>} >>> print(bwr.detectors) - {'xymesh': <serpentTools.objects.Detector object at 0x7f6df2162a90>, - 'spectrum': <serpentTools.objects.Detector object at 0x7f6df2162b00>} + {'xymesh': <serpentTools.Detector object at 0x7f6df2162a90>, + 'spectrum': <serpentTools.Detector object at 0x7f6df2162b00>} These detectors were defined for a single fuel pin with 16 axial layers and a separate BWR assembly, with a description of the detectors provided in @@ -103,11 +103,6 @@ Here, only three columns, shown as rows for readability, are changing: - column 10: tally column - column 11: errors -.. note:: - - For SERPENT-1, there would be an additional column 12 that - contained the scores for each bin - Detectors can also be obtained by indexing into the \|DetectorReader\|, as .. code:: @@ -115,14 +110,11 @@ Detectors can also be obtained by indexing into the \|DetectorReader\|, as >>> nf = pin['nodeFlx'] >>> assert nf is nodeFlx -Once each detector is given this binned tally data, the -:meth:`~serpentTools.objects.Detector.reshape` -method is called to recast the -\|DetTallies\|, \|DetErrors\|, and, if applicable, -the :attr:`~serpentTools.objects.Detector.scores` columns into -individual, multidimensional arrays. For this case, -since the only variable bin quantity is that of the universe, these -will all be 1D arrays. +Tally data is reshaped corresponding to the bin information provided +by Serpent. The tally and errors columns are recast into multi-dimensional +arrays where each dimension is some unique bin type like energy or spatial +bin index. For this case, since the only variable bin quantity is that +of the universe, the ``tallies`` and ``errors`` attributes will be 1D arrays. .. code:: @@ -138,36 +130,21 @@ will all be 1D arrays. 0.00251, 0.00282, 0.00307, 0.00359, 0.00415, 0.00511, 0.00687, 0.00809, 0.01002]) -Bin information is retained through the \|DetIndx\| attribute. This is an -:class:`~collections.OrderedDict` as the keys are placed according to their column -position. These positions can be found in the SERPENT Manual, and are -provided in the ``DET_COLS`` tuple. - .. note:: Python and numpy arrays are zero-indexed, meaning the first item is accessed with ``array[0]``, rather than ``array[1]``. -.. code:: - - >>> from serpentTools.objects import DET_COLS - >>> print(DET_COLS) - ('value', 'energy', 'universe', 'cell', 'material', 'lattice', 'reaction', - 'zmesh', 'ymesh', 'xmesh', 'tally', 'error', 'scores') - >>> print(DET_COLS.index('cell')) - 3 - >>> nodeFlx.indexes - OrderedDict([('universe', - array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))]) - -Each item in the \|DetIndx\| ordered dictionary corresponds to the -unique values of that bin in the original \|DetBins\| array. Here, -``universe`` is the first item and contains an equal number of elements -to the size of the first (and only) axis in the ``nodeFlx`` tally matrix +Bin information is retained through the \|DetIndx\| attribute. +Each entry indicates what bin type is changing along that dimension of +``tallies`` and ``errors``. Here, ``universe`` is the first item and +indicates that the first dimension of ``tallies`` and ``errors`` +corresponds to a changing universe bin. .. code:: - >>> assert nodeFlx.indexes['universe'].size == nodeFlx.tallies.size + >>> nodeFlx.indexes + ("universe", ) For detectors that include some grid matrices, such as spatial or energy meshes ``DET<name>E``, these arrays are stored in the \|DetGrids\| dictionary @@ -175,7 +152,7 @@ meshes ``DET<name>E``, these arrays are stored in the \|DetGrids\| dictionary .. code:: >>> spectrum = bwr.detectors['spectrum'] - >>> print(spectrum.grids['E'][:5, :]) + >>> print(spectrum.grids['E'][:5]) [[1.00002e-11 4.13994e-07 2.07002e-07] [4.13994e-07 5.31579e-07 4.72786e-07] [5.31579e-07 6.25062e-07 5.78320e-07] @@ -196,33 +173,30 @@ fission and capture rates (two ``dr`` arguments) in an XY mesh. .. code:: >>> xy = bwr.detectors['xymesh'] - >>> for key in xy.indexes: - ... print(key, xy.indexes[key]) - energy [0 1] - ymesh [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] - xmesh [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] + >>> xy.indexes + ('energy', 'ymesh', 'xmesh') Traversing the first axis in the \|DetTallies\| array corresponds to -changing the value of the ``reaction``. The second axis corresponds to +changing the value of the ``energy``. The second axis corresponds to changing ``ymesh`` values, and the final axis reflects changes in ``xmesh``. .. code:: >>> print(xy.bins.shape) - >>> print(xy.tallies.shape) - >>> print(xy.bins[:5, 10]) - >>> print(xy.tallies[0, 0, :5]) (800, 12) + >>> print(xy.tallies.shape) (2, 20, 20) + >>> print(xy.bins[:5, 10]) [8.19312e+17 7.18519e+17 6.90079e+17 6.22241e+17 5.97257e+17] + >>> print(xy.tallies[0, 0, :5]) [8.19312e+17 7.18519e+17 6.90079e+17 6.22241e+17 5.97257e+17] Slicing ~~~~~~~ -As the detectors produced by SERPENT can contain multiple bin types, as -seen in ``DET_COLS``, obtaining data from the tally data can become +As the detectors produced by SERPENT can contain multiple bin types, +obtaining data from the tally data can become complicated. This retrieval can be simplified using the \|DetSlice\| method. This method takes an argument indicating what bins (keys in \|DetIndx\|) to fix at what position. @@ -236,9 +210,7 @@ reaction tally is stored in column 1. .. code:: - >>> print(spectrum.indexes['reaction']) >>> spectrum.slice({'reaction': 1})[:20] - [0 1] array([3.66341e+22, 6.53587e+20, 3.01655e+20, 1.51335e+20, 3.14546e+20, 7.45742e+19, 4.73387e+20, 2.82554e+20, 9.89379e+19, 9.49670e+19, 8.98272e+19, 2.04606e+20, 3.58272e+19, 1.44708e+20, 7.25499e+19, @@ -492,8 +464,8 @@ from hexagonal detectors in >>> hexFile = 'hexplot_det0.m' >>> hexR = serpentTools.readDataFile(hexFile) >>> hexR.detectors - {'hex2': <serpentTools.objects.HexagonalDetector at 0x7f1ad03d5da0>, - 'hex3': <serpentTools.objects.HexagonalDetector at 0x7f1ad03d5c88>} + {'hex2': <serpentTools.HexagonalDetector at 0x7f1ad03d5da0>, + 'hex3': <serpentTools.HexagonalDetector at 0x7f1ad03d5c88>} Here, two \|hexDet\| objects are produced, with similar \|DetTallies\| and slicing methods as demonstrated above. @@ -535,12 +507,11 @@ Here, two \|hexDet\| objects are produced, with similar [ 3. , 1.732051 ]]), 'Z': array([[0., 0., 0.]])} >>> hex2.indexes - OrderedDict([('ycoord', array([0, 1, 2, 3, 4])), - ('xcoord', array([0, 1, 2, 3, 4]))]) + ('ycoord', 'xcoord') Creating hexagonal mesh plots with these objects requires setting the -:attr:`~serpentTools.objects.HexagonalDetector.pitch` -and :attr:`~serpentTools.objects.HexagonalDetector.hexType` attributes. +:attr:`~serpentTools.HexagonalDetector.pitch` +and :attr:`~serpentTools.HexagonalDetector.hexType` attributes. .. code:: diff --git a/examples/Detector.ipynb b/examples/Detector.ipynb index 637713d..36eacc1 100644 --- a/examples/Detector.ipynb +++ b/examples/Detector.ipynb @@ -59,8 +59,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'nodeFlx': <serpentTools.objects.detectors.Detector object at 0x7fd09d5530b8>}\n", - "{'xymesh': <serpentTools.objects.detectors.CartesianDetector object at 0x7fd09d553208>, 'spectrum': <serpentTools.objects.detectors.Detector object at 0x7fd09d553198>}\n" + "{'nodeFlx': <serpentTools.detectors.Detector object at 0x7fe29eba2290>}\n", + "{'xymesh': <serpentTools.detectors.CartesianDetector object at 0x7fe29eb984d0>, 'spectrum': <serpentTools.detectors.Detector object at 0x7fe29f58ee50>}\n" ] } ], @@ -139,9 +139,7 @@ "\n", " column 0: universe column\n", "* column 10: tally column\n", - "* column 11: errors\n", - "\n", - "Note For SERPENT-1, there would be an additional column 12 that contained the scores for each bin" + "* column 11: errors" ] }, { @@ -165,7 +163,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Once each detector is given this binned tally data, the `reshape` method is called to recast the tallies, errors, and, if applicable, the scores columns into individual, multidimensional arrays. For this case, since the only variable bin quantity is that of the universe, these will all be 1D arrays." + "Tally data is reshaped corresponding to the bin information provided by Serpent. The tally and error columns are recast into multi-dimensional arrays where each dimension is some unique bin type like energy or spatial bin index. For this case, since the only variable bin quantity is that of the changing universe, the ``tallies`` and ``errors`` attributes will be 1D arrays." ] }, { @@ -219,44 +217,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Bin information is retained through the `indexes` attribute. This is an [`OrderedDictionary`](https://docs.python.org/2/library/collections.html#collections.OrderedDict), as the keys are placed according to their column position. These postions can be found in the SERPENT Manual, and are provided in the `DET_COLS` tuple.\n", + "Note: Python and numpy arrays are zero-indexed, meaning the first item is accessed with `array[0]`, rather than `array[1]`.\n", "\n", - "Note: Python and numpy arrays are zero-indexed, meaning the first item is accessed with `array[0]`, rather than `array[1]`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('value', 'energy', 'universe', 'cell', 'material', 'lattice', 'reaction', 'zmesh', 'ymesh', 'xmesh', 'tally', 'error', 'scores')\n", - "3\n" - ] - } - ], - "source": [ - "from serpentTools.objects.detectors import DET_COLS\n", - "print(DET_COLS)\n", - "print(DET_COLS.index('cell'))" + "Bin information is retained through the ``indexes`` attribute. Each entry indicates what bin type is changing along that dimension of ``tallies`` and ``errors``. Here, ``universe`` is the first item and indicates that the first dimension of ``tallies`` corresponds to a changing universe bin." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "OrderedDict([('universe',\n", - " array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))])" + "('universe',)" ] }, - "execution_count": 9, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -265,23 +242,6 @@ "nodeFlx.indexes" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each item in the `indexes` ordered dictionary corresponds to the unique values of that bin in the original `bin` array.\n", - "Here, `universe` is the first item and contains an equal number of elements to the size of the first (and only) axis in the nodeFlx tally matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "assert nodeFlx.indexes['universe'].size == nodeFlx.tallies.size" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -291,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -327,23 +287,23 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "energy [0 1]\n", - "ymesh [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n", - "xmesh [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" - ] + "data": { + "text/plain": [ + "('energy', 'ymesh', 'xmesh')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "xy = bwr.detectors['xymesh']\n", - "for key in xy.indexes:\n", - " print(key, xy.indexes[key])" + "xy.indexes" ] }, { @@ -355,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -381,23 +341,16 @@ "metadata": {}, "source": [ "### Slicing\n", - "As the detectors produced by SERPENT can contain multiple bin types, as seen in `DET_COLS`, obtaining data from the tally data can become complicated. This retrieval can be simplified using the `slice` method. This method takes an argument indicating what bins (keys in `indexes`) to fix at what position.\n", + "As the detectors produced by SERPENT can contain multiple bin types, obtaining data from the tally data can become complicated. This retrieval can be simplified using the `slice` method. This method takes an argument indicating what bins (keys in `indexes`) to fix at what position.\n", "\n", "If we want to retrive the tally data for the capture reaction in the `spectrum` detector, you would instruct the `slice` method to use column 1 along the axis that corresponds to the reaction bin, as the fission reaction corresponded to reaction tally 2 in the original matrix. Since python and numpy arrays are zero indexed, the second reaction tally is stored in column 1." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0 1]\n" - ] - }, { "data": { "text/plain": [ @@ -407,13 +360,12 @@ " 6.31722e+20, 2.89445e+20, 2.15484e+20, 3.59303e+20, 3.15000e+20])" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(spectrum.indexes['reaction'])\n", "spectrum.slice({'reaction': 1})[:20]" ] }, @@ -426,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -439,7 +391,7 @@ " 0.02165, 0.0192 , 0.02048, 0.01715, 0.02055, 0.0153 ])" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -482,17 +434,19 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -502,17 +456,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xd4VNX2//H3SoFAqAGCQMBQgyBF\niLTQBKWogOWqgAjYonjhp15ArKCCfgFpNtAICl7vRRAsoRlFEVRACEWkGNoNEIr0ToCQ9ftjJjGE\nkASS4UyS9XqePJk5c86ZTyhZs/c5e29RVYwxxpjM+DgdwBhjjPezYmGMMSZLViyMMcZkyYqFMcaY\nLFmxMMYYkyUrFsYYY7JkxcIYY0yWrFgYY4zJkhULY4wxWfJzOkBuKVu2rIaGhjodwxhj8pRVq1Yd\nVNVyWe3n0WIhIp2AtwFfYLKqjkz3emtgAlAf6K6qs9K9XgLYBHylqv0ze6/Q0FBiY2NzM74xxuR7\nIrIjO/t5rBtKRHyB94HOQB2gh4jUSbfbTqAv8N/LnGY4sNhTGY0xxmSPJ69ZNAG2qup2VT0HfA50\nS7uDqsar6jogOf3BItIYKA9858GMxhhjssGTxaISsCvN8wT3tiyJiA8wFhicxX6RIhIrIrEHDhy4\n6qDGGGMy58lrFpLBtuzOh/4UMF9Vd4lkdBr3yVSjgCiA8PBwm2vdGJOh8+fPk5CQQGJiotNRHBMQ\nEEBISAj+/v5Xdbwni0UCUDnN8xBgTzaPbQ60EpGngGJAIRE5qarP53JGY0wBkJCQQPHixQkNDSWz\nD6D5lapy6NAhEhISqFq16lWdw5PFYiVQU0SqAruB7kDP7Byoqg+mPBaRvkC4FQpjzNVKTEwssIUC\nQEQoU6YMOemu99g1C1VNAvoDMbhuf52pqhtE5HUR6QogIjeLSAJwH/ChiGzwVB5jTMFWUAtFipz+\n/B4dZ6Gq84H56bYNTfN4Ja7uqczOMRWY6oF4xhhjssmm+zC5YuzYsYwdO9bpGMbkKRMmTOD06dNO\nx8gWKxbGGOMQKxbG5JC1VEx+c+rUKe644w4aNGjAjTfeyGuvvcaePXu45ZZbuOWWWwD47rvvaN68\nOY0aNeK+++7j5MmTgGs6oyFDhtCkSROaNGnC1q1bAfjiiy+48cYbadCgAa1bt/Zo/nwzkaAxxmTH\na3M2sHHP8Vw9Z52KJRjWpW6m+3z77bdUrFiRefPmAXDs2DE++eQTFi1aRNmyZTl48CAjRoxg4cKF\nBAYGMmrUKMaNG8fQoa7LvCVKlGDFihV8+umnPPPMM8ydO5fXX3+dmJgYKlWqxNGjR3P1Z0rPWhbG\nGHMN1KtXj4ULFzJkyBB+/vlnSpYsedHry5cvZ+PGjURERNCwYUOmTZvGjh1/z/HXo0eP1O/Lli0D\nICIigr59+/LRRx9x4cIFj+a3loUxpkDJqgXgKbVq1WLVqlXMnz+fF154gQ4dOlz0uqpy2223MX36\n9AyPT3vra8rjDz74gN9++4158+bRsGFD1q5dS5kyZTyS31oWxhhzDezZs4eiRYvSq1cvBg0axOrV\nqylevDgnTpwAoFmzZvz666+p1yNOnz7N5s2bU4+fMWNG6vfmzZsDsG3bNpo2bcrrr79O2bJl2bVr\nF55iLQtjjLkG/vjjDwYPHoyPjw/+/v5MmjSJZcuW0blzZypUqMCiRYuYOnUqPXr04OzZswCMGDGC\nWrVqAXD27FmaNm1KcnJyautj8ODBbNmyBVWlffv2NGjQwGP5rVgYY8w10LFjRzp27HjRtvDwcAYM\nGJD6vF27dqxcuTLD4//5z38ybNiwi7Z9+eWXuR/0MqwbyhhjTJasZVFApYxhGDhwoMNJjDFZiY+P\ndzqCtSyMMcZkzYqFMcaYLFmxMAWCTR9iTM5YsTDGmAzYB4yLWbEwxhiHPPbYY2zcuNHpGNlid0MZ\nY0waMTExhIaGXrQtLi6O+Pj4S8ZJ5NTkyZNz9XyeZC0LY4xJIzQ0lKioKPbv3w+4CkVUVNQlBeRK\npZ+ifMaMGbRt25bY2FgApkyZQq1atWjbti2PP/44/fv3B6Bv377069ePW265hWrVqrF48WIeeeQR\nbrjhBvr27Zt6/n79+hEeHk7dunUvGbyXG6xlYYwpUGbOnJnlHEp+fn7MmjWLoKAgoqOjadasGXPn\nzmXu3LkZ7l+5cmXuv//+TM+Z0RTlkyZNAlzzRg0fPjx1vqh27dpdNHXHkSNH+PHHH4mOjqZLly78\n+uuvTJ48mZtvvpm1a9fSsGFD3njjDYKCgrhw4QLt27dn3bp11K9f/0r+aDJlLQtjjEknODiYoKAg\n9u/fT/Xq1QkODs7xOTObonzFihW0adOGoKAg/P39ue+++y46tkuXLogI9erVo3z58tSrVw8fHx/q\n1q2bOmBv5syZNGrUiJtuuokNGzbk+rUQj7YsRKQT8DbgC0xW1ZHpXm8NTADqA91VdZZ7e0NgElAC\nuAC8oaozPJnVGFMwZNUCAFfXU3R0NO3bt6d06dLceeedhIWF5eh9M5uiXFUzPbZw4cIA+Pj4pD5O\neZ6UlMT//vc/xowZw8qVKyldujR9+/YlMTExR3nT81jLQkR8gfeBzkAdoIeI1Em3206gL/DfdNtP\nA71VtS7QCZggIqU8ldUYY1KkXKNo1qwZdevWJTIykqioKOLi4nJ03oymKE/RpEkTFi9ezJEjR0hK\nSmL27NlXdO7jx48TGBhIyZIl+euvv1iwYEGOsmbEky2LJsBWVd0OICKfA92A1LaRqsa7X0tOe6Cq\nbk7zeI+I7AfKAZ5dN9AYU+DFx8cTGRmZen0iLCyMyMhI4uPjc9S6yGiK8kGDBgFQqVIlXnzxRZo2\nbUrFihWpU6fOJSvpZaZBgwbcdNNN1K1bl2rVqhEREXHVOS/Hk8WiEpD2KlIC0PRKTyIiTYBCwLYM\nXosEIgGqVKlydSmNMSaNlNtj017MDgsLy3E3VEZTlP/000+pj3v27ElkZCRJSUncfffdqd1UU6dO\nTd0nNDSU9evXpz5P+1rax57gyWIhGWzLvGMu/QlEKgD/BvqoanL611U1CogCCA8Pv6JzG2NMZq71\njMyvvvoqCxcuJDExkQ4dOnDXXXdd0/fPiieLRQJQOc3zEGBPdg8WkRLAPOBlVV2ey9mMMcarjBkz\nxukImfLkrbMrgZoiUlVECgHdgejsHOje/yvgU1X9woMZjTEFRFZ3HOV3Of35PVYsVDUJ6A/EAJuA\nmaq6QUReF5GuACJys4gkAPcBH4rIBvfh9wOtgb4istb91dBTWY0x+VtAQACHDh0qsAVDVTl06BAB\nAQFXfQ6PjrNQ1fnA/HTbhqZ5vBJX91T64z4DPvNkNmNMwRESEkJCQgIHDhxwOopjAgICCAm55Ndt\nttl0H8ZcJVuaNu/w9/enatWqTsfI02y6D2OMMVmyYmGMMSZLViyMMcZkyYqFMcaYLFmxMDkSExNz\nyQRrcXFxxMTEOJTIGOMJVixMjuT2qmJWfIzxTnbrrMmRsLAwHn/8ce7s8QjnSl3P29MXULFha37/\neAni8wuIDyKC+PimfkdAxBfxEUR8wUcQ936nDu4lfsWHFK0Wzk3XB6UWn8jISKd/VGMKNCsWJkf2\n7NnD/PnzOXj8NKd3/UJQ+Yoc2LyanAx9Onf6BLvmfcS2EuX44evp3NjlUYb+dBh+WnbV50w4WoVa\nhY/lIJUxBZsVizzAGwd/JSYmMmfOHH788UdOnz6NX+GihLZuS4eKSfTq1Yvq1auTnJzMhQsXSE5O\nTv1KeZ52e0b7/HPoW2z5cyMXzgvxy+ZzXd1mlKvVCF8//6vKeyCpMJD99QGMMRezYmGuiKqyYsUK\nZs+ezfHjx6levTo7d+6kStsHKFKmIgN61U/tNrra+f/j4uIodvYg/7i1Ob6+vgQHB3Pw4CZK7tjD\n7bffTsuWLfHzu7J/uhGv2HyUxuSEXeA22bZnzx7Gjh3Lxx9/TKlSpXj++eepX78+/fv3p0iZisDF\nq4pdjfRLWg4aNIjExETuuecegoODmT59OkOHDmXp0qUkJ1+yxIkxxkOsZWGydObMGebMmcOiRYso\nUqQIvXr1IiIiAh8fnzR3Pa1L3T8nq4pltqTlwIED2bhxI9988w3Tpk3j22+/pWvXrjRu3BiRjNba\nMsbkFisW5rJSupxmzZrFiRMnaNWqFXfddReBgYEee8+slrSsW7cuderU4ffff+ebb77ho48+YsGC\nBXTr1o169epZ0TDGQ6xYmAwlJCTw+eefs2XLFkJDQ+nfvz/XX3+907EAEBEaNmxI/fr1iY2NZc6c\nObz//vtUrVqVu+66i9q1azsd0Zh8x4qFuUj6LqeHHnqIiIgIr/zE7uPjQ5MmTWjcuDHLli1j7ty5\njB8/nrCwMLp160b16tWdjmhMvmHFwgCuLqfffvuN2bNnX7Mup9zi6+tLy5Ytadq0KT///DPz589n\n9OjR3HjjjZQuXZqbb775ov3j4uKIj49P7fIyxmTNikUBExMTc8lUHIsXL+bTTz/Fz8+PqlWrelWX\n05Xw9/enXbt2REREsGjRImJiYtizZw/Tpk3jeMVWlKhyg40IN+YqWbEoYFLmcvLz86NUqVKMGzeO\nadOm0ahRI3r37k2LFi28ssvpShQuXJhOnTrRpk0bFi5cyMyZM1n67RT8K9fnzl8CqNH23hyPCIfc\nHxXujYMvjUlhxaKASZnL6fb7e3PsrHLh1BFCm3Xm+E138856H95Zv/yqznsgqTDl/M7mctqcKVKk\nCF26dOGWW25h7e33sW3TWs6EVAc0V85vo8JNQeLRYiEinYC3AV9gsqqOTPd6a2ACUB/orqqz0rzW\nB3jZ/XSEqk7zZNaCZPfu3Rw+dY7EM6epd+v9VIvokuNzlvM767VzL+3evZsgOU2ddi3YuXMnJTd9\nw/317qdr165XPBI8LRsVbgoSjxULEfEF3gduAxKAlSISraob0+y2E+gLDEp3bBAwDAjH9TFwlfvY\nI57KW1D8+eefTJ48GZIvENryLlqXO0tk26CrHkSXIqULxdukHREeHBzMyy+/zJAhQ5gxYwZ//vkn\njz32GMHBwU7HNMbreXK6jybAVlXdrqrngM+Bbml3UNV4VV0HpJ+3oSPwvaoedheI74FOHsxaIBw8\neJCRI0cSHx9P6G29KRN2M5GRkURFRV2yhkR+kTIiPKUg1KtXj/Hjx9OqVSsOHDjAiBEjWLZsGaq5\n0zVlTH7lyWJRCdiV5nmCe1uuHSsikSISKyKxBw7kZFLs/O/s2bNMmjSJY8eOMXbsWALLhwI5n8vJ\n23Xs2PGSVlNYWBj9+/dn6NChXH/99UydOpUpU6Zw5swZh1Ia4/08WSwyuqUmux/fsnWsqkapariq\nhpcrV+6KwhUkqsqnn37K7t27GTFiBC1atLjo9bCwsAI55qB06dI8++yzdOvWjVWrVjF8+HC2bdvm\ndCxjvJIni0UCUDnN8xBgzzU41qTz3XffERsby913303dunWdjuNVfHx8uP322xk8eDAiwpgxY5g7\nd67NaGtMOp4sFiuBmiJSVUQKAd2B6GweGwN0EJHSIlIa6ODeZq7Qhg0b+OqrrwgPD6dDhw5Ox/Fa\n1apV4+WXXyY8PJw5c+Ywbtw4Dh8+7HQsY7yGx4qFqiYB/XH9kt8EzFTVDSLyuoh0BRCRm0UkAbgP\n+FBENriPPQwMx1VwVgKvu7eZK7B//34mT55MpUqV6N27d54fbOdpRYoU4dFHH+Xhhx9m586dDB8+\nnNWrVzsdyxiv4NFxFqo6H5ifbtvQNI9X4upiyujYj4GPPZkvP0tMTGTSpEmICP369aNw4cJOR8oz\nmjVrRvXq1Zk8eTIffvghERERPPDAA/ZnaAo0WykvH1JVpk6dyt69e4mMjKRs2bJOR8pzypUrx3PP\nPUfnzp1ZunQpb7zxBjt37nQ6ljGOsWKRDy1YsIA1a9bwj3/8w9Z2yAFfX1/uuusunn32Wc6ePcvI\nkSP5/vvvbUyGKZCsWOQz69atIzo6mqZNm9K+fXun4+QLYWFhDB06lHr16jFr1iz69evHqlWrLton\nLi6OmBi7B8PkX1Ys8pF9+/YxZcoUKleuzEMPPWQXtHNRYGAgTz75JA8++CAnTpzgiSee4NDmWODv\nKUXST/1uTH5is87mE2fOnGHixIn4+/vTr18//P39nY6U74gIrVu3pmbNmowcOZLJ0z9n/87tdF32\nJdVadcvxtOe5PeW5MbnJWhb5gKry8ccfc+DAAZ544gmCgoKcjpSvVahQgTFjxhBaoxaJW5Zx4fxZ\nAstWzPF5DyQVZvNZm/LceCdrWeQD0dHRrFu3jh49elCzZk2n4xQI27dvp6z/OUIa12Xfvm1U2fY1\nw4YNo2TJq/9lb1OeG29mxSKPW716NfPnzyciIoI2bdo4HadASD/teY0aNXj11Vc5ceIEw4YNIyQk\nw6FDxuRp1g2Vh+3Zs4epU6dSrVo1evbsaRe0r5H0055369aN8ePHc+zYMUaPHs26descTmhM7rNi\nkUedOnWK999/n8KFC/PEE0/kaMU3c2Uymva8bdu2REVFUb58eSZOnMgPP/xg4zFMvmLFIg9KTk5m\n8uTJHDlyhH79+lGqVCmnIxmgVKlSDBo0iAYNGjBz5kymT59us9eafMOKRR709ddfs3HjRnr27Em1\natWcjmPSKFy4ME8++SQdO3Zk8eLFvPvuu44tqjR27FivXe7W5D1WLPKYlStXEhMTQ5s2bWjZsqXT\ncUwGRIR77rmH3r178+effzJq1CgOHjzodCxjcsSKRR6ya9cupk2bRo0aNbj//vudjmOyEBERwTPP\nPMOxY8cYOXKkrcJn8jQrFl4sJiaGuLg44O81tBMTE6levbpd0M4jwsLCeP755wkICGDcuHGsWLHC\n6UjGXBX7jePFQkNDiYqKwsfHh7i4OIoVK0bRokULxNKoAwcOdDpCrilfvjwvvPACkyZNYsqUKezf\nv5877rjDbnU2eYoVCy8WFhZGZGQkHe7uwbGzyZQMCqbOnY/keA4icE0tUc7vbC4lzV+/3D0hMDCQ\nZ555hs8++4w5c+bw119/0bt3b5vDy+QZViy8XEhICCfOC2fPnaPyzbdS4rrrc+W85fzO2qR115if\nnx99+vShfPnyfP311xw6dIh+/fpRvHhxp6MZk6VsFQsR6QjUBQJStqnqm54KZf42depUzh4/SKUm\nd9C0yF9Etg26ZEDY1bBbKp0hInTu3Jny5cvz8ccf83//938MGDCAChUqOB3NmExleYFbRCYCfYB/\nAUWAXkAND+cyuOZ9mjx5MmVuaE75hrcQGRlJVFRU6kVvk3c1atSIQYMGcf78efr168e8efMuet0W\nUzLeJjt3Q7VU1Z7AIVV9BWgKZGumNBHpJCJxIrJVRJ7P4PXCIjLD/fpvIhLq3u4vItNE5A8R2SQi\nL2T/R8o/Zs+eTVhYGOVvcq14l3INIz4+3tlgJleEhobywgsvUKVKFV588UX2r1sC2GJKxjtlpxsq\nZfhpoohcBxwCQrM6SER8gfeB24AEYKWIRKvqxjS7PQocUdUaItIdGAU8ANwHFFbVeiJSFNgoItNV\nNT6bP1eed/ToUY4dO0anTp34aFtg6vawsLBc6YYy3iEoKIjRo0fzxhtvMO7DTzn4125bTMl4pey0\nLBaISClgDLAWiAdmZ+O4JsBWVd2uqueAz4Fu6fbpBkxzP54FtBfX/YQKBIqIH66ur3PA8Wy8Z76x\nYMECLly4wJ133ul0FONhAQEBvPbaa1StVZuzW1egyckUD66c4/PaYkomN2WnZTFCVZOAL0RkLq5f\n3tn5xV0J2JXmeQKuLqwM91HVJBE5BpTBVTi6AXuBosCzqno4G++ZLxw6dIiff/6ZiIgIypYt63Qc\ncw1s2bKFMr6JXNewNvv3b6Lu/oW89NJLORp8aYspmdyUnX+JK4BGAKp6BjgjIqtTtmUioxFH6eds\nvtw+TYALQEWgNPCziCxU1e0XHSwSCUQCVKlSJYs4ecf8+fMREe644w6no5hrIP1iSkFBQYwfP55z\n584xdOhQChcu7HREYy7fDSUiwSLSACgiIvVEpL77qyWuT/tZSQDStqVDgD2X28fd5VQSOAz0BL5V\n1fOquh/4FQhP/waqGqWq4aoaXq5cuWxE8n779+9n6dKltGrVitKlSzsdx1wD6RdTevjhh3nhhRdY\nu3Ytb7/9NqdPn3Y4oTGZtyzuAB7B9Ut+YprtJ4BXsnHulUBNEakK7Aa64yoCaUXjui13GfAP4EdV\nVRHZCbQTkc9wFaZmwIRsvGeeN2/ePHx9fencubPTUfIVbx5h3rFjRwDmzp2buq1Hjx7UqlWLKVOm\nMG7cOJ5++mkbvGccddmWhap+oqqtgEdVtVWar9tVNcvOUPd1jv5ADLAJmKmqG0TkdRHp6t5tClBG\nRLbiGseRcnvt+0AxYD2uovOJqub7tSr37dvHb7/9Rtu2bSlZ0i5MFnSNGzfmn//8J/v27eOtt97i\nyJEjTkcyBViW1yxUdebVjuBW1fnA/HTbhqZ5nIjrNtn0x53MaHtekTI6+ko/zc6ZM4dChQqlftI0\npm7dujz99NO89957jB49mmeffTa1u8qYa8lGcHuJhIQEYmNjadeunXU3mIvUrFmTgQMHcu7cOd56\n6y0SEhKcjmQKII+O4DbZN2fOHIoUKUKHDh2cjmK8UJUqVRg8eDA+Pj6MHTuW7du3Z32QMbkoO8Ui\n/QjuRLIxgttk344dO1i7di233norRYtm50aznBs4cKBXX/Q1l7ruuut47rnnCAwMZMKECWzatOma\nZ7B1vQsuT47gNtkUHR1NYGAgt956q9NRjJcrU6YMgwcPpkyZMrz33nv8/vvvTkcyBUSWxUJVX1XV\no+47oKoC9VS1QE7s5wnbtm1j/fr1dOzYkYCAgKwPMAVeyZIlGTRoEJUrV+aDDz5g+fLlTkcyBcBl\n74ZKc3trRq+hqtGeiVSwREdHU7x4cdq2bet0FJOHpKy8N2nSJD755BMSExPt35DxqMxunU25dbUs\n0AL4yf28DbAY14A6kwNxcXH8+eef3H///Talg7liAQEB9O/fn48++ojp06dz5swZOnXqZGt7G4/I\nbFDeQ6r6EHAeqKOq3VS1G67xFknXKmB+pap88803lCpVitatWzsdx+RR/v7+PPHEEzRt2pSvv/6a\nr776CtX0U7AZk3PZmUiwmqruTvN8D2ALKuTQxo0b2bZtGz179sTf39/pOCYP8/X15eGHHyYgIICY\nmBhOnz5Nz57pZ9YxJmeyUyyWiMg8YDquGWG7A0s8miqfS2lVlClThoiICKfjmHxAROjRowdFixYl\nKiqK+Ph4NLkm4uPqPIiLiyM+Pt5mBzBXLTvF4p+4JvlL6Sv5FNd6E+YqrVu3jh07dtC7d+8crVdg\nnOVt41REhLvuuouDBw/yzjvvcLhkLSq3ujd1CvTIyEinI5o8LDtzQynwhfvL5JCqEh0dTXBwMM2b\nN3c6jsmHHnvsMQICAnhi4EscP3aULsu/onqbe3K8TCvYUq0FWXYG5ZlctHr1ahISErjzzjvx8bE/\nfuMZvXr1os5NTUja+ydnTx2jSKncWXHRlmotuKwP5BpKTk5mzpw5VKhQgZtvvtnpOCYfi4uLo+iZ\n/XSICCc+Pp7r4r5kxIgRBAUF5ei8tlRrwZWdWWc7id24nStWrlzJ3r176dKli7UqjMekXaa1VatW\njB49muXLlzNkyBD27dvndDyTR2XnN1ZfYIuIvCkiNT2cJ9+6cOECc+fOJSQkhEaNslq+3Jirl36Z\n1g4dOjB+/HiOHDnC6NGj2bFjh8MJTV6UnbmhuuNa/3o3MF1EfhaRR0Qk0OPp8pHly5ezf/9+unbt\naiNsjUd17NiRsLCLh0K1adOGiRMnEhAQwNixY4mLi3MoncmrstUXoqpHgf8CU4EqQA/gdxF5ynPR\n8o+kpCTmzp1LaGgo9evXdzqOKaCCg4N57rnnKFOmDO+88w5r1651OpLJQ7JzzaKziHwB/AwUB5qp\n6m1AA2CIh/PlC7/88guHDx+2VoVxXKlSpRg0aBBVqlThgw8+YOnSpU5HMnlEdloWDwGTVPVGVf0/\nVd0LoKqngMc9mi4fOH/+PAsWLKBGjRrUqVPH6TjGpM5YW7t2baZNm8bChQudjmTygOxcs+ipqj9e\n5rXvMjvWfSdVnIhsFZHnM3i9sIjMcL/+m4iEpnmtvogsE5ENIvKHiOTJxR6WLFnC0aNH6datm7Uq\njNcoXLgw/fv3p3HjxnzxxRd8/fXXNgGhyVRm61kcwTUX1CUv4RrYnekN2yLiC7wP3AYkACtFJFpV\nN6bZ7VHgiKrWEJHuwCjgARHxAz4DHlLV30WkDK7Zb/OUs2fPsmDBAmrXrk2tWrWcjmPMRfz8/Hjs\nsccoWrQoCxYs4NSpU/To0cNu6zYZymxQXk6HfDYBtqrqdgAR+RzoBqQtFt2AV92PZwHvucd0dADW\nqervAKp6KIdZHPHTTz9x4sQJuna97DpSxjjKx8eHBx98kGLFirFgwQJOnz7Nww8/bHOWmUtk9i8i\nq1tjj2fxeiVgV5rnCUDTy+2jqkkicgwoA9QCVERigHLA56o6Oov38yrnz58nJiaGG2+8kerVqzsd\nx5jLSpmAsGjRosyePZvTp0/z5JNP2oJc5iKZtTc3AOvd39N/rc/GuTPqoE/frXW5ffyAlsCD7u93\ni0j7S95AJFJEYkUk9sCBA9mI5FkxMTGp969v2bKFU6dOUbduXWJiYnJ03pCQEEJCQnIjojGX1aFD\nB3r37s2mTZuYMGECp06d8vh7jh07lrFjx3r8fUzOZbZSXmVVreL+nv6rSjbOnQBUTvM8BNfCSRnu\n475OURI47N6+WFUPquppYD5wybBnVY1S1XBVDS9Xrlw2InlWaGgoUVFRJCQksHnzZipUqMC8efMI\nDQ11Opox2RIREcETTzzBzp07GTNmDEePHnU6kvES2eqYFJGSQHUg9Y4kVc3qBu2VQE0RqYpr9Hd3\nIP3yXdFAH2AZrjUzflTVlO6n50SkKHAO17rf47OT1UlhYWFERkZyW7cHOH4e9vywllq3ds/x1NAb\n9x6nToUSuZjUeCNvWR/jppuQAGEEAAAZ20lEQVRuYsCAAUycOJG33nqLZ555Bm/4MGaclZ1BeY8C\nS4Efcd2t9CPwZlbHqWoS0B+IATYBM1V1g4i8LiIpV3ynAGVEZCvwL+B597FHgHG4Cs5aYLWqzrvC\nn80RVatW5WSyH2fPnadCvRaUuO76HJ+zToUSdGtYKRfSGZM9tWvX5l//+hdnzpxh9OjRJCQkOB3J\nOCw7LYtncM0NtUxVW4lIXeDl7JxcVefj6kJKu21omseJwH2XOfYzXLfP5imzZ8/m7NEDVAzvQNMi\nfxHZNuiSeXqMyQtCQ0MZPHgwb7/9NgMGDOCppy6e3ceWai1YsnNDdaKqngEQkUKqugGo7dlYeVNc\nXBwTJ06keJUbKN/oNiIjI4mKirJJ20yeVaFCBZ577jkqVKjA4MGDObQ5Fvh7GnS7HldwZDYoz8/d\nlbRXREoBc4AYETkM/HWtAuYlv/32GxUrVuRIodqISOo1jPj4eGtdmDwrKCiIUaNG8corr/DR9M85\ndOAAXZd/SbWW3XJ8Pc6Wac07MmtZrABQ1a6qelRVXwFGAP/BNZjOZCA4OJhilf4erR0WFmbNdJPn\nFS9enJEjR1IxpDKn/viOQkVL5Mr1OFumNe/I7JrFJWMgVPUHD2bJ006fPs2KFSto2rQpqzcXcjqO\nMblux44dVAj0oWyNypz63zLaJjfkySefzNGcZ7ZMa96RWbEoJyL/utyLqjrOA3nyrOXLl3P+/Hna\ntGnDB5t/czqOMbkq5RpF8+bNKVu2LMnJyUyaNIlTp07xr3/9y+aTKgAyKxa+QDEyHmVt0lBVlixZ\nQmhoKFWqVAGsWJj8JWWp1rlz5wIwaNCg1AkIS5YsafNJFQCZ/e3uVdXXr1mSPGzLli3s3buXPn36\nOB3FGI9Iue6WUixEhAEDBlC7dm1mzZrFqVOn6Nevn80nlY9l1na0FkU2LV68mKJFi3LzzTc7HcWY\na+q2226jb9++xMXFMW7cOE6ePOl0JOMhmRWLSybuM5c6fvw4a9asoXnz5vj7+zsdx5hrrnnz5vTr\n14/du3fz1ltvcfjwYacjGQ/IbCJB+xvPhl9//ZULFy7Qpk0bp6MY45j69evz9NNPc+zYMUaPHs3e\nvXudjmRymd3CkAPJycksWbKE2rVrU758eafjGOOomjVrMmjQIC5cuMBbb71FfHy805FMLrJikQPr\n16/n8OHD1qowxi0kJIQhQ4ZQpEgRxo0bx6ZNm5yOZHKJFYscWLx4MSVLlqRBgwZORzHGa5QtW5Yh\nQ4ZQrlw53n33XWJjY52OZHKB3Rh9lQ4dOsSGDRu4/fbb8fX1dTqOMZdwcn2MEiVKMHDgQCZOnMjk\nyZM5efIkbdu2dSyPyTlrWVyln3/+GYBWrVo5nMQY71S0aFGefvpp6tevz/Tp05k7dy6q6VdWzl22\nTKvnWLG4CklJSfzyyy/Ur1+f0qVLOx3HGK/l7+/Pk08+SYsWLZgzZw4zZszweMEwnmHdUFdhzZo1\nnDhxwi5sG5MNPj4+9O7dm2LFivHdd99x8uRJ+vbta9OD5DH2t3UVFi9eTNmyZalTp47TUYzJE0SE\ne++9l+LFizN79mxWrVplK+/lMdYNdYX27NnDli1baN26dY6mZjamIOrQoQN9+vThyJEjPPXUU5zc\n+z/AVt7LC6xlcYWWLFmCn58fLVq0cDqKMXlSixYtCAwMZNSoUcz5djKBjbrYynt5gEdbFiLSSUTi\nRGSriDyfweuFRWSG+/XfRCQ03etVROSkiAzyZM7sOnv2LMuWLaNx48YUL17c6TjG5FkNGjTg1Vdf\npVRgEU78NoviwVVs5T0v57GWhYj4Au8DtwEJwEoRiVbVjWl2exQ4oqo1RKQ7MAp4IM3r44EFnsp4\npVauXEliYqJd2DYmF6gqIUFFKVNESNoQQ9fb6/Pggw/m6Jy28p7neLIbqgmwVVW3A4jI57jW7k5b\nLLoBr7ofzwLeExFRVRWRu4DtwCkPZsw2VWXx4sVUrFiRatWqOR3HmDwt5RpF69atKVWqFCdPnmTU\nqFHs2bOHZ5991u6U8kKe7IaqBOxK8zzBvS3DfVQ1CTgGlBGRQGAI8JoH812RHTt2sHPnTtq0aWMX\nto3JoZSV94KDgylUqBDDhw8nMjKSRYsWMW7cOI4fP+50RJOOJ4tFRr9R04/Gudw+rwHjVTXTlVRE\nJFJEYkUk9sCBA1cZM3sWL15M4cKFadasmUffx5iCoGPHjoSFhaU+9/HxoX///rz++uvs2rWLN954\nw2at9TKeLBYJQOU0z0OAPZfbR0T8gJLAYaApMFpE4oFngBdFpH/6N1DVKFUNV9XwcuXK5f5P4Hbq\n1ClWrlxJ06ZNCQgI8Nj7GFPQhYeHM2TIEHx9fXnrrbdYunSp05GMmyeLxUqgpohUFZFCQHcgOt0+\n0UDKwtX/AH5Ul1aqGqqqocAE4E1Vfc+DWTO1fPlyzp8/bxe2jbkGQkJCeOmll6hRowbTpk1jxowZ\nXLhwwelYBZ7HriKpapK7NRAD+AIfq+oGEXkdiFXVaGAK8G8R2YqrRdHdU3muVsqF7WrVqhESEpKt\nY7K7nzEmY4GBgTz99NPMnj2bhQsXkpCQQGRkpN2y7iCP3nKgqvOB+em2DU3zOBG4L4tzvOqRcNkU\nFxfHX3/9xcMPP+xkDGMKHB8fH+677z6qVKnCv//9b958802eeuopKleunPXBJtfZdB9ZWLx4MYGB\ngTRu3NjpKMYUSE2bNmXw4MGoKqNGjWLFihVORyqQrFhk4tixY6xdu5YWLVrg7+/vdBxjCqzrr7+e\nl156idDQUKZMmcKsWbNITk72+Pva+hh/s5Evmfjll19ITk6mdevWTkcxxis4ufpe8eLFefbZZ/ni\niy/4/vvvSUhI4PHHHycwMNCxTAWJtSzI+NNDcnIyP//8MzfccAPBwcEOJTPGpOXr60v37t3p3bs3\nW7Zs4c033yQhIcHpWAWCFYvL+OOPPzhy5IjdLmuMF4qIiGDQoEEkJSUxatQoVq9e7XSkfM+KxWUs\nXryYUqVK0aBBA6ejGGMyULVqVV588UVCQkJ4+eWXef/991H9+zpGXFwcMTExDibMX6xYZODgwYNs\n3LiRVq1a4eNjf0TGeKuSJUsycOBAbrnlFj788EN2Lp7JhfNnbTElD7AL3BlYsmQJIkLLli2djmKM\nyYKfnx+DBw+mQoUKPDHwJY4e/IvOv8wk7LaetphSLrKPzekkJSXx66+/0qBBA0qVKuV0HGNMNvXq\n1Yv6zVqjR3Zz5ugBTh5IQHN4e60tpvQ3a1mks2rVKk6ePGkXto3JY+Li4ih0dCf3dmjFvn37KH1g\nJY1PJPPII49QtmzZqzqnLab0N2tZpLNkyRKCg4OpXbu201GMMdmUco2iWbNmNGjQgHfffRdfX1/W\nr1/P8OHDWb58OarpV0gwV8KKRRq7d+9m69attG7d2hY4MiYPSbuYEkBYWBjDhg2jS5cuhISE8Mkn\nnzB58mROnz7tcNK8y7qh0liyZAl+fn60aNHC6SjGmCvQsWNHAObOnZu6LSwsjLCwMJKTk4mJiSE6\nOppt27bxyCOPUKtWLaei5lnWsnBLSkpi+fLlhIeH2/QBxuQjPj4+dO7cmSFDhuDv78+4ceP48ssv\nSUpKcjpanmLFwm3Hjh0kJibahW1j8qnQ0FBefvllWrZsSUxMDKNGjWLfvn1Ox8ozrFjgWuBo+/bt\nhISEULVqVafjGGM8pHDhwvTq1Yt+/fpx6NAhRowYwZIlS+zidzYU6GIRExNDXFwchw8f5tixY7Rp\n04bNmzfbFAHG5HMNGzZk2LBh1KpVi//85z9MnDiREydOOB3LqxXoYhEaGkpUVBRr1qzBz8+P0qVL\n2xQBxlxDAwcOdGza85IlSzJgwAAeeOABNm7cyGuvvcb69esdyZIXFOi7ocLCwujVqxdtO99FYGh9\n/vGvN6nWsluOpwjYuPc4dSqUyMWkxhhPEBHatWtHWFgYU6ZM4d133+WWW27h3nvvtQXP0inQLQtw\nFYzytRpy/tQxytW8iRLXXZ/jc9apUIJuDSvlQjpjzLVQqVIlXnjhBW699VYWLVrEww8/zE8//XTR\nPgV9FluPtixEpBPwNuALTFbVkeleLwx8CjQGDgEPqGq8iNwGjAQKAeeAwar6oycy7tq1iwpylJY3\n16B0kb+IbBtEWFiYJ97KGOPF/P39ue+++6hbty7jxo3jmWee4WRIc4Ib3pI6QjwyMjLH75Oy0JqT\nqw5eDY8VCxHxBd4HbgMSgJUiEq2qG9Ps9ihwRFVriEh3YBTwAHAQ6KKqe0TkRiAGyPWP6mmnCAgO\nDubOO+9M/QdhBcOYgqlOnTpMmDCBMWPG8NbET9i/cyt3/Fycmu3uz3EXNeTdmWw92Q3VBNiqqttV\n9RzwOdAt3T7dgGnux7OA9iIiqrpGVfe4t28AAtytkFyV0RQBkZGRxMfH5/ZbGWPykGLFijFs2DBq\nNwwnad+fnD60j1MH95B8IecD+fLqTLae7IaqBOxK8zwBaHq5fVQ1SUSOAWVwtSxS3AusUdWzuR0w\nsykCjDEF2+bNmyl29iB339aa/fv3E7T/N24oc5wHH3yQmjVrXvV58+pMtp5sWWQ0E1/6kS+Z7iMi\ndXF1TT2R4RuIRIpIrIjEHjhw4KqDGmNMWmm7qBs1asR7771HQEAAe/fuZcyYMUybNo2TJ086HfOa\n8mSxSAAqp3keAuy53D4i4geUBA67n4cAXwG9VXVbRm+gqlGqGq6q4eXKlcvl+MaYgiqjLuoXX3yR\nLl260LlzZ5YvX87QoUP59ddfC8zob092Q60EaopIVWA30B3omW6faKAPsAz4B/CjqqqIlALmAS+o\n6q8ezGiMMZfIqou6SZMm/Pe//+XTTz9l6dKlPPjgg1SsWNGRrNeKx1oWqpoE9Md1J9MmYKaqbhCR\n10Wkq3u3KUAZEdkK/At43r29P1ADeEVE1rq/gj2V1RhjrkTFihUZOHAgffr0Ye/evQwfPpwvv/yS\ns2dz/dKq1/DoOAtVnQ/MT7dtaJrHicB9GRw3AhjhyWzGGJMTIkKLFi2oX78+X375JTExMcTGxtKj\nRw/q1avndLxcV+BHcBtjTE4UK1aM3r17M2jQIAoVKsR7773HBx98wJEjR5yOlqusWBhjTC6oWbMm\nL7/8MnfffTfr169n2LBhLFy4kOTkZKej5YoCPZGgMSZ/cXoKDT8/Pzp16kR4eDjTp0/niy++YNmy\nZfTq1SvPr5VjxcIYY3JZ2bJl6d+/P2vWrGHGjBmMGjWKUqVK0b1794v2i4uLIz4+PvXuK29m3VDG\nGOMBIkKjRo147bXXaNeuHfHx8Tz22GMc3LgMVU0d+JdX1s+R/DKgJDw8XGNjY52OYYzJR3Jzhthd\nu3Yxfvx4PvxsNr7X1aBsiUDCbuuZK8sidGtYiZ5Nq1zVsSKySlXDs9rPWhbGGHMNVK5cmTFjxlC7\nQWMu/LWV00f+4sCWNSSeyNldUxv3HuebtbtzKeXl2TULY4y5RrZs2UKxc4e4t2Mbjh8/TjF2U2L7\nXNq0acMdd9xBsWLFrvicD3yYsynTs8taFsYYcw2knZywQYMGqRe9q1atyqJFi3jppZf49ttvOX/+\nvNNRM2TFwhhjroGMJiccMGAA9evXZ9iwYYSFhfHVV1/xyiuvsHTpUq8bn2HdUMYYcw1kNTnhU089\nxebNm5k9ezbTpk3jhx9+4J577qFu3bqZnjchIcFzodOwloUxxniJWrVq8fzzz/P444+TmJjIO++8\nw4QJE9i1a1fWB3uYtSyMMcaLiAjh4eE0bNiQxYsXM2/ePN544w2aNm1Kt27dCAoKciSXFQtjjPFC\nfn5+tG/fnubNm/Ptt9/yww8/EBsbS/v27enUqRNFixa9tnmu6bsZY0we4vRcUwBFixblnnvuoW3b\ntnzzzTd89913/PLLLwQHB9OlS5eL9vXk9CF2zcIYY/KAoKAgHn74YV566SWqVKnCmjVreOSRRzi4\nafk1mT7EWhbGGJOHVK5cmWeeeYYOHTrw4YcfMnn6LI5sXc2Hp2/iiSeeSL27KrdZy8IYY/KgOnXq\nMH78eK5v0Bw5fZS2bdt6rFCAFQtjjMmztmzZQrlze7mj5U0sXryYuLg4j72XFQtjjMmD0k4fUrdu\nXSIjI4mKivJYwfBosRCRTiISJyJbReT5DF4vLCIz3K//JiKhaV57wb09TkS8f2UQY4zJhoEDB+bK\nXVYZTR8SGRlJfHx8js+dEY8VCxHxBd4HOgN1gB4iUifdbo8CR1S1BjAeGOU+tg7QHagLdAImus9n\njDEG1/Qh6a9RhIWFeWzVPU+2LJoAW1V1u6qeAz4HuqXbpxswzf14FtBeRMS9/XNVPauq/wO2us9n\njDHGAZ4sFpWAtBOaJLi3ZbiPqiYBx4Ay2TzWGGPMNeLJcRaSwbb0a7hebp/sHIuIRAKRAFWqXN2S\ngsYYk5ddq1HmnmxZJACV0zwPAfZcbh8R8QNKAoezeSyqGqWq4aoaXq5cuVyMbowxJi1PFouVQE0R\nqSoihXBdsI5Ot0800Mf9+B/Aj6qq7u3d3XdLVQVqAis8mNUYY0wmPNYNpapJItIfiAF8gY9VdYOI\nvA7Eqmo0MAX4t4hsxdWi6O4+doOIzAQ2AknAP1X1gqeyGmOMyZy4PsjnfeHh4RobG+t0DGOMyVNE\nZJWqhme1n43gNsYYkyUrFsYYY7JkxcIYY0yWrFgYY4zJkhULY4wxWco3d0OJyAFgRw5OURY4mEtx\nPMHb84H3Z/T2fGAZc4O35wPvyni9qmY5qjnfFIucEpHY7Nw+5hRvzwfen9Hb84FlzA3eng/yRsb0\nrBvKGGNMlqxYGGOMyZIVi79FOR0gC96eD7w/o7fnA8uYG7w9H+SNjBexaxbGGGOyZC0LY4wxWSrw\nxUJEOolInIhsFZHnnc6TnohUFpFFIrJJRDaIyNNOZ8qIiPiKyBoRmet0loyISCkRmSUif7r/LJs7\nnSktEXnW/fe7XkSmi0iAF2T6WET2i8j6NNuCROR7Edni/l7aCzO+5f57XiciX4lIKW/LmOa1QSKi\nIlLWiWxXokAXCxHxBd4HOgN1gB4iUsfZVJdIAgaq6g1AM+CfXpgR4Glgk9MhMvE28K2q1gYa4EVZ\nRaQS8P+AcFW9EdeU/t2dTQXAVKBTum3PAz+oak3gB/dzJ03l0ozfAzeqan1gM/DCtQ6VzlQuzYiI\nVAZuA3Ze60BXo0AXC6AJsFVVt6vqOeBzoJvDmS6iqntVdbX78Qlcv+S8aj1yEQkB7gAmO50lIyJS\nAmiNa/0UVPWcqh51NtUl/IAi7hUji5LBypDXmqouwbXOTFrdgGnux9OAu65pqHQyyqiq36lqkvvp\nclwrbTrmMn+OAOOB58hgyWhvVNCLRSVgV5rnCXjZL+K0RCQUuAn4zdkkl5iA6x99stNBLqMacAD4\nxN1VNllEAp0OlUJVdwNjcH3C3AscU9XvnE11WeVVdS+4PsgAwQ7nycojwAKnQ6QnIl2B3ar6u9NZ\nsqugFwvJYJtXVnkRKQbMBp5R1eNO50khIncC+1V1ldNZMuEHNAImqepNwCmc7z5J5e737wZUBSoC\ngSLSy9lUeZ+IvISrG/c/TmdJS0SKAi8BQ53OciUKerFIACqneR6CFzT/0xMRf1yF4j+q+qXTedKJ\nALqKSDyubrx2IvKZs5EukQAkqGpKi2wWruLhLW4F/qeqB1T1PPAl0MLhTJfzl4hUAHB/3+9wngyJ\nSB/gTuBB9b7xAdVxfTD43f3/JgRYLSLXOZoqCwW9WKwEaopIVREphOuiYrTDmS4iIoKrr32Tqo5z\nOk96qvqCqoaoaiiuP78fVdWrPhWr6j5gl4iEuTe1x7W+u7fYCTQTkaLuv+/2eNEF+HSigT7ux32A\nbxzMkiER6QQMAbqq6mmn86Snqn+oarCqhrr/3yQAjdz/Tr1WgS4W7otg/YEYXP85Z6rqBmdTXSIC\neAjXJ/a17q/bnQ6VBw0A/iMi64CGwJsO50nlbvHMAlYDf+D6f+n4CF8RmQ4sA8JEJEFEHgVGAreJ\nyBZcd/KM9MKM7wHFge/d/18+8MKMeY6N4DbGGJOlAt2yMMYYkz1WLIwxxmTJioUxxpgsWbEwxhiT\nJSsWxhhjsmTFwhg3EbngvtXydxFZLSIt3NsrisisKzzXTyJyRWssi8jJK9nfmGvJz+kAxniRM6ra\nEEBEOgL/B7RR1T3APxxNZozDrGVhTMZKAEfANYFjyloEItJXRL4UkW/dazqMzupEInJSRN5wt1iW\ni0h59/aqIrJMRFaKyPB0xwx2b18nIq+5t93sfh4gIoHu9S9uzPWf3JgMWLEw5m9F3N1Qf+Kabn34\nZfZrCDwA1AMecK9LkJlAYLmqNgCWAI+7t7+Na3LDm4HUqR5EpANQE9cU+g2BxiLSWlVX4ppuYwQw\nGvhMVS9ZUMcYT7BiYczfzqhqQ/cCSZ2AT91zNaX3g6oeU9VEXHNMXZ/Fec8BKSsIrgJC3Y8jgOnu\nx/9Os38H99caXFOA1MZVPABexzXNRjiugmHMNWHXLIzJgKoucy91WS6Dl8+meXyBrP8fnU8z82n6\n/TOab0eA/1PVDzN4LQgoBvgDAbimWzfG46xlYUwGRKQ2ruVND3nwbX7l7+VTH0yzPQZ4xL2GCSJS\nSURSFhmKAl7BtUbDKA9mM+Yi1rIw5m9FRGSt+7EAfVT1QsY9UbniaeC/IvI0rvVKANeyoCJyA7DM\n/d4ngV7uqbeTVPW/7vXjl4pIO1X90VMBjUlhs84aY4zJknVDGWOMyZIVC2OMMVmyYmGMMSZLViyM\nMcZkyYqFMcaYLFmxMMYYkyUrFsYYY7JkxcIYY0yW/j+vzHynxHc7jAAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -531,17 +487,19 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEKCAYAAAA4t9PUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xl4VdW5x/HvLwnzKBCQOSABBQSH\nCCgOtVjFMdrSCg7XWlo6OLa31+q111pre2sHra22t9ThKg6IWFtqteJY1DIFZQpjDATCGKYwhpDk\nvX/sDTeGDAeSk32SvJ/n4eGcvdfe5z1ieFl7rfUumRnOOefc8UqKOgDnnHMNmycS55xzteKJxDnn\nXK14InHOOVcrnkicc87ViicS55xzteKJxDnnXK14InHOOVcrnkicc87VSkrUAdSHLl26WFpaWtRh\nOOdcg7FgwYJtZpYaS9smkUjS0tLIysqKOgznnGswJOXF2tYfbTnnnKsVTyTOOedqxROJc865WvFE\n4pxzrlY8kTjnnKuVuCYSSWMlrZSUI+nuSs63kPRSeH6upLTweGdJ70naK+mxCtecKWlJeM1vJSme\n38E551z14pZIJCUDjwOXAoOBCZIGV2g2EdhpZgOAR4CHwuNFwH8B36/k1n8AJgHp4a+xdR+9c865\nWMWzRzICyDGzXDMrBqYCmRXaZALPhK+nA2Mkycz2mdmHBAnlCEndgfZmNtuCPYKfBa6O43dwzrkG\n6b0VW3n6ozUUl5TF/bPimUh6AuvLvc8Pj1XaxsxKgEKgcw33zK/hns451+Q9/l4Oz/xrLSlJ8X/6\nH89EUln0dhxtjqu9pEmSsiRlFRQUVHNL55xrXJZt3E1W3k5uGNWXpAaeSPKB3uXe9wI2VtVGUgrQ\nAdhRwz171XBPAMxsspllmFlGampM5WKcc65RmDInjxYpSYw7s1fNjetAPBPJfCBdUj9JzYHxwIwK\nbWYAN4WvxwHvhmMflTKzTcAeSaPC2Vr/Bvy17kN3zrmGaXfRIf7yyQauGt6Djq2b18tnxq1oo5mV\nSLoVeBNIBp4ys2xJDwBZZjYDeBKYIimHoCcy/vD1ktYC7YHmkq4GLjazZcC3gf8FWgFvhL+cc84B\nf16Qz4FDpdx4dt96+8y4Vv81s9eB1yscu6/c6yLgy1Vcm1bF8SxgaN1F6ZxzjYOZMWVOHsN7d2RY\nr4719rm+st055xqJ2bnb+bRgHzeOqr/eCHgicc65RuO5OXl0bN2MK4Z1r9fP9UTinHONwObCIt7M\n3sJXMnrTsllyvX62JxLnnGsEXpy3jjIzrh/Zp94/2xOJc841cIdKy3hx3jouGJhK385t6v3zPZE4\n51wD99ayLWzdc7DeB9kP80TinHMN3JTZefTs2IrPDeoayed7InHOuQZs9ZY9zM7dzvWj+pBcD3W1\nKuOJxDnnGrDn5uTRPDmJazN619w4TjyROOdcA7XvYAmvfLyBy4d1p3PbFpHF4YnEOecaqL8s3MDe\ngyXcENEg+2GeSJxzrgEyM6bMzmNw9/ac0af+6mpVxhOJc841QFl5O1mxeQ83nt2XYFeN6Hgicc65\nBmjK7DzatUwh87QeUYfiicQ55xqagj0HeWPpJsad2YvWzeO6G0hMPJE451wDMy1rPYdKLfJB9sPi\nmkgkjZW0UlKOpLsrOd9C0kvh+bmS0sqduyc8vlLSJeWO3yFpqaRsSXfGM37nnEs0pWXG83PyGD2g\nMyelto06HCCOiURSMvA4cCkwGJggaXCFZhOBnWY2AHgEeCi8djDBtrtDgLHA7yUlSxoKfAMYAQwH\nrpCUHq/v4Jxziead5VvYWFgUWV2tysSzRzICyDGzXDMrBqYCmRXaZALPhK+nA2MUTD/IBKaa2UEz\nWwPkhPc7BZhjZvvNrAT4J3BNHL+Dc84llClz8jixfUsuOqVb1KEcEc9E0hNYX+59fnis0jZhYigE\nOldz7VLgfEmdJbUGLgOiqwvgnHP1aM22fXywehvXjexDSnLiDHHHc7i/sonNFmObSo+b2XJJDwFv\nAXuBRUBJpR8uTQImAfTpU/8bvTjnXF17fk4eKUli/FmJ9e/neKa0fD7bW+gFbKyqjaQUoAOwo7pr\nzexJMzvDzM4P266u7MPNbLKZZZhZRmpqah18Heeci86B4lJeXpDPJUNPpGv7llGH8xnxTCTzgXRJ\n/SQ1Jxg8n1GhzQzgpvD1OOBdM7Pw+PhwVlc/IB2YByCpa/h7H+CLwItx/A7OOZcQ/rZoI4UHDiXU\nIPthcXu0ZWYlkm4F3gSSgafMLFvSA0CWmc0AngSmSMoh6F2MD6/NljQNWEbw6OoWMysNb/2KpM7A\nofD4znh9B+ecSwRmxrNz1jKwW1tG9usUdThHieuSSDN7HXi9wrH7yr0uAr5cxbU/BX5ayfHz6jhM\n55xLaIvyC1m6YTc/yRwSeV2tyiTOsL9zzrlKTZmdR5vmyVx9esWJr4nBE4lzziWwnfuK+dvijVxz\nRk/atWwWdTiV8kTinHMJbFrWeopLyrhxVFrUoVTJE4lzziWosjLjubl5jOjXiUEntos6nCp5InHO\nuQT1z9UFrN9xICGn/JbnicQ55xLUc7Pz6NK2BZcMOTHqUKrlicQ55xLQ+h37eXflViaM6E3zlMT+\nqzqxo3POuSbq+bnrEDBhROLXCvRE4pxzCaboUCnTstbzhcHd6NGxVdTh1MgTiXPOJZg3lm5ix77i\nhJ7yW54nEuecSzBTZufRv0sbzjmpc9ShxMQTiXPOJZClGwr5eN0urh/Vl6SkxKurVRlPJM45l0Ce\nm5NHy2ZJjDujV9ShxKzK6r+SKu4dUpkdZvbVugvHOeearsIDh/jLwg1cfVpPOrROzLpalamujPwp\nwNerOS/g8boNxznnmq5XFuRTdKiMGxJ8JXtF1SWSe83sn9VdLOnHdRyPc841SWbGc3PyOL1PR4b2\n7BB1OMekyjESM5tW8ZiklpLaV9fGOefcsfvXp9vJ3bYv4etqVSbmwXZJXyfYNvfvkn4W4zVjJa2U\nlCPp7krOt5D0Unh+rqS0cufuCY+vlHRJuePflZQtaamkFyW1jPU7OOdconrmX2vp1KY5l53aPepQ\njlmViUTSlRUOXWRmF4Rb3V5e040lJROMoVwKDAYmSBpcodlEYKeZDQAeAR4Krx1MsH/7EGAs8HtJ\nyZJ6ArcDGWY2lGAv+PE1f03nnEtcH6wuYOayLdwwqi8tmyVHHc4xq65HMlzSXyUND98vlvS8pOeA\n7BjuPQLIMbNcMysGpgKZFdpkAs+Er6cDYxRsSJwJTDWzg2a2BsgJ7wfBuE4rSSlAa2BjDLE451xC\n2newhLtfWUL/Lm34zudOijqc41LlYLuZPSjpROCBcLP5+4C2QGszWxzDvXsC68u9zwdGVtXGzEok\nFQKdw+NzKlzb08xmS/oVsA44AMw0s5mVfbikScAkgD59Er/omXOuafrFP1awsfAAL3/z7AbZG4Ga\nx0j2AXcSPKKaDEwAVsV478qWZFqMbSo9LukEgt5KP6AH0EbSDZV9uJlNNrMMM8tITU2NMWTnnKs/\n89bs4JnZedx0dhoZaZ2iDue4VTdG8iDwd+Ad4EIzuwpYRDDYfmMM984Hepd734ujH0MdaRM+quoA\n7Kjm2ouANWZWYGaHgD8D58QQi3POJZQDxaXcNX0RvTu14q6xg6IOp1aq65FcYWbnE/xF/W8AZjYD\nuASIJXXOB9Il9ZPUnGBQvOJq+RnATeHrccC7Zmbh8fHhrK5+QDowj+CR1ihJrcOxlDHA8hhicc65\nhPLI26tYu30/P//iMFo3r25JX+KrLvqlkqYArYAjCxPNrAR4tKYbh2MetxJMGU4GnjKzbEkPAFlh\nUnoSmCIph6AnMj68NlvSNGAZUALcYmalwFxJ04GPw+OfEDxyc865BuOTdTt54oNcJozow+gBXaIO\np9YUdACqOCmdChwysxX1F1Ldy8jIsKysrKjDcM45DpaUcsVvP2TvwRLe/O75tG+ZmDW1JC0ws4xY\n2lY3RnKGmS2pLolIOuN4AnTOuabqsXdzWL11Lz+75tSETSLHqrpHW09L+hyVz6A67Eng9DqNyDnn\nGqmlGwr5/fuf8sUzenLhyV2jDqfOVJdIOgALqD6RFNRtOM451zgdKi3jrumLOaF1c+67omKRj4at\nugWJafUYh3PONWp//OenLNu0m/+54Uw6tm4edTh1yndIdM65OFu1ZQ+/fSeHy4d1Z+zQE6MOp855\nInHOuTgqLTP+Y/pi2rRI5sdXDYk6nLioNpEo0Lu6Ns4556r21IdrWLR+F/dfNYQubVtEHU5cVJtI\nwlXmf6mnWJxzrlFZs20fv5q5kotO6cZVw3tEHU7cxPJoa46ks+IeiXPONSJlZcYPpi+meUoSP71m\nKGEV9UYplgIvFwLflJRHUA1YBJ2VYXGNzDnnGrDn5uYxb+0OfjFuGN3aN+6NXGNJJJfGPQrnnGtE\n1u/Yz8/fWMF56V348pm9og4n7mp8tGVmeUBH4MrwV8fwmHPOuQrMjHv+vAQB//3FUxv1I63Dakwk\nku4Ange6hr+ek3RbvANzzrmGaFrWej7M2cbdl51CrxNaRx1OvYjl0dZEYKSZ7QOQ9BAwG/hdPANz\nzrmGZnNhEQ++tpyR/Tpx/Yims8V3LLO2BJSWe19K9fW3nHOuyTEz7n11CYfKynjoS8NISmo6f03G\n0iN5mmBDqVfD91cTVP11zjkX+uvCjbyzYis/vPwU0rq0iTqcehXLYPvDwM0EOxjuBG42s9/EcnNJ\nYyWtlJQj6e5KzreQ9FJ4fq6ktHLn7gmPr5R0SXhskKSF5X7tlnRnbF/VOefio2DPQe7/Wzan9+nI\nzaP7RR1Ovau2RyIpCVhsZkMJtreNmaRk4HHgC0A+MF/SDDNbVq7ZRGCnmQ2QNB54CLhW0mCCbXeH\nAD2AtyUNNLOVwGnl7r8BeBXnnIvQj2YsZf/BUn45bhjJTeiR1mE1lUgpAxZJOp5RoxFAjpnlmlkx\nMBXIrNAmE3gmfD0dGKNgrlwmMNXMDprZGiAnvF95Y4BPfSqycy5Kry/ZxOtLNnPHRekM6Nou6nAi\nEcsYSXcgW9I8gpXtAJjZVTVc1xNYX+59PjCyqjZmViKpEOgcHp9T4dqeFa4dD7wYQ/zOORcXO/cV\nc99flzK0Z3smnd8/6nAiE0si+fFx3ruy/p3F2KbaayU1B64C7qnyw6VJwCSAPn2azjQ851z9eeC1\nZezaf4hnvzaSZslNd1eOmsZIkoH/MrOLjuPe+UD5EvS9gI1VtMmXlEKwve+OGK69FPjYzLZU9eFm\nNhmYDJCRkVExgTnnXK28s3wLr36ygdvHpDO4R/uow4lUTWMkpcB+SR2O497zgXRJ/cIexHhgRoU2\nM4CbwtfjgHfD0vUzgPHhrK5+QDowr9x1E/DHWs65iOw9WMK9ry5lULd23HrhgKjDiVwsj7aKgCWS\n3uKzYyS3V3dROOZxK/AmkAw8ZWbZkh4AssxsBsF6lCmScgh6IuPDa7MlTQOWASXALWFSQ1Jrgplg\n3zy2r+qcc3Xjxbnr2Ly7iMevP53mKU33kdZhCjoA1TSQbqrsuJk9U9nxRJSRkWFZWVlRh+GcawQO\nlZZx/i/eo0+n1rz0zbOjDiduJC0ws4xY2tbYIzGzZyS1AvqE6zicc67Jem3xRjYVFvHg1UOjDiVh\nxFL990pgIfCP8P1pkiqOdTjnXKNnZkyetYYBXdty4aCuUYeTMGJ5uHc/wWLAXQBmthBoejUAnHNN\n3kc521m+aTffOK9fkyrKWJNYEkmJmRVWOObTaZ1zTc4fZ31Kl7YtuPr0iuujm7ZYEslSSdcByZLS\nJf0O+Fec43LOuYSyfNNuPli9jZtHp9EiJTnqcBJKLInkNoLiiQeBF4BCwCvuOuealD/NyqV182Su\nH+mVMiqKZdbWfuDe8JdzzjU5mwoPMGPRRm4Y1ZeOrZtHHU7C8ZU0zjlXg6c/WkuZGRPP9XlGlfFE\n4pxz1dhddIgX5q7jslO707tT66jDSUixrCPpVB+BOOdcIpo6bx17D5Y06TLxNYmlRzJX0suSLgs3\nnXLOuSahuKSMpz5cy6j+nRjWq2PU4SSsWBLJQIJy7DcCOZJ+JmlgfMNyzrno/X3JRjbvLuKb558U\ndSgJrcZEYoG3zGwC8HWCsu/zJP1TUuOtWOaca9LMjD/+M5f0rm25YGBq1OEktBqn/0rqDNxA0CPZ\nQrCuZAZwGvAyXi7FOdcIfZizjRWb9/CLccO8HEoNYtmPZDYwBbjazPLLHc+S9D/xCcs556I1eVYu\nXdu1IPO0HlGHkvBiSSSDrIpNS8zsoTqOxznnIrdsY1AO5a6xg7wcSgyqTCSS/kZYnLGyyVpmdlVN\nN5c0FniUYIfEJ8zs5xXOtwCeBc4EtgPXmtna8Nw9wESgFLjdzN4Mj3cEngCGhvF9zcxm1xSLc87F\n6k8fhOVQRvSNOpQGoboeya9qc2NJycDjBNvi5gPzJc0ws2Xlmk0EdprZAEnjgYeAayUNJth2dwjQ\nA3hb0sBwu91HgX+Y2bhwL3hfIeScqzMbdx3gb4s28m9np9GhdbOow2kQqkwkZvbPWt57BJBjZrkA\nkqYCmQT7sB+WSbDfCcB04LFwrUomMNXMDgJrwj3dR0jKBs4HvhrGWAwU1zJO55w74umP1mDAzaPT\nog6lwaju0dYSqtl3xMyG1XDvnsD6cu/zgZFVtTGzEkmFQOfw+JwK1/YEDgAFwNOShgMLgDvMbF8N\nsTjnXI12Fx3ixXnrvRzKMaru0dYVtbx3ZfPlKiamqtpUdTwFOAO4zczmSnoUuBv4r6M+XJoETALo\n08fLPjvnanakHMp5Xg7lWFT3aCuvlvfOB3qXe98L2FhFm3xJKUAHYEc11+YD+WY2Nzw+nSCRVBb/\nZIIV+WRkZPiOjs65ah0uh3J2/86c2qtD1OE0KLEUbRwlab6kvZKKJZVK2h3DvecD6ZL6hYPi4wkW\nMpY3g2ClPMA44N1wqvEMYLykFpL6AenAPDPbDKyXNCi8ZgyfHXNxzrnj8trioBzKpAu8N3KsYllH\n8hhBEngZyAD+DRhQ00XhmMetwJsE03+fMrNsSQ8AWWY2A3gSmBIOpu8IP4ew3TSCJFEC3BLO2IJg\nZf3zYXLKBW6O+ds651wlzIzJs3IZ2K0tn/NyKMcslkSCmeVISg7/Mn9aUkx7tpvZ68DrFY7dV+51\nEfDlKq79KfDTSo4vJEhozjlXJz5YHZRD+eW4YZWum3PViyWR7A//9b9Q0i+ATUCb+IblnHP153A5\nlKu8HMpxiaWM/I1hu1uBfQSD4F+MZ1DOOVdfsjcW8mHONm4e3c/LoRynWBLJ1WZWZGa7zezHZvY9\naj812DnnEsITH6yhTfNkrhvpywSOVyyJ5KZKjn21juNwzrl6d7gcyvgRfejQysuhHK/qVrZPAK4D\n+kkqP223HUGBReeca9C8HErdqG6w/V8EA+tdgF+XO74HWBzPoJxzLt4Ol0O5Ylh3ep3g5VBqo6aV\n7XmAb6frnGt0XpwblEP5hpdDqbVYxkicc65RKS4p4+mP1jJ6QGeG9vRyKLXlicQ51+T8bVFQDsV7\nI3UjpkQiqVW5+lbOOddgmRl/+iCXQd3acYGXQ6kTsRRtvBJYCPwjfH9ahVlczjnXYMwKy6F84/z+\nXg6ljsTSI7mfYLfDXXCk1lVa/EJyzrn4+dOsXLq1b8FVw70cSl2JJZGUmFlh3CNxzrk4W7rh/8uh\nNE/xIeK6EkvRxqWSrgOSJaUDtxOsMXHOuQbliQ9yadM8mQkjvBxKXYolJd8GDAEOAi8AhcCd8QzK\nOefq2oZdB/jb4k1M8HIodS6WHskgM7sXuDfewTjnXLw8/eEaAG4+t1/EkTQ+sfRIHpa0QtJPJA2J\ne0TOOVfHCg8c4sV567hyWHd6dmwVdTiNTo2JxMwuBD4HFACTJS2R9MNYbi5prKSVknIk3V3J+RaS\nXgrPz5WUVu7cPeHxlZIuKXd8bRjDQklZscThnGvafvP2KvYVl/J1X4AYFzFNWzCzzWb2W+BbBGtK\n7qvhEiQlA48DlwKDgQmSBldoNhHYaWYDgEeAh8JrBxPs3z4EGAv8PrzfYRea2Wlm5lvuOueq9dL8\ndTz90Vq+ek6al0OJk1gWJJ4i6X5JS4HHCGZs9Yrh3iOAHDPLNbNiYCqQWaFNJvBM+Ho6MEbBCqFM\nYKqZHTSzNUBOeD/nnIvZ3Nzt/PAvSzkvvQs/vPyUqMNptGLpkTwN7AQuNrMLzOwPZrY1hut6AuvL\nvc8Pj1XaxsxKCGaEda7hWgNmSlogaVJVHy5pkqQsSVkFBQUxhOuca0zWbd/Pt55bQO9OrXnsujNI\nSfZ1I/FS46wtMxt1nPeurPaAxdimumtHm9lGSV2BtyStMLNZRzU2mwxMBsjIyKj4uc65RmxP0SEm\nPjOfMoMnbzrLp/vGWXU7JE4zs69IWsJnE4AAM7NhNdw7H+hd7n0vYGMVbfIlpQAdgB3VXWtmh3/f\nKulVgkdeRyUS51zTVFpm3P7iJ6zZto9nJ46gX5c2UYfU6FXXI7kj/P2K47z3fCBdUj9gA8Hg+XUV\n2swg2BN+NjAOeNfMLCwK+YKkh4EeQDowT1IbIMnM9oSvLwYeOM74nHON0H+/vpz3Vhbw02uGcs5J\nXaIOp0mobofETeHL75jZD8qfk/QQ8IOjr/rM9SWSbgXeBJKBp8wsW9IDQJaZzQCeBKZIyiHoiYwP\nr82WNA1YBpQAt5hZqaRuwKthxc4U4AUz+8cxf2vnXKP00vx1PPHhGr56ThrXj+wbdThNhsyqHz6Q\n9LGZnVHh2OIYHm0ljIyMDMvK8iUnzjVmc3K3c+OTczn7pC48dVOGD67XkqQFsS6xqG6M5NvAd4D+\nkhaXO9UO+Kh2ITrnXN1Zt30/335uAX06teZ3E073JFLPqhsjeQF4A/hvoPyq9D1mtiOuUTnnXIx2\nhzO0DJ+hFZXqxkgKCdZ1TAAIp9u2BNpKamtm6+onROecq1zFGVppPkMrEjFttStpNbAG+CewlqCn\n4pxzkfrZ68t5f2UBD2T6DK0oxfIg8UFgFLDKzPoBY/AxEudcxKbOW8eTH67h5tFpXDfSN6qKUiyJ\n5JCZbQeSJCWZ2XvAaXGOKyG8tngja7btizoM51wFsz8NamidPzCVey/zGlpRi2Vjq12S2hKsHn9e\n0laCtR2N2q79xdz76lL6dWnD9G+d7bNAnEsQedv38e3nF5DWpQ2PXecztBJBLH8CmcAB4LvAP4BP\ngSvjGVQi6Ni6OQ9ePZSF63fx+/c/jToc5xyHZ2gFa8KevCmD9i19hlYiiKVoY/lnO89U2bARunJ4\nD95ZvoVH31nNBQNTGd67Y9QhOddklZSWcdsLn7B22z6mTBxJ384+QytRVNkjkbRH0u5yv/aU/70+\ng4zSjzOH0rVdC747bSEHikujDse5Jutnr6/gn6sK+MnVQzn7pM5Rh+PKqTKRmFk7M2tf7le78r/X\nZ5BR6tCqGb/+8nByC/bx328sjzoc55qkF+au46mP1vC10f2YMMJnaCWamEapJJ0r6ebwdZewom+T\ncc6ALkw8tx/Pzs7j/ZWx7OnlnKsr//p0G/f9dSkXDEzlPy87OepwXCViWZD4I4JKv/eEh5oDz8Uz\nqET0H5cMYmC3ttw1fTE79xVHHY5zTcLabfv4zvMfk9alDb/zGVoJK5Y/lWuAq4B9cGRjqXbxDCoR\ntWyWzCPXnsbO/cXc+5cl1FQ12TlXO4UHghpa4DO0El0siaTYgr81DSDcUKpJGtKjA9/7wiBeX7KZ\nVz/ZEHU4zjVaJaVl3PbiJ+Rt38//3HCmz9BKcLEkkmmS/gh0lPQN4G3gifiGlbgmnd+fs9JO4Ed/\nzSZ/5/6ow3Gu0Sk6VMrdf17CrFUFPHj1UEb19xlaia7GRGJmvwKmA68Ag4D7zOy3sdxc0lhJKyXl\nSLq7kvMtJL0Unp8rKa3cuXvC4yslXVLhumRJn0h6LZY46lJyknj4K6dRZsa/T1tEWZk/4nKurizd\nUMgVv/uQ6QvyuX1MOuN9hlaDENPIlZm9ZWb/YWbfB96VdH1N10hKBh4HLgUGAxMkDa7QbCKw08wG\nAI8AD4XXDibYdncIMBb4fXi/w+4AIpuL27tTa3501RDmrtnBEx/mRhWGc41GaZnx+Hs5XPP7j9hT\ndIgpE0fwvS8MjDosF6PqFiS2D3sFj0m6WIFbgVzgKzHcewSQY2a5ZlYMTCUot1JeJv+/Wn46MEbB\nhuyZwFQzO2hma4Cc8H5I6gVcTsSP1758Zi8uGdKNX725iuWbmsz6TOfq3Pod+xk/eTa/fHMlFw8+\nkTfvPJ/z0lOjDssdg+p6JFMIHmUtAb4OzAS+DGSaWcWEUJmewPpy7/PDY5W2MbMSgo20Otdw7W+A\nu4CyGGKIG0n87JpTad+qGd99aSEHS3zVu3PHwsyYviCfSx/9gBWb9vDwV4bz2HWn07F186hDc8eo\nukTS38y+amZ/JNglMQO4wswWxnhvVXKs4oBCVW0qPS7pCmCrmS2o8cOlSZKyJGUVFBTUHO1x6Ny2\nBb8YdyorNu/h4Zmr4vIZzjVGO/YV853nP+b7Ly9icI/2vHHneXzxjF4EDyRcQ1NdIjl0+IWZlQJr\nzGzPMdw7H+hd7n0vYGNVbSSlAB2AHdVcOxq4StJagkdln5dU6eJIM5tsZhlmlpGaGr9u8udP7sZ1\nI/sw+YNc5uRuj9vnONdYvL9yK5f8ZhZvL9/C3ZeezIvfGEWvE1pHHZarheoSyfDyBRuBYcdYtHE+\nkC6pn6TmBIPnMyq0mQHcFL4eB7wbrlmZAYwPZ3X1A9KBeWZ2j5n1MrO08H7vmtkNMX/bOPnh5afQ\nt1Nr/n3aInYXHar5AueaoAPFpdz316V89en5nNC6GX+5ZTTfuuAkkpO8F9LQVVe0MblCwcaUYyna\nGI553Aq8STDDapqZZUt6QNJVYbMngc6ScoDvAXeH12YD04BlBHug3BL2ihJS6+YpPHLtaWzeXcT9\nM7KjDse5hLMkv5ArfvcBz87O42uj+zHj1nMZ0qND1GG5OqKmUOojIyPDsrKy4v45D7+1it++s5rf\nX38Gl53aPe6f51yiKy0z/vAX8RA1AAATL0lEQVR+Dr95ezVd2rbgV18ezrnpXaIOy8VA0gIzy4il\nbSxb7boY3fb5Aby/civ/+eoSzux7At3at4w6JOcis277fr47bSEL8nZy+bDu/PTqoT4jq5HyUpp1\nqFlyEo9cexpFh0r5j+mLvbCja5LMjGlZ67n00Vms2ryHR64dzmMTfFpvY+aJpI6dlNqWey87hVmr\nCnhuTl7U4ThXr3bsK+Zbzy3grumLGdqzA2/ceR7XnO7Tehs7f7QVBzeM6stby7fy09eXc86ALpyU\n2jbqkJyLu/dWbuWu6YvZtb+Yey49ma+f199nZDUR3iOJA0n8ctwwWjZL5rsvLeRQaaSL8J2Lq30H\nS/ivvyzl5nBa719vOZdv+rTeJsUTSZx0a9+Sn11zKovzC/nduzlRh+NcXMxaVcDFj8ziubl5TDw3\nmNY7uEeNqwNcI+OPtuLoslO788XTe/L4ezl8blAqZ/Q5IeqQnKsTu/YX8+DflzN9QT79U9vw8jfP\nJiOtU9RhuYh4jyTO7s8cwontW/K9lxayv7gk6nCcq7U3lmzioodn8eonG7jlwpN4/fbzPIk0cZ5I\n4qx9y2b8+ivDyduxnwf/HtkWKs7V2tY9RXz7uQV8+/mP6da+BTNuHc1/XHIyLZsl13yxa9T80VY9\nGNW/M984rz+TZ+Uy5uSujDmlW9QhORezw+Xef/LaMopKyrhr7CC+cV5/miX7v0NdwBNJPfn3iwcy\na1UB35yygOtH9uHWz6eT2q5F1GE5V631O/bzn68u4YPV2zgr7QR+/qVhPp3dHcUTST1pkZLMlIkj\neeTtVTw3dx0vL8jn6+f1Z9L5/Wnbwv8YXGIpKzOenb2WX7y5EgE/yRzC9SP7kuRTel0lvGhjBD4t\n2MuvZ67k9SWb6dymObd9fgDXjexL8xR/VOCil7N1Dz94ZQkL8nZywcBUfnrNUN8vpAk6lqKNnkgi\ntHD9Lh56YwWzc7fTu1Mrvn/xIK4c1sP/1ecicai0jMmzcnn07dW0bpHMfVcM5prTe3p5kybKE0kF\niZpIIBjInLV6Gz9/YwXLN+1mcPf2/ODSkzk/vYv/ALt6s3RDIXdNX8yyTbu5/NTu3H/VEB/Da+I8\nkVSQyInksLIyY8aijfz6rZWs33GAs/t35u5LT2Z4745Rh+YasaJDpTz6zmomz8qlU5vm/CRzKGOH\nnhh1WC4BHEsiietDeUljJa2UlCPp7krOt5D0Unh+rqS0cufuCY+vlHRJeKylpHmSFknKlvTjeMZf\nn5KSxNWn9+Sd732O+68czMote8h8/CO+8/wCcgv2Rh2ea4Tmr93BZY9+wB/e/5RxZ/Ti7e9e4EnE\nHZe49UgkJQOrgC8A+QR7uE8ws2Xl2nwHGGZm35I0HrjGzK6VNBh4ERgB9ADeBgYCZUAbM9srqRnw\nIXCHmc2pLpaG0COpaE/RIf70wRqe+CCXgyVlXHtWb+4ck05X3yzL1dL6Hfv50we5PDs7j14ntOLn\nXxzmuxa6oyTKDokjgBwzyw2DmgpkEuzDflgmcH/4ejrwmIKBgUxgqpkdBNaEe7qPMLPZwOF/njcL\nfzXKZ3PtWjbje18YyI2j+vLYu6t5fu46Xv14A187N41vXnAS7Vs2izpE10CYGdkbdzNz2RZmZm9m\nxeY9SPC10f34/iUDad3cp5+72onn/0E9gfXl3ucDI6tqY2YlkgqBzuHxORWu7QlHejoLgAHA42Y2\nNy7RJ4jUdi34ceZQvnZuP349cxWPv/cpz89dx60XDuCGUX29PIWrVElpGfPW7mBm9hbeWraFDbsO\nkCTISOvEDy8/hUuGnEjvTj6l19WNeCaSyqYcVew9VNWmymvNrBQ4TVJH4FVJQ81s6VEfLk0CJgH0\n6dPnWOJOSH07t+G3E05n0vn9eegfK3jw78t5+qO13HFROpmn9aBFiieUpm5/cQmzVm1j5rLNvLti\nK7v2H6JFShLnpadyx0XpjDm5K53b+kwsV/fimUjygd7l3vcCNlbRJl9SCtAB2BHLtWa2S9L7wFjg\nqERiZpOByRCMkdTmiySSoT07MGXiSD5cvY2H/rGCu6Yv5udvrGDcmb0Yf1Zv+nv5iiZl+96DvLNi\nKzOzt/DB6gIOlpTRoVUzxpzSlYsHn8j5A7v4oysXd/H8P2w+kC6pH7ABGA9cV6HNDOAmYDYwDnjX\nzEzSDOAFSQ8TDLanA/MkpQKHwiTSCrgIeCiO3yFhnZvehXNOGs2HOdt4cd46nvpwDZNn5TKqfyeu\nG9mXS4Z0815KI7Vu+35mLtvMzGVbyFq7gzKDnh1bcd3IPlw8+ETOSjuBFC+o6OpR3BJJOOZxK/Am\nkAw8ZWbZkh4AssxsBvAkMCUcTN9BkGwI200jGJgvAW4xs1JJ3YFnwnGSJGCamb0Wr++Q6JKSxPkD\nUzl/YCpb9xTxclY+U+ev4/YXP+GE1s0Yd2YvJozo472UBu7IYHl2kDxWbN4DwCnd23Pb59O5eEg3\nBndv7wtYXWR8QWIjU1ZmR3opby3bQkmZMap/JyaM6MPYoSd6L6UB2bq7iOkf5/NyVj5rtu0jSXBW\nWicuHnIiFw/u5oPlLq58ZXsFTSmRlFe+l7J+x4EjvZTxI/p4KfAEdai0jPdWbGVa1nreW1lAaZkx\nol8nxp3Ri4sGd6NTm+ZRh+iaCE8kFTTVRHJYWZnx0adBL2VmdtBLGdmvE9eN9F5Kosgt2Mu0rHxe\n+Tifgj0HSW3XgnFn9uIrGb3p16VN1OG5JsgTSQVNPZGUt3VPEdMX5DN13nrW7djPCa2b8aUzgl7K\ngK7eS6lP+4tLeH3JZqbNX8+8tTtIThIXDurK+LN687lBqT5g7iLliaQCTyRHq66XcsmQE32hY5yY\nGYvyC3lp/nr+tmgjew+W0L9LG76c0ZsvndHTS+C4hOGJpAJPJNUr2HOQlxes915KHO3YV8yrn2xg\n2vz1rNyyh1bNkrns1O5ce1Zvzko7wWdcuYTjiaQCTySxKSsz/vXpdl6Yl3eklzKiXyeu917KcTk8\ng+6lrPW8lb2F4tIyhvfuyLUZvblyeHfaeb00l8A8kVTgieTYFew5yPQF+bw4bx3rduynY9hLmeC9\nlGqVlhlrtu3jb4s2Mn1BPht2HaBj62Zcc3pPrj2rNyef2D7qEJ2LiSeSCjyRHL/DvZQX563jzezN\nR3op14XrUppqL6W4pIy12/eRs3Uvq7fsZfXWPeRs3Uvutn0Ul5QhwbkDunDtWb35wmCvMuAaHk8k\nFXgiqRuHeylT568jb3vT6KUcKC7l04K95GwNfq3euofVW/eSt30/pWXBz44EvU5oxYDUtqR3a8eA\n1LacM6AzvU7wBYOu4fJEUoEnkrpVVmbMzt3OC3MbTy9ld9GhI8ki6GXsIadgL/k7D3D4RyQ5SaR1\nbs2Arm1J79qOAV3bMqBrW05KbUur5g3vOztXHU8kFXgiiZ+CPQd55eNgLOWzvZTeDOjaLurwKrV1\ndxFLNhSyOL+QpRsKyd64m827i46cb56SRP8ubY70LtK7BQkjrXMbmqf42g7XNHgiqcATSfwd6aXM\nW8fM7M0cKjVGpAXrUi48uSvtW6ZEMsV1y+4iluQXsmRDkDSWbChk656DQPBI6qTUtgzt0Z5BJ7YP\nexpt6d2pNclJPh3XNW2eSCrwRFK/tu39/xlfedv3A9CmeTLdO7aie4eW9OjQihM7tKRHx5Z07xAc\n696xFW1b1K4Y9eGksbhc0igolzQGpLbl1J4dGNqzA6f26sDg7u1pU8vPdK6x8kRSgSeSaJSVGXPW\nbCd7w242Fh5g064iNu0uYtOuAxTsPUjF//XatUypPMl0aEX3jkECatU8GTNjy+6DLNnw2Z7G4aSR\nFPY0PGk4d/yOJZH4T5aLm6Qkcc5JXTjnpC5HnSsuKWPL7iI27y5i464DbCoMEszGwiI2FxaRvbGQ\nbXuLj7quY+tmpCTpyLnDSeO8AV0Y2rMDw3p1YHCP9r4roHP1yH/aXCSapyTRu1PravfUKDpUypbd\nRUGSKTzAxl3B7wcPlTG4R3tO7elJw7lE4D+BLmG1bJZM385t6NvZy6g7l8jiOpdR0lhJKyXlSLq7\nkvMtJL0Unp8rKa3cuXvC4yslXRIe6y3pPUnLJWVLuiOe8TvnnKtZ3BJJuK/648ClwGBggqTBFZpN\nBHaa2QDgEeCh8NrBBPu3DwHGAr8P71cC/LuZnQKMAm6p5J7OOefqUTx7JCOAHDPLNbNiYCqQWaFN\nJvBM+Ho6MEbBYoNMYKqZHTSzNUAOMMLMNpnZxwBmtgdYDvSM43dwzjlXg3gmkp7A+nLv8zn6L/0j\nbcysBCgEOsdybfgY7HRgbmUfLmmSpCxJWQUFBcf9JZxzzlUvnomksqXBFRetVNWm2msltQVeAe40\ns92VfbiZTTazDDPLSE1NjTFk55xzxyqeiSQf6F3ufS9gY1VtJKUAHYAd1V0rqRlBEnnezP4cl8id\nc87FLJ6JZD6QLqmfpOYEg+czKrSZAdwUvh4HvGvBUvsZwPhwVlc/IB2YF46fPAksN7OH4xi7c865\nGMVtHYmZlUi6FXgTSAaeMrNsSQ8AWWY2gyApTJGUQ9ATGR9emy1pGrCMYKbWLWZWKulc4EZgiaSF\n4Uf9p5m9Hq/v4ZxzrnpNotaWpAIg7zgv7wJsq8Nw6lqixwceY11I9Pgg8WNM9PggsWLsa2YxDTA3\niURSG5KyYi1cFoVEjw88xrqQ6PFB4seY6PFBw4ixMr5Lj3POuVrxROKcc65WPJHUbHLUAdQg0eMD\nj7EuJHp8kPgxJnp80DBiPIqPkTjnnKsV75E455yrFU8kVaipBH7UGkpJfUnJkj6R9FrUsVRGUkdJ\n0yWtCP9bnh11TBVJ+m74Z7xU0ouSWiZATE9J2ippabljnSS9JWl1+PsJCRbfL8M/58WSXpXUMar4\nqoqx3LnvSzJJR28vmoA8kVQixhL4UWsoJfXvIKjSnKgeBf5hZicDw0mwWCX1BG4HMsxsKMHi3vHR\nRgXA/xJs8VDe3cA7ZpYOvBO+j8r/cnR8bwFDzWwYsAq4p76DquB/OTpGJPUGvgCsq++AjpcnksrF\nUgI/Ug2hpL6kXsDlwBNRx1IZSe2B8wkqLGBmxWa2K9qoKpUCtArr0bXm6Jp19c7MZhFUoyiv/LYQ\nzwBX12tQ5VQWn5nNDKuMA8whqOEXmSr+G0KwN9NdHF3kNmF5IqlcLCXwE0ZNJfUj9BuCH4iyqAOp\nQn+gAHg6fPz2hKSE2tfXzDYAvyL41+kmoNDMZkYbVZW6mdkmCP6hA3SNOJ7qfA14I+ogKpJ0FbDB\nzBZFHcux8ERSuVhK4CeEWErqR0HSFcBWM1sQdSzVSAHOAP5gZqcD+4j2ccxRwnGGTKAf0ANoI+mG\naKNq2CTdS/Bo+PmoYylPUmvgXuC+qGM5Vp5IKhdLCfzIJXhJ/dHAVZLWEjwa/Lyk56IN6Sj5QL6Z\nHe7JTSdILInkImCNmRWY2SHgz8A5EcdUlS2SugOEv2+NOJ6jSLoJuAK43hJv7cNJBP9gWBT+3PQC\nPpZ0YqRRxcATSeViKYEfqUQvqW9m95hZLzNLI/jv966ZJdS/pM1sM7Be0qDw0BiCitOJZB0wSlLr\n8M98DAk2IaCc8ttC3AT8NcJYjiJpLPAD4Coz2x91PBWZ2RIz62pmaeHPTT5wRvj/aULzRFKJcEDu\ncAn85cA0M8uONqqjjCYoqf95SQvDX5dFHVQDdBvwvKTFwGnAzyKO5zPC3tJ04GNgCcHPbOSrnyW9\nCMwGBknKlzQR+DnwBUmrCWYd/TzB4nsMaAe8Ff68/E9U8VUTY4PkK9udc87VivdInHPO1YonEuec\nc7XiicQ551yteCJxzjlXK55InHPO1YonEufiRFKGpN9GHYdz8ebTf51LcJJSyhUbdC7heI/EuRhJ\nSquwv8X3Jd0v6X1JD0maJ2mVpPPC85+T9JqkJElry+9/Ee5z001SqqRXJM0Pf40Oz98vabKkmcCz\nkoaE918Y7qeRHra7odzxP4ZbIDhXrzyROFc3UsxsBHAn8KPyJ8ysjKBcyDUAkkYCa81sC8F+KI+Y\n2VnAl/hsyf0zgUwzuw74FvComZ0GZAD5kk4BrgVGh8dLgevj+B2dq1RK1AE410gcLpq5AEir5PxL\nBFVdnyaoPfZSePwiYHBQRguA9pLaha9nmNmB8PVs4N5wj5c/m9lqSWMIks388PpWJGChRNf4eSJx\nLnYlfLYXX37L24Ph76VU/nM1GxggKZVgw6cHw+NJwNnlEgYAYWLYd/i9mb0gaS7BRmFvSvo6wXYH\nz5hZ1Dv9uSbOH205F7stQFdJnSW1IChHHpOwZPmrwMMEFZu3h6dmEhQIBUDSaZVdL6k/kGtmvyWo\nsjuMYDvbcZK6hm06Sep77F/LudrxROJcjML9QB4g2InyNWDFMd7iJeAG/v+xFoT7sYcD6MsIxkIq\ncy2wVNJC4GTgWTNbBvwQmBlWL34L6H6MMTlXaz791znnXK14j8Q551yteCJxzjlXK55InHPO1Yon\nEuecc7XiicQ551yteCJxzjlXK55InHPO1YonEuecc7Xyf3Lzt5q71ZtRAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -572,17 +530,19 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEYCAYAAABSnD3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMEyLAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -605,17 +565,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEQCAYAAABFtIg2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIjoKEkiIiI6SpKIiIiOkiQiIqKj/w/dVhz7kUZNiwAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -639,17 +601,19 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAERCAYAAABowZDXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -698,17 +662,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7MAAAEVCAYAAAAyxLK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npB01ZkVERERERCTt/H+5ie+7fzfy7QAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "<Figure size 1152x288 with 3 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -731,17 +697,19 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEOCAYAAACaQSCZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSJI6MTAkSZ0YGJKkTv4CW7ucsX2pLgIAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -754,17 +722,19 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEnCAYAAABcy78jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEnCAYAAACkK0TUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de3xddZnv8c+ThKRN2xRqLymXQlGZobQUJVh6RLmMDjKgKCBVZhypR9qjI+ANjyhivYzgZV6Hm5cWXxZROFbrAUYQWy9VgamVoIVicRi1pVAbeoWEZpJ0Zz/nj7VWsrK7k+yV7vv+vl+vvLLXXmvvPLtJ17PW7/L8zN0REZHaVVfqAEREpLSUCEREapwSgYhIjVMiEBGpcUoEIiI1TolARKTGNZQ6gLF47LHHpjc0NHwTmIuSmYjIaNLAk6lU6r2nnnrqzsydFZkIGhoavtna2nritGnT9tXV1WkihIjICNLptO3atWtOR0fHN4G3ZO6v1KvpudOmTetUEhARGV1dXZ1PmzbtRYJWlIP3FzmefKlTEhARyV14zsx6zq/URCAiInmiRCAiUkEOHDhAf39/Xt+zZhLBhbc9/DcX3vbw35Q6DhEpEyvO/htWnF3Qc0IqleINb3jDy++///5J6XSaq6666sjdu3fXD3f8r371q+arr776yLe85S2zAdrb28d95StfmXrttde27tq1q/5973vfUZ/97Gdn1NcP+xZjUjOJoBC2b9/e8MUvfnFae3v7uFLHIiLlp66ujrq6Oi644IKuuro6Xv3qV3dPnTp12Mv5U0899b9vvvnmvx5zzDF9AG1tbT1z587tmTx5cv8jjzzS3NHRcdjMmTMP5D3OfL9hrbjhhhumrV69evKaNWsmb926tbHU8YhI+Xn00UfHt7W17Yfgav+MM87YD3D77bcfceGFF86Ovn7yk59MBJg4caJv2rSp6corr9wVvceZZ565/9FHH53w1re+teuee+7Z+thjj03Id5wVOY+gHCxfvnzG1q1bn1y7du3kUsciIuXh3//93yddf/31Rz333HNNkyZNSn3ta1975sUXX6z/3ve+N/nAgQN25plndgNcccUV+6644op9ma9fuXLlEevWrZt0+OGHpz7wgQ/s/u1vf9vc0tLSf/HFF+/7xje+MWXKlCmpc845pxNg2bJlM2bOnHng8ssv33fWWWe98pFHHnn6iiuuOOYjH/nI83PmzOlLEnfFJ4JrVj9+zNMdXc2jHfennS+Nh6CvYLRjT2id1P3lS+Y/O9z+H/7why179uxpWLFixRHpdBqADRs2jD///PNP2L59+xPvec97jpkxY0bqYx/72PNvectbXnHRRRftffrpp8ctXrx4z+te97ruBB9PRJK691+OYefmUc8J7H56PEBO/QTT53Tz1q8Oe04A+N3vfjfu6quvnrVu3bqnv/SlL02/7rrrnj/yyCNT55133ks5Rs7ixYv3LV68eCBBnHDCCcOe0I866qi+LVu2NP36179unj9/fve9997bMn78+HTSJABqGhqTiy++uLOpqcmXLFmyr7m5OQ2wYMGC/wZoamry+fPn/zdAa2tr/+233771jjvumPb617++S0lApHrddNNN0z/84Q93HHfccQfGjx+fXrNmzaRC/rxHH310woknntizbt26SWeeeWbX2rVrW97whjd0juW9Kv6OYKQr97joTuC+D5zxn4WNaKj58+f3zpgxo2///v1KuiLFMMqV+4DoTmDJurycE5577rnGD33oQzsBHn/88earr75616pVqyZ3dXXVvfe97z2oGehQ7dq1q+H444/vXbly5dQrr7xy91e+8pXWm2++eftY3ksnpzxKpVKWTqfp6emx6Lk1a9ZM/OAHP7jzpptumvniiy/q31ukSr3jHe/Y86Mf/ahl+fLlUz760Y8+f/TRR6d+//vfjy9EEgC45JJL9t13332HP/fcc4233nrr1H/913/d3tTUNKaKCxV/R1AKa9asmdjV1VX/0EMPNf/pT38a197e3nzRRRd1nn766Z0XX3zxcccee2zf3r17G55++unG22+/ferq1au3/uAHP+i6/PLLZ911113PNDc3qzyGSJVZsmTJkBP+t771rSPq6uro7Oysa2lpSef75/3jP/7ji8ccc8yBffv21X/hC1/oOJT3UiIYg3PPPfel3t7e3wE8/vjjf4ye/8lPfvKXzGNXr169FeDuu+/eVrQARaTkOjo6GpYuXbqnEEkg8uCDD7a88Y1vHFO/QFzNJIJi9w2ISJnLU9/AcF588cX6Y489Nu+Tv+JuuOGGQ7oTiKjNWkQkz1asWHHE6aefXjGjBGvmjkBEpFgy+wvKne4IRERqnBKBiEiNUyIQEalxSgQiIjWuUhNBOp1O2+iHiYgIQHjOzDqnoVITwZO7du2arGQgIjK6dDptu3btmgw8mW1/RQ4fTaVS7+3o6PhmR0fHXCo3mYmIFEsaeDKVSr03205zV9kbEZFapqtpEZEap0QgIlLjKrKPYOrUqX7ccceVOgwRkYoxdepU1qxZs8bd35S5ryITwXHHHUd7e3upwxARqShmNjXb82oaEhGpcUoEIiI1TolARKTGKRGIiNQ4JQIRkRqnRCAiUuOUCEREapwSgYhIjVMiEJGat2j5ehYtX1/qMEpGiUBEZAS1kCSUCKTyrDw/+BKRvKjIWkMiIvm0eUdnqUMoqaLeEZjZPDOrL+bPFMk73ZHUjEXL19dEkijaHYGZLQB+AbwM6I89fw4wFzDgN+6+oVgxiYhIEROBu28ws13x58K7gy8Bp4VP/Rw4p1gxiYhI6TuLZwG7PQQcMLPjSxyTlLuuDuh4YrCJpn1lqSOSCteXStPdm+LuDdsGnquVZiEofSJoBbpi213AjGwHmtkSM2s3s/Zdu3ZlO0Rqxf6d0Lc/eNyxCTatLm08UvFS/Wn6He7buL3UoZREqRPBHmBibHsisDvbge6+wt3b3L1t2rRpRQlOyljjBFj8ALTOK3UkUkVq5Q4gU0mGj5pZHTDV3Z82s0lmZuGuie7+X6WISUSkVhVz1FAbMA34e2Ab8AngUuBa4CPhYdcWKx4RkdF096bod3iqyu8UEiUCM5vr7k+O5Qe5ezswIfbUpeHzDwEPjeU9RUTyqbs3NWQ77cH3zp5UlqOrR9I+gu+b2cvM7CIzm16QiETKnUYt1QwvdQBFkjQRHEHQjDMX+IaZvTb/IYmMovXk4KtUNGqp5kwaV93VeJImgi3ALe7+WXe/CDi6ADGJlD+NWqpK/U7VVxrNJmkiuA74hZn9k5mdwDBj/kVEKklz0+AVfy0OIU2UCNz9F8DFwALgy0BHIYISEZHiGUvD15+BO4C/uPu+/IYjIlJaXT0p5i1bw9QJjaUOpWhySgRmdg3wRuB24HpgA/CCmf04vEsQEakaXT0pumJDRjOHlVabXJuG3gicC/wV2ODu73X3jwJNBYtMRKRI5sxsKXUIJZVr09DmsDroI2b219jzFwMP5j8sEREpllzvCD5tZqcAuPuW2PPP5T8kEZHiq7fcnqtGOSUCd3/R3TcCmNnfxZ5fFt8WEakU85atYd6yNQPb8SGkcfU2/L5qMZYy1B8ZZVtERCpIPtJcjdw8iYRKWd5CpAByTgRmdgXwP4B5Zvat8OnV1E5dJhGRqpRz05C73+7ui4En3f094dePCxibiEjBdPemcp4f0NUzdD3japOPpSrVNCQiVamhvo6G+uA0Wc3rGY8lEXx7lG0RkYqxaPl6Nu/ozDqprLGhjsaGOpWhzuTu3xtpW0SkGtQbbFp2bk3MOq7uNCciMoy0ByNddnb2DDxn1Obol0R3BGb2LjPLR7+CiEhJRSf83fv7Bp6ri/V4Rn0DtSDpHcE+YKmZGbDR3f+jADGJiBRULquQNTYoEQxnDXAY8G7gu2Z2P/CfwLfd/aV8ByciUmjxctO1KmnK+zHwKDAZONXdrwL+L/CzfAcmIlIIi5avZ8OWvQc9f+EpR434us07Oqt2PeOkieDXwCnufmNsdbJe4Hf5DUtEpHgmjWvgsgWzqr643HCSJoJNxBajMbPzgHOAj+czKBGRUqq3wcVqVi1dyJyZLXT3pqp2Yfuk6W8lsMbMbgk7ij8HnA5cDnwzz7GJiOTNoTbr9Hv1LlmZ9I7gand/BzAl3E67ewp4VX7DEhEpH9V6JxBJekdwupm9C/i5mc0E6szsNcCr8x+aiEhpNDc1sGrpwlKHUTRJE8GPgC3u/kczGwd8D7gIWJ73yERE8iyXK/tJ4xrYtOzcIc9Va5NQJGkieJe7Xwbg7j1mNtndVXRORCpa1DGcmQCyifoaqumOIWkiOGBmDxLMMDZgNkFn8YjMrAH4NMEw0xOBG909He77n8ALwCuATVrjQEQK6VCv7quxvyBpIvglsBVIh9uvyfF1VwDb3f0eM2sF3g6sCvf9k7ufbWYtwF0Ek9ZERIom2wSzWpIoEbj7yvi2meW6ZM/pwNfDxxuB9zGYCHaZ2TVAJ3BTknhERA5FnQVVSGtdokRgZm8GLiQYdloHzAXacnhpK9AVPu4CZsT2XUlQoqIrfO/hfvYSYAnArFmzkoQtIsLOzh66e1P0x078E5oaVGuI5E1DJwG3AkcDTwNn5Pi6PcDE8PFEYHds35eABQSF7L4BXJztDdx9BbACoK2tTTlcRBLZtrd7SBIYi0N9fblKOqFsPNBMkAhOIbxCz8FaYH74+GRgrZlND7ePdvdud/86MDVhPCIicoiSJoLvAAcI1imeS1ByIhd3ArPM7FJgFvAkcFu4b7WZLTWzy4H/kzAeEZGi6kul6e5NcfeGXLtIy1/SpqHnCMpLvAb4BTAvlxeFQ0WvCze/H36/NNz39awvEhEpEw31dfSngsGSqf40/Q73bdzOZQuqo78yaSJ4GNhGUHoagnkEtw1/uIhI+ZozsyWnoaONDXX0hokg6ieopvkESZuG7nP3i9z9ne7+TuBthQhKRKQYqml28KEY9Y7AzC4B/iHcPNbMXgX0hNvHk8PM4rKx8vzg++IHShuH5E/HE6WOQMpYvPR0tY74yYdcmob2APcQTPjKpKqjUn6U8CW0eUcn3b2pml15LFej/uu4+7rosZktcPcNse1JhQqsIPb+BfpeGjxRSGXq2AStOY1TEAGy1xdaMDtYVqVl3OhJIte+hEqVU5o0s6nA54G5sbIS9QRDSO8vUGwi2bXOg3mXlDoKqQDRrOF6G/6YE8PKo7Usp0Tg7rvN7CbgLOCPgBMUnvvPwoVWAFOOD76ryUCk6o20NOUIeaEm5TxqyN3/SFAzaIm7/4ogEbyhUIGJiORbvQULz0zMoTmoliQdPpoC7gVw90cIFq2vHIsf0N1ANerbDzccA7ecGmx3dQSjiVaeH3y15zoBXqpVfMTQnJktAwvR5GrV0oUHNS919VTP7OKkiWAP0GRmzWb2fmBaAWISyd2E6dA4IUgG+3cGz+3fGWxD0LG8aXXp4pOqdt/G7aUOIS+S3h+tBj4GvBN4lmEqhYoUzaTW4CtzPkHjhODuTyPEatKi5etHnPk7lolkzRklqydVUfNS0oVpdgHXRNtm9vK8RyQicoii+QOSm1xmFl8DfJhgpNCQXcAkBtcZEBEpuUXL1ydKAmO5O6i3YG5CtdQbyuWOYB1wu7u/kLnDzF6b/5BERPLLCK5kZ01pzlt9oX7PPlGtEo3aWezu7dmSQLjvkfyHJCIyNouWr6d9696D6grVhcNG111zdl5/Xr+PPF+hUiQdNSRS/lpPDr6k5mze0TmQBPJdZG7OzBYmjWsYcZZypUqUCMxsQcb2BfkNRySh+NyQ3k7NGZCsmpsaEs8dqCWqNSTVYcL0IBFozoBkkc8kEFUyjQ8lrXRjqTX0VPh05dUakuo1qXVwQplITCGacuLVSHd29oxydPnLeR5BWGvoj/HnzOx4QP/7RKQs9IXLSRbT7v19Rf+Z+ZZTH4GZ3WVmh5nZ1Wa2xcz+YmZbgMcKHJ+ISM5S/QcngnwtSrNq6cKq7WfI9V/o4+5+wMzWAt+OhpOa2WmFC01EpLzE5yC8/BM/pj/tVTGXINc+gmfDh1uA+WbWFG6fDDxaiMBEaoKW1SyKQixS39xYXzUdxknvmR4GtgG94fYrgdvyGpHIodKC9lJE+Z6vUAqJq4+6+43Rhpkdled4REQSG252b73ld+hotUqaCE4zs9VA1E0+G8j/PZeISI6iktPxE341zv4tpKSJ4AHgz7Ht+XmMRWTsFj8QrFJWac1CXR3B/Ieor2DeJdC2uLQxlbH4SX/V0oXDrjvQdtwUgIJWB43PJah0SWsN/YmgiF/01ZX3iERqiVZTy6vmpgYmjWsoSOdwNUt6R/BFYGv4eBywF1BxF5FDodXUDllfKh0sRtPXT3Nj/cDz0Z2DjCxpIrjA3fdEG2b2mXwEYWYTgEuBre6+Lh/vKSKVLbMDODqhd/emaN+6l0XL17Ozs4fu3hT9Dr2pNAtmT+HCU44acnyhrFq6kOM+/sBArJWccJImglvNBnphxgMvBz492ovMrCE87nfAicCN7p4O900F7gaucPdnEsYjMlTffvD+UkdRlaITczmd8Hbv76Pfg87hfC46k1Slr1SWNBGsB6LeuP7Y49FcAWx393vMrBV4O7Aq3PdvBLOVlQTk0EyYDuwMqpBK3hX6ZJdroskct19vQd/A9JZxhQqt6iXqLHb3W939V+HXw+6e61/G6cDG8PFG4HwAMzuMICnMNLM789XUJDVqUmvlLUijRXSGtXlHJ5t3dLKzs4fNOzq5e8O2IZU+oz6BcvBST6qiVyrLTzWm0bUyOMKoC5gRPp5G0C/wFQAz+4OZ3e7uz2W+gZktAZYAzJo1q/ARS+Uad3ipI5CE5i1bQ3dvamDYZ6S7N8W2sA/gvo3bh1T67OpJMWlcQ0kqjmaq9MnFSVcoe9MYf84eYGL4eCKwO3z8AkETU+Rp4Mhsb+DuK9y9zd3bpk2bNsYwpCbMmBt8SUXp98Hmp0XL1x9UzC2zaWr2y5rZtOxcTjnm8JKNDopPXKvkdQmS3hH8M/CTaMPMZrj78zm8bi3B5LMNBIXq1prZdHffaWa7zGySu3cRdED/V8KYRKQIitEM092bWxNLvTHQJ1Aundfb9naXOoQxSzqh7ICZPWhmd5vZ3cB9Ob7uTmCWmV0KzAKeZLBY3f8GPmNmlwHfcfd9CWMSCcTXLy61ledX3byA/rTTn3bu3rBt9INzsGj5+hFP+v1+cMdwdJfQ3FR+k8b6PWjimrdsTcX1FyS9I/glwYSyNDAVOD6XF4VDRa8LN78ffr803PcoKmUtUjHu27idyxYUr58uSgbdff1VUemzHCVNBMcCb3T3y8zstUDTaC8QkepQb7mVXB7LfIOow7ff4bdb9jJx3NBT04TGeurqbKD+f7lWFK3URWqSJoIUcC+Auz9iZp8mmAwmIqOpskVohpv5OxbxJSYzc82kcQ0DJ/5yK/LWdtwU2rfuHUiQlXrHkjQR7AGazKwZuJxg+KeISGI7O3vYvb+PecvWHHQCHW1IaDn1DzQ3NVT8SmVJO4tXE4z6+UH4/eK8RyRSDbo6gpLYK8+H9uqqyzjWGcbxzuFFy9ezbW833X39WUcj9cYSwdQJjQOPW8Y10DKuWNOfRlctC9on/Rd9GUEncRp4Fshl6KhI7YnKS3dsCrYrbI2BbO38mVe+0RX91AmNQ8o7RM+f/eV1TG8Zd9DVe3wNgahSaLYragPqYsNEAU6sgpNuOUqaCFYB/4egeNwM4KOAykJIeSmXNvjGCdA6r9RRFMzu/X0DJ/D4yXq455Noaqgj1Z8+aJhopQzLrLQidEmbhh5w9zvc/Ql3/ymwBcDMZuY/NJEqE28uqsAmo6gJpCujrs5YRsr0pdLDdqzWGzQ2JD01lU459VeMVdI7gr/NWLP4lWHZCa1dLDKazNXIylS2NYAzbd7ROZAA+h2eGuEKOHq/vlR64AQf9QFE7f/dvSnSPnTEUHNTQ1W0v1eCpIngp8BTHDzCa0F+whGpchWyGll3X/+Qk/twawNHOrO08Xf1pAZe05dKk+pPk0o7zY31A+sHrLvmbBYtXz+kmFx08q+05pVKligRuPvXh9n1qzzEIiJloj/tAyf30ZLASAZGBHlw7djcWM+cmS1s3tE5Yv9BtDB9tucrQaVNLCufcVgiUnJJTvqZo4jmLVvDnJktzJnZMjDxKxoVVGknxlqjRCBSCFWy2Ey8fT8+tr+7NwVmQ44daRLYaDNu430BlXLVH2dU9poEoyYCMzsCmJRtF3C6u6/Ksk9EMlVIcsg8oceTQJ1BOjzjNTfWk047+8Pmn3iZiLipExrp7k3R3NTAteedeFDBuujEXylDQ7Opy7EOU7nK5Y7gHcA5wEvASQSdxWmCoafNDK49LCKZOnJd1rv0Fi1fz8ZnXxhy5Q+DJ/jZL2tm9/6+gZN6dBUf1dqJLywDQY2ga887cWBlsTkzW0asWpp5J1BJdwaZzWT9Hvx7VspnyCUR3BV1EpvZm939R9GOsOiciFSJ+FX9pFgph2ghmPjonrhoFjAEzTw7O3uY3jKOyxbM4r6N2wsZsuTBqLM2Mhaon2dm55jZPDN7DzDWpStFpEzV2+ASjDs7e0Zt8mhuamDiuIaBu4RVSxeOeUaxlEbSzuKbgY8ArwE6gHfnPSKRatO3P5g30LGpokpOdPf1s6UnWH6xoT7ZTN94k0ilNI8civhIqUqUNBFcA5zg7heEC9O0ESw4LyLZTJgO7Awet86DeZeUNJyRRFf/9Rac+KNZwFMnNLLumrOzvmbR8vUVP+KnUNqf2Vcx/QRamEakkCa1Bl9lUAgvXlE0W3XRqP0/SgK5lHeohJNcqfSnnc07OisiGWhhGpFDUUajgsayRGT8dRB0EG9adm5FD+UslUqeS5A0EawGPga8E9iGFqYRKajhTu5jOelH6wTEZw9Hj+OLv8SV+5VsuVi1dCHzlq05aF2FSplRnbTW0C6CfgLM7HDgQCGCEqkaRWwSyjzRZ7bt797fl3U1sK6eFF09KerrbKAkhCQ3Z2bLkPWLK0mioQBmtt7MXhVupoAv5j8kERnNzs6egfbnuzdsA4ae6Lv7+geSQryZp7mxfmB5xWio5+yXNTNpXAPNjfUDdwarli7U3UANSdo09Avga2b2e+CTwC/zHpFIPpVRG/5YxK/yAS485SguWzBr4KTf/sw+Nu/oHJixG53o5y1bQ3df/5AmICBrB/D0lnFZl5SU2pE0EfzO3T8ZTiZ7CNhE0G8gUruieQIQDA/N4/rE8av86KQeP+nHxU/yUyc0spu+g/ZfeMpRQO2N8y+VSmkmSroeXIuZNbv7twhWJMvewyRSQJnNHSU1YXqw2AwEE8Y25f+6KLrKh+yLtXT39R9UPnp6y7iB5p+oCWjV0oUj1vqRQ7Nq6ULajpty0PM7O3tKEE0yOd0RmNldBMNFW4A/mJkTjJY6vHChiWQ3XHPJsAp4xT5knkARVx2Lrv53dvYMXPnHr/ilfAxXn6mc5No09HF3P2Bma4Fvu/sLAGZ2WuFCE8kuW3NJVNjsoGaO+MzeaJ3gfCaCMfrDjhcBOGnm5BGPy2zTj1/9z5nZMmz7vpp7Sieq01QpzUKQYyJw92fD709l7Krc4hpS0aLmklGbiEp0xT6aA6k0B9LOH3a8yNSJTcwglhyGeU283T+6+ldTT/lpbgpOq9GcgkqYS5DLwjTXAB/m4ElzRrBgzcQCxCVS1Q6knf60B8M8X+plRsb+bAlOo3ukUHK5I1gH3B41B8WFhedGZWYNwKeB3wEnAje6ezrjmNXAR919ay7vKbVrtBo4Q2bdFnpC1yG8f31YwD9q5oruEuITwlTOufJEf5+VVI101ETg7u0j7P5rjj/nCmC7u99jZq3A24mtbGZmbwOacnwvkQFRH8HUCY3s3t83MLmqEkV3CTA42zda2QvU7l8JKnXZzaQzi99sZt80s2+Z2R3AD3J86enAxvDxRmCgsTacqfwsQUE7kTHZvb+Prp4UNzyY2Y1Vnpob6wfmAfSHdwH9aae+zobM9tVIoMpXCZ3GSSeUnQTcChxNsA7BGTm+rhXoCh93QdAkamZHAK9w9x+Y2YhvYGZLgCUAs2apg0wGZaufU46yFYo7rG7w776+zga21R8gxZR0Qtl4ggXrjwZeRXhizsEeBjuVJwK7w8fnA/9kZvcC5wArzCzr5Y+7r3D3NndvmzZN1a8lMHVC45Cr66isQrlM4olPfovXB4qS12ENdQMjoE47dgqnHHMEoFo/lW7V0oUDaz7Xj3yNWxaSJoLvEBSbuwN4NXBPjq9bC8wPH58MrDWz6e7+XXe/0N3fSlDHaIm7a6VryVl8Bm1TeFKNCq6Vm/j8h2Z6mNq/m5NmTh51LoFUplwW9ikXSRPBZ4AfAk8Bi4CP5/i6O4FZZnYpMAt4Ergt4c8WGVG0qlY5lVLOvAuIrv5PatrNjPTOwUluUnVWLV1IvQV9BOU+iCFpH8Fz7j7QQG9mx+TyonCo6HXh5vfD75dmHHN5wlhEhjSflONIjSF3AbEyzwMznst8HWM5NA31dfSn0ty3cXtZT/5LmggeMrN/YXBBmpOAq/MbksjYREnh5deWfn3guHjRuAFltJaxFE5jQx2p/vToB5ZY0kRwLfAroDfcVuOm1Jx4KYjhloyMjqmkdmKpXUkTwT3u/pVoI5wcJlJ2+p1hl2w8VNlmAA93TFQc7iC6E5AykjQRnBt2+KYIag1NA16R96hEDsGsKc0Do4aiGbqLlq/nozv2clidccohvn+2GcCZ7x8dM+coTQiT8pc0EXwVeByIGr3mj3CsSEnEJ2Od/eV1A0khOnnHm3OGa9rJJjr2ozAwAzjz/aNZwh+KzRIWKXdJE0GPu2+JbT+Tz2BE8iF+8o0nhUeXGf1hc0225pwkSSHb+z+7bC8vpJsBmDSugakTVT6r1s2Z2UL71r1ZV5YrJ0kTwT8DP4k2zGyGuz+f35BE8idbOYd4c058cffo8bxla7L2K0Qro8HgesHx959iLzGl/qXguZUaRyGVI2kiOGBmDwL7CPoIZhMUlBMpe4c11HEYMHtC84gzj+OJAgaXwozmBKbLwtkAABLtSURBVDRPrM96tT+hsX5wWcyOTcEcAalpq5YuZN6yNaUOY1RJE8Evga0M9hG8Jp/BiBRSVMphet9gc062/oL4lX/7M/vYvKNzYDJQc2P98CUh4stiaqKYhKpihbIM3wM+AJxAUE76prxHJFJgw/UBrGr8PACLWq4bSBTzlq0ZWCcYRpkXoEliUqGSJoKbgf8CVgNTCJaw/GK+gxIpliFJoasD9u9kVevnw6v5hVnXCWZzaWIVKZSkiWCtu6+ONsKVxUSqw/6dQRt/VAiubXH2dQGGSwS6E5AKlbT66LFmtsDM2szsfwHnFSIokZJpnKBOXsm7cq9AmvSO4A7gE8DfAk8A1+Q7IJFyoglhcqiiCqQ3PPhU2VYgTZoI9hOsU9xIMHz0XWhdAakWrSeXOoJBK8NlvUdrbsr1OCmZxoY6elPlXYE0aSJ4mGA2cTQI+5UoEYiIDCuaXVzOkiaC1e5+Y7Qx3PrCInKIwhFMrDw/GMHUtnj042DkY0WGkTQRnGZmqxm8I5gNqBFVJN+yjGAa8TgY/ViRYSRNBA8Af45tq/qoSKHkOoKpcULQRxDdFUjZ6ffynmGcKBG4+7cynvpVHmMRqW0dTww+zrXjupw6uCWrVUsXlt3yqZmSziMQkUKKF63LtPJ8XfVLQSRtGhrCzGZnrE8gImOVrWjdT68f3J+tY1gkDxIlAjN7M3AhwZ1EHTAXaCtAXCK1J1vRungiiHcMb1sfNCWpaUjyIGnT0EnArcAPgX8lWLpSRPJh8QPZJ4ZFzUV9+wc7hhsnDB1VJGWtuamhrMtMJE0E44Fm4GjgVcCSvEckIoMmTA9O+gCzFsIbPzv0ea17UBGipVE/dW95Ju7E1UeBA8C3gWuBu/IekYgMGm6NA619UFGmt4xjy57uUocxrJwSgZlNBT5P0CcQ3dvUA5egEhNSzUpdy2e4n6sEIHmUUyJw991mdhNwFvBU+HQa+M8CxSUiIkWSc9OQu/8R+GP8OTObnfeIRMqJhmxKHvU7LFq+vuzKm2v4qMhIstXyEUkoml3c77B5R2epwzlIUYaPmlmDmX3OzN5mZp8ws7rYvneY2SNm9icz+x8J4xEpvGjIplYukzx4qaf8ag4dyvDRU8h9+OgVwHZ3vwfYB7wdwMzGA/3u/lrgeuBTCeMRKazWkzVpS/KioT443XqJ48gmaSL4DoPDR+cSLF2Zi9OBjeHjjUBUMOUAwd0FwO+BPQnjERGpCI0N5VvabdQ+AjM7ApgUbvYRFEOZDqwEFuT4c1qBrvBxFzADwN3j90ivB740QhxLCO9AZs0qz3U/RUqq44nBWcgQdGxvWh081nDTkpszs4UNW8pzpbJcOovfAZwDvETQR/AUwdDROoJmolU5vMceYGL4eCKwO77TzI4Htrn7E5kvjLj7CmAFQFtbWzneXYmUVrxoXdSxrRXMJAe5JIK73P3rEIwacvcfRTvM7NM5/py1BIvYbABOBtaa2XR332lm04G/dfcfm9k4oMXddyb7GCIyZLZxdOLXCmaSg1Ebrdw9PtZpnpmdY2bzzOw9wJty/Dl3ArPM7FJgFvAkcJuZNQP3AV8ysyeBR4HyvHcSKXfDFa3TqCcZRdJaQzcDHyLo/O0A3p3Li9w9DVwXbn4//H5p+L28ZlaIVBONeCobq5Yu5LiPl2dfTdKlKvcT1BwSEZEqcUgrlFWaz/zoD2z+a/nN6pNkNu/oZM7MllKHUd6i9Y91RyA5SDSw1czeFZ8VXGke3LSjLKd3SzJzZrZw4SlHlTqM8pe5cE3HE8HKZtHax+0rSxdbjSu3BWqS3hHsA5aamQEb3f0/ChBTwfzmE28odQhSSvFx9tU+jDIaShpfuCbb8NJq/jcoQ00NdfSm0ty3cTuXLSif+VBJE8Ea4DCCTuLvmtn9BKWov+3uL+U7OJG8ik6EzzwcfEWTraoxKVz12MjPRcNLpagaG+pI9adLHcZBkiaCHwNHEpSaONXd95nZFOBnBCOJRMpXdCK85dRgfD0MTQodmzTEUgquHCuQJk0EvwZudPcDsed6gd/lLySRAotfGceTQtSMEt0piOTZnJkttG8tv6lSiTp+3f1z8SRgZi939/3u/v78hyZSBJNag5E10WSsamsikrKyaulCmpsa6OpJcfaX15U6nAG5FJ27BvgwB1dPNYJidBMPepFIpajVYmzZCtQpCRbF1AmNdPWk2L2/j0XL1wOUfMWyXO4I1gEnuvuRGV8zgXMLHJ+IFMKE6UHpCQj6RtQcVjTTW8YB0NWTKpu+glHvCNy9fYTdHXmMRaS8RVfRjRMrv1NZI4gkJmnTkGXsVtOQ1I5sY/OrQZTgbjgm2H7jZ9VMVGOSNg3NjH8B/1Dg+ETKR7xjuZpOlPFmot5OuP+DsGwy3HhsaeOqRCvPDxLqCHdZ8f6Al8qkeShR05CZXQBcCNQT3B3MA9oKFp1IOanWjuXM4bR7/xQ87nlh6Czs6ORWrf8O+fDMw4kOL5cVtpLOI5gL3EaweP3TwBl5j0hESueqx4ITfnRCi5eieP7J0sVVaZ55pNQRJJK0gNx4guUpjwZOIVxDWESqUFML4EFSaF8Z3CH0vFDqqIqrfWXQ1HPLqcMfc8upwTFDivjlfq3f3dc/9vjyJOkdwXeAw4FvA9cCd+Q7IBEpsXgJ666OwX6DWvTT64PP39sZnOjbFsPnZ0D/AThsPPTFSqxl/hvdcmr2mk9AfZ3Rnw6SRfS9lHJKBGb2EPBJd/917Olc1ysWkUoyYXrwPeobuOVU2LcFPLxyXTY5uFuo9tFFt5waJIBINNci1RN87xulzua+LcPuam6sp6sndYgB5k+uTUM/zUgCAJiZisKLVJurHoNrnx08yV/1GHx6b9hUFIruEm44pjpHGH1+xmCn+QU3BZ+944ngDiFTU8YiSdHx3j9sk1K5LayUayJ4rZldn/kFfKOQwYlIGYnuFCCYVGf1g1fM1dJ3cOeFQWKLrvovuClIiH37B5uIjj1j8OR/7BlB0owsezE4Pvq32vunrAsAlbqkRKYkfQSZk8mGe05EqlG29u74cNNKW/Bn2eGAD57s21fCX345uD96HuCI2YOfs+OJoc1nECSAuKseG3z/n15f9v8muSaCR9z9s5lPmllrnuMRKQ8dm4ITm9YoGNlVjw32IcTXdijnhNC+cmjHbvQ43uwTTwIQfM7PTAmae6JJhaNZ9sLgbO0sJo1rGOgnWLR8fUnvEnJNBK81s9e5+0PxJ91dtYak+nR1MDD8r9rKSRRC5oI/UUKAwSvtUiaH6Odv+w/wjNXBGicGnb5RMjj2jOFP8tFrk/49xEccDWNnZ0+y98yznBKBu6vKqNSOYYb8ySiif7foivv+Dw4Ov4Shy4NGouRw54XB9j/fd2gxRCd9CJpwerOUb2hqCZ4//qzg58Wbt0aybAz9IP19wfdNq0dMBFv2dCd/7zxKOo9ARGRk0QkvftLveym4+obBInfeP5gcojuIz0yBWQsHj/M0zDxl8PXRcccOU9QgW4kHqw/u7BonZr8ryZxNnU/XPR98pmceDr6f/28DP3/OzBY2bCmP1crMvfSTGZJqa2vz9vaRqmOLSNmLrt7jV+5WP7jfR5hxa/VBobzWk4dOgIOxNUEVsvnq8zMGRyEBTHnFwN3TcR8fbIaa/bJm1l1zdn5/dgYze8zdD6oPpzsCESmNtsUjn3Sjk3N0NzDvEth8b7DvUJuQksZyKK57PvgeJYRYM9SC2VMG7gq27S1d85DuCEREimXZ5OD7sWcEiW3Tan6zZQ/v6PsUEIwk2rSscF2yw90RJC06JyIiYzXlFUGz1jMPB53pzzzM/Lo/D+zu6klx94ZtRQ9LiUBEpFiich0x4+njqw03DWzft3F7saMqTiIwswYz+5yZvc3MPmFmdbF955jZVWZ2tZktKEY8IiIldfxZQzbPb/gtW8ddxtZxl3H8th8U/a6gKH0EZvY+wN39G+Hjve6+yszqgQ3AaeGhP3f3c0Z7P/URiEjVyJzpDPwmfSL1dUZzY/2Q57sOP5HT33/7mH9UqfsITgc2ho83AtGCnrOA3R4CDpjZ8dnewMyWmFm7mbXv2rWr8BGLiBRD2+KgVtHxZwFBEpjEfprTL0HPi9DTWfDFa4o1fLQV6AofdwEzsjwf3/eXzDdw9xXACgjuCAoWqYhIKYRDYk8vwY8u1h3BHiCcVshEYHeW5zP3iYhIERQrEawF5oePTwbWmtl0d38amGQhYKK7/1eRYhIREYqXCO4EZpnZpQT9Ak8Ct4X7rgU+En5dW6R4REQkpJnFIiI1otSjhkREpEwpEYiI1DglAhGRGqdEICJS45QIRERqXEWOGjKzXcAzpY5jBFOp/Ilx1fAZoDo+RzV8BqiOz1HJn2E3gLu/KXNHRSaCcmdm7dmGaFWSavgMUB2foxo+A1TH56iGz5CNmoZERGqcEoGISI1TIiiMFaUOIA+q4TNAdXyOavgMUB2foxo+w0HURyAiUuN0RyAiUuOUCEREapwSgYhIjVMiKAIz+3szO7zUcWQys3lmVj/6kQPHX2Zmc83s44WMqxjM7CQzOy3J5y+kpL+L8DVzzays1vAYw9/UaWb2OTO7q5BxjdVon8fMLjSz15nZ1cWMK9+UCBIawx/6YcDZwOHhdln84ZjZAuA3wGFm1hD+Z3ybmX3CzIb7u3gJeCfw16IFmoMx/E5eC7zW3R9198KuCp5bPIl/F2b2NwS/j6ZixjqSMf5NPenunwK2FS/S3OT4ec5z94eAfjObV7poD40SQQJj+UN39wNAb+ypsvjDcfcNwK5w8wpgu7vfA+wD3g5gZovM7LvRF/AqYA3wRjNrKEXcmcZ48nk/sMvMvmZmE4c5pmjG+Lt4B/AmoM3Mppci7kxj/BxtZvZKynBYZi6fB7Do8PCrIpXFf+ZK4e4bwjpHEPvDMLNWgj+MVWa2CHhz7GXLM96mHP9wTge+Hj7eCLwPWOXuq4BV0UFm9i6CK9DfE/ztpIoc50HG+DtpjR0zB/htcaMeUU6/i4iZtbr7ziLGl6tc/6YuARYBL5jZ7e5erjXEsn4e4GdmthBodPcnSxXcoVIiGLtc/9APA94DHA9spTz/cFqBrvBxFzAj20Hu/p3w4U+LEdQY5Po7eb2ZvRkYDzxe9ChHltPvIuLuywod0Bjl+je1GlhdrKAOQdbP4+4/CJ9bX4qg8kWJYOxy/UM/ACyObZfjH84eIGoimUjlVlfM9Xfy66JFlFy1/C6q5XNEqu3zDKE+grGrpj+MtcD88PHJ4XYlqobfSbX8Lqrlc0Sq7fMMoUQwdhX9h2FmbcA04O+BO4FZZnYpMAv4biljOwQV+Tuplt9FtXyOSLV9npGo1lAC4R/GrwiGUN4PfBZ4guCk8+lyGIpYa/Q7ETl0SgQiIjVOTUMiIjVOiUBEpMYpEYiI1DglAhGRGqdEICJS45QIRERqnBKBVC0zu8DM3MyuMrP/ZWY3mNmVRfz5d5jZB8zs2LAiqpvZP2Qcs9rMHjOzY7O8fq6ZPWNmt4Y1qzCzc8zsZ2Y23czONbOtRfo4UsVUa0iqlrvfb2a4+y3Rc2Y2u8hh3B9W1HzGzP4MXA38OIzlSIKZq5uyVd109yfN7AvAhWHNKggK5V0bVhxdo0Qg+aA7AqkZZnapu28xs/PM7Kdm9kEz22hmR5lZs5n9i5ldbWa3m9lkM/uOmX3SzDaY2Tgz+7qZvdvMXjCzi8zs+vDqvMnM3mxm7x4lhB8A883sb8PttwPfj8X3GjNbHP78C8KnvwO8xsxeHm6/xt0fzes/jNQ8JQKpeuEJ/xqCBWkAngJe5u43AeuA1xOUCh8PPA80hl8O/AVYCMwGTnD3bxPUMPoZcCNwFHAYcAww2nKLvcDtwJUWrKjWCOyP7f8I8N8EJTJOA3D3buBbwPvNbBqDC6WI5I2ahqTqhSd8zOzHsaf7wu+9BIvtnAR8NVwj4nvh8Wlgn7ungafM7Odm9hbgFnfvDI/5DvDPQJ2757JQz9eBPxAshnM/sCC27yh3/16W13wVaAd2Aitz+BkiieiOQGqGu//BzN42zO5ngXcDmNkcC9YEHmBm44Hd7v7v7v5wbNc3gGsIFsIZlpkZQbL4K8Fyn29396cyDntZtHxpPM6w/+AhoK1MVyOTCqc7Aqla4dU7ZvZJ4AXg5cBkIA3MNLNjCKqUNgJfAO41s4eA/wfcAbySoH3+5wTNP0vNbAnBBdSn3P0Bd99rZg8Aj4wSzv8EXmdmRwO3hj//cOAs4JSwE/tjwD1m9jhBs1PczcARY/7HEBmBqo+K5MDMLgf+6O6/CYdy/gPwc4Jmpcvc/dYsr7kD+Iy7bylgXL9097MK9f5SG3RHIJIbA24ys98DvycY7fNl4FTgDcO85j+AvzOzn+c7GZhZI/B3BB3fIodEdwQiIjVOncUiIjVOiUBEpMYpEYiI1DglAhGRGqdEICJS45QIRERq3P8Hv/gv03eYXS8AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -790,17 +760,17 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'hex2': <serpentTools.objects.detectors.HexagonalDetector at 0x7fd09bfa2b70>,\n", - " 'hex3': <serpentTools.objects.detectors.HexagonalDetector at 0x7fd09bfa2ac8>}" + "{'hex2': <serpentTools.detectors.HexagonalDetector at 0x7fe2b1baec50>,\n", + " 'hex3': <serpentTools.detectors.HexagonalDetector at 0x7fe29f43b0d0>}" ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -820,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -833,7 +803,7 @@ " [0.198196, 0.198623, 0.195612, 0.174804, 0.178053]])" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -845,63 +815,16 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'COORD': array([[-3. , -1.732051 ],\n", - " [-2.5 , -0.8660254],\n", - " [-2. , 0. ],\n", - " [-1.5 , 0.8660254],\n", - " [-1. , 1.732051 ],\n", - " [-2. , -1.732051 ],\n", - " [-1.5 , -0.8660254],\n", - " [-1. , 0. ],\n", - " [-0.5 , 0.8660254],\n", - " [ 0. , 1.732051 ],\n", - " [-1. , -1.732051 ],\n", - " [-0.5 , -0.8660254],\n", - " [ 0. , 0. ],\n", - " [ 0.5 , 0.8660254],\n", - " [ 1. , 1.732051 ],\n", - " [ 0. , -1.732051 ],\n", - " [ 0.5 , -0.8660254],\n", - " [ 1. , 0. ],\n", - " [ 1.5 , 0.8660254],\n", - " [ 2. , 1.732051 ],\n", - " [ 1. , -1.732051 ],\n", - " [ 1.5 , -0.8660254],\n", - " [ 2. , 0. ],\n", - " [ 2.5 , 0.8660254],\n", - " [ 3. , 1.732051 ]]), 'Z': array([[0., 0., 0.]])}" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hex2.grids" - ] - }, - { - "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "OrderedDict([('ycoord', array([0, 1, 2, 3, 4])),\n", - " ('xcoord', array([0, 1, 2, 3, 4]))])" + "('ycoord', 'xcoord')" ] }, - "execution_count": 28, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -919,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -929,17 +852,19 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -949,17 +874,19 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzAripG9mVhAnfTOzgjjpm5kV5DsEhvNSVaGdkgAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1020,7 +947,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/serpentTools/__init__.py b/serpentTools/__init__.py index 668ecb6..92591d3 100644 --- a/serpentTools/__init__.py +++ b/serpentTools/__init__.py @@ -1,4 +1,5 @@ # flake8: noqa +from serpentTools.detectors import * from serpentTools.parsers import * from serpentTools.messages import * from serpentTools.data import * diff --git a/serpentTools/_compat.py b/serpentTools/_compat.py index 3ba52a0..60de40d 100644 --- a/serpentTools/_compat.py +++ b/serpentTools/_compat.py @@ -8,6 +8,6 @@ Provided objects: import six if six.PY2: - from collections import Iterable, Callable + from collections import Iterable, Callable, Mapping else: - from collections.abc import Iterable, Callable + from collections.abc import Iterable, Callable, Mapping diff --git a/serpentTools/detectors.py b/serpentTools/detectors.py new file mode 100644 index 0000000..c8eb59d --- /dev/null +++ b/serpentTools/detectors.py @@ -0,0 +1,1500 @@ +""" +Module containing classes for storing detector data. + +``SERPENT`` is capable of producing detectors, or tallies from MCNP, +with a variety of bin structures. These include, but are not limited to, +material regions, reaction types, energy bins, spatial meshes, and +universe bins. + +The detectors contained in this module are tasked with storing such +data and proving easy access to the data, as well as the underlying +bins used to define the detector. + +Detector Types +-------------- + +* :class:`~serpentTools.objects.detectors.Detector` +* :class:`~serpentTools.objects.detectors.CartesianDetector` +* :class:`~serpentTools.objects.detectors.HexagonalDetector` +* :class:`~serpentTools.objects.detectors.CylindricalDetector` +* :class:`~serpentTools.objects.detectors.SphericalDetector` +""" + +from math import sqrt, pi +from warnings import warn +from numbers import Real + +from six import iteritems +from numpy import ( + unique, empty, inf, hstack, arange, log, divide, asfortranarray, + ndarray, asarray, +) +from matplotlib.patches import RegularPolygon +from matplotlib.collections import PatchCollection +from matplotlib.pyplot import gca + +from serpentTools.messages import ( + warning, SerpentToolsException, error, BAD_OBJ_SUBJ, + debug, +) +from serpentTools.objects.base import NamedObject +from serpentTools.plot import plot, cartMeshPlot +from serpentTools.utils import ( + magicPlotDocDecorator, formatPlot, setAx_xlims, setAx_ylims, + addColorbar, normalizerFactory, DETECTOR_PLOT_LABELS, + compareDictOfArrays, +) +from serpentTools.utils.compare import getLogOverlaps +from serpentTools.io.hooks import matlabHook +# PY2 compatibility +from serpentTools._compat import Mapping + +__all__ = ['Detector', 'CartesianDetector', 'HexagonalDetector', + 'CylindricalDetector', 'SphericalDetector'] + + +class Detector(NamedObject): + """Class for storing detector data with multiple bins + + For detectors with spatial meshes, including rectilinear, + hexagonal, cylindrical, or spherical meshes, refer to companion + classes :class:`serpentTools.CartesianDetector`, + :class:`serpentTools.HexagonalDetector`, + :class:`serpentTools.CylindricalDetector`, or + :class:`serpentTools.SphericalDetector` + + If simply the tally bins are available, it is recommended + to use the :meth:`fromTallyBins` class method. This will + reshape the data and separate the mean tally [second to last + column] and relative errors [last column]. + + Parameters + ---------- + name : str + Name of this detector + bins : numpy.ndarray, optional + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray, optional + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray, optional + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : iterable of string, optional + Iterable naming the bins that correspond to reshaped + :attr:`tally` and :attr:`errors`. + grids : dict, optional + Supplemental grids that may be supplied to this detector, + including energy points or spatial coordinates. + + Attributes + ---------- + name : str + Name of this detector + bins : numpy.ndarray or None + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray or float or None + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray or float or None + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : tuple or None + Iterable naming the bins that correspond to reshaped + :attr:`tally` and :attr:`errors`. The tuple + ``(energy, ymesh, xmesh)`` indicates that :attr:`tallies` + should have three dimensions corresponding to various + energy, y-position, and x-position bins. Must be set + after :attr:`tallies` or :attr:`errors` and agree with + the shape of each + grids : dict + Dictionary containing grid information for binned quantities like + energy or time. + energy : numpy.ndarray or None + Potential underlying energy grid in MeV. Will be ``(n_ene, 3)``, where + ``n_ene`` is the number of values in the energy grid. Each + row ``energy[j]`` will be the low point, high point, and mid point + of the energy bin ``j``. + + Raises + ------ + ValueError + If some spatial grid is found in ``indexes`` during creation. This + class is ill-suited for these problems. Refer to the companion + classes mentioned above. + IndexError + If the shapes of ``bins``, ``tallies``, and ``errors`` are inconsistent + """ + + DET_COLS = ( + 'value', 'time', 'energy', 'universe', 'cell', 'material', 'lattice', + 'reaction', 'zmesh', 'ymesh', 'xmesh', 'tally', 'error') + + _CBAR_LABELS = { + 'tallies': 'Tally data', + 'errors': 'Relative Uncertainty', + } + + def __init__(self, name, bins=None, tallies=None, errors=None, + indexes=None, grids=None): + NamedObject.__init__(self, name) + self._bins = None + self._tallies = None + self._errors = None + self.bins = bins + self.tallies = tallies + self.errors = errors + self._indexes = None + if indexes is not None: + self.indexes = indexes + self.grids = grids + + @property + def bins(self): + return self._bins + + @bins.setter + def bins(self, value): + if value is None: + self._bins = None + return + + # Coerce to numpy array, check shape + # Ensure data is ordered columnwise, and + # the array owns the underlying data + bins = asfortranarray(value).copy() + if len(bins.shape) != 2 or ( + len(bins.shape) == 2 and bins.shape[1] not in {12, 13}): + raise ValueError( + "Data does not appear to be Serpent 2 detector data. Shape " + "should be (N, 12) or (N, 13), is {}".format(bins.shape)) + + # Check that this is not a Serpent 1 detector + if bins[0, -3] != 1: + raise ValueError( + "Data does not appear to be Serpent 2 detector data. Appears " + "to have a scores column, indicated unsupported Serpent 1 " + "data: {}.".format(bins[0])) + + self._bins = bins + + @property + def tallies(self): + return self._tallies + + @tallies.setter + def tallies(self, tallies): + if tallies is None: + self._tallies = tallies + return + + if not isinstance(tallies, (Real, ndarray)): + raise TypeError("Tallies must be array or scalar, not {}".format( + type(tallies))) + + if self._tallies is None: + self._tallies = tallies + return + + # Handle scalar / single values arrays + # TODO Kind of clunky. Maybe a dedicated ScalarDetector? + + if isinstance(tallies, Real): + if isinstance(self._tallies, Real): + self._tallies = tallies + else: + raise TypeError("Tallies are current array, not scalar") + else: + if isinstance(self._tallies, Real): + raise TypeError("Tallies are currently scalar, nor array") + if tallies.shape != self._tallies.shape: + raise IndexError( + "Shape of tally data is not consistent. Should be {}, " + "is {}".format(self._tallies.shape, tallies.shape)) + self._tallies = tallies + + @property + def errors(self): + return self._errors + + @errors.setter + def errors(self, errors): + if errors is None: + self._errors = errors + return + + if not isinstance(errors, (Real, ndarray)): + raise TypeError( + "Tallies must be array or scalar, not {}".format(type(errors))) + + if self._errors is None: + self._errors = errors + return + + # Handle scalar / single values arrays + # TODO Kind of clunky. Maybe a dedicated ScalarDetector? + + if isinstance(errors, Real): + if isinstance(self._errors, Real): + self._errors = errors + else: + raise TypeError("Tallies are current array, not scalar") + else: + if isinstance(self._errors, Real): + raise TypeError("Tallies are currently scalar, nor array") + if errors.shape != self._errors.shape: + raise IndexError( + "Shape of tally data is not consistent. Should be {}, " + "is {}".format(self._errors.shape, errors.shape)) + self._errors = errors + + @property + def energy(self): + return self.grids.get("E") + + @property + def grids(self): + return self._grids + + @grids.setter + def grids(self, grids): + if grids is None: + self._grids = {} + else: + if not isinstance(grids, Mapping): + raise TypeError( + "Grids must be Mapping type, not {}".format(type(grids))) + self._grids = grids + + @property + def indexes(self): + return self._indexes + + @indexes.setter + def indexes(self, ix): + if self._tallies is None: + if self._errors is None: + raise AttributeError("Tally and error attributes not set") + nItems = len(self._errors.shape) + else: + nItems = len(self._tallies.shape) + if len(ix) != nItems: + raise ValueError( + "Expected {} items for indexes, got {}".format( + nItems, len(ix))) + self._indexes = tuple(ix) + + @classmethod + def fromTallyBins(cls, name, bins, grids=None): + """Create a detector instance from 2D detector data + + Parameters + ---------- + name : str + Name of this detector + bins : numpy.ndarray + 2D array taken from Serpent. Expected to have + either 12 or 13 columns, where the latter indicates + a time bin has been added. + grids : dict, optional + Dictionary of underlying energy, space, and/or time data. + + Returns + ------- + Detector + + Raises + ------ + ValueError + If the tally data does not appear to be Serpent 2 tally data + + """ + bins = asfortranarray(bins) + if len(bins.shape) != 2: + raise ValueError( + "Array does not appear to be Serpent tally data. Shape is {}, " + "should be 2D".format(bins.shape)) + + if bins.shape[1] not in (12, 13): + raise ValueError( + "Array does not appear to be Serpent tally data. Expected 12 " + " or 13 columns, not {}".format(bins.shape[1])) + + if grids is None: + grids = {} + elif not isinstance(grids, Mapping): + raise TypeError("Grid data is not dictionary-like") + + det = cls(name, bins=bins, grids=grids) + + tallies, errors, indexes = det.reshapedBins() + + det.tallies = tallies + det.errors = errors + det.indexes = indexes + + return det + + def reshapedBins(self): + """Obtain multi-dimensional tally, error, and index data + + Returns + ------- + tallies : numpy.ndarray + Potentially multi-dimensional array corresponding to + tally data along each bin index + errors : numpy.ndarray + Potentially multi-dimensional array corresponding to + tally relative error along each bin index + indexes : list of str + Ordering of named bin information, e.g. ``"xmesh"``, + ``"energy"``, corresponding to axis in ``tallies`` and ``errors`` + + Examples + -------- + + A detector is created with a single bin with two bins values. + These could represent tallying two different reaction rates + + >>> import numpy + >>> from serpentTools import Detector + >>> bins = numpy.ones((2, 12)) + >>> bins[1, 0] = 2 + >>> bins[1, 4] = 2 + >>> bins[:, -2:] = [ + ... [5.0, 0.1], + ... [10.0, 0.2]] + >>> det = Detector("reshape", bins=bins) + >>> tallies, errors, indexes = det.reshapedBins() + >>> tallies + array([5.0, 10.0]) + >>> errors + array([0.1, 0.2]) + >>> indexes + ["reaction", ] + + """ + assert self.bins is not None, "No bin data present on {}".format(self) + + if self.bins.shape[0] == 1: + return self.bins[0, -2], self.bins[0, -1], {} + + shape = [] + indexes = [] + + # See if the time column has been inserted + nameStart = 2 if self.bins.shape[1] == 12 else 1 + for colIx, indexName in enumerate(self.DET_COLS[nameStart:-2], + start=1): + uniqueVals = unique(self.bins[:, colIx]) + if len(uniqueVals) > 1: + indexes.append(indexName) + shape.append(len(uniqueVals)) + + tallies = self.bins[:, -2].reshape(shape) + errors = self.bins[:, -1].reshape(shape) + + return tallies, errors, indexes + + def slice(self, fixed, data='tallies'): + """ + Return a view of the reshaped array where certain axes are fixed + + Parameters + ---------- + fixed: dict + dictionary to aid in the restriction on the multidimensional + array. Keys correspond to the various grids present in + :attr:`indexes` while the values are used to + data: {'tallies', 'errors'} + Which data set to slice + + Returns + ------- + :class:`numpy.ndarray` + View into the respective data where certain dimensions + have been removed + + Raises + ------ + AttributeError + If ``data`` is not supported + + """ + if data not in {"tallies", "errors"}: + raise AttributeError( + 'Data argument {} not in allowed options' + '\ntallies, errors') + work = getattr(self, data) + if work is None: + raise AttributeError("{} not setup on {}".format(data, self)) + if not fixed: + return work + return work[self._getSlices(fixed)] + + def _getSlices(self, fixed): + """ + Return a list of slice operators for each axis in reshaped data + + Parameters + ---------- + fixed: dict or None + Dictionary where keys are strings pointing to dimensions in + """ + keys = set(fixed) + slices = tuple() + for key in self.indexes: + if key in keys: + slices += fixed[key], + keys.remove(key) + else: + slices += slice(None), + if any(keys): + warning( + 'Could not find arguments in index that match the following' + ' requested slice keys: {}'.format(', '.join(keys))) + return slices + + def _getPlotGrid(self, qty): + grids = self.grids.get(qty[0].upper()) + if grids is not None: + return hstack((grids[:, 0], grids[-1, 1])) + nItems = self.tallies.shape[self.indexes.index(qty)] + return arange(nItems + 1) + + def _getPlotXData(self, qty, ydata): + binIndex = arange(len(ydata)) + xlabel = DETECTOR_PLOT_LABELS.get(qty, 'Bin Index') + if qty is None: + return binIndex, xlabel + xdata = self.grids.get(qty[0].upper()) + if xdata is not None: + return xdata[:, 0], xlabel + return binIndex, xlabel + + def _compare(self, other, lower, upper, sigma): + myShape = self.tallies.shape + otherShape = other.tallies.shape + if myShape != otherShape: + error("Detector tallies do not have identical shapes" + + BAD_OBJ_SUBJ.format('tallies', myShape, otherShape)) + return False + similar = compareDictOfArrays(self.grids, other.grids, 'grids', + lower=lower, upper=upper) + + similar &= getLogOverlaps('tallies', self.tallies, other.tallies, + self.errors, other.errors, sigma, + relative=True) + return similar + + @magicPlotDocDecorator + def spectrumPlot(self, fixed=None, ax=None, normalize=True, xlabel=None, + ylabel=None, steps=True, logx=True, logy=False, + loglog=False, sigma=3, labels=None, legend=None, ncol=1, + title=None, *kwargs): + """ + Quick plot of the detector value as a function of energy. + + Parameters + ---------- + fixed: None or dict + Dictionary controlling the reduction in data + {ax} + normalize: bool + Normalize quantities per unit lethargy + {xlabel} + {ylabel} + steps: bool + Plot tally as constant inside bin + {logx} + {logy} + {loglog} + {sigma} + {labels} + {legend} + {ncol} + {title} + {kwargs} :py:func:`matplotlib.pyplot.plot` or + :py:func:`matplotlib.pyplot.errorbar` + + Returns + ------- + {rax} + + Raises + ------ + :class:`~serpentTools.SerpentToolsException` + if number of rows in data not equal to + number of energy groups + + See Also + -------- + :meth:`slice` + """ + slicedTallies = self.slice(fixed, 'tallies').copy() + if len(slicedTallies.shape) > 2: + raise SerpentToolsException( + 'Sliced data cannot exceed 2-D for spectrum plot, not ' + '{}'.format(slicedTallies.shape) + ) + elif len(slicedTallies.shape) == 1: + slicedTallies = slicedTallies.reshape(slicedTallies.size, 1) + lowerE = self.grids['E'][:, 0] + if normalize: + lethBins = log( + divide(self.grids['E'][:, -1], lowerE)) + for indx in range(slicedTallies.shape[1]): + scratch = divide(slicedTallies[:, indx], lethBins) + slicedTallies[:, indx] = scratch / scratch.max() + + if steps: + if 'drawstyle' in kwargs: + debug('Defaulting to drawstyle specified in kwargs as {}' + .format(kwargs['drawstyle'])) + else: + kwargs['drawstyle'] = 'steps-post' + + if sigma: + slicedErrors = sigma * self.slice(fixed, 'errors').copy() + slicedErrors = slicedErrors.reshape(slicedTallies.shape) + slicedErrors = slicedTallies + else: + slicedErrors = None + ax = plot(lowerE, slicedTallies, ax=ax, labels=labels, + yerr=slicedErrors, kwargs) + if ylabel is None: + ylabel = 'Tally data' + ylabel += ' normalized per unit lethargy' if normalize else '' + ylabel += r' $\pm{}\sigma$'.format(sigma) if sigma else '' + + if legend is None and labels: + legend = True + ax = formatPlot(ax, loglog=loglog, logx=logx, ncol=ncol, + xlabel=xlabel or "Energy [MeV]", ylabel=ylabel, + legend=legend, title=title) + return ax + + @magicPlotDocDecorator + def plot(self, xdim=None, what='tallies', sigma=None, fixed=None, ax=None, + xlabel=None, ylabel=None, steps=False, labels=None, logx=False, + logy=False, loglog=False, legend=None, ncol=1, title=None, + kwargs): + """ + Simple plot routine for 1- or 2-D data + + Parameters + ---------- + xdim: str, optional + Plot the data corresponding to changing this bin, + e.g. ``"energy"``. Must exist in :attr:`indexes` + what: {'tallies', 'errors'} + Primary data to plot + {sigma} + fixed: None or dict + Dictionary controlling the reduction in data down to one dimension + {ax} + {xlabel} If ``xdim`` is given and ``xlabel`` is ``None``, then + ``xdim`` will be applied to the x-axis. + {ylabel} + steps: bool + If true, plot the data as constant inside the respective bins. + Sets ``drawstyle`` to be ``steps-post`` unless ``drawstyle`` + given in ``kwargs`` + {labels} + {logx} + {logy} + {loglog} + {legend} + {ncol} + {title} + {kwargs} :py:func:`~matplotlib.pyplot.plot` or + :py:func:`~matplotlib.pyplot.errorbar` function. + + Returns + ------- + {rax} + + Raises + ------ + :class:`~serpentTools.SerpentToolsException` + If data contains more than 2 dimensions + AttributeError + If plot data or :attr:`indexes` set up. + + See Also + -------- + :meth:`slice` + * :meth:`spectrumPlot` + better options for plotting energy spectra + """ + + if xdim is not None and fixed and self.indexes is None: + raise AttributeError( + "indexes not setup. Unsure how to slice and plot") + + if sigma and what == "errors": + raise ValueError("Refusing to plot error bars on uncertainties") + + data = self.slice(fixed, what) + if len(data.shape) > 2: + raise SerpentToolsException( + 'Data must be constrained to 1- or 2-D, not {}' + .format(data.shape)) + elif len(data.shape) == 1: + data = data.reshape(data.size, 1) + + if sigma: + yerr = (self.slice(fixed, 'errors').reshape(data.shape) + * data * sigma) + else: + yerr = None + + xdata, autoX = self._getPlotXData(xdim, data) + xlabel = xlabel or autoX + ylabel = ylabel or "Tally data" + ax = ax or gca() + + if steps: + kwargs.setdefault("drawstyle", "steps-post") + + ax = plot(xdata, data, ax, labels, yerr, kwargs) + + if legend is None and labels: + legend = True + + ax = formatPlot(ax, loglog=loglog, logx=logx, logy=logy, ncol=ncol, + xlabel=xlabel, ylabel=ylabel, legend=legend, + title=title) + return ax + + @magicPlotDocDecorator + def meshPlot(self, xdim, ydim, what='tallies', fixed=None, ax=None, + cmap=None, cbarLabel=None, logColor=False, xlabel=None, + ylabel=None, logx=False, logy=False, loglog=False, + title=None, thresh=None, kwargs): + """ + Plot tally data as a function of two bin types on a cartesian mesh. + + Parameters + ---------- + xdim: str + Primary dimension - will correspond to x-axis on plot + ydim: str + Secondary dimension - will correspond to y-axis on plot + what: {'tallies', 'errors'} + Color meshes from tally data or uncertainties + fixed: None or dict + Dictionary controlling the reduction in data down to one dimension + {ax} + {cmap} + logColor: bool + If true, apply a logarithmic coloring to the data positive + data + {xlabel} + {ylabel} + {logx} + {logy} + {loglog} + {title} + {mthresh} + cbarLabel: str + Label to apply to colorbar. If not given, infer from ``what`` + {kwargs} :py:func:`~matplotlib.pyplot.pcolormesh` + + Returns + ------- + {rax} + + Raises + ------ + :class:`serpentTools.SerpentToolsException` + If data to be plotted, with or without constraints, is not 1D + KeyError + If ``fixed`` is given and ``xdim`` or ``ydim`` are contained + in ``fixed`` + AttributeError + If the data set by ``what`` not in the allowed selection + ValueError + If the data contains negative quantities and ``logColor`` is + ``True`` + + See Also + -------- + * :meth:`slice` + * :func:`matplotlib.pyplot.pcolormesh` + """ + if fixed: + if xdim in fixed: + raise KeyError("Cannot constrain {} and use as x data for " + "plot".format(xdim)) + if ydim in fixed: + raise KeyError("Cannot constrain {} and use as y data for " + "plot".format(ydim)) + data = self.slice(fixed, what) + if len(data.shape) != 2: + raise SerpentToolsException( + 'Data must be 2D for mesh plot, currently is {}.\nConstraints:' + '{}'.format(data.shape, fixed) + ) + xgrid = self._getPlotGrid(xdim) + ygrid = self._getPlotGrid(ydim) + if data.shape != (ygrid.size - 1, xgrid.size - 1): + data = data.T + if cbarLabel is None: + cbarLabel = self._CBAR_LABELS[what] + ax = cartMeshPlot( + data, xgrid, ygrid, ax, cmap, logColor, + cbarLabel=cbarLabel, thresh=thresh, *kwargs) + if xlabel is None: + xlabel = DETECTOR_PLOT_LABELS.get(xdim, xdim) + if ylabel is None: + ylabel = DETECTOR_PLOT_LABELS.get(ydim, ydim) + ax = formatPlot(ax, loglog=loglog, logx=logx, logy=logy, + xlabel=xlabel, ylabel=ylabel, + title=title) + return ax + + def _gather_matlab(self, reconvert): + """Gather bins and grids for exporting to matlab""" + + converter = deconvert if reconvert else prepToMatlab + + data = { + converter(self.name, 'bins'): self.bins, + } + + for key, value in iteritems(self.grids): + data[converter(self.name, key)] = value + + return data + + +Detector.toMatlab = matlabHook + + +class CartesianDetector(Detector): + """Class for storing detector data with Cartesian grid + + If simply the tally bins are available, it is recommended + to use the :meth:`fromTallyBins` class method. This will + reshape the data and separate the mean tally [second to last + column] and relative errors [last column]. + + .. note:: + + In order to get full functionality from this class, + :attr:`x`, :attr:`y`, and :attr:`z` must be set. + All grids are expected to be ``(N, 3)`` arrays, not + necessarily of equal shape. + + Parameters + ---------- + name : str + Name of this detector + bins : numpy.ndarray + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : dict or None + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}``. Optional + x : numpy.ndarray, optional + Directly set the x grid + y : numpy.ndarray, optional + Directly set the y grid + z : numpy.ndarray, optional + Directly set the z grid + grids : dict, optional + Supplemental grids that may be supplied to this detector, + including energy points or spatial coordinates. If spatial + grids are not provided directly with the ``x``, ``y``, or + ``z`` arguments, the grids will be pulled from this dictionary + in the following manner. + + Key ``"X"`` denotes the :attr:`x` grid + * Key ``"Y"`` denotes the :attr:`y` grid + * Key ``"Z"`` denotes the :attr:`z` grid + + If the keys are not present, or the grids are directly provided, + no actions are taken. + + Attributes + ---------- + name : str + Name of this detector + bins : numpy.ndarray or None + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray or None + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray or None + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : dict + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}``. + energy : numpy.ndarray or None + Potential underlying energy grid in MeV. Will be ``(n_ene, 3)``, where + ``n_ene`` is the number of values in the energy grid. Each + row ``energy[j]`` will be the low point, high point, and mid point + of the energy bin ``j``. + x : numpy.ndarray or None + X grid + y : numpy.ndarray or None + Y grid + z : numpy.ndarray or None + Z grid + + Raises + ------ + ValueError + If the values for ``x``, ``y``, and ``z`` are not 2D arrays + with 3 columns + """ + + def __init__(self, name, bins=None, tallies=None, errors=None, + indexes=None, grids=None, x=None, y=None, z=None): + Detector.__init__(self, name, bins, tallies, errors, indexes, grids) + self._x = None + self._y = None + self._z = None + x = x if x is not None else self.grids.get("X") + y = y if y is not None else self.grids.get("Y") + z = z if z is not None else self.grids.get("Z") + if x is not None: + self.x = x + if y is not None: + self.y = y + if z is not None: + self.z = z + + @property + def x(self): + return self._x + + @x.setter + def x(self, value): + value = asfortranarray(value) + if self._x is None: + if len(value.shape) != 2 or value.shape[1] != 3: + raise ValueError( + "Expected shape of x grid to be (N, 3), not {}".format( + value.shape)) + elif value.shape != self._x.shape: + raise ValueError( + "Expected shape of x grid to be {}, not {}".format( + value.shape, self.x.shape)) + self._x = value + + @property + def y(self): + return self._y + + @y.setter + def y(self, value): + value = asfortranarray(value) + if self._y is None: + if len(value.shape) != 2 or value.shape[1] != 3: + raise ValueError( + "Expected shape of y grid to be (N, 3), not {}".format( + value.shape)) + elif value.shape != self._y.shape: + raise ValueError( + "Expected shape of y grid to be {}, not {}".format( + value.shape, self.y.shape)) + self._y = value + + @property + def z(self): + return self._z + + @z.setter + def z(self, value): + value = asfortranarray(value) + if self._z is None: + if len(value.shape) != 2 or value.shape[1] != 3: + raise ValueError( + "Expected shape of z grid to be (N, 3), not {}".format( + value.shape)) + elif value.shape != self._z.shape: + raise ValueError( + "Expected shape of z grid to be {}, not {}".format( + value.shape, self.z.shape)) + self._z = value + + @magicPlotDocDecorator + def meshPlot(self, xdim='x', ydim='y', what='tallies', fixed=None, ax=None, + cmap=None, cbarLabel=None, logColor=False, xlabel=None, + ylabel=None, logx=False, logy=False, loglog=False, + title=None, thresh=None, *kwargs): + """ + Plot tally data as a function of two bin types on a cartesian mesh. + + Parameters + ---------- + xdim: str + Primary dimension - will correspond to x-axis on plot. Defaults + to "x" + ydim: str + Secondary dimension - will correspond to y-axis on plot. Defaults + to "y" + what: {'tallies', 'errors'} + Color meshes from tally data or uncertainties + fixed: None or dict + Dictionary controlling the reduction in data down to one dimension + {ax} + {cmap} + logColor: bool + If true, apply a logarithmic coloring to the data positive + data + {xlabel} + {ylabel} + {logx} + {logy} + {loglog} + {title} + {mthresh} + cbarLabel: str + Label to apply to colorbar. If not given, infer from ``what`` + {kwargs} :py:func:`~matplotlib.pyplot.pcolormesh` + + Returns + ------- + {rax} + + Raises + ------ + :class:`serpentTools.SerpentToolsException` + If data to be plotted, with or without constraints, is not 1D + KeyError + If ``fixed`` is given and ``xdim`` or ``ydim`` are contained + in ``fixed`` + AttributeError + If the data set by ``what`` not in the allowed selection + ValueError + If the data contains negative quantities and ``logColor`` is + ``True`` + + See Also + -------- + :meth:`slice` + * :func:`matplotlib.pyplot.pcolormesh` + """ + Detector.meshPlot( + self, xdim=xdim, ydim=ydim, what=what, fixed=fixed, ax=ax, + cmap=cmap, cbarLabel=cbarLabel, logColor=logColor, xlabel=xlabel, + ylabel=ylabel, logx=logx, logy=logy, loglog=loglog, + title=title, thresh=thresh, kwargs) + + def _getPlotGrid(self, qty): + if qty is not None and hasattr(self, qty[0]): + grid = getattr(self, qty[0]) + if grid is not None: + return hstack((grid[:, 0], grid[-1, 1])) + raise AttributeError("{} not set".format(qty[0], stacklevel=2)) + return Detector._getPlotGrid(self, qty) + + def _getPlotXData(self, qty, ydata): + if qty is not None and hasattr(self, qty[0]): + grid = getattr(self, qty[0]) + if grid is not None: + return grid[:, 0], DETECTOR_PLOT_LABELS.get(qty, "Bin Index") + raise AttributeError("{} not set".format(qty[0], stacklevel=2)) + return Detector._getPlotXData(self, qty, ydata) + + +class HexagonalDetector(Detector): + """Class for storing detector data with a hexagonal grid + + If simply the tally bins are available, it is recommended + to use the :meth:`fromTallyBins` class method. This will + reshape the data and separate the mean tally [second to last + column] and relative errors [last column]. + + .. note:: + + In order to get full functionality from this class, + :attr:`z`, :attr:`centers`, :attr:`pitch`, and :attr:`hexType` + must be set. This can be done by having ``"Z"`` and + ``"COORDS"`` as keys in ``grids`` and passing ``pitch`` and + ``hexType`` directly, or by setting the attributes directly. + Z grid is expected to be ``(N, 3)`` array. + + Parameters + ---------- + name : str + Name of this detector + bins : numpy.ndarray, optional + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray, optional + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray, optional + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : dict, optional + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}`` + centers : numpy.ndarray, optional + ``(N, 2)`` array with centers of each hexagonal element + pitch : float, optional + Center to center distance between adjacent hexagons + hexType : {2, 3}, optional + Integer orientation, identical to Serpent detector structure. 2 + corresponds to a hexagon with flat faces perpendicular to y-axis. + 3 corresponds to a flat faces perpendicular to x-axis + grids : dict, optional + Supplemental grids that may be supplied to this detector, + including energy points or spatial coordinates. + + Attributes + ---------- + name : str + Name of this detector + bins : numpy.ndarray or None + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray or None + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + pitch : float or None + Center to center distance between hexagons. Must be set before + calling :meth:`hexPlot` + hexType : {2, 3} or None + Integer orientation, identical to Serpent detector structure. 2 + corresponds to a hexagon with flat faces perpendicular to y-axis. + 3 corresponds to a flat faces perpendicular to x-axis + errors : numpy.ndarray or None + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : dict or None + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}``. + energy : numpy.ndarray or None + Potential underlying energy grid in MeV. Will be ``(n_ene, 3)``, where + ``n_ene`` is the number of values in the energy grid. Each + row ``energy[j]`` will be the low point, high point, and mid point + of the energy bin ``j``. + coords: numpy.ndarray or None + Centers of hexagonal meshes in XY plane + z : numpy.ndarray or None + Z Grid + + """ + + DET_COLS = ( + 'value', 'time', 'energy', 'universe', 'cell', 'material', 'lattice', + 'reaction', 'zmesh', 'ycoord', 'xcoord', 'tally', 'error') + + def __init__(self, name, bins=None, tallies=None, errors=None, + indexes=None, z=None, centers=None, pitch=None, hexType=None, + grids=None): + + Detector.__init__(self, name, bins, tallies, errors, indexes, grids) + + if pitch is not None: + self.pitch = pitch + else: + self._pitch = None + + if hexType is not None: + self.hexType = hexType + else: + self._hexType = None + + self._z = None + self._centers = None + + z = z if z is not None else self.grids.get("Z") + if z is not None: + self.z = z + + centers = centers if centers is not None else self.grids.get("COORD") + if centers is not None: + self.centers = centers + + @property + def z(self): + return self._z + + @z.setter + def z(self, value): + value = asfortranarray(value) + if self._z is None: + if len(value.shape) != 2 or value.shape[1] != 3: + raise ValueError( + "Expected shape of z grid to be (N, 3), not {}".format( + value.shape)) + elif value.shape != self._z.shape: + raise ValueError( + "Expected shape of z grid to be {}, not {}".format( + value.shape, self.z.shape)) + self._z = value + + @property + def pitch(self): + return self._pitch + + @pitch.setter + def pitch(self, value): + if isinstance(value, Real): + if value <= 0: + raise ValueError("Pitch must be positive") + else: + raise TypeError( + "Cannot set pitch to {}. Must be positive value".format( + type(value))) + self._pitch = value + + @property + def hexType(self): + return self._hexType + + @hexType.setter + def hexType(self, value): + if value not in {2, 3}: + raise ValueError( + "Hex type must be 2 or 3, not {}".format(value)) + self._hexType = value + + @property + def centers(self): + return self._centers + + @centers.setter + def centers(self, value): + value = asarray(value) + + if self._centers is None: + if len(value.shape) != 2 or value.shape[1] != 2: + raise ValueError( + "Expected centers to have shape (N, 2), got {}".format( + value.shape)) + else: + if value.shape != self._centers.shape: + raise ValueError( + "Expected centers to have shape {}, got {}".format( + self._centers.shape, value.shape)) + self._centers = value + + def meshPlot(self, xdim, ydim, what='tallies', fixed=None, ax=None, + cmap=None, logColor=False, xlabel=None, ylabel=None, + logx=False, logy=False, loglog=False, title=None, kwargs): + opts = dict(what=what, + fixed=fixed, + ax=ax, + cmap=cmap, + logColor=logColor, + xlabel=xlabel, + logx=logx, + logy=logy, + loglog=loglog, + title=title, + kwargs) + if any(arg in {"ycoord", "xcoord"} for arg in (xdim, ydim)): + warn("Use hexPlot if plotting xcoord vs ycoord") + return self.hexPlot(opts) + return Detector.meshPlot(self, xdim, ydim, opts) + + meshPlot.__doc__ = Detector.meshPlot.__doc__ + + @magicPlotDocDecorator + def hexPlot(self, what='tallies', fixed=None, ax=None, cmap=None, + logColor=False, xlabel=None, ylabel=None, logx=False, + logy=False, loglog=False, title=None, normalizer=None, + cbarLabel=None, borderpad=2.5, kwargs): + """ + Create and return a hexagonal mesh plot. + + Parameters + ---------- + what: {'tallies', 'errors'} + Quantity to plot + fixed: None or dict + Dictionary of slicing arguments to pass to :meth:`slice` + {ax} + {cmap} + {logColor} + {xlabel} + {ylabel} + {logx} + {logy} + {loglog} + {title} + borderpad: int or float + Percentage of total plot to apply as a border. A value of + zero means that the extreme edges of the hexagons will touch + the x and y axis. + {kwargs} :class:`matplotlib.patches.RegularPolygon` + + Raises + ------ + AttributeError + If :attr:`pitch` and :attr:`hexType` are not set. + """ + if self.centers is None or self.pitch is None or self.hexType is None: + raise AttributeError("centers, pitch, and hexType not set") + + if fixed and ('xcoord' in fixed or 'ycoord' in fixed): + raise KeyError("Refusing to restrict along one of the hexagonal " + "dimensions {x/y}coord") + + if not isinstance(borderpad, Real) or borderpad < 0: + raise ValueError( + "borderpad should be postive, not {}".format(borderpad)) + + kwargs.setdefault("ec", "k") + kwargs.setdefault("edgecolor", "k") + alpha = kwargs.get('alpha') + + data = self.slice(fixed, what) + + ny = getattr(self, what).shape[self.indexes.index("ycoord")] + nx = getattr(self, what).shape[self.indexes.index("xcoord")] + + if data.shape != (ny, nx): + raise IndexError("Constrained data does not agree with hexagonal " + "grid structure. Coordinate grid: {}. " + "Constrained shape: {}" + .format((ny, nx), data.shape)) + + patches = empty(ny * nx, dtype=object) + values = empty(ny * nx) + coords = self.centers + + ax = ax or gca() + xmax, ymax = [-inf, ] * 2 + xmin, ymin = [inf, ] * 2 + radius = self.pitch / sqrt(3) + rotation = 0 if self.hexType == 2 else (pi / 2) + + for pos, (xy, val) in enumerate(zip(coords, data.flat)): + values[pos] = val + h = RegularPolygon(xy, 6, radius, rotation, kwargs) + verts = h.get_verts() + vmins = verts.min(0) + vmaxs = verts.max(0) + xmax = max(xmax, vmaxs[0]) + xmin = min(xmin, vmins[0]) + ymax = max(ymax, vmaxs[1]) + ymin = min(ymin, vmins[1]) + patches[pos] = h + + normalizer = normalizerFactory(values, normalizer, logColor, + coords[:, 0], coords[:, 1]) + pc = PatchCollection(patches, cmap=cmap, alpha=alpha) + pc.set_array(values) + pc.set_norm(normalizer) + ax.add_collection(pc) + + addColorbar(ax, pc, None, cbarLabel) + + formatPlot(ax, loglog=loglog, logx=logx, logy=logy, + xlabel=xlabel or "X [cm]", + ylabel=ylabel or "Y [cm]", title=title) + + setAx_xlims(ax, xmin, xmax, pad=borderpad) + setAx_ylims(ax, ymin, ymax, pad=borderpad) + + return ax + + +class CylindricalDetector(Detector): + """Class for storing detector data with a cylindrical mesh + + If simply the tally bins are available, it is recommended + to use the :meth:`fromTallyBins` class method. This will + reshape the data and separate the mean tally [second to last + column] and relative errors [last column]. + + Parameters + ---------- + name : str + Name of this detector + bins : numpy.ndarray, optional + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray, optional + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray, optional + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : collections.OrderedDict, optinal + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}`` + grids : dict, optional + Supplemental grids that may be supplied to this detector, + including energy points or spatial coordinates. + + Attributes + ---------- + name : str + Name of this detector + bins : numpy.ndarray + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : collections.OrderedDict + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}``. + energy : numpy.ndarray or None + Potential underlying energy grid in MeV. Will be ``(n_ene, 3)``, where + ``n_ene`` is the number of values in the energy grid. Each + row ``energy[j]`` will be the low point, high point, and mid point + of the energy bin ``j``. + + """ + + # column indices in full (time-bin included) bins matrix + DET_COLS = ( + 'value', 'time', 'energy', 'universe', 'cell', 'material', 'lattice', + 'reaction', 'zmesh', 'phi', 'rmesh', 'tally', 'error') + + def meshPlot(self, xdim, ydim, what='tallies', fixed=None, ax=None, + cmap=None, logColor=False, xlabel=None, ylabel=None, + logx=False, logy=False, loglog=False, title=None, kwargs): + if any(arg in {"rmesh", "phi"} for arg in (xdim, ydim)): + warn("Cylindrical plotting is not fully supported. See GitHub " + "issue 169") + return Detector.meshPlot( + self, xdim, ydim, what=what, fixed=fixed, ax=ax, cmap=cmap, + logColor=logColor, xlabel=xlabel, logx=logx, logy=logy, + loglog=loglog, title=title, kwargs) + + meshPlot.__doc__ = Detector.meshPlot.__doc__ + + +class SphericalDetector(Detector): + """Class for storing detector data with multiple bins + + If simply the tally bins are available, it is recommended + to use the :meth:`fromTallyBins` class method. This will + reshape the data and separate the mean tally [second to last + column] and relative errors [last column]. + + Parameters + ---------- + name : str + Name of this detector + bins : numpy.ndarray + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : dict + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}``. Optional + grids : dict + Supplemental grids that may be supplied to this detector, + including energy points or spatial coordinates. + + Attributes + ---------- + name : str + Name of this detector + bins : numpy.ndarray + Full 2D tally data from detector file, including tallies and + errors in last two columns + tallies : numpy.ndarray + Reshaped tally data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. + errors : numpy.ndarray + Reshaped error data such that each dimension corresponds + to a unique bin, such as energy or spatial bin. Note: + this is a relative error as it would appear in the + output file + indexes : dict + Dictionary mapping the bin name to its corresponding + axis in :attr:`tallies` and :attr:`errors`, e.g. + ``{"energy": 0}``. + energy : numpy.ndarray or None + Potential underlying energy grid in MeV. Will be ``(n_ene, 3)``, where + ``n_ene`` is the number of values in the energy grid. Each + row ``energy[j]`` will be the low point, high point, and mid point + of the energy bin ``j``. + """ + + DET_COLS = ( + 'value', 'time', 'energy', 'universe', 'cell', 'material', 'lattice', + 'reaction', 'theta', 'phi', 'rmesh', 'tally', 'error') + + def meshPlot(self, xdim, ydim, what='tallies', fixed=None, ax=None, + cmap=None, logColor=False, xlabel=None, ylabel=None, + logx=False, logy=False, loglog=False, title=None, kwargs): + if any(arg in {"theta", "phi", "rmesh"} for arg in (xdim, ydim)): + warn("Spherical mesh plotting is not fully supported.") + return Detector.meshPlot( + self, xdim, ydim, what=what, fixed=fixed, ax=ax, cmap=cmap, + logColor=logColor, xlabel=xlabel, logx=logx, logy=logy, + loglog=loglog, title=title, *kwargs) + + meshPlot.__doc__ = Detector.meshPlot.__doc__ + + +def detectorFactory(name, bins, grids=None): + grids = {} if grids is None else grids + if "X" in grids: + detClass = CartesianDetector + elif "COORD" in grids: + detClass = HexagonalDetector + elif "R" in grids: + detClass = CylindricalDetector if "Z" in grids else SphericalDetector + else: + # fall back to base detector + detClass = Detector + return detClass.fromTallyBins(name, bins, grids) + + +def deconvert(detectorName, quantity): + """Restore the original name of the detector data""" + return "DET{}{}".format( + detectorName, "" if quantity == "bins" else quantity) + + +def prepToMatlab(detectorName, quantity): + """Create a name of a variable for MATLAB""" + return detectorName + "_" + quantity diff --git a/serpentTools/objects/__init__.py b/serpentTools/objects/__init__.py index 10e3fb5..1cd0130 100644 --- a/serpentTools/objects/__init__.py +++ b/serpentTools/objects/__init__.py @@ -2,5 +2,4 @@ # flake8: noqa from serpentTools.objects.materials import from serpentTools.objects.containers import * -from serpentTools.objects.detectors import * from serpentTools.objects.xsdata import * diff --git a/serpentTools/objects/base.py b/serpentTools/objects/base.py index 66116f7..414d934 100644 --- a/serpentTools/objects/base.py +++ b/serpentTools/objects/base.py @@ -1,27 +1,16 @@ """ Collection of base classes from which other objects inherit. """ -from abc import ABCMeta, abstractmethod - -from six import add_metaclass - -from numpy import arange, hstack, log, divide -from matplotlib.pyplot import gca from serpentTools.messages import ( - debug, warning, SerpentToolsException, info, - error, - BAD_OBJ_SUBJ, - + warning, info, ) -from serpentTools.plot import plot, cartMeshPlot from serpentTools.utils import ( - magicPlotDocDecorator, formatPlot, DETECTOR_PLOT_LABELS, compareDocDecorator, DEF_COMP_LOWER, DEF_COMP_SIGMA, - DEF_COMP_UPPER, compareDictOfArrays, + DEF_COMP_UPPER ) from serpentTools.utils.compare import ( - getLogOverlaps, finalCompareMsg, + finalCompareMsg, ) from serpentTools.settings import rc @@ -143,452 +132,8 @@ class NamedObject(BaseObject): """Class for named objects like materials and detectors.""" def __init__(self, name): + BaseObject.__init__(self) self.name = name def __str__(self): return '<{} {}>'.format(self.__class__.__name__, self.name) - - -@add_metaclass(ABCMeta) -class DetectorBase(NamedObject): - """ - Base class for classes that store detector data - - Parameters - ---------- - {params:s} - - Attributes - ---------- - {attrs:s} - """ - - _CBAR_LABELS = { - 'tallies': 'Tally data', - 'errors': 'Relative Uncertainty', - 'scores': 'Tally scores', - } - - baseParams = """name: str - Name of this detector""" - baseAttrs = """grids: dict - Dictionary with additional data describing energy grids or mesh points - tallies: None, float, or {np} - Reshaped tally data to correspond to the bins used - errors: None, float, or {np} - Reshaped relative error data corresponding to bins used - scores: None, float, or {np} - Reshaped array of tally scores. SERPENT 1 only - indexes: None or :class:`collections.OrderedDict` - Collection of unique indexes for each requested bin - """.format(np=':class:`numpy.ndarray`') + baseParams - __doc__ = __doc__.format(params=baseParams, attrs=baseAttrs) - - def __init__(self, name): - NamedObject.__init__(self, name) - self.tallies = None - self.errors = None - self.scores = None - self.grids = {} - self.indexes = None - self._map = None - - @abstractmethod - def _isReshaped(self): - raise NotImplementedError - - def slice(self, fixed, data='tallies'): - """ - Return a view of the reshaped array where certain axes are fixed - - Parameters - ---------- - fixed: dict - dictionary to aid in the restriction on the multidimensional - array. Keys correspond to the various grids present in - ``indexes`` while the values are used to - data: {'tallies', 'errors', 'scores'} - Which data set to slice - - Returns - ------- - :class:`numpy.ndarray` - View into the respective data where certain dimensions - have been removed - - Raises - ------ - :class:`~serpentTools.SerpentToolsException` - If the data has not been reshaped or is None [e.g. scores] - KeyError - If the data set to slice not in the allowed selection - - """ - if not self._isReshaped(): - raise SerpentToolsException( - 'Slicing requires detector to be reshaped') - if data not in self._map: - raise KeyError( - 'Data argument {} not in allowed options' - '\n{}'.format(data, ', '.join(self._map.keys()))) - work = self._map[data] - if work is None: - raise SerpentToolsException( - '{} data for detector {} is None. ' - 'Cannot perform slicing'.format(data, self.name)) - if not fixed: - return work - return work[self._getSlices(fixed)] - - def _getSlices(self, fixed): - """ - Return a list of slice operators for each axis in reshaped data - - Parameters - ---------- - fixed: dict - Dictionary where keys are strings pointing to dimensions in - """ - fixed = fixed if fixed is not None else {} - keys = set(fixed.keys()) - slices = tuple() - for key in self.indexes: - if key in keys: - slices += fixed[key], - keys.remove(key) - else: - slices += slice(0, len(self.indexes[key])), - if any(keys): - warning( - 'Could not find arguments in index that match the following' - ' requested slice keys: {}'.format(', '.join(keys))) - return slices - - @magicPlotDocDecorator - def spectrumPlot(self, fixed=None, ax=None, normalize=True, xlabel=None, - ylabel=None, steps=True, logx=True, logy=False, - loglog=False, sigma=3, labels=None, legend=None, ncol=1, - title=None, *kwargs): - """ - Quick plot of the detector value as a function of energy. - - Parameters - ---------- - fixed: None or dict - Dictionary controlling the reduction in data - {ax} - normalize: bool - Normalize quantities per unit lethargy - {xlabel} - {ylabel} - steps: bool - Plot tally as constant inside bin - {logx} - {logy} - {loglog} - {sigma} - {labels} - {legend} - {ncol} - {title} - {kwargs} :py:func:`matplotlib.pyplot.plot` or - :py:func:`matplotlib.pyplot.errorbar` - - Returns - ------- - {rax} - - Raises - ------ - :class:`~serpentTools.SerpentToolsException` - if number of rows in data not equal to - number of energy groups - - See Also - -------- - :meth:`slice` - """ - slicedTallies = self.slice(fixed, 'tallies').copy() - if len(slicedTallies.shape) > 2: - raise SerpentToolsException( - 'Sliced data cannot exceed 2-D for spectrum plot, not ' - '{}'.format(slicedTallies.shape) - ) - elif len(slicedTallies.shape) == 1: - slicedTallies = slicedTallies.reshape(slicedTallies.size, 1) - lowerE = self.grids['E'][:, 0] - if normalize: - lethBins = log( - divide(self.grids['E'][:, -1], lowerE)) - for indx in range(slicedTallies.shape[1]): - scratch = divide(slicedTallies[:, indx], lethBins) - slicedTallies[:, indx] = scratch / scratch.max() - - if steps: - if 'drawstyle' in kwargs: - debug('Defaulting to drawstyle specified in kwargs as {}' - .format(kwargs['drawstyle'])) - else: - kwargs['drawstyle'] = 'steps-post' - - if sigma: - slicedErrors = sigma * self.slice(fixed, 'errors').copy() - slicedErrors = slicedErrors.reshape(slicedTallies.shape) - slicedErrors = slicedTallies - else: - slicedErrors = None - ax = plot(lowerE, slicedTallies, ax=ax, labels=labels, - yerr=slicedErrors, kwargs) - if ylabel is None: - ylabel = 'Tally data' - ylabel += ' normalized per unit lethargy' if normalize else '' - ylabel += r' $\pm{}\sigma$'.format(sigma) if sigma else '' - - if legend is None and labels: - legend = True - ax = formatPlot(ax, loglog=loglog, logx=logx, ncol=ncol, - xlabel=xlabel or "Energy [MeV]", ylabel=ylabel, - legend=legend, title=title) - return ax - - @magicPlotDocDecorator - def plot(self, xdim=None, what='tallies', sigma=None, fixed=None, ax=None, - xlabel=None, ylabel=None, steps=False, labels=None, logx=False, - logy=False, loglog=False, legend=None, ncol=1, title=None, - kwargs): - """ - Simple plot routine for 1- or 2-D data - - Parameters - ---------- - xdim: None or str - If not None, use the array under this key in ``indexes`` as - the x axis - what: {'tallies', 'errors', 'scores'} - Primary data to plot - {sigma} - fixed: None or dict - Dictionary controlling the reduction in data down to one dimension - {ax} - {xlabel} If ``xdim`` is given and ``xlabel`` is ``None``, then - ``xdim`` will be applied to the x-axis. - {ylabel} - steps: bool - If true, plot the data as constant inside the respective bins. - Sets ``drawstyle`` to be ``steps-post`` unless ``drawstyle`` - given in ``kwargs`` - {labels} - {logx} - {logy} - {loglog} - {legend} - {ncol} - {title} - {kwargs} :py:func:`~matplotlib.pyplot.plot` or - :py:func:`~matplotlib.pyplot.errorbar` function. - - Returns - ------- - {rax} - - Raises - ------ - :class:`~serpentTools.SerpentToolsException` - If data contains more than 2 dimensions - - See Also - -------- - :meth:`slice` - * :meth:`spectrumPlot` - better options for plotting energy spectra - """ - - data = self.slice(fixed, what) - if len(data.shape) > 2: - raise SerpentToolsException( - 'Data must be constrained to 1- or 2-D, not {}' - .format(data.shape)) - elif len(data.shape) == 1: - data = data.reshape(data.size, 1) - - if sigma: - if what != 'errors': - yerr = (self.slice(fixed, 'errors').reshape(data.shape) - * data * sigma) - else: - warning( - 'Will not plot error bars on the error plot. Data to be ' - 'plotted: {}. Sigma: {}'.format(what, sigma)) - yerr = None - else: - yerr = None - - xdata, autoX = self._getPlotXData(xdim, data) - xlabel = xlabel or autoX - ylabel = ylabel or "Tally data" - ax = ax or gca() - - if steps: - if 'drawstyle' in kwargs: - debug('Defaulting to drawstyle specified in kwargs as {}' - .format(kwargs['drawstyle'])) - else: - kwargs['drawstyle'] = 'steps-post' - ax = plot(xdata, data, ax, labels, yerr, kwargs) - - if legend is None and labels: - legend = True - - ax = formatPlot(ax, loglog=loglog, logx=logx, logy=logy, ncol=ncol, - xlabel=xlabel, ylabel=ylabel, legend=legend, - title=title) - return ax - - @magicPlotDocDecorator - def meshPlot(self, xdim='x', ydim='y', what='tallies', fixed=None, ax=None, - cmap=None, cbarLabel=None, logColor=False, xlabel=None, - ylabel=None, logx=False, logy=False, loglog=False, - title=None, thresh=None, kwargs): - """ - Plot tally data as a function of two bin types on a cartesian mesh. - - Parameters - ---------- - xdim: str - Primary dimension - will correspond to x-axis on plot - ydim: str - Secondary dimension - will correspond to y-axis on plot - what: {'tallies', 'errors', 'scores'} - Color meshes from tally data, uncertainties, or scores - fixed: None or dict - Dictionary controlling the reduction in data down to one dimension - {ax} - {cmap} - logColor: bool - If true, apply a logarithmic coloring to the data positive - data - {xlabel} - {ylabel} - {logx} - {logy} - {loglog} - {title} - {mthresh} - cbarLabel: str - Label to apply to colorbar. If not given, infer from ``what`` - {kwargs} :py:func:`~matplotlib.pyplot.pcolormesh` - - Returns - ------- - {rax} - - Raises - ------ - :class:`serpentTools.SerpentToolsException` - If data to be plotted, with or without constraints, is not 1D - KeyError - If the data set by ``what`` not in the allowed selection - ValueError - If the data contains negative quantities and ``logColor`` is - ``True`` - - See Also - -------- - * :meth:`slice` - * :func:`serpentTools.plot.cartMeshPlot` - * :func:`matplotlib.pyplot.pcolormesh` - """ - if fixed: - for qty, name in zip((xdim, ydim), ('x', 'y')): - if qty in fixed: - raise SerpentToolsException( - 'Requested {} dimension {} is one of the axis to be ' - 'constrained. ' - .format(name, qty)) - - data = self.slice(fixed, what) - dShape = data.shape - if len(dShape) != 2: - raise SerpentToolsException( - 'Data must be 2D for mesh plot, currently is {}.\nConstraints:' - '{}'.format(dShape, fixed) - ) - xgrid = self._getGrid(xdim) - ygrid = self._getGrid(ydim) - if data.shape != (ygrid.size - 1, xgrid.size - 1): - data = data.T - if cbarLabel is None: - cbarLabel = self._CBAR_LABELS[what] - ax = cartMeshPlot( - data, xgrid, ygrid, ax, cmap, logColor, - cbarLabel=cbarLabel, thresh=thresh, *kwargs) - if xlabel is None: - xlabel = DETECTOR_PLOT_LABELS.get(xdim, xdim) - if ylabel is None: - ylabel = DETECTOR_PLOT_LABELS.get(ydim, ydim) - ax = formatPlot(ax, loglog=loglog, logx=logx, logy=logy, - xlabel=xlabel, ylabel=ylabel, - title=title) - return ax - - def _getGrid(self, qty): - grids = self._inGridsAs(qty) - if grids is not None: - lowBounds = grids[:, 0] - return hstack((lowBounds, grids[-1, 1])) - if qty not in self.indexes: - raise KeyError("No index {} found on detector. Bin indexes: {}" - .format(qty, ', '.join(self.indexes.keys()))) - bins = self.indexes[qty] - return hstack((bins, len(bins))) - - def _dimInGrids(self, key): - return key[0].upper() in self.grids - - def _inGridsAs(self, qty): - if self._dimInGrids(qty): - return self.grids[qty[0].upper()] - return None - - def _getPlotXData(self, qty, ydata): - fallbackX = arange(len(ydata)) - xlabel = DETECTOR_PLOT_LABELS.get(qty, 'Bin Index') - if qty is None: - return fallbackX, xlabel - xdata = self._inGridsAs(qty) - if xdata is not None: - return xdata[:, 0], xlabel - if qty in self.indexes: - return self.indexes[qty], xlabel - return fallbackX, xlabel - - def _compare(self, other, lower, upper, sigma): - myShape = self.tallies.shape - otherShape = other.tallies.shape - if myShape != otherShape: - error("Detector tallies do not have identical shapes" - + BAD_OBJ_SUBJ.format('tallies', myShape, otherShape)) - return False - similar = compareDictOfArrays(self.grids, other.grids, 'grids', - lower=lower, upper=upper) - - similar &= getLogOverlaps('tallies', self.tallies, other.tallies, - self.errors, other.errors, sigma, - relative=True) - hasScores = [obj.scores is not None for obj in (self, other)] - - similar &= hasScores[0] == hasScores[1] - - if not any(hasScores): - return similar - if all(hasScores): - similar &= getLogOverlaps('scores', self.scores, other.scores, - self.errors, other.errors, sigma, - relative=True) - return similar - firstK, secondK = "first", "second" - if hasScores[1]: - firstK, secondK = secondK, firstK - error("{} detector has scores while {} does not" - .format(firstK.capitalize(), secondK)) - return similar diff --git a/serpentTools/objects/detectors.py b/serpentTools/objects/detectors.py index 3f3eccc..3b80328 100644 --- a/serpentTools/objects/detectors.py +++ b/serpentTools/objects/detectors.py @@ -1,583 +1,5 @@ -""" -Module containing classes for storing detector data. - -``SERPENT`` is capable of producing detectors, or tallies from MCNP, -with a variety of bin structures. These include, but are not limited to, -material regions, reaction types, energy bins, spatial meshes, and -universe bins. - -The detectors contained in this module are tasked with storing such -data and proving easy access to the data, as well as the underlying -bins used to define the detector. - -Detector Types --------------- - - :class:`~serpentTools.objects.detectors.Detector` -* :class:`~serpentTools.objects.detectors.CartesianDetector` -* :class:`~serpentTools.objects.detectors.HexagonalDetector` -* :class:`~serpentTools.objects.detectors.CylindricalDetector` -* :class:`~serpentTools.objects.detectors.SphericalDetector` -""" - -from math import sqrt, pi from warnings import warn -from collections import OrderedDict - -from six import iteritems -from numpy import unique, array, empty, inf -from matplotlib.figure import Figure -from matplotlib.patches import RegularPolygon -from matplotlib.collections import PatchCollection -from matplotlib.pyplot import gca - -from serpentTools.messages import warning, SerpentToolsException -from serpentTools.objects.base import DetectorBase -from serpentTools.utils import ( - magicPlotDocDecorator, formatPlot, setAx_xlims, setAx_ylims, - addColorbar, normalizerFactory, linkToWiki, -) -from serpentTools.io.hooks import matlabHook - -__all__ = ['Detector', 'CartesianDetector', 'HexagonalDetector', - 'CylindricalDetector', 'SphericalDetector'] - - -DET_COLS = ( - 'value', 'time', 'energy', 'universe', - 'cell', 'material', 'lattice', - 'reaction', 'zmesh', 'ymesh', 'xmesh', - 'tally', 'error', 'scores', -) -"""Name of the columns of the data""" - - -class Detector(DetectorBase): - docAttrs = """bins: :class:`numpy.ndarray` - Tally data directly from ``SERPENT`` file""" - __doc__ = """ - Class that stores data from a detector without a spatial mesh. - - Parameters - ---------- - {params:s} - - Attributes - ---------- - {docAttrs:s} - {baseAttrs:s} - """.format(params=DetectorBase.baseParams, - docAttrs=docAttrs, - baseAttrs=DetectorBase.baseAttrs) - - def __init__(self, name): - DetectorBase.__init__(self, name) - self.bins = None - self.__reshaped = False - - def _isReshaped(self): - return self.__reshaped - - def addTallyData(self, bins, hasTime=False): - """Add tally data to this detector""" - self.__reshaped = False - self.bins = bins - self.reshape(hasTime) - - def reshape(self, hasTime=False): - """ - Reshape the tally data into a multidimensional array - - This method reshapes the tally and uncertainty data into arrays - where the array axes correspond to specific bin types. - If a detector was set up to tally two group flux in a 5 x 5 - xy mesh, then the resulting tally data would be in a 50 x 12/13 - matrix in the original ``detN.m`` file. - The tally data and relative error would be rebroadcasted into - 2 x 5 x 5 arrays, and the indexing information is stored in - ``self.indexes`` - - Returns - ------- - shape: list - Dimensionality of the resulting array - - Raises - ------ - SerpentToolsException: - If the bin data has not been loaded - """ - if self.bins is None: - raise SerpentToolsException('Tally data for detector {} has not ' - 'been loaded'.format(self.name)) - if self.__reshaped: - warning('Data has already been reshaped') - return - shape = [] - tallyCol = 10 - errorCol = 11 - scoreCol = 12 - if hasTime: - tallyCol += 1 - errorCol += 1 - scoreCol += 1 - self.indexes = OrderedDict() - hasScores = self.bins.shape[1] == (scoreCol + 1) - - if self.bins.shape[0] == 1: - # single tally value - self.tallies = self.bins[0, tallyCol] - self.errors = self.bins[0, errorCol] - if hasScores: - self.scores = self.bins[0, scoreCol] - else: - for tallyIx, colIx in enumerate( - range(1, tallyCol), - start=1 if hasTime else 2): - uniqueVals = unique(self.bins[:, colIx]) - if len(uniqueVals) > 1: - indexName = self._indexName(tallyIx) - self.indexes[indexName] = array(uniqueVals, dtype=int) - 1 - shape.append(len(uniqueVals)) - self.tallies = self.bins[:, tallyCol].reshape(shape) - self.errors = self.bins[:, errorCol].reshape(shape) - if hasScores: - self.scores = self.bins[:, scoreCol].reshape(shape) - self._map = {'tallies': self.tallies, 'errors': self.errors, - 'scores': self.scores} - self.__reshaped = True - return shape - - def _indexName(self, indexPos): - """Return the name of this index position""" - if hasattr(self, '_INDEX_MAP') and indexPos in self._INDEX_MAP: - return self._INDEX_MAP[indexPos] - return DET_COLS[indexPos] - - def _gather_matlab(self, reconvert): - """Gather bins and grids for exporting to matlab""" - - converter = deconvert if reconvert else prepToMatlab - - data = { - converter(self.name, 'bins'): self.bins, - } - - for key, value in iteritems(self.grids): - data[converter(self.name, key)] = value - - return data - - -Detector.toMatlab = matlabHook - - -class CartesianDetector(Detector): - __doc__ = """ - Class that stores detector data containing cartesian meshing. - - .. versionadded:: 0.5.1 - - Parameters - ---------- - {params:s} - - Attributes - ---------- - {docAttrs:s} - {baseAttrs:s} - - See Also - -------- - {seeAlso:s} - """.format(params=DetectorBase.baseParams, docAttrs=Detector.docAttrs, - baseAttrs=DetectorBase.baseAttrs, seeAlso='* ' + linkToWiki( - 'Input_syntax_manual#det_dx', - 'Setting up a cartesian mesh detector')) - pass - - -class HexagonalDetector(Detector): - docSeeAlso = '* ' + linkToWiki('Input_syntax_manual#det_dh', - 'Setting up a hexagonal detector') - docAttrs = """pitch: None or int - Mesh size [cm] - hexType: None or {2, 3} - Type of hexagonal mesh. - - 2. Flat face perpendicular to x-axis - 3. Flat face perpendicular to y-axis.""" - __doc__ = """ - Class that stores detector data containing a hexagonal meshing. - - .. versionadded:: 0.5.1 - - Parameters - ---------- - {params:s} - - Attributes - ---------- - {docAttrs:s} - {detAttrs:s} - {baseAttrs:s} - - See Also - -------- - {seeAlso:s}""".format(seeAlso=docSeeAlso, docAttrs=docAttrs, - params=DetectorBase.baseParams, - detAttrs=Detector.docAttrs, - baseAttrs=DetectorBase.baseAttrs) - - # column indicies in full (time-bin included) bins matrix - _INDEX_MAP = { - 9: 'ycoord', - 10: 'xcoord', - } - - _NON_CART = _INDEX_MAP.values() - - def __init__(self, name): - Detector.__init__(self, name) - self.__pitch = None - self.__hexType = None - self.__hexRot = None - - @property - def pitch(self): - return self.__pitch - - @pitch.setter - def pitch(self, value): - f = float(value) - if f <= 0: - raise ValueError("Pitch must be positive") - self.__pitch = f - - @property - def hexType(self): - return self.__hexType - - @hexType.setter - def hexType(self, value): - if value not in {2, 3}: - raise ValueError("Hex type must be 2 or 3, not {}" - .format(value)) - self.__hexType = value - self.__hexRot = 0 if value == 2 else (pi / 2) - - def meshPlot(self, xdim, ydim, what='tallies', fixed=None, ax=None, - cmap=None, logColor=False, xlabel=None, ylabel=None, - logx=False, logy=False, loglog=False, title=None, kwargs): - opts = dict(what=what, - fixed=fixed, - ax=ax, - cmap=cmap, - logColor=logColor, - xlabel=xlabel, - logx=logx, - logy=logy, - loglog=loglog, - title=title, - kwargs) - if xdim in self._NON_CART or ydim in self._NON_CART: - return self.hexPlot(opts) - return DetectorBase.meshPlot(self, xdim, ydim, opts) - - meshPlot.__doc__ = DetectorBase.meshPlot.__doc__ - - @magicPlotDocDecorator - def hexPlot(self, what='tallies', fixed=None, ax=None, cmap=None, - logColor=False, xlabel=None, ylabel=None, logx=False, - logy=False, loglog=False, title=None, normalizer=None, - cbarLabel=None, borderpad=2.5, kwargs): - """ - Create and return a hexagonal mesh plot. - - Parameters - ---------- - what: {'tallies', 'errors', 'scores'} - Quantity to plot - fixed: None or dict - Dictionary of slicing arguments to pass to :meth:`slice` - {ax} - {cmap} - {logColor} - {xlabel} - {ylabel} - {logx} - {logy} - {loglog} - {title} - borderpad: int or float - Percentage of total plot to apply as a border. A value of - zero means that the extreme edges of the hexagons will touch - the x and y axis. - {kwargs} :class:`matplotlib.patches.RegularPolygon` - - Raises - ------ - AttributeError - If :attr:`pitch` and :attr:`hexType` are not set. - """ - borderpad = max(0, float(borderpad)) - if fixed and ('xcoord' in fixed or 'ycoord' in fixed): - raise KeyError("Refusing to restrict along one of the hexagonal " - "dimensions {x/y}coord") - for attr in {'pitch', 'hexType'}: - if getattr(self, attr) is None: - raise AttributeError("{} is not set.".format(attr)) - - for key in {'color', 'fc', 'facecolor', 'orientation'}: - checkClearKwargs(key, 'hexPlot', kwargs) - ec = kwargs.get('ec', None) or kwargs.get('edgecolor', None) - if ec is None: - ec = 'k' - kwargs['ec'] = kwargs['edgecolor'] = ec - if 'figure' in kwargs and kwargs['figure'] is not None: - fig = kwargs['figure'] - if not isinstance(fig, Figure): - raise TypeError( - "Expected 'figure' to be of type Figure, is {}" - .format(type(fig))) - if len(fig.axes) != 1 and not ax: - raise TypeError("Don't know where to place the figure since" - "'figure' argument has multiple axes.") - if ax and fig.axes and ax not in fig.axes: - raise IndexError("Passed argument for 'figure' and 'ax', " - "but ax is not attached to figure.") - ax = ax or (fig.axes[0] if fig.axes else gca()) - alpha = kwargs.get('alpha', None) - - ny = len(self.indexes['ycoord']) - nx = len(self.indexes['xcoord']) - data = self.slice(fixed, what) - if data.shape != (ny, nx): - raise IndexError("Constrained data does not agree with hexagonal " - "grid structure. Coordinate grid: {}. " - "Constrained shape: {}" - .format((ny, nx), data.shape)) - nItems = ny * nx - patches = empty(nItems, dtype=object) - values = empty(nItems) - coords = self.grids['COORD'] - - ax = ax or gca() - pos = 0 - xmax, ymax = [-inf, ] * 2 - xmin, ymin = [inf, ] * 2 - radius = self.pitch / sqrt(3) - - for xy, val in zip(coords, data.flat): - values[pos] = val - h = RegularPolygon(xy, 6, radius, self.__hexRot, *kwargs) - verts = h.get_verts() - vmins = verts.min(0) - vmaxs = verts.max(0) - xmax = max(xmax, vmaxs[0]) - xmin = min(xmin, vmins[0]) - ymax = max(ymax, vmaxs[1]) - ymin = min(ymin, vmins[1]) - patches[pos] = h - pos += 1 - normalizer = normalizerFactory(values, normalizer, logColor, - coords[:, 0], coords[:, 1]) - pc = PatchCollection(patches, cmap=cmap, alpha=alpha) - pc.set_array(values) - pc.set_norm(normalizer) - ax.add_collection(pc) - - addColorbar(ax, pc, None, cbarLabel) - - formatPlot(ax, loglog=loglog, logx=logx, logy=logy, - xlabel=xlabel or "X [cm]", - ylabel=ylabel or "Y [cm]", title=title, - ) - setAx_xlims(ax, xmin, xmax, pad=borderpad) - setAx_ylims(ax, ymin, ymax, pad=borderpad) - - return ax - - -class CylindricalDetector(Detector): - docSeeAlso = " " + linkToWiki('Input_syntax_manual#det_dn', - 'Setting up a curvilinear detector') - __doc__ = """ - Class that stores detector data containing a cylindrical mesh. - - .. versionadded:: 0.5.1 - - Parameters - ---------- - {params:s} - - Attributes - ---------- - {detAttrs:s} - {baseAttrs:s} - - See Also - -------- - {seeAlso:s}""".format( - params=DetectorBase.baseParams, detAttrs=Detector.docAttrs, - baseAttrs=DetectorBase.baseAttrs, seeAlso=docSeeAlso) - # column indices in full (time-bin included) bins matrix - _INDEX_MAP = { - 9: 'phi', - 10: 'rmesh' - } - - _NON_CART = _INDEX_MAP.values() - - def meshPlot(self, xdim, ydim, what='tallies', fixed=None, ax=None, - cmap=None, logColor=False, xlabel=None, ylabel=None, - logx=False, logy=False, loglog=False, title=None, kwargs): - if xdim in self._NON_CART or ydim in self._NON_CART: - warnMeshPlot('Cylindrical', 169) - return DetectorBase.meshPlot(self, - xdim, ydim, - what=what, - fixed=fixed, - ax=ax, - cmap=cmap, - logColor=logColor, - xlabel=xlabel, - logx=logx, - logy=logy, - loglog=loglog, - title=title, - kwargs) - - meshPlot.__doc__ = DetectorBase.meshPlot.__doc__ - - -class SphericalDetector(Detector): - __doc__ = """ - Class that stores detector data containing a spherical mesh. - - .. versionadded:: 0.5.1 - - Paramters - --------- - {params:s} - - Attributes - ---------- - {detAttrs:s} - {baseAttrs:s} - - See Also - -------- - {seeAlso:s}""".format( - params=DetectorBase.baseParams, detAttrs=Detector.docAttrs, - baseAttrs=DetectorBase.baseAttrs, - seeAlso=CylindricalDetector.docSeeAlso) - - # column indices in full (time-bin included) bins matrix - _INDEX_MAP = { - 8: 'theta', - 9: 'phi', - 10: 'rmesh', - } - - _NON_CART = _INDEX_MAP.values() - - def meshPlot(self, xdim, ydim, what='tallies', fixed=None, ax=None, - cmap=None, logColor=False, xlabel=None, ylabel=None, - logx=False, logy=False, loglog=False, title=None, kwargs): - if xdim in self._NON_CART or ydim in self._NON_CART: - warnMeshPlot('Spherical', 169) - return DetectorBase.meshPlot(self, - xdim, ydim, - what=what, - fixed=fixed, - ax=ax, - cmap=cmap, - logColor=logColor, - xlabel=xlabel, - logx=logx, - logy=logy, - loglog=loglog, - title=title, - kwargs) - - meshPlot.__doc__ = DetectorBase.meshPlot.__doc__ - - -DET_UNIQUE_GRIDS = { - CartesianDetector: {'X', 'Y'}, - HexagonalDetector: {"COORD"}, -} - - -def _getDetectorType(dataDict): - if not dataDict: - return Detector - for cls, uniqGrids in iteritems(DET_UNIQUE_GRIDS): - if any([key in uniqGrids for key in dataDict]): - return cls - if 'R' in dataDict: - return CylindricalDetector if 'Z' in dataDict else SphericalDetector - return CartesianDetector if 'Z' in dataDict else Detector - - -def detectorFactory(name, dataDict): - """ - Return a proper Detector subclass depending upon the attached grids - - Parameters - ---------- - name: str - Name of this specific detector. - dataDict: dict - Dictionary of detector data. Expects at least ``'tally'`` - - Returns - ------- - object: - Subclass of :class:`serpentTools.objects.base.DetectorBase` - depending on grid data passed - - Raises - ------ - KeyError: - If ``'tally'`` is missing from the data dictionary - """ - tallyD = dataDict.pop('tally') - detCls = _getDetectorType(dataDict) - det = detCls(name) - det.addTallyData(tallyD, 'T' in dataDict) - for gridK, value in iteritems(dataDict): - det.grids[gridK] = value - return det - - -def warnMeshPlot(plotType, ghIssue): - msg = ("{} plotting is not fully supported yet - #{}" - .format(plotType, ghIssue)) - warn(msg, FutureWarning) - - -def checkClearKwargs(key, fName, *kwargs): - """Log a warning and clear non-None values from kwargs""" - if key in kwargs and kwargs[key] is not None: - warning("{} will be set by {}. Worry not.".format(key, fName)) - kwargs[key] = None - - -# Functions for converting varible names for matlab - -_DET_FMT_ORIG = "DET{}{}" -_DET_FMT_CC = "{}_{}" - - -def deconvert(detectorName, quantity): - """Restore the original name of the detector data""" - if quantity == 'bins': - return _DET_FMT_ORIG.format(detectorName, '') - return _DET_FMT_ORIG.format(detectorName, quantity) - +from serpentTools.detectors import -def prepToMatlab(detectorName, quantity): - """Create a name of a variable for MATLAB""" - return _DET_FMT_CC.format(detectorName, quantity) +warn("Importing from serpentTools.objects.detectors is deprecated. " + "Prefer serpentTools.detectors") diff --git a/serpentTools/parsers/detector.py b/serpentTools/parsers/detector.py index 516dd89..063be38 100644 --- a/serpentTools/parsers/detector.py +++ b/serpentTools/parsers/detector.py @@ -1,17 +1,19 @@ """Parser responsible for reading the ``det<n>.m`` files""" +from warnings import warn from six import iteritems from numpy import empty from serpentTools.utils import str2vec from serpentTools.utils.compare import getKeyMatchingShapes from serpentTools.engines import KeywordParser -from serpentTools.objects.detectors import detectorFactory +from serpentTools.detectors import detectorFactory from serpentTools.parsers.base import BaseReader -from serpentTools.messages import error, debug, warning, SerpentToolsException -from serpentTools.settings import rc +from serpentTools.messages import SerpentToolsException +# After py2 removal, subclass this from collections.abc.Mapping +# Gain full dictionary-like behavior by defining a few methods class DetectorReader(BaseReader): """ Parser responsible for reading and working with detector files. @@ -23,63 +25,63 @@ class DetectorReader(BaseReader): Attributes ---------- - {attrs:s} - """ - docAttrs = """detectors: dict + detectors : dict Dictionary where key, value pairs correspond to detector names and their respective :class:`~serpentTools.objects.detector.Detector` - representations.""" - __doc__ = __doc__.format(attrs=docAttrs) + instances + """ def __init__(self, filePath): BaseReader.__init__(self, filePath, 'detector') self.detectors = {} - if not self.settings['names']: - self._loadAll = True - else: - self._loadAll = False - self.__numCols = 13 if rc['serpentVersion'][0] == '1' else 12 def __getitem__(self, name): """Retrieve a detector from :attr:`detectors`""" return self.detectors[name] + def __len__(self): + return len(self.detectors) + + def __contains__(self, key): + return key in self.detectors + + def __iter__(self): + return iter(self.detectors) + def iterDets(self): """Yield name, detector pairs by iterating over :attr:`detectors`.""" - for name, detector in iteritems(self.detectors): - yield name, detector + for key, det in iteritems(self.detectors): + yield key, det def _read(self): """Read the file and store the detectors.""" - recentName = None - lenRecent = 0 - recentGrids = {} - keys = ['DET'] - separators = ['\n', '];'] - with KeywordParser(self.filePath, keys, separators) as parser: + currentName = "" + grids = {} + bins = None + with KeywordParser(self.filePath, ["DET"], ["\n", "];"]) as parser: for chunk in parser.yieldChunks(): name, data = cleanDetChunk(chunk) - if recentName is None or name[:lenRecent] != recentName: - if recentName is not None: - self.__processDet(recentName, recentGrids) - recentName = name - lenRecent = len(name) - recentGrids = {'tally': data} - continue - gridName = name[lenRecent:] - recentGrids[gridName] = data - self.__processDet(recentName, recentGrids) - - def __processDet(self, name, grids): + if currentName and name[:len(currentName)] != currentName: + self._processDet(currentName, bins, grids) + bins = data + grids = {} + currentName = name + elif bins is None: + currentName = name + bins = data + else: + gridName = name[len(currentName):] + grids[gridName] = data + self._processDet(currentName, bins, grids) + + def _processDet(self, name, bins, grids): """Add this detector with it's grid data to the reader.""" - if not self._loadAll and name in self.settings['names']: - debug("Skipping detector {} due to setting <detector.names>" - .format(name)) + if self.settings['names'] and name in self.settings['names']: return if name in self.detectors: raise KeyError("Detector {} already stored on reader" .format(name)) - self.detectors[name] = detectorFactory(name, grids) + self.detectors[name] = detectorFactory(name, bins, grids) def _precheck(self): """ Count how many detectors are in the file.""" @@ -90,11 +92,11 @@ class DetectorReader(BaseReader): continue if 'DET' in sline[0]: return - error("No detectors found in {}".format(self.filePath)) + warn("No detectors in pre-checking {}".format(self.filePath)) def _postcheck(self): if not self.detectors: - warning("No detectors stored from file {}".format(self.filePath)) + warn("No detectors stored from file {}".format(self.filePath)) def _compare(self, other, lower, upper, sigma): """Compare two detector readers.""" @@ -114,8 +116,8 @@ class DetectorReader(BaseReader): """Collect data from all detectors for exporting to matlab""" data = {} - for detector in self.detectors.values(): - data.update(detector._gather_matlab(reconvert)) + for det in self.detectors.values(): + data.update(det._gather_matlab(reconvert)) return data diff --git a/serpentTools/samplers/base.py b/serpentTools/samplers/base.py index 821d5af..602b5f3 100644 --- a/serpentTools/samplers/base.py +++ b/serpentTools/samplers/base.py @@ -39,41 +39,34 @@ def extendFiles(files): class Sampler(object): - docFiles = """files: str or iterable - Single file or iterable (list) of files from which to read. - Supports file globs, ``det0.m`` expands to all files that - end with ``det0.m``""" - docAttrs = """files: set - Unordered set containing full paths of unique files read - settings: dict - Dictionary of sampler-wide settings - parsers: set - Unordered set of all parsers that were successful - map: dict - Dictionary where key, value pairs are files and their corresponding - parsers""" - - __doc__ = """ - Base class for reading multiple files of of a similar type + """Base class for reading multiple files of of a similar type Parameters ---------- - {files:s} - parser: subclass of BaseReader + files: str or iterable + Single file or iterable (list) of files from which to read. + Supports file globs, ``det0.m`` expands to all files that + end with ``det0.m`` +parser: subclass of BaseReader Class that will be used to read all files Attributes ---------- - {attrs:s} + files: set + Unordered set containing full paths of unique files read + settings: dict + Dictionary of sampler-wide settings + parsers: set + Unordered set of all parsers that were successful + map: dict + Dictionary where key, value pairs are files and their corresponding + parsers Raises ------ serpentTools.messages.SamplerError If ``parser`` is not a subclass of ``BaseReader`` - """.format(files=docFiles, attrs=docAttrs) - - docSkipChecks = """These tests can be skipped by setting - ``<sampler.skipPrecheck>`` to be ``False``""" + """ def __init__(self, files, parser): if not issubclass(parser, BaseReader): diff --git a/serpentTools/samplers/depletion.py b/serpentTools/samplers/depletion.py index e3ef397..adc8511 100644 --- a/serpentTools/samplers/depletion.py +++ b/serpentTools/samplers/depletion.py @@ -36,15 +36,25 @@ class DepletionSampler(DepPlotMixin, Sampler): 2. Metadata keys are consistent for all parsers 3. Isotope names and ZZAAA metadata are equal for all parsers - {skip:s} + These tests can be skipped by settings ``<sampler.skipPrecheck>`` to be + ``False``. Parameters ---------- - {files:s} + files: str or iterable + Single file or iterable (list) of files from which to read. + Supports file globs, ``dep.m`` expands to all files that + end with ``dep.m`` Attributes ---------- - {depAttr:s} + materials: dict + Dictionary with material names as keys and the corresponding + :py:class:`~serpentTools.objects.materials.DepletedMaterial` class + for that material as values + metadata: dict + Dictionary with file-wide data names as keys and the + corresponding data, e.g. ``'zai'``: [list of zai numbers] metadataUncs: dict Dictionary containing uncertainties in file-wide metadata, such as burnup schedule @@ -52,10 +62,17 @@ class DepletionSampler(DepPlotMixin, Sampler): Dictionary where key, value pairs are name of metadata and metadata arrays for all runs. Arrays with be of one greater dimension, as the first index corresponds to the file index. - {samplerAttr:s} - - """.format(files=Sampler.docFiles, samplerAttr=Sampler.docAttrs, - skip=Sampler.docSkipChecks, depAttr=DepletionReader.docAttrs) + files: set + Unordered set containing full paths of unique files read + settings: dict + Dictionary of sampler-wide settings + parsers: set + Unordered set of all parsers that were successful + map: dict + Dictionary where key, value pairs are files and their corresponding + parsers + + """ def __init__(self, files): self.materials = {} diff --git a/serpentTools/samplers/detector.py b/serpentTools/samplers/detector.py index 9a4502a..78bd384 100644 --- a/serpentTools/samplers/detector.py +++ b/serpentTools/samplers/detector.py @@ -1,24 +1,22 @@ """ Class to read and process a batch of similar detector files """ -from six import iteritems -from six.moves import range +from collections import defaultdict +import warnings -from numpy import empty, empty_like, square, sqrt, sum, where +from six import iteritems +from numpy import empty, square, sqrt, allclose, asarray from matplotlib import pyplot -from serpentTools.messages import (MismatchedContainersError, warning, - SamplerError, SerpentToolsException) -from serpentTools.utils import magicPlotDocDecorator +from serpentTools.messages import SerpentToolsException +from serpentTools.utils import magicPlotDocDecorator, formatPlot from serpentTools.parsers.detector import DetectorReader -from serpentTools.objects.base import DetectorBase -from serpentTools.samplers.base import (Sampler, SampledContainer, - SPREAD_PLOT_KWARGS) +from serpentTools.detectors import Detector +from serpentTools.samplers.base import Sampler, SPREAD_PLOT_KWARGS class DetectorSampler(Sampler): - __doc__ = """ - Class responsible for reading multiple detector files + """Class responsible for reading multiple detector files The following checks are performed to ensure that all detectors are of similar structure and content @@ -26,19 +24,32 @@ class DetectorSampler(Sampler): 1. Each parser must have the same detectors 2. The reshaped tally data must be of the same size for all detectors - {skip:s} + These tests can be skipped by settings ``<sampler.skipPrecheck>`` to be + ``False``. Parameters ---------- - {files:s} + files: str or iterable + Single file or iterable (list) of files from which to read. + Supports file globs, ``det0.m`` expands to all files that + end with ``det0.m`` Attributes ---------- - {detAttrs:s} - {samplerAttrs:s} - - """.format(detAttrs=DetectorReader.docAttrs, samplerAttrs=Sampler.docAttrs, - files=Sampler.docFiles, skip=Sampler.docSkipChecks) + detectors : dict + Dictionary of key, values pairs for detector names and corresponding + :class:`~serpentTools.samplers.SampledDetector` instances + files: set + Unordered set containing full paths of unique files read + settings: dict + Dictionary of sampler-wide settings + parsers: set + Unordered set of all parsers that were successful + map: dict + Dictionary where key, value pairs are files and their corresponding + parsers + + """ def __init__(self, files): self.detectors = {} @@ -69,18 +80,16 @@ class DetectorSampler(Sampler): self._raiseErrorMsgFromDict(misMatches, 'shape', 'detector') def _process(self): - numParsers = len(self.parsers) + individualDetectors = defaultdict(list) for parser in self: for detName, detector in parser.iterDets(): - if detName not in self.detectors: - self.detectors[detName] = SampledDetector(detName, - numParsers) - self.detectors[detName].loadFromContainer(detector) - for _detName, sampledDet in self.iterDets(): - sampledDet.finalize() + individualDetectors[detName].append(detector) + for name, detList in iteritems(individualDetectors): + self.detectors[name] = SampledDetector.fromDetectors( + name, detList) def _free(self): - for _detName, sampledDet in self.iterDets(): + for sampledDet in self.detectors.values(): sampledDet.free() def iterDets(self): @@ -88,122 +97,124 @@ class DetectorSampler(Sampler): yield name, detector -class SampledDetector(SampledContainer, DetectorBase): - __doc__ = """ +class SampledDetector(Detector): + """ Class to store aggregated detector data - .. note :: - - :py:func:`~serpentTools.samplers.detector.SampledDetector.free` - sets ``allTallies``, ``allErrors``, and ``allScores`` to - ``None``. {free:s} - Parameters ---------- - {detParams:s} - numFiles: int - Number of files that have been/will be read + name : str + Name of the detector to be sampled + allTallies : numpy.ndarray or iterable of arrays + Array of tally data for each individual detector + allErrors : numpy.ndarray or iterable of arrays + Array of absolute tally errors for individual detectors + indexes : iterable of string, optional + Iterable naming the bins that correspond to reshaped + :attr:`tally` and :attr:`errors`. + grids : dict, optional + Additional grid information, like spatial or energy-wise + grid information. Attributes ---------- - {detAttrs:s} - - - """.format(detAttrs=DetectorBase.baseAttrs, free=SampledContainer.docFree, - detParams=DetectorBase.baseParams) - - def __init__(self, name, numFiles): - SampledContainer.__init__(self, numFiles, DetectorBase) - DetectorBase.__init__(self, name) - self.allTallies = None - self.allErrors = None - self.allScores = None - - def _isReshaped(self): - return True - - def _loadFromContainer(self, detector): - """ - Load data from a detector - - Parameters - ---------- - detector - - Returns - ------- - - """ - if detector.name != self.name: - warning("Attempting to read from detector with dissimilar names: " - "Base: {}, incoming: {}".format(self.name, detector.name)) - if not self._index: - self.__shape = tuple([self.N] + list(detector.tallies.shape)) - self.__allocate(detector.scores is not None) - if self.__shape[1:] != detector.tallies.shape: - raise MismatchedContainersError( - "Incoming detector {} tally data shape does not match " - "sampled shape. Base: {}, incoming: {}".format( - detector.name, self.__shape[1:], detector.tallies.shape)) - self.__load(detector.tallies, detector.errors, detector.scores, - detector.name) - if self.indexes is None: - self.indexes = detector.indexes - if not self.grids: - self.grids = detector.grids - - def __allocate(self, scoreFlag): - self.allTallies = empty(self.__shape) - self.allErrors = empty_like(self.allTallies) - if scoreFlag: - self.allScores = empty_like(self.allTallies) - - def free(self): - self.allTallies = None - self.allScores = None - self.allErrors = None - - def __load(self, tallies, errors, scores, oName): - index = self._index - otherHasScores = scores is not None - selfHasScores = self.allScores is not None - if otherHasScores and selfHasScores: - self.allScores[index, ...] = scores - elif otherHasScores and not selfHasScores: - warning("Incoming detector {} has score data, while base does " - "not. Skipping score data".format(oName)) - elif not otherHasScores and selfHasScores: - raise MismatchedContainersError( - "Incoming detector {} does not have score data, while base " - "does.".format(oName) - ) - self.allTallies[index] = tallies - self.allErrors[index] = tallies errors - - def _finalize(self): - if self.allTallies is None: - raise SamplerError( - "Detector data has not been loaded and cannot be processed") - N = self._index - self.tallies = self.allTallies[:N].mean(axis=0) - self.__computeErrors(N) - - self.scores = (self.allScores[:N].mean( - axis=0) if self.allScores is not None else None) - self._map = {'tallies': self.tallies, 'errors': self.errors, - 'scores': self.scores} - - def __computeErrors(self, N): - nonZeroT = where(self.tallies > 0) - zeroIndx = where(self.tallies == 0) - self.errors = sqrt(sum(square(self.allErrors), axis=0)) / N - self.errors[nonZeroT] /= self.tallies[nonZeroT] - self.errors[zeroIndx] = 0 + name : str + Name of this detector + tallies : numpy.ndarray + Average of tallies from all detectors + errors : numpy.ndarray + Uncertainty on :attr:`tallies` after propagating uncertainty from all + individual detectors + deviation : numpy.ndarray + Deviation across all tallies + allTallies : numpy.ndarray + Array of tally data from sampled detectors. First dimension is the + file index ``i``, followed by the tally array for detector ``i``. + allErrors : numpy.ndarray + Array of uncertainties for sampled detectors. Structure is identical + to :attr:`allTallies` + grids : dict or None + Dictionary of additional grid information + indexes : tuple or None + Iterable naming the bins that correspond to reshaped + :attr:`tally` and :attr:`errors`. The tuple + ``(energy, ymesh, xmesh)`` indicates that :attr:`tallies` + should have three dimensions corresponding to various + energy, y-position, and x-position bins. Must be set + after :attr:`tallies` or :attr:`errors` and agree with + the shape of each + + See Also + -------- + :meth:`fromDetectors` + + """ + + def __init__(self, name, allTallies, allErrors, indexes=None, grids=None): + # average tally data, propagate uncertainty + self._allTallies = allTallies + self._allErrors = allErrors + tallies = self.allTallies.mean(axis=0) + + # propagate absolute uncertainty + # assume no covariance + inner = square(allErrors).sum(axis=0) + errors = sqrt(inner) / allTallies.shape[0] + nz = tallies.nonzero() + errors[nz] /= tallies[nz] + + Detector.__init__(self, name, tallies=tallies, errors=errors, + grids=grids, indexes=indexes) + self.deviation = self.allTallies.std(axis=0) + + @property + def allTallies(self): + return self._allTallies + + @allTallies.setter + def allTallies(self, tallies): + if tallies is None: + self._allTallies = None + return + + tallies = asarray(tallies) + + if self._allTallies is None: + self._allTallies = tallies + return + + if tallies.shape != self._tallies.shape: + raise ValueError("Expected shape to be {}, is {}".format( + self._allTallies.shape, tallies.shape)) + + self._allTallies = tallies + + @property + def allErrors(self): + return self._allErrors + + @allErrors.setter + def allErrors(self, errors): + if errors is None: + self._allErrors = None + return + + errors = asarray(errors) + + if self._allErrors is None: + self._allErrors = errors + return + + if errors.shape != self._errors.shape: + raise ValueError("Expected shape to be {}, is {}".format( + self._allErrors.shape, errors.shape)) + + self._allErrors = errors @magicPlotDocDecorator def spreadPlot(self, xdim=None, fixed=None, ax=None, xlabel=None, ylabel=None, logx=False, logy=False, loglog=False, - autolegend=True): + legend=True): """ Plot the mean tally value against all sampled detector data. @@ -219,8 +230,7 @@ class SampledDetector(SampledContainer, DetectorBase): {logx} {logy} {loglog} - autolegend: bool - If true, apply a label to this plot. + {legend} Returns ------- @@ -228,7 +238,7 @@ class SampledDetector(SampledContainer, DetectorBase): Raises ------ - :class:`~serpentTools.SamplerError` + AttributeError If ``allTallies`` is None, indicating this object has been instructed to free up data from all sampled files :class:`~serpentTools.SerpentToolsException` @@ -237,8 +247,9 @@ class SampledDetector(SampledContainer, DetectorBase): """ if self.allTallies is None: - raise SamplerError("Data from all sampled files has been freed " - "and cannot be used in this plot method") + raise AttributeError( + "allTallies is None, cannot plot all tally data") + samplerData = self.slice(fixed, 'tallies') slices = self._getSlices(fixed) if len(samplerData.shape) != 1: @@ -248,19 +259,98 @@ class SampledDetector(SampledContainer, DetectorBase): xdata, autoX = self._getPlotXData(xdim, samplerData) xlabel = xlabel or autoX ax = ax or pyplot.gca() - N = self._index - allTallies = self.allTallies.copy(order='F') - for n in range(N): - plotData = allTallies[n][slices] - ax.plot(xdata, plotData, SPREAD_PLOT_KWARGS) - - ax.plot(xdata, samplerData, label='Mean value - N={}'.format(N)) - if autolegend: - ax.legend() - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel or "Tally Data") - if loglog or logx: - ax.set_xscale('log') - if loglog or logy: - ax.set_yscale('log') + for data in self.allTallies: + ax.plot(xdata, data[slices], SPREAD_PLOT_KWARGS) + + ax.plot(xdata, samplerData, label='Mean value - N={}'.format( + self.allTallies.shape[0])) + formatPlot(ax, logx=logx, logy=logy, loglog=loglog, xlabel=xlabel, + ylabel=ylabel, legend=legend) return ax + + @classmethod + def fromDetectors(cls, name, detectors): + """ + Create a :class:`SampledDetector` from similar detectors + + Parameters + ---------- + name : str + Name of this detector + detectors : iterable of :class:`serpentTools.Detector` + Iterable that contains detectors to be averaged. These + should be structured identically, in shape of the tally + data and the underlying grids and indexes. + + Returns + ------- + SampledDetector + + Raises + ------ + TypeError + If something other than a :class:`serpentTools.Detector` is found + ValueError + If tally data are not shaped consistently + KeyError + If some grid or index information is missing + AttributeError + If one detector is missing grids entirely but grids are + present on other grids + """ + shape = None + indexes = None + grids = {} + differentGrids = set() + + for d in detectors: + if not isinstance(d, Detector): + raise TypeError( + "All items should be Detector. Found {}".format(type(d))) + + if shape is None: + shape = d.tallies.shape + elif shape != d.tallies.shape: + raise ValueError( + "Shapes do not agree. Found {} and {}".format( + shape, d.tallies.shape)) + + # Inspect tally structure via indexes + if indexes is None and d.indexes is not None: + indexes = d.indexes + else: + if d.indexes != indexes: + raise KeyError( + "Detector indexes do not agree. Found {} and " + "{}".format(d.indexes, indexes)) + + # Inspect tally structure via grids + if d.grids and not grids: + grids = d.grids + elif not d.grids and grids: + raise AttributeError( + "Detector {} is missing grid structure".format(d)) + elif d.grids and grids: + for key, refGrid in iteritems(grids): + thisGrid = d.grids.get(key) + if thisGrid is None: + raise KeyError( + "Detector {} is missing {} grid".format(d, key)) + if not allclose(refGrid, thisGrid): + differentGrids.add(key) + + if differentGrids: + warnings.warn( + "Found some potentially different grids {}".format( + ", ".join(differentGrids)), RuntimeWarning) + + shape = (len(detectors), ) + shape + + allTallies = empty(shape) + allErrors = empty(shape) + + for ix, d in enumerate(detectors): + allTallies[ix] = d.tallies + allErrors[ix] = d.tallies * d.errors + + return cls(name, allTallies, allErrors, indexes=indexes, grids=grids)	API Remove support for SERPENT 1 detectors After a discussion with @DanKotlyar, we decided that support for SERPENT 1 detectors will be removed at release 0.8.0. Notice will be sent out to all users in the 0.7.1 release notice.	CORE-GATECH-GROUP/serpent-tools	diff --git a/tests/test_detector.py b/tests/test_detector.py index 099fd13..9193d6a 100644 --- a/tests/test_detector.py +++ b/tests/test_detector.py @@ -10,7 +10,7 @@ from numpy import arange, array from numpy.testing import assert_equal from serpentTools.parsers import read from serpentTools.data import getFile -from serpentTools.objects.detectors import ( +from serpentTools.detectors import ( CartesianDetector, HexagonalDetector, CylindricalDetector) from tests import compareDictOfArrays @@ -51,13 +51,7 @@ class DetectorHelper(TestCase): """Verify that the detector tally index is properly constructed.""" for detName, expectedIndex in iteritems(self.EXPECTED_INDEXES): actualIndex = self.detectors[detName].indexes - expectedKeys = list(expectedIndex.keys()) - actualKeys = list(actualIndex.keys()) - self.assertListEqual(actualKeys, expectedKeys) - for key in expectedIndex: - assert_equal( - actualIndex[key], expectedIndex[key], - err_msg="Key: {}, Detector: {}".format(key, detName)) + self.assertEqual(actualIndex, expectedIndex) def test_detectorSlice(self): """Verify that the detector slicing is working well.""" @@ -119,11 +113,7 @@ class CartesianDetectorTester(DetectorHelper): EXPECTED_GRIDS = { DET_NAME: _EXPECTED_GRIDS } - _INDEXES = OrderedDict([ - ['reaction', arange(2)], - ['ymesh', arange(5)], - ['xmesh', arange(5)], - ]) + _INDEXES = ("reaction", "ymesh", "xmesh") EXPECTED_INDEXES = {DET_NAME: _INDEXES} SLICING = {DET_NAME: { @@ -165,10 +155,7 @@ class HexagonalDetectorTester(DetectorHelper): 'hex2': HexagonalDetector, 'hex3': HexagonalDetector, } - _INDEXES = OrderedDict([ - ['ycoord', arange(5)], - ['xcoord', arange(5)], - ]) + _INDEXES = ("ycoord", "xcoord") EXPECTED_INDEXES = {'hex2': _INDEXES} EXPECTED_INDEXES['hex3'] = EXPECTED_INDEXES['hex2'] @@ -252,10 +239,7 @@ class CylindricalDetectorTester(DetectorHelper): 'Z': array([[0.00000E+00, 0.00000E+00, 0.00000E+00]]) } EXPECTED_GRIDS = {DET_NAME: _EXPECTED_GRIDS} - _INDEXES = OrderedDict([ - ['phi', arange(4)], - ['rmesh', arange(5)], - ]) + _INDEXES = ("phi", "rmesh") EXPECTED_INDEXES = {DET_NAME: _INDEXES} SLICING = { diff --git a/tests/test_detectors.py b/tests/test_detectors.py new file mode 100644 index 0000000..f60aaeb --- /dev/null +++ b/tests/test_detectors.py @@ -0,0 +1,233 @@ +""" +Test various aspects of the detectors classes +""" + +import numpy +import pytest +from serpentTools import detectors + [email protected](scope="module") +def meshedBinData(): + bins = numpy.ones((25, 12), order="f") + bins[:, -1] = range(25) + bins[:, -2] = range(25) + bins[:, -3] = numpy.tile(range(1, 6), 5) + bins[:, -4] = numpy.repeat(range(1, 6), 5) + + tallies = numpy.arange(25).reshape(5, 5) + errors = tallies.copy() + + return bins, tallies, errors + + +def testDetectorProperties(meshedBinData): + bins, tallies, errors = meshedBinData + + detector = detectors.Detector( + "test", bins=bins, tallies=tallies, errors=errors) + + # Modify the tally data + detector.tallies = detector.tallies * 2 + assert (detector.tallies == tallies * 2).all() + + detector.errors = detector.errors * 2 + assert (detector.errors == errors * 2).all() + + energies = numpy.arange(bins.shape[0] * 3).reshape(bins.shape[0], 3) + energyDet = detectors.Detector( + "energy", bins=bins, grids={"E": energies}) + + assert (energyDet.energy == energies).all() + + # Test setting indexes + + detector.indexes = tuple(range(len(tallies.shape))) + + with pytest.raises(ValueError, match="indexes"): + detector.indexes = detector.indexes[:1] + [email protected]("how", ["bins", "grids", "bare", "init"]) +def testCartesianDetector(meshedBinData, how): + + xgrid = numpy.empty((5, 3)) + xgrid[:, 0] = range(5) + xgrid[:, 1] = xgrid[:, 0] + 1 + xgrid[:, 2] = xgrid[:, 0] + 0.5 + + ygrid = xgrid.copy() + + zgrid = numpy.array([[-1, 1, 0]]) + + bins, tallies, errors = meshedBinData + + grids = {"X": xgrid.copy(), "Y": ygrid.copy(), "Z": zgrid.copy()} + + if how == "bare": + detector = detectors.CartesianDetector("xy_bare") + detector.bins = bins + detector.tallies = tallies + detector.errors = errors + detector.x = grids["X"] + detector.y = grids["Y"] + detector.z = grids["Z"] + elif how == "grids": + detector = detectors.CartesianDetector( + "xy_full", bins=bins, tallies=tallies, errors=errors, + grids=grids) + elif how == "init": + detector = detectors.CartesianDetector( + "xy_full", bins=bins, tallies=tallies, errors=errors, + x=grids["X"], y=grids["Y"], z=grids["Z"]) + elif how == "bins": + detector = detectors.CartesianDetector.fromTallyBins( + "xyBins", bins=bins, grids=grids) + + # Modify the tally data + detector.tallies = tallies * 2 + assert (detector.tallies == tallies * 2).all() + + detector.errors = errors * 2 + assert (detector.errors == errors * 2).all() + + assert (detector.x == xgrid).all() + assert (detector.y == ygrid).all() + assert (detector.z == zgrid).all() + + # Emulate scaling by some conversion factor + detector.x = 100 + assert (detector.x == xgrid 100).all() + detector.y = 100 + assert (detector.y == ygrid 100).all() + detector.z = 100 + assert (detector.z == zgrid 100).all() + + # Test failure modes + + for gridk in ["x", "y", "z"]: + msg = ".shape of {} grid".format(gridk) + with pytest.raises(ValueError, match=msg): + setattr(detector, gridk, [1, 2, 3]) + + # Test setting indexes + + detector.indexes = tuple(range(len(tallies.shape))) + + with pytest.raises(ValueError, match="indexes"): + detector.indexes = detector.indexes[:1] + [email protected]("how", ["grids", "bins", "bare", "init"]) +def testHexagonalDetector(meshedBinData, how): + centers = numpy.array([ + [-3.000000E+00, -1.732051E+00], + [-2.500000E+00, -8.660254E-01], + [-2.000000E+00, 0.000000E+00], + [-1.500000E+00, 8.660254E-01], + [-1.000000E+00, 1.732051E+00], + [-2.000000E+00, -1.732051E+00], + [-1.500000E+00, -8.660254E-01], + [-1.000000E+00, 0.000000E+00], + [-5.000000E-01, 8.660254E-01], + [0.000000E+00, 1.732051E+00], + [-1.000000E+00, -1.732051E+00], + [-5.000000E-01, -8.660254E-01], + [0.000000E+00, 0.000000E+00], + [5.000000E-01, 8.660254E-01], + [1.000000E+00, 1.732051E+00], + [0.000000E+00, -1.732051E+00], + [5.000000E-01, -8.660254E-01], + [1.000000E+00, 0.000000E+00], + [1.500000E+00, 8.660254E-01], + [2.000000E+00, 1.732051E+00], + [1.000000E+00, -1.732051E+00], + [1.500000E+00, -8.660254E-01], + [2.000000E+00, 0.000000E+00], + [2.500000E+00, 8.660254E-01], + [3.000000E+00, 1.732051E+00], + ]) + z = numpy.array([[0, 0, 0]]) + + bins, tallies, errors = meshedBinData + + pitch = 1.0 + hexType = 2 + + if how == "init": + detector = detectors.HexagonalDetector( + "hexInit", bins=bins, tallies=tallies, errors=errors, + z=z, centers=centers, pitch=pitch, hexType=hexType) + elif how == "grids": + detector = detectors.HexagonalDetector( + "hexGrids", bins=bins, tallies=tallies, errors=errors, + grids={"Z": z, "COORD": centers}) + elif how == "bins": + detector = detectors.HexagonalDetector.fromTallyBins( + "hexBins", bins, grids={"Z": z, "COORD": centers}) + elif how == "bare": + detector = detectors.HexagonalDetector("hexBins") + detector.bins = bins + detector.tallies = tallies + detector.errors = errors + detector.z = z + detector.centers = centers + + if how != "init": + detector.pitch = pitch + detector.hexType = hexType + + assert (detector.bins == bins).all() + assert (detector.tallies == tallies).all() + assert (detector.errors == errors).all() + assert (detector.z == z).all() + assert (detector.centers == centers).all() + assert detector.pitch == pitch + assert detector.hexType == hexType + + detector.tallies = detector.tallies 2 + detector.errors = detector.errors * 2 + detector.z = detector.z * 2 + detector.centers = detector.centers * 2 + detector.pitch = detector.pitch * 2 + + assert (detector.tallies == tallies * 2).all() + assert (detector.errors == errors * 2).all() + assert (detector.z == z * 2).all() + assert (detector.centers == centers * 2).all() + assert detector.pitch == pitch * 2 + + # Test failure modes + + with pytest.raises(ValueError, match="Hex type"): + detector.hexType = -1 + + with pytest.raises(ValueError, match="Pitch must be positive"): + detector.pitch = 0 + + with pytest.raises(ValueError, match="Pitch must be positive"): + detector.pitch = -1 + + with pytest.raises(TypeError, match="Cannot set pitch"): + detector.pitch = [1, 2] + + with pytest.raises(ValueError, match="Expected centers"): + detector.centers = detector.centers[:5] + + with pytest.raises(ValueError, match="Expected shape of z"): + detector.z = [[-1, 0, -0.5], [0, 1, 0.5]] + + [email protected](scope="module") +def binsWithScores(): + bins = numpy.ones((2, 13)) + bins[0, -3] = 1.5 + return bins + +def testNoScores(binsWithScores): + + with pytest.raises(ValueError, match=".scores"): + detectors.Detector("scored", bins=binsWithScores) + + with pytest.raises(ValueError, match=".scores"): + detectors.Detector("scored").bins = binsWithScores + + with pytest.raises(ValueError, match=".scores"): + detectors.Detector.fromTallyBins("scored", bins=binsWithScores) diff --git a/tests/test_toMatlab.py b/tests/test_toMatlab.py index 1b96c8f..19d9ecd 100644 --- a/tests/test_toMatlab.py +++ b/tests/test_toMatlab.py @@ -3,21 +3,22 @@ Tests for testing the MATLAB conversion functions """ from six import BytesIO, iteritems -from numpy import arange +from numpy import arange, ones from numpy.testing import assert_array_equal -from serpentTools.objects import Detector +from serpentTools import Detector from serpentTools.data import getFile, readDataFile from serpentTools.parsers import DepmtxReader from tests import MatlabTesterHelper + class Det2MatlabHelper(MatlabTesterHelper): """Helper class for testing detector to matlab conversion""" NBINS = 10 NCOLS = 12 # approximate some detector data - BINS = arange( + BINS = ones( NCOLS NBINS, dtype=float).reshape(NBINS, NCOLS) # emulate energy grid GRID = arange(3 * NBINS).reshape(NBINS, 3) @@ -32,7 +33,7 @@ class Det2MatlabHelper(MatlabTesterHelper): def setUp(self): MatlabTesterHelper.setUp(self) - from serpentTools.objects.detectors import deconvert, prepToMatlab + from serpentTools.detectors import deconvert, prepToMatlab # instance methods and/or rename them # potential issues sending putting many such functions in this # test suite	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_added_files", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": -1, "issue_text_score": 2, "test_score": -1 }, "num_modified_files": 14 }	0.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 coverage==6.2 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.19.5 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 pytest-cov==4.0.0 python-dateutil==2.9.0.post0 PyYAML==5.1.1 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@88b5707ebb49f9fe3d4755999c0659fa6b5ee131#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - coverage==6.2 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.19.5 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - pytest-cov==4.0.0 - python-dateutil==2.9.0.post0 - pyyaml==5.1.1 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "tests/test_detector.py::CartesianDetectorTester::test_detectorGrids", "tests/test_detector.py::CartesianDetectorTester::test_detectorIndex", "tests/test_detector.py::CartesianDetectorTester::test_detectorSlice", "tests/test_detector.py::CartesianDetectorTester::test_getitem", "tests/test_detector.py::CartesianDetectorTester::test_iterDets", "tests/test_detector.py::CartesianDetectorTester::test_loadedDetectors", "tests/test_detector.py::CartesianDetectorTester::test_sharedPlot", "tests/test_detector.py::HexagonalDetectorTester::test_detectorGrids", "tests/test_detector.py::HexagonalDetectorTester::test_detectorIndex", "tests/test_detector.py::HexagonalDetectorTester::test_detectorSlice", "tests/test_detector.py::HexagonalDetectorTester::test_getitem", "tests/test_detector.py::HexagonalDetectorTester::test_iterDets", "tests/test_detector.py::HexagonalDetectorTester::test_loadedDetectors", "tests/test_detector.py::CylindricalDetectorTester::test_detectorGrids", "tests/test_detector.py::CylindricalDetectorTester::test_detectorIndex", "tests/test_detector.py::CylindricalDetectorTester::test_detectorSlice", "tests/test_detector.py::CylindricalDetectorTester::test_getitem", "tests/test_detector.py::CylindricalDetectorTester::test_iterDets", "tests/test_detector.py::CylindricalDetectorTester::test_loadedDetectors", "tests/test_detector.py::CombinedDetTester::test_detectorGrids", "tests/test_detector.py::CombinedDetTester::test_detectorIndex", "tests/test_detector.py::CombinedDetTester::test_detectorSlice", "tests/test_detector.py::CombinedDetTester::test_getitem", "tests/test_detector.py::CombinedDetTester::test_iterDets", "tests/test_detector.py::CombinedDetTester::test_loadedDetectors", "tests/test_detector.py::SingleTallyTester::test_singleTally", "tests/test_detector.py::TimeBinnedDetectorTester::test_timeDetector", "tests/test_detectors.py::testDetectorProperties", "tests/test_detectors.py::testCartesianDetector[bins]", "tests/test_detectors.py::testCartesianDetector[grids]", "tests/test_detectors.py::testCartesianDetector[bare]", "tests/test_detectors.py::testCartesianDetector[init]", "tests/test_detectors.py::testHexagonalDetector[grids]", "tests/test_detectors.py::testHexagonalDetector[bins]", "tests/test_detectors.py::testHexagonalDetector[bare]", "tests/test_detectors.py::testHexagonalDetector[init]", "tests/test_detectors.py::testNoScores" ]	[]	[]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-346	09a5feab5b95f98aef4f7dc7b07edbf8e458f955	2019-10-07 19:21:44	09a5feab5b95f98aef4f7dc7b07edbf8e458f955		diff --git a/docs/changelog.rst b/docs/changelog.rst index 2c68415..5e70766 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -13,6 +13,12 @@ Next * \|HomogUniv\| objects no longer automatically convert data to arrays * Serpent 2.1.31 is the default version for :ref:`serpentVersion` setting +Bug Fixes +--------- + +* Burnup and days are properly set on homogenized universes when reading a + result file with multiple universes but no burnup + Incompatible API Changes ------------------------ diff --git a/serpentTools/parsers/results.py b/serpentTools/parsers/results.py index d9f83ce..796a7ff 100644 --- a/serpentTools/parsers/results.py +++ b/serpentTools/parsers/results.py @@ -45,7 +45,7 @@ MapStrVersions = { '2.1.29': { 'meta': 'VERSION ', 'rslt': 'MIN_MACROXS', 'univ': 'GC_UNIVERSE_NAME', 'days': 'BURN_DAYS', - 'burn': 'BURNUP', 'infxs': 'INF_', 'b1xs': 'B1_', + 'burnup': 'BURNUP', 'infxs': 'INF_', 'b1xs': 'B1_', 'varsUnc': ['MICRO_NG', 'MICRO_E', 'MACRO_NG', 'MACRO_E'], }, } @@ -204,15 +204,22 @@ class ResultsReader(XSReader): def __init__(self, filePath): XSReader.__init__(self, filePath, 'results') - self.__serpentVersion = rc['serpentVersion'] + + self.metadata = {} + self.resdata = {} + self.universes = {} + + def _read(self): + """Read through the results file and store requested data.""" + self._counter = {'meta': 0, 'rslt': 0} - self._numUniverses = 0 self._univlist = [] - self.metadata, self.resdata, self.universes = {}, {}, {} self._varTypeLookup = {} - def _read(self): - """Read through the results file and store requested data.""" + self.metadata.clear() + self.resdata.clear() + self.universes.clear() + with open(self.filePath, 'r') as fObject: for tline in fObject: self._processResults(tline) @@ -303,22 +310,24 @@ class ResultsReader(XSReader): def _getBUstate(self): """Define unique branch state""" burnIdx = self._counter['rslt'] - 1 - varPyDays = convertVariableName(self._keysVersion['days']) # Py style - varPyBU = convertVariableName(self._keysVersion['burn']) - if varPyDays in self.resdata.keys(): - if burnIdx > 0: - days = self.resdata[varPyDays][-1, 0] - else: - days = self.resdata[varPyDays][-1] + dayVec = self.resdata.get(self._burnupKeys["days"]) + + if dayVec is None: + days = 0 + elif burnIdx: + days = dayVec[-1, 0] else: - days = self._counter['meta'] - 1 - if varPyBU in self.resdata.keys(): - if burnIdx > 0: - burnup = self.resdata[varPyBU][-1, 0] - else: - burnup = self.resdata[varPyBU][-1] + days = dayVec[0] + + burnupVec = self.resdata.get(self._burnupKeys["burnup"]) + + if burnupVec is None: + burnup = 0 + elif burnIdx: + burnup = burnupVec[-1, 0] else: - burnup = self._counter['meta'] - 1 + burnup = burnupVec[-1] + return UnivTuple(self._univlist[-1], burnup, burnIdx, days) def _getVarName(self, tline): @@ -397,13 +406,20 @@ class ResultsReader(XSReader): def _precheck(self): """do a quick scan to ensure this looks like a results file.""" - if self.__serpentVersion in MapStrVersions: - self._keysVersion = MapStrVersions[self.__serpentVersion] - else: + serpentV = rc['serpentVersion'] + keys = MapStrVersions.get(serpentV) + + if keys is None: warning("SERPENT {} is not supported by the " - "ResultsReader".format(self.__serpentVersion)) + "ResultsReader".format(serpentV)) warning(" Attemping to read anyway. Please report strange " "behaviors/failures to developers.") + keys = MapStrVersions[max(MapStrVersions)] + + self._keysVersion = keys + + self._burnupKeys = {k: convertVariableName(keys[k]) for k in {"days", "burnup"}} + univSet = set() verWarning = True with open(self.filePath) as fid: @@ -415,11 +431,11 @@ class ResultsReader(XSReader): if verWarning and self._keysVersion['meta'] in tline: verWarning = False varType, varVals = self._getVarValues(tline) # version - if self.__serpentVersion not in varVals: + if serpentV not in varVals: warning("SERPENT {} found in {}, but version {} is " "defined in settings" .format(varVals, self.filePath, - self.__serpentVersion)) + serpentV)) warning(" Attemping to read anyway. Please report " "strange behaviors/failures to developers.") if self._keysVersion['univ'] in tline: @@ -451,8 +467,8 @@ class ResultsReader(XSReader): def _postcheck(self): self._inspectData() self._cleanMetadata() - del self._varTypeLookup - self._univList = [] + del (self._varTypeLookup, self._burnupKeys, self._keysVersion, + self._counter, self._univlist) def _compare(self, other, lower, upper, sigma): similar = self.compareMetadata(other)	BUG Burnup and day values wrong for results with multiple gcu, no burnup ## Summary of issue When reading a result file with multiple homogenized universes and no burnup, the homogenized universes have incorrect values of burnup and days. ## Code for reproducing the issue Reading a file with four homogenized universes, (50, 60, 70, 80) and no burnup ``` >>> r = serpentTools.read("core_res.m") >>> r.universes.keys() dict_keys([ UnivTuple(universe='50', burnup=0, step=0, days=0), UnivTuple(universe='60', burnup=1, step=0, days=1), UnivTuple(universe='70', burnup=2, step=0, days=2), UnivTuple(universe='80', burnup=3, step=0, days=3)]) ``` ## Expected outcome All values of `burnup` and `days` should be zero. The bug exists on the develop branch and master branch [0.7.1] but the keys are plain tuples. ## Versions * Version from ``serpentTools.__version__`` `0.7.1-48-g09a5fea` and `0.7.1` * Python version - ``python --version`` 3.7.4	CORE-GATECH-GROUP/serpent-tools	diff --git a/tests/test_results.py b/tests/test_results.py new file mode 100644 index 0000000..057debd --- /dev/null +++ b/tests/test_results.py @@ -0,0 +1,40 @@ +import re +import os + +import pytest +import serpentTools + + [email protected] +def multipleGcuNoBu(): + origFile = serpentTools.data.getFile("InnerAssembly_res.m") + newFile = "MultipleGcuNoBu_res.m" + nGcu = 3 + counter = re.compile("Increase counter") + burnKey = re.compile("BURN") + counts = 0 + + with open(origFile, "r") as orig, open(newFile, "w") as new: + for line in orig: + if burnKey.match(line): + continue + if counter.search(line): + counts += 1 + if counts > nGcu: + break + new.write(line) + + with serpentTools.settings.rc as temprc: + temprc["serpentVersion"] = "2.1.30" + yield newFile + + os.remove(newFile) + + +def test_multipleGcuNoBu(multipleGcuNoBu): + r = serpentTools.read(multipleGcuNoBu) + assert len(r.universes) == 3 + for key in r.universes: + assert key.step == 0 + assert key.burnup == 0 + assert key.days == 0	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 3 }, "num_modified_files": 2 }	0.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler==0.11.0 importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver==1.3.1 matplotlib==3.3.4 numpy==1.19.5 packaging==21.3 Pillow==8.4.0 pluggy==1.0.0 py==1.11.0 pyparsing==3.1.4 pytest==7.0.1 python-dateutil==2.9.0.post0 PyYAML==5.1.1 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@09a5feab5b95f98aef4f7dc7b07edbf8e458f955#egg=serpentTools six==1.12.0 tomli==1.2.3 typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==22.2.0 - cycler==0.11.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - kiwisolver==1.3.1 - matplotlib==3.3.4 - numpy==1.19.5 - packaging==21.3 - pillow==8.4.0 - pluggy==1.0.0 - py==1.11.0 - pyparsing==3.1.4 - pytest==7.0.1 - python-dateutil==2.9.0.post0 - pyyaml==5.1.1 - six==1.12.0 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "tests/test_results.py::test_multipleGcuNoBu" ]	[]	[]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-346
CORE-GATECH-GROUP__serpent-tools-377	3d11b98046c48eb8a1c625e1dd534bc3af0a7be6	2020-01-17 15:52:18	d8f02d1f064b40e23db4c5ad6bf0bb31b9e8a6da		diff --git a/docs/changelog.rst b/docs/changelog.rst index e651937..3862113 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -6,6 +6,18 @@ Changelog ========= +.. _v0.9.2: + +0.9.2 +===== + +.. _v0.9.2-bugs: + +Bug Fixes +--------- + +* Detector reader can handle sequential detectors with very similar + names - :issue:`374`. .. _v0.9.1: @@ -21,7 +33,6 @@ Bug Fixes overwrite the primary result arrays - :pull:`366`. These arrays are not currently stored - :issue:`367` - .. _v0.9.0: :release-tag:`0.9.0` diff --git a/serpentTools/parsers/detector.py b/serpentTools/parsers/detector.py index 52f6f43..c5a28c4 100644 --- a/serpentTools/parsers/detector.py +++ b/serpentTools/parsers/detector.py @@ -30,6 +30,9 @@ class DetectorReader(BaseReader): instances """ + # Update this if new detector grids are introduced + _KNOWN_GRIDS = ("E", "X", "Y", "Z", "T", "COORD", "R", "PHI", "THETA") + def __init__(self, filePath): BaseReader.__init__(self, filePath, 'detector') self.detectors = {} @@ -60,7 +63,18 @@ class DetectorReader(BaseReader): with KeywordParser(self.filePath, ["DET"], ["\n", "];"]) as parser: for chunk in parser.yieldChunks(): name, data = cleanDetChunk(chunk) - if currentName and name[:len(currentName)] != currentName: + + # Determine if this is a new detector + if not currentName: + isNewDetector = False + elif not name.startswith(currentName): + isNewDetector = True + else: + isNewDetector = not any( + name == "".join((currentName, g)) + for g in self._KNOWN_GRIDS) + + if isNewDetector: self._processDet(currentName, bins, grids) bins = data grids = {}	BUG DetectorReader misses detectors that start with and contain identical names ## Summary of issue A colleague has a detector file with the following energy dependent detectors: `spectrum`, `spectrumA`, and `spectrumB`. The DetectorReader fails to capture the later two detectors, and messes up the data stored on `spectrum`. ## Code for reproducing the issue Process a detector file with the above names. One has been created for reproducibility using the `bwr_det0.m` file and the following commands ```sh $ sed "s/spectrum/spectrumA/" bwr_det0.m > a $ sed "s/spectrum/spectrumB/" bwr_det0.m > b $ cat bwr_det0.m a b > joined_det0.m ``` Process with python ```python >>> import serpentTools >>> det = serpentTools.read("joined_det0.m") ``` ## Actual outcome including console output and error traceback if applicable ```python >>> det.detectors {'spectrum' : <serpentTools.detector.Detector object at ...>} >>> det["spectrum"].grids.keys() dict_keys(["E", "A", "AE", "B", "BE"]) ``` ## Expected outcome All three detectors are properly defined. ## Versions Please provide the following: * Version from ``serpentTools.__version__`` `0.9.1` * Python version - ``python --version`` - `3.7` ## Likely culprit https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/2dc5a9d03bc510d358032f270575f0ab3120584f/serpentTools/parsers/detector.py#L63 # Workaround If the file has already been produced, then users can change the detector names to be more distinct, e.g. ```sh $ sed -e "s/spectrumA/Aspectrum/" -e "s/spectrumB/Bspectrum/" joined_det0.m > mod_det0.m ``` then serpentTools reveals that `spectrum`, `Aspectrum`, and `Bspectrum` detectors have been found. If the detector names must be present in the file, users can put the longer names first, or break up the ordering with other detectors. This will "confuse" the matching. Putting `spectrum` last yields the correct detectors.	CORE-GATECH-GROUP/serpent-tools	diff --git a/tests/test_detectors.py b/tests/test_detectors.py index f60aaeb..0462578 100644 --- a/tests/test_detectors.py +++ b/tests/test_detectors.py @@ -4,8 +4,10 @@ Test various aspects of the detectors classes import numpy import pytest +import serpentTools from serpentTools import detectors + @pytest.fixture(scope="module") def meshedBinData(): bins = numpy.ones((25, 12), order="f") @@ -46,6 +48,7 @@ def testDetectorProperties(meshedBinData): with pytest.raises(ValueError, match="indexes"): detector.indexes = detector.indexes[:1] + @pytest.mark.parametrize("how", ["bins", "grids", "bare", "init"]) def testCartesianDetector(meshedBinData, how): @@ -115,6 +118,7 @@ def testCartesianDetector(meshedBinData, how): with pytest.raises(ValueError, match="indexes"): detector.indexes = detector.indexes[:1] + @pytest.mark.parametrize("how", ["grids", "bins", "bare", "init"]) def testHexagonalDetector(meshedBinData, how): centers = numpy.array([ @@ -221,6 +225,7 @@ def binsWithScores(): bins[0, -3] = 1.5 return bins + def testNoScores(binsWithScores): with pytest.raises(ValueError, match=".scores"): @@ -231,3 +236,41 @@ def testNoScores(binsWithScores): with pytest.raises(ValueError, match=".scores"): detectors.Detector.fromTallyBins("scored", bins=binsWithScores) + + [email protected] +def similarDetectorFile(tmp_path): + detFile = tmp_path / "similar_det0.m" + detFile.absolute() + + with detFile.open("w") as stream: + stream.write("""DETspectrum = [ + 1 1 1 1 1 1 1 1 1 1 8.19312E+17 0.05187 +]; + +DETspectrumA = [ + 1 1 1 1 1 1 1 1 1 1 8.19312E+17 0.05187 +]; + +DETspectrumAE = [ + 0.00000E-11 4.13994E-07 2.07002E-07 +]; + +DETspectrumACOORD = [ + 0.00000E-11 4.13994E-07 +]; + +DETspectrumB = [ + 1 1 1 1 1 1 1 1 1 1 8.19312E+17 0.05187 +];""") + + yield str(detFile) + + detFile.unlink() + + +def test_similarDetectors(similarDetectorFile): + reader = serpentTools.read(similarDetectorFile) + + assert set(reader.detectors) == {"spectrum", "spectrumA", "spectrumB"} + assert isinstance(reader["spectrumA"], detectors.HexagonalDetector)	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 2 }	0.9	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.7", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	certifi @ file:///croot/certifi_1671487769961/work/certifi cycler==0.11.0 exceptiongroup==1.2.2 fonttools==4.38.0 importlib-metadata==6.7.0 iniconfig==2.0.0 kiwisolver==1.4.5 matplotlib==3.5.3 numpy==1.21.6 packaging==24.0 Pillow==9.5.0 pluggy==1.2.0 pyparsing==3.1.4 pytest==7.4.4 python-dateutil==2.9.0.post0 PyYAML==6.0.1 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@3d11b98046c48eb8a1c625e1dd534bc3af0a7be6#egg=serpentTools six==1.17.0 tomli==2.0.1 typing_extensions==4.7.1 zipp==3.15.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - cycler==0.11.0 - exceptiongroup==1.2.2 - fonttools==4.38.0 - importlib-metadata==6.7.0 - iniconfig==2.0.0 - kiwisolver==1.4.5 - matplotlib==3.5.3 - numpy==1.21.6 - packaging==24.0 - pillow==9.5.0 - pluggy==1.2.0 - pyparsing==3.1.4 - pytest==7.4.4 - python-dateutil==2.9.0.post0 - pyyaml==6.0.1 - six==1.17.0 - tomli==2.0.1 - typing-extensions==4.7.1 - zipp==3.15.0 prefix: /opt/conda/envs/serpent-tools	[ "tests/test_detectors.py::test_similarDetectors" ]	[]	[ "tests/test_detectors.py::testDetectorProperties", "tests/test_detectors.py::testCartesianDetector[bins]", "tests/test_detectors.py::testCartesianDetector[grids]", "tests/test_detectors.py::testCartesianDetector[bare]", "tests/test_detectors.py::testCartesianDetector[init]", "tests/test_detectors.py::testHexagonalDetector[grids]", "tests/test_detectors.py::testHexagonalDetector[bins]", "tests/test_detectors.py::testHexagonalDetector[bare]", "tests/test_detectors.py::testHexagonalDetector[init]", "tests/test_detectors.py::testNoScores" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-377
CORE-GATECH-GROUP__serpent-tools-4	606a90d665a15a16c437dd1fb72ef014aa480142	2017-09-19 15:34:09	606a90d665a15a16c437dd1fb72ef014aa480142		diff --git a/serpentTools/__init__.py b/serpentTools/__init__.py index 8af8d0f..d38090d 100644 --- a/serpentTools/__init__.py +++ b/serpentTools/__init__.py @@ -1,7 +1,7 @@ from serpentTools import settings from serpentTools import parsers -__version__ = '0.1.1' +__version__ = '0.1.2' # List TODOS/feature requests here for now # Messages/Errors diff --git a/serpentTools/objects/__init__.py b/serpentTools/objects/__init__.py index 42ee6c0..8f2e2cd 100644 --- a/serpentTools/objects/__init__.py +++ b/serpentTools/objects/__init__.py @@ -146,8 +146,18 @@ class DepletedMaterial(_SupportingObject): AttributeError If the names of the isotopes have not been obtained and specific isotopes have been requested + KeyError + If at least one of the days requested is not present """ - returnX = timePoints is None + if timePoints is not None: + returnX = False + timeCheck = self._checkTimePoints(xUnits, timePoints) + if any(timeCheck): + raise KeyError('The following times were not present in file {}' + '\n{}'.format(self._container.filePath, + ', '.join(timeCheck))) + else: + returnX = True if names and 'names' not in self._container.metadata: raise AttributeError('Parser {} has not stored the isotope names.' .format(self._container)) @@ -164,6 +174,12 @@ class DepletedMaterial(_SupportingObject): return yVals, xVals return yVals + def _checkTimePoints(self, xUnits, timePoints): + valid = self[xUnits] + badPoints = [str(time) for time in timePoints if time not in valid] + return badPoints + + def _getXSlice(self, xUnits, timePoints): allX = self[xUnits] if timePoints is not None: diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index 6817810..8efd2f1 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -1,9 +1,9 @@ """Parser responsible for reading the ``coe.m`` files""" -from serpentTools.parsers import _BaseReader +from serpentTools.objects.readers import BaseReader -class BranchingReader(_BaseReader): +class BranchingReader(BaseReader): """ Parser responsible for reading and working with automated branching files. diff --git a/serpentTools/parsers/bumat.py b/serpentTools/parsers/bumat.py index 70ead0a..27c155b 100644 --- a/serpentTools/parsers/bumat.py +++ b/serpentTools/parsers/bumat.py @@ -1,9 +1,9 @@ """Parser responsible for reading the ``bumat<n>.m`` files""" -from serpentTools.parsers import _MaterialReader +from serpentTools.objects.readers import MaterialReader -class BumatReader(_MaterialReader): +class BumatReader(MaterialReader): """ Parser responsible for reading and working with burned material files. diff --git a/serpentTools/parsers/detector.py b/serpentTools/parsers/detector.py index ea40920..9c4c33c 100644 --- a/serpentTools/parsers/detector.py +++ b/serpentTools/parsers/detector.py @@ -1,9 +1,9 @@ """Parser responsible for reading the ``det<n>.m`` files""" -from serpentTools.parsers import _BaseReader +from serpentTools.objects.readers import BaseReader -class DetectorReader(_BaseReader): +class DetectorReader(BaseReader): """ Parser responsible for reading and working with detector files. diff --git a/serpentTools/parsers/fissionMatrix.py b/serpentTools/parsers/fissionMatrix.py index 58af81d..3923415 100644 --- a/serpentTools/parsers/fissionMatrix.py +++ b/serpentTools/parsers/fissionMatrix.py @@ -1,9 +1,9 @@ """Parser responsible for reading the ``fmtx<n>.m`` files""" -from serpentTools.parsers import _BaseReader +from serpentTools.objects.readers import BaseReader -class FissionMatrixReader(_BaseReader): +class FissionMatrixReader(BaseReader): """ Parser responsible for reading and working with fission matrix files. diff --git a/serpentTools/parsers/results.py b/serpentTools/parsers/results.py index f33b68c..ae4f1ef 100644 --- a/serpentTools/parsers/results.py +++ b/serpentTools/parsers/results.py @@ -1,9 +1,9 @@ """Parser responsible for reading the ``*res.m`` files""" -from serpentTools.parsers import _BaseReader +from serpentTools.objects.readers import BaseReader -class ResultsReader(_BaseReader): +class ResultsReader(BaseReader): """ Parser responsible for reading and working with result files.	`getXY` for `DepletedMaterial` raises unhelpful value error if days missing from object If a depleted material is asked to get some quantity for days that are not present, the following error is raised: ``` File "C:\Users\ajohnson400\AppData\Local\Continuum\Anaconda3\lib\site-packages\serpenttools-0.1.1rc0-py3.5.egg\serpentTools\objects\__init__.py", line 162, in getXY else allY[rowId][:]) ValueError: could not broadcast input array from shape (19) into shape (0) ``` The value of `colIndices` is an empty list for this case.	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_depletion.py b/serpentTools/tests/test_depletion.py index 0574305..d7f6d52 100644 --- a/serpentTools/tests/test_depletion.py +++ b/serpentTools/tests/test_depletion.py @@ -156,6 +156,17 @@ class DepletedMaterialTester(_DepletionTestHelper): names=['Xe135']) numpy.testing.assert_equal(actualDays, self.reader.metadata['days']) + def test_getXY_raisesError_badTime(self): + """Verify that a ValueError is raised for non-present requested days.""" + badDays = [-1, 0, 50] + with self.assertRaises(KeyError): + self.material.getXY('days', 'adens', timePoints=badDays) + + def test_fetchData(self): + """Verify that key errors are raised when bad data are requested.""" + with self.assertRaises(KeyError): + _ = self.material['fake units'] + if __name__ == '__main__': unittest.main()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 7 }	unknown	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	contourpy==1.3.0 cycler==0.12.1 drewtils==0.1.9 exceptiongroup @ file:///croot/exceptiongroup_1706031385326/work fonttools==4.56.0 importlib_resources==6.5.2 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work kiwisolver==1.4.7 matplotlib==3.9.4 numpy==2.0.2 packaging @ file:///croot/packaging_1734472117206/work pillow==11.1.0 pluggy @ file:///croot/pluggy_1733169602837/work pyparsing==3.2.3 pytest @ file:///croot/pytest_1738938843180/work python-dateutil==2.9.0.post0 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@606a90d665a15a16c437dd1fb72ef014aa480142#egg=serpentTools six==1.17.0 tomli @ file:///opt/conda/conda-bld/tomli_1657175507142/work zipp==3.21.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - exceptiongroup=1.2.0=py39h06a4308_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.2=py39h06a4308_0 - pip=25.0=py39h06a4308_0 - pluggy=1.5.0=py39h06a4308_0 - pytest=8.3.4=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tomli=2.0.1=py39h06a4308_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - contourpy==1.3.0 - cycler==0.12.1 - drewtils==0.1.9 - fonttools==4.56.0 - importlib-resources==6.5.2 - kiwisolver==1.4.7 - matplotlib==3.9.4 - numpy==2.0.2 - pillow==11.1.0 - pyparsing==3.2.3 - python-dateutil==2.9.0.post0 - six==1.17.0 - zipp==3.21.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_raisesError_badTime" ]	[]	[ "serpentTools/tests/test_depletion.py::DepletionTester::test_ReadMaterials", "serpentTools/tests/test_depletion.py::DepletionTester::test_metadata", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_fetchData", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_adens", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_adensAndTime", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_burnup_full", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_burnup_slice", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_materials" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-40	ebd70724628e6108a10b47338c31d3fcb3af7d03	2017-10-19 21:23:42	224ef748f519903554f346d48071e58b43dcf902		diff --git a/.travis.yml b/.travis.yml index 19f06e3..98b5729 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,3 +1,5 @@ +env: + - ONTRAVIS=True language: python python: - "3.6" diff --git a/serpentTools/objects/__init__.py b/serpentTools/objects/__init__.py index 2ef74d4..53949d9 100644 --- a/serpentTools/objects/__init__.py +++ b/serpentTools/objects/__init__.py @@ -1,7 +1,7 @@ """Objects used to support the parsing.""" -class _SupportingObject(object): +class SupportingObject(object): """ Base supporting object. @@ -9,27 +9,15 @@ class _SupportingObject(object): ---------- container: Some parser from serpentTools.parsers Container that created this object - name: str - Name of this specific object, e.g. material name, detector name, etc. """ def __init__(self, container): - self._container = container - self._filePath = container.filePath + self.origin = container.filePath - def __repr__(self): - return '<{} from {}>'.format(self.whatAmI(), self._filePath) + def __str__(self): + return '<{} from {}>'.format(self.__class__.__name__, self.origin) - def whatAmI(self): - return type(self).__name__ - - def __getattr__(self, item): - """Search for the item in the containers metadata.""" - if item in self._container.metadata: - return self._container.metadata[item] - raise AttributeError('{} has object has no attribute \'{}\'' - .format(self, item)) @staticmethod def _convertVariableName(variable): @@ -42,13 +30,13 @@ class _SupportingObject(object): for item in lowerSplits[1:]]) -class _NamedObject(_SupportingObject): +class NamedObject(SupportingObject): """Class for named objects like materials and detectors.""" def __init__(self, container, name): - _SupportingObject.__init__(self, container) + SupportingObject.__init__(self, container) self.name = name - def __repr__(self): - return '<{} {} from {}>'.format(self.whatAmI(), - self.name, self._filePath) \ No newline at end of file + def __str__(self): + return '<{} {} from {}>'.format(self.__class__.__name__, + self.name, self.origin) diff --git a/serpentTools/objects/materials.py b/serpentTools/objects/materials.py index 49e2ff4..ac65c6a 100644 --- a/serpentTools/objects/materials.py +++ b/serpentTools/objects/materials.py @@ -4,11 +4,12 @@ import numpy from matplotlib import pyplot from serpentTools.settings import messages -from serpentTools.objects import _NamedObject +from serpentTools.objects import NamedObject -class DepletedMaterial(_NamedObject): - """Class for storing material data from ``_dep.m`` files. +class DepletedMaterial(NamedObject): + """ + Class for storing material data from ``_dep.m`` files. Parameters ---------- @@ -20,39 +21,67 @@ class DepletedMaterial(_NamedObject): Attributes ---------- - zai: numpy.array + zai: numpy.array or None Isotope id's - names: numpy.array + names: numpy.array or None Names of isotopes - days: numpy.array + days: numpy.array or None Days overwhich the material was depleted - adens: numpy.array + adens: numpy.array or None Atomic density over time for each nuclide + mdens: numpy.array or None + Mass density over time for each nuclide + burnup: numpy.array or None + Burnup of the material over time """ def __init__(self, parser, name): - _NamedObject.__init__(self, parser, name) - self._varData = {} - - def __getattr__(self, item): - """ - Allows the user to get items like ``zai`` and ``adens`` - with ``self.zai`` and ``self.adens``, respectively. - """ - if item in self._varData: - return self._varData[item] - return _NamedObject.__getattr__(self, item) + NamedObject.__init__(self, parser, name) + self.data = {} + self.zai = parser.metadata.get('zai', None) + self.names = parser.metadata.get('names', None) + self.days = parser.metadata.get('days', None) + self.__burnup__ = None + self.__adens__ = None + self.__mdens__ = None def __getitem__(self, item): - if item not in self._varData: - if item not in self._container.metadata: - raise KeyError('{} has no item {}'.format(self, item)) - return self._container.metadata[item] - return self._varData[item] + if item not in self.data: + raise KeyError('Key {} not found on material {}' + .format(item, self.name)) + return self.data[item] + + @property + def burnup(self): + if 'burnup' not in self.data: + raise AttributeError('Burnup for material {} has not been loaded' + .format(self.name)) + if self.__burnup__ is None: + self.__burnup__ = self.data['burnup'] + return self.__burnup__ + + @property + def adens(self): + if 'adens' not in self.data: + raise AttributeError('Atomic densities for material {} have not ' + 'been loaded'.format(self.name)) + if self.__adens__ is None: + self.__adens__ = self.data['adens'] + return self.__adens__ + + @property + def mdens(self): + if 'mdens' not in self.data: + raise AttributeError('Mass densities for material {} has not been ' + 'loaded'.format(self.name)) + if self.__mdens__ is None: + self.__mdens__ = self.data['mdens'] + return self.__mdens__ def addData(self, variable, rawData): - """Add data straight from the file onto a variable. + """ + Add data straight from the file onto a variable. Parameters ---------- @@ -70,10 +99,20 @@ class DepletedMaterial(_NamedObject): for line in rawData: if line: scratch.append([float(item) for item in line.split()]) - self._varData[newName] = numpy.array(scratch) + self.data[newName] = numpy.array(scratch) + @messages.depreciated def getXY(self, xUnits, yUnits, timePoints=None, names=None): - """Return x values for given time, and corresponding isotope values. + """Depreciated. Use getValues instead""" + if timePoints is None: + timePoints = self.days + return self.getValues(xUnits, yUnits, timePoints, names), self.days + else: + return self.getValues(xUnits, yUnits, timePoints, names) + + def getValues(self, xUnits, yUnits, timePoints=None, names=None): + """ + Return x values for given time, and corresponding isotope values. Parameters ---------- @@ -92,8 +131,6 @@ class DepletedMaterial(_NamedObject): ------- numpy.array Array of values. - numpy.array - Vector of time points only if ``timePoints`` is ``None`` Raises ------ @@ -104,20 +141,18 @@ class DepletedMaterial(_NamedObject): If at least one of the days requested is not present """ if timePoints is not None: - returnX = False timeCheck = self._checkTimePoints(xUnits, timePoints) if any(timeCheck): raise KeyError('The following times were not present in file {}' - '\n{}'.format(self._container.filePath, + '\n{}'.format(self.origin, ', '.join(timeCheck))) - else: - returnX = True - if names and 'names' not in self._container.metadata: - raise AttributeError('Parser {} has not stored the isotope names.' - .format(self._container)) + if names and self.names is None: + raise AttributeError( + 'Isotope names not stored on DepletedMaterial {}.' + .format(self.name)) xVals, colIndices = self._getXSlice(xUnits, timePoints) rowIndices = self._getIsoID(names) - allY = self[yUnits] + allY = self.data[yUnits] if allY.shape[0] == 1 or len(allY.shape) == 1: # vector yVals = allY[colIndices] if colIndices else allY else: @@ -125,17 +160,15 @@ class DepletedMaterial(_NamedObject): for isoID, rowId in enumerate(rowIndices): yVals[isoID, :] = (allY[rowId][colIndices] if colIndices else allY[rowId][:]) - if returnX: - return yVals, xVals return yVals def _checkTimePoints(self, xUnits, timePoints): - valid = self[xUnits] + valid = self.days if xUnits == 'days' else self.data[xUnits] badPoints = [str(time) for time in timePoints if time not in valid] return badPoints def _getXSlice(self, xUnits, timePoints): - allX = self[xUnits] + allX = self.days if xUnits == 'days' else self.data[xUnits] if timePoints is not None: colIndices = [indx for indx, xx in enumerate(allX) if xx in timePoints] @@ -157,7 +190,8 @@ class DepletedMaterial(_NamedObject): return rowIDs def plot(self, xUnits, yUnits, timePoints=None, names=None, ax=None): - """Plot some data as a function of time for some or all isotopes. + """ + Plot some data as a function of time for some or all isotopes. Parameters ---------- @@ -185,9 +219,10 @@ class DepletedMaterial(_NamedObject): getXY """ - xVals, yVals = self.getXY(xUnits, yUnits, timePoints, names) - ax = ax or pyplot.subplots(1, 1)[1] - labels = names or [None] + xVals = timePoints or self.days + yVals = self.getValues(xUnits, yUnits, xVals, names) + ax = ax or pyplot.axes() + labels = names or [''] for row in range(yVals.shape[0]): ax.plot(xVals, yVals[row], label=labels[row]) return ax diff --git a/serpentTools/objects/readers.py b/serpentTools/objects/readers.py index b26ef67..b67b9d6 100644 --- a/serpentTools/objects/readers.py +++ b/serpentTools/objects/readers.py @@ -17,7 +17,7 @@ class BaseReader(object): self.metadata = {} self.settings = rc.getReaderSettings(readerSettingsLevel) - def __repr__(self): + def __str__(self): return '<{} reading {}>'.format(self.__class__.__name__, self.filePath) def read(self): diff --git a/serpentTools/parsers/depletion.py b/serpentTools/parsers/depletion.py index b6f0dda..87355bc 100644 --- a/serpentTools/parsers/depletion.py +++ b/serpentTools/parsers/depletion.py @@ -69,6 +69,9 @@ class DepletionReader(MaterialReader): self._addTotal(chunk) else: self._addMetadata(chunk) + if 'days' in self.metadata: + for mKey in self.materials: + self.materials[mKey].days = self.metadata['days'] messages.debug('Done reading depletion file') messages.debug(' found {} materials'.format(len(self.materials)))	Explicit attributes for supporting objects Currently, supporting objects look for their attributes first in their own metadata, then in the metadata of the metadata of the parent. This poses some introspection ugliness, as it is difficult to determine what attributes are on the object. Each supporting object should have its attributes explicitly stated and stored. This should improve performance and usability. Maybe use `__slots__` as well?	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_depletion.py b/serpentTools/tests/test_depletion.py index 2e00eec..6e8e0d5 100644 --- a/serpentTools/tests/test_depletion.py +++ b/serpentTools/tests/test_depletion.py @@ -120,18 +120,18 @@ class DepletedMaterialTester(_DepletionTestHelper): self.assertListEqual(self.material.zai, self.reader.metadata['zai']) numpy.testing.assert_equal(self.material.adens, expectedAdens) - numpy.testing.assert_equal(self.material.ingTox, expectedIngTox) + numpy.testing.assert_equal(self.material['ingTox'], expectedIngTox) def test_getXY_burnup_full(self): """ Verify the material can produce the full burnup vector through getXY. """ - actual, _days = self.material.getXY('days', 'burnup', ) + actual = self.material.getValues('days', 'burnup', ) numpy.testing.assert_equal(actual, self.fuelBU) def test_getXY_burnup_slice(self): """Verify depletedMaterial getXY correctly slices a vector.""" - actual = self.material.getXY('days', 'burnup', self.requestedDays) + actual = self.material.getValues('days', 'burnup', self.requestedDays) expected = [0.0E0, 1.90317E-2, 3.60163E-2, 1.74880E-1, 3.45353E-01, 8.49693E-01, 1.66071E0] numpy.testing.assert_equal(actual, expected) @@ -147,27 +147,28 @@ class DepletedMaterialTester(_DepletionTestHelper): [0.00000E+00, 2.90880E-14, 5.57897E-14, 2.75249E-13, 5.46031E-13, 1.35027E-12, 2.64702E-12], ], float) - actual = self.material.getXY('days', 'adens', names=names, - timePoints=self.requestedDays) + actual = self.material.getValues('days', 'adens', names=names, + timePoints=self.requestedDays) numpy.testing.assert_allclose(actual, expected, rtol=1E-4) - def test_getXY_adensAndTime(self): - """Verify correct atomic density and time slice are returned.""" - actualAdens, actualDays = self.material.getXY('days', 'adens', - names=['Xe135']) - numpy.testing.assert_equal(actualDays, self.reader.metadata['days']) - def test_getXY_raisesError_badTime(self): """Verify that a ValueError is raised for non-present requested days.""" badDays = [-1, 0, 50] with self.assertRaises(KeyError): - self.material.getXY('days', 'adens', timePoints=badDays) + self.material.getValues('days', 'adens', timePoints=badDays) def test_fetchData(self): """Verify that key errors are raised when bad data are requested.""" with self.assertRaises(KeyError): _ = self.material['fake units'] + @unittest.skipIf(os.getenv('ONTRAVIS') is not None, + "Plotting doesn't play well with Travis") + def test_plotter(self): + """Verify the plotting functionality is operational.""" + self.material.plot('days', 'adens', timePoints=self.requestedDays, + names=['Xe135', 'U235']) + if __name__ == '__main__': unittest.main()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 5 }	1.00	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	contourpy==1.3.0 cycler==0.12.1 drewtils==0.1.9 exceptiongroup @ file:///croot/exceptiongroup_1706031385326/work fonttools==4.56.0 importlib_resources==6.5.2 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work kiwisolver==1.4.7 matplotlib==3.9.4 numpy==2.0.2 packaging @ file:///croot/packaging_1734472117206/work pillow==11.1.0 pluggy @ file:///croot/pluggy_1733169602837/work pyparsing==3.2.3 pytest @ file:///croot/pytest_1738938843180/work python-dateutil==2.9.0.post0 PyYAML==6.0.2 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@ebd70724628e6108a10b47338c31d3fcb3af7d03#egg=serpentTools six==1.17.0 tomli @ file:///opt/conda/conda-bld/tomli_1657175507142/work versioneer==0.29 zipp==3.21.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - exceptiongroup=1.2.0=py39h06a4308_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.2=py39h06a4308_0 - pip=25.0=py39h06a4308_0 - pluggy=1.5.0=py39h06a4308_0 - pytest=8.3.4=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tomli=2.0.1=py39h06a4308_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - contourpy==1.3.0 - cycler==0.12.1 - drewtils==0.1.9 - fonttools==4.56.0 - importlib-resources==6.5.2 - kiwisolver==1.4.7 - matplotlib==3.9.4 - numpy==2.0.2 - pillow==11.1.0 - pyparsing==3.2.3 - python-dateutil==2.9.0.post0 - pyyaml==6.0.2 - six==1.17.0 - versioneer==0.29 - zipp==3.21.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_adens", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_burnup_full", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_burnup_slice", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_getXY_raisesError_badTime", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_plotter" ]	[]	[ "serpentTools/tests/test_depletion.py::DepletionTester::test_ReadMaterials", "serpentTools/tests/test_depletion.py::DepletionTester::test_metadata", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_fetchData", "serpentTools/tests/test_depletion.py::DepletedMaterialTester::test_materials" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-412	a69471eb211a18768aa55c695be906e006b4b4a6	2020-08-10 14:22:31	d8f02d1f064b40e23db4c5ad6bf0bb31b9e8a6da		diff --git a/serpentTools/parsers/depletion.py b/serpentTools/parsers/depletion.py index aa730d8..6e5e05a 100644 --- a/serpentTools/parsers/depletion.py +++ b/serpentTools/parsers/depletion.py @@ -262,7 +262,7 @@ class DepletionReader(DepPlotMixin, MaterialReader): if varName == 'NAMES': values = [item[1:item.find(" ")] for item in cleaned] else: - values = str2vec(cleaned, int, list) + values = [int(v) for v in cleaned] else: line = self._cleanSingleLine(chunk) values = str2vec(line) diff --git a/serpentTools/parsers/microxs.py b/serpentTools/parsers/microxs.py index 039480f..0002e43 100644 --- a/serpentTools/parsers/microxs.py +++ b/serpentTools/parsers/microxs.py @@ -128,15 +128,15 @@ class MicroXSReader(BaseReader): if 'E' in currVar.split('_')[-1]: # e.g., NFY_902270_1E sclVal = SCALAR_REGEX.search(chunk[0]) # energy must be stored on the reader - self._energyFY = float(str2vec( - chunk[0][sclVal.span()[0] + 1:sclVal.span()[1] - 2])) + self._energyFY = float(sclVal.groups()[1]) return # thermal/epi/fast for tline in chunk: if '[' in tline or ']' in tline: continue tline = tline[:tline.find('%')] - if len(tline.split()) == 3: - val1, val2, val3 = str2vec(tline, out=list) + items = tline.split() + if len(items) == 3: + val1, val2, val3 = (float(i) for i in items) fissProd.append(val1) indYield.append(val2) cumYield.append(val3) @@ -151,7 +151,7 @@ class MicroXSReader(BaseReader): # obtain the universe id univ = currVar.split('_')[-1] search = VEC_REGEX.search(chunk0) # group flux values - vals = str2vec(chunk0[search.span()[0] + 1:search.span()[1] - 2]) + vals = str2vec(search.groups()[1]) self.fluxRatio[univ], self.fluxUnc[univ] = splitValsUncs(vals) def _storeMicroXS(self, chunk): diff --git a/serpentTools/parsers/results.py b/serpentTools/parsers/results.py index f0444a0..557e2b5 100644 --- a/serpentTools/parsers/results.py +++ b/serpentTools/parsers/results.py @@ -133,8 +133,8 @@ def varTypeFactory(key): return lambda x: (typeFunc(x), None) # Perform array conversion, return expected values and uncertainties if UNIV_K_RE.search(key) is not None: - return lambda x: str2vec(x, out=tuple) - return lambda x: splitValsUncs(x) + return lambda s: tuple(float(i) for i in s.split()) + return splitValsUncs __all__ = ['ResultsReader', ] @@ -427,13 +427,13 @@ class ResultsReader(XSReader): varType, varVals = [], [] if strMatch is not None: varType = 'string' - varVals = tline[strMatch.span()[0] + 1:strMatch.span()[1] - 1] + varVals = strMatch.groups()[1] elif vecMatch is not None: varType = 'vector' - varVals = tline[vecMatch.span()[0] + 1:vecMatch.span()[1] - 2] + varVals = vecMatch.groups()[1].strip() elif sclMatch is not None: varType = 'scalar' - varVals = tline[sclMatch.span()[0] + 1:sclMatch.span()[1] - 2] + varVals = sclMatch.groups()[1] return varType, varVals def getUniv(self, univ, burnup=None, index=None, timeDays=None): diff --git a/serpentTools/parsers/sensitivity.py b/serpentTools/parsers/sensitivity.py index 98a249c..ef1c253 100644 --- a/serpentTools/parsers/sensitivity.py +++ b/serpentTools/parsers/sensitivity.py @@ -4,7 +4,7 @@ Class to read Sensitivity file from collections import OrderedDict from itertools import product -from numpy import transpose, hstack +from numpy import transpose, hstack, array from matplotlib.pyplot import gca, axvline from serpentTools.utils.plot import magicPlotDocDecorator, formatPlot @@ -219,7 +219,7 @@ class SensitivityReader(BaseReader): "in energy chunk {}".format(chunk[:3])) splitLine = line.split() varName = splitLine[0].split('_')[1:] - varValues = str2vec(splitLine[3:-1]) + varValues = array(splitLine[3:-1], dtype=float) if varName[0] == 'E': self.energies = varValues elif varName == ['LETHARGY', 'WIDTHS']: diff --git a/serpentTools/utils/core.py b/serpentTools/utils/core.py index c1b35ad..8460b51 100644 --- a/serpentTools/utils/core.py +++ b/serpentTools/utils/core.py @@ -9,68 +9,43 @@ from numpy import array, ndarray, fromiter # Regular expressions -STR_REGEX = compile(r'\'.+\'') # string -VEC_REGEX = compile(r'(?<==.)\[.+?\]') # vector -SCALAR_REGEX = compile(r'=.+;') # scalar +STR_REGEX = compile(r"([A-Z_]+).'(.)'") +VEC_REGEX = compile(r"([A-Z_]+).=\s+\[\s([0-9-\+\.Ee ]+)\]") +SCALAR_REGEX = compile(r"([A-Z_]+).*=\s+([0-9-\+Ee\.]+)") FIRST_WORD_REGEX = compile(r'^\w+') # first word in the line -def str2vec(iterable, dtype=float, out=array): +def str2vec(s, dtype=float): """ - Convert a string or other iterable to vector. + Convert a string to a numpy array Parameters ---------- - iterable: str or iterable + s : str If a string containing spaces, will be split using ```iterable.split()``. If no spaces are found, the outgoing type is filled with a single string, e.g. a list with a single string as the first and only entry. This is returned directly, avoiding conversion - with ``dtype``. - Every item in this split list, or original - iterable, will be iterated over and converted accoring - to the other arguments. - dtype: type + with ``dtype``. Every item in this split list will be iterated + over and converted according to the other arguments. + dtype : type Convert each value in ``iterable`` to this data type. - out: type - Return data type. Will be passed the iterable of - converted items of data dtype ``dtype``. Returns ------- - vector - Iterable of all values of ``iterable``, or split variant, - converted to type ``dtype``. + numpy.ndarray + Vector of all the elements converted to ``dtype`` Examples -------- - :: - - >>> v = "1 2 3 4" - >>> str2vec(v) - array([1., 2., 3., 4.,]) - - >>> str2vec(v, int, list) - [1, 2, 3, 4] - - >>> x = [1, 2, 3, 4] - >>> str2vec(x) - array([1., 2., 3., 4.,]) - >>> str2vec("ADF") - array(['ADF', dtype='<U3') + >>> v = "1 2 3 4" + >>> str2vec(v) + array([1., 2., 3., 4.,]) """ - if isinstance(iterable, str): - if ' ' in iterable: - iterable = iterable.split() - else: - return out([iterable]) - cmap = map(dtype, iterable) - if out is array: - return fromiter(cmap, dtype) - return out(cmap) + return array(s.split(), dtype=dtype) def splitValsUncs(iterable, copy=False):	BUG ResultsReader drops last digit of scalar data ## Summary of issue When reading a result file, we expect scalar data to have a space between the last digit and the colon due to https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/dd886ff2b2cd19e8dc681245134cef8815d61f31/serpentTools/utils/core.py#L14 and https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/dd886ff2b2cd19e8dc681245134cef8815d61f31/serpentTools/parsers/results.py#L280-L282 The `- 2` was done to remove the colon and the trailing space. However, there are some values that don't have this trailing space https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/a69471eb211a18768aa55c695be906e006b4b4a6/data/pwr_res.m#L110-L118 ## Code for reproducing the issue ```python >>> from serpentTools.data import readDataFile >>> res = readDataFile("pwr_res.m") # read the above data file >>> res.resdata["miscMemsize"] array([[6.5], [6.5]]) ``` ## Actual outcome including console output and error traceback if applicable See above ## Expected outcome For this value, we should see `miscMemsize` give us `[[6.59], [6.59]]` ## Versions Please provide the following: * Version from ``serpentTools.__version__`` `0.10.0.dev0` and `0.9.3` * Python version - ``python --version`` `3.8` ## Potential fix Improving the flexibility of the regular expressions. Possibly even using a match group to present the string / scalar / vector data in `match.groups()` rather than relying on `match.span`	CORE-GATECH-GROUP/serpent-tools	diff --git a/tests/test_pt_results.py b/tests/test_pt_results.py new file mode 100644 index 0000000..5b4fab3 --- /dev/null +++ b/tests/test_pt_results.py @@ -0,0 +1,28 @@ +"""pytest tests for reading the result file""" + +import pytest +import serpentTools.data +from serpentTools.settings import rc + + [email protected](scope="module") +def read_2_1_29(): + with rc: + rc["serpentVersion"] = "2.1.29" + yield + + [email protected] +def fullPwrFile(read_2_1_29): + return serpentTools.data.readDataFile("pwr_res.m") + + +def test_scalars(fullPwrFile): + """Ensure that the full precision of scalars are captured + + Related: GH issue #411 + """ + assert fullPwrFile["miscMemsize"] == pytest.approx([6.59, 6.59], abs=0, rel=0) + assert fullPwrFile["availMem"] == pytest.approx([15935.20, 15935.20], abs=0, rel=0) + assert fullPwrFile["totCpuTime"] == pytest.approx([0.282078, 0.494889], abs=0, rel=0) + diff --git a/tests/test_pt_utils.py b/tests/test_pt_utils.py new file mode 100644 index 0000000..e16cc27 --- /dev/null +++ b/tests/test_pt_utils.py @@ -0,0 +1,15 @@ +"""Tests for using utility features""" + +import pytest +from numpy import random + +import serpentTools.utils as sutils + + [email protected]("N", [1, 5, 10]) +def test_str2vec(N): + """Compare the string to vector conversion on a random vector""" + r = random.random(N) + s = " ".join(map(str, r)) + v = sutils.str2vec(s) + assert v == pytest.approx(r, abs=0, rel=0) diff --git a/tests/test_utils.py b/tests/test_utils.py index 56a2837..1f0c50f 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -44,48 +44,6 @@ class VariableConverterTester(TestCase): self.assertEqual(expected, actual, msg=serpentStyle) -class VectorConverterTester(TestCase): - """Class for testing the str2vec function""" - - @classmethod - def setUpClass(cls): - cls.testCases = ("0 1 2 3", [0, 1, 2, 3], (0, 1, 2, 3), arange(4)) - - def test_str2Arrays(self): - """Verify that the str2vec converts to arrays.""" - expected = arange(4) - for case in self.testCases: - actual = str2vec(case) - assert_array_equal(expected, actual, err_msg=case) - - def test_listOfInts(self): - """Verify that a list of ints can be produced with str2vec.""" - expected = [0, 1, 2, 3] - self._runConversionTest(int, expected, list) - - def test_vecOfStr(self): - """Verify a single word can be converted with str2vec""" - key = 'ADF' - expected = array('ADF') - actual = str2vec(key) - assert_array_equal(expected, actual) - - def _runConversionTest(self, valType, expected, outType=None): - if outType is None: - outType = array - compareType = ndarray - else: - compareType = outType - for case in self.testCases: - actual = str2vec(case, dtype=valType, out=outType) - self.assertIsInstance(actual, compareType, msg=case) - ofRightType = [isinstance(xx, valType) for xx in actual] - self.assertTrue(all(ofRightType), - msg="{} -> {}, {}".format(case, actual, - type(actual))) - self.assertEqual(expected, actual, msg=case) - - class SplitValsTester(TestCase): """Class that tests splitValsUncs."""	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 5 }	0.9	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.7", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	certifi @ file:///croot/certifi_1671487769961/work/certifi cycler==0.11.0 exceptiongroup==1.2.2 fonttools==4.38.0 importlib-metadata==6.7.0 iniconfig==2.0.0 kiwisolver==1.4.5 matplotlib==3.5.3 numpy==1.21.6 packaging==24.0 Pillow==9.5.0 pluggy==1.2.0 pyparsing==3.1.4 pytest==7.4.4 python-dateutil==2.9.0.post0 PyYAML==6.0.1 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@a69471eb211a18768aa55c695be906e006b4b4a6#egg=serpentTools six==1.17.0 tomli==2.0.1 typing_extensions==4.7.1 zipp==3.15.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - cycler==0.11.0 - exceptiongroup==1.2.2 - fonttools==4.38.0 - importlib-metadata==6.7.0 - iniconfig==2.0.0 - kiwisolver==1.4.5 - matplotlib==3.5.3 - numpy==1.21.6 - packaging==24.0 - pillow==9.5.0 - pluggy==1.2.0 - pyparsing==3.1.4 - pytest==7.4.4 - python-dateutil==2.9.0.post0 - pyyaml==6.0.1 - serpenttools==0.10.0.dev0 - six==1.17.0 - tomli==2.0.1 - typing-extensions==4.7.1 - zipp==3.15.0 prefix: /opt/conda/envs/serpent-tools	[ "tests/test_pt_utils.py::test_str2vec[1]" ]	[]	[ "tests/test_pt_utils.py::test_str2vec[5]", "tests/test_pt_utils.py::test_str2vec[10]", "tests/test_utils.py::VariableConverterTester::test_variableConversion", "tests/test_utils.py::SplitValsTester::test_splitAtCols", "tests/test_utils.py::SplitValsTester::test_splitCopy", "tests/test_utils.py::SplitValsTester::test_splitVals", "tests/test_utils.py::CommonKeysTester::test_getKeys_missing", "tests/test_utils.py::CommonKeysTester::test_goodKeys_dict", "tests/test_utils.py::DirectCompareTester::test_acceptableHigh", "tests/test_utils.py::DirectCompareTester::test_acceptableLow", "tests/test_utils.py::DirectCompareTester::test_badTypes", "tests/test_utils.py::DirectCompareTester::test_diffShapes", "tests/test_utils.py::DirectCompareTester::test_dissimilarString", "tests/test_utils.py::DirectCompareTester::test_identicalString", "tests/test_utils.py::DirectCompareTester::test_notImplemented", "tests/test_utils.py::DirectCompareTester::test_outsideTols", "tests/test_utils.py::DirectCompareTester::test_stringArrays", "tests/test_utils.py::OverlapTester::test_overlap_absolute", "tests/test_utils.py::OverlapTester::test_overlap_badshapes", "tests/test_utils.py::OverlapTester::test_overlap_identical_1D", "tests/test_utils.py::OverlapTester::test_overlap_identical_2D", "tests/test_utils.py::OverlapTester::test_overlap_identical_3D", "tests/test_utils.py::OverlapTester::test_overlap_relative", "tests/test_utils.py::SplitDictionaryTester::test_keySet_all", "tests/test_utils.py::SplitDictionaryTester::test_noKeys", "tests/test_utils.py::PlotTestTester::test_formatPlot", "tests/test_utils.py::PlotTestTester::test_plotAttrs_fail", "tests/test_utils.py::PlotTestTester::test_plotAttrs_gca" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-412
CORE-GATECH-GROUP__serpent-tools-447	f7eb3e1c843f7d2506fe7ffa378584d7602a3bb9	2021-05-14 18:34:11	d8f02d1f064b40e23db4c5ad6bf0bb31b9e8a6da		diff --git a/docs/variableGroups.rst b/docs/variableGroups.rst index 8b2fd56..723a8a5 100644 --- a/docs/variableGroups.rst +++ b/docs/variableGroups.rst @@ -30,19 +30,227 @@ variable names converted to ``mixedCaseNames``. This conversion is done to fit the style of the project and reduce a few keystrokes for accessing variable names. +.. _vars-2-1-32: + +---------- +``2-1-32`` +---------- + +.. _burnup-coeff-2-1-32: + + +``burnup-coeff`` +---------------- + + + * ``BURNUP`` → ``burnup`` + * ``BURN_DAYS`` → ``burnDays`` + * ``BURN_MATERIALS`` → ``burnMaterials`` + * ``BURN_MODE`` → ``burnMode`` + * ``BURN_RANDOMIZE_DATA`` → ``burnRandomizeData`` + * ``BURN_STEP`` → ``burnStep`` + * ``COEF_BRANCH`` → ``coefBranch`` + * ``COEF_BU_STEP`` → ``coefBuStep`` + * ``COEF_IDX`` → ``coefIdx`` + * ``FIMA`` → ``fima`` + +.. _energy-deposition-2-1-32: + + +``energy-deposition`` +--------------------- + + + * ``EDEP_CAPT_E`` → ``edepCaptE`` + * ``EDEP_COMP`` → ``edepComp`` + * ``EDEP_DELAYED`` → ``edepDelayed`` + * ``EDEP_KEFF_CORR`` → ``edepKeffCorr`` + * ``EDEP_LOCAL_EGD`` → ``edepLocalEgd`` + * ``EDEP_MODE`` → ``edepMode`` + +.. _n-balance-2-1-32: + + +``n-balance`` +------------- + + + * ``BALA_LOSS_NEUTRON_CAPT`` → ``balaLossNeutronCapt`` + * ``BALA_LOSS_NEUTRON_CUT`` → ``balaLossNeutronCut`` + * ``BALA_LOSS_NEUTRON_ERR`` → ``balaLossNeutronErr`` + * ``BALA_LOSS_NEUTRON_FISS`` → ``balaLossNeutronFiss`` + * ``BALA_LOSS_NEUTRON_LEAK`` → ``balaLossNeutronLeak`` + * ``BALA_LOSS_NEUTRON_TOT`` → ``balaLossNeutronTot`` + * ``BALA_NEUTRON_DIFF`` → ``balaNeutronDiff`` + * ``BALA_SRC_NEUTRON_FISS`` → ``balaSrcNeutronFiss`` + * ``BALA_SRC_NEUTRON_NXN`` → ``balaSrcNeutronNxn`` + * ``BALA_SRC_NEUTRON_SRC`` → ``balaSrcNeutronSrc`` + * ``BALA_SRC_NEUTRON_TOT`` → ``balaSrcNeutronTot`` + * ``BALA_SRC_NEUTRON_VR`` → ``balaSrcNeutronVr`` + +.. _optimization-2-1-32: + + +``optimization`` +---------------- + + + * ``DOUBLE_INDEXING`` → ``doubleIndexing`` + * ``MG_MAJORANT_MODE`` → ``mgMajorantMode`` + * ``OPTIMIZATION_MODE`` → ``optimizationMode`` + * ``RECONSTRUCT_MACROXS`` → ``reconstructMacroxs`` + * ``RECONSTRUCT_MICROXS`` → ``reconstructMicroxs`` + * ``SPECTRUM_COLLAPSE`` → ``spectrumCollapse`` + +.. _rad-data-2-1-32: + + +``rad-data`` +------------ + + + * ``ACTINIDE_ACTIVITY`` → ``actinideActivity`` + * ``ACTINIDE_DECAY_HEAT`` → ``actinideDecayHeat`` + * ``ACTINIDE_ING_TOX`` → ``actinideIngTox`` + * ``ACTINIDE_INH_TOX`` → ``actinideInhTox`` + * ``ALPHA_DECAY_SOURCE`` → ``alphaDecaySource`` + * ``CS134_ACTIVITY`` → ``cs134Activity`` + * ``ELECTRON_DECAY_SOURCE`` → ``electronDecaySource`` + * ``FISSION_PRODUCT_ACTIVITY`` → ``fissionProductActivity`` + * ``FISSION_PRODUCT_DECAY_HEAT`` → ``fissionProductDecayHeat`` + * ``FISSION_PRODUCT_ING_TOX`` → ``fissionProductIngTox`` + * ``FISSION_PRODUCT_INH_TOX`` → ``fissionProductInhTox`` + * ``INGENSTION_TOXICITY`` → ``ingenstionToxicity`` + * ``INHALATION_TOXICITY`` → ``inhalationToxicity`` + * ``NEUTRON_DECAY_SOURCE`` → ``neutronDecaySource`` + * ``PHOTON_DECAY_SOURCE`` → ``photonDecaySource`` + * ``SR90_ACTIVITY`` → ``sr90Activity`` + * ``TE132_ACTIVITY`` → ``te132Activity`` + * ``TOT_ACTIVITY`` → ``totActivity`` + * ``TOT_DECAY_HEAT`` → ``totDecayHeat`` + * ``TOT_SF_RATE`` → ``totSfRate`` + +.. _six-ff-2-1-32: + + +``six-ff`` +---------- + + + * ``SIX_FF_EPSILON`` → ``sixFfEpsilon`` + * ``SIX_FF_ETA`` → ``sixFfEta`` + * ``SIX_FF_F`` → ``sixFfF`` + * ``SIX_FF_KEFF`` → ``sixFfKeff`` + * ``SIX_FF_KINF`` → ``sixFfKinf`` + * ``SIX_FF_LF`` → ``sixFfLf`` + * ``SIX_FF_LT`` → ``sixFfLt`` + * ``SIX_FF_P`` → ``sixFfP`` + .. _vars-2-1-31: ---------- -``2.1.31`` +``2-1-31`` ---------- -Variable groups for Serpent 2.1.31 are identical to those from -Serpent 2.1.30 +.. _burnup-coeff-2-1-31: + + +``burnup-coeff`` +---------------- + + + * ``BURNUP`` → ``burnup`` + * ``BURN_DAYS`` → ``burnDays`` + * ``BURN_MATERIALS`` → ``burnMaterials`` + * ``BURN_MODE`` → ``burnMode`` + * ``BURN_RANDOMIZE_DATA`` → ``burnRandomizeData`` + * ``BURN_STEP`` → ``burnStep`` + * ``COEF_BRANCH`` → ``coefBranch`` + * ``COEF_BU_STEP`` → ``coefBuStep`` + * ``COEF_IDX`` → ``coefIdx`` + +.. _n-balance-2-1-31: + + +``n-balance`` +------------- + + + * ``BALA_LOSS_NEUTRON_CAPT`` → ``balaLossNeutronCapt`` + * ``BALA_LOSS_NEUTRON_CUT`` → ``balaLossNeutronCut`` + * ``BALA_LOSS_NEUTRON_ERR`` → ``balaLossNeutronErr`` + * ``BALA_LOSS_NEUTRON_FISS`` → ``balaLossNeutronFiss`` + * ``BALA_LOSS_NEUTRON_LEAK`` → ``balaLossNeutronLeak`` + * ``BALA_LOSS_NEUTRON_TOT`` → ``balaLossNeutronTot`` + * ``BALA_NEUTRON_DIFF`` → ``balaNeutronDiff`` + * ``BALA_SRC_NEUTRON_FISS`` → ``balaSrcNeutronFiss`` + * ``BALA_SRC_NEUTRON_NXN`` → ``balaSrcNeutronNxn`` + * ``BALA_SRC_NEUTRON_SRC`` → ``balaSrcNeutronSrc`` + * ``BALA_SRC_NEUTRON_TOT`` → ``balaSrcNeutronTot`` + * ``BALA_SRC_NEUTRON_VR`` → ``balaSrcNeutronVr`` + +.. _optimization-2-1-31: + + +``optimization`` +---------------- + + + * ``DOUBLE_INDEXING`` → ``doubleIndexing`` + * ``MG_MAJORANT_MODE`` → ``mgMajorantMode`` + * ``OPTIMIZATION_MODE`` → ``optimizationMode`` + * ``RECONSTRUCT_MACROXS`` → ``reconstructMacroxs`` + * ``RECONSTRUCT_MICROXS`` → ``reconstructMicroxs`` + * ``SPECTRUM_COLLAPSE`` → ``spectrumCollapse`` + +.. _rad-data-2-1-31: + + +``rad-data`` +------------ + + + * ``ACTINIDE_ACTIVITY`` → ``actinideActivity`` + * ``ACTINIDE_DECAY_HEAT`` → ``actinideDecayHeat`` + * ``ACTINIDE_ING_TOX`` → ``actinideIngTox`` + * ``ACTINIDE_INH_TOX`` → ``actinideInhTox`` + * ``ALPHA_DECAY_SOURCE`` → ``alphaDecaySource`` + * ``CS134_ACTIVITY`` → ``cs134Activity`` + * ``ELECTRON_DECAY_SOURCE`` → ``electronDecaySource`` + * ``FISSION_PRODUCT_ACTIVITY`` → ``fissionProductActivity`` + * ``FISSION_PRODUCT_DECAY_HEAT`` → ``fissionProductDecayHeat`` + * ``FISSION_PRODUCT_ING_TOX`` → ``fissionProductIngTox`` + * ``FISSION_PRODUCT_INH_TOX`` → ``fissionProductInhTox`` + * ``INGENSTION_TOXICITY`` → ``ingenstionToxicity`` + * ``INHALATION_TOXICITY`` → ``inhalationToxicity`` + * ``NEUTRON_DECAY_SOURCE`` → ``neutronDecaySource`` + * ``PHOTON_DECAY_SOURCE`` → ``photonDecaySource`` + * ``SR90_ACTIVITY`` → ``sr90Activity`` + * ``TE132_ACTIVITY`` → ``te132Activity`` + * ``TOT_ACTIVITY`` → ``totActivity`` + * ``TOT_DECAY_HEAT`` → ``totDecayHeat`` + * ``TOT_SF_RATE`` → ``totSfRate`` + +.. _six-ff-2-1-31: + + +``six-ff`` +---------- + + + * ``SIX_FF_EPSILON`` → ``sixFfEpsilon`` + * ``SIX_FF_ETA`` → ``sixFfEta`` + * ``SIX_FF_F`` → ``sixFfF`` + * ``SIX_FF_KEFF`` → ``sixFfKeff`` + * ``SIX_FF_KINF`` → ``sixFfKinf`` + * ``SIX_FF_LF`` → ``sixFfLf`` + * ``SIX_FF_LT`` → ``sixFfLt`` + * ``SIX_FF_P`` → ``sixFfP`` .. _vars-2-1-30: ---------- -``2.1.30`` +``2-1-30`` ---------- .. _n-balance-2-1-30: diff --git a/serpentTools/settings.py b/serpentTools/settings.py index 5d0239e..28a90ae 100644 --- a/serpentTools/settings.py +++ b/serpentTools/settings.py @@ -106,7 +106,7 @@ defaultSettings = { }, 'serpentVersion': { 'default': '2.1.31', - 'options': ['2.1.29', '2.1.30', '2.1.31'], + 'options': ['2.1.29', '2.1.30', '2.1.31', '2.1.32'], # When adding new version of Serpent, add / update # MapStrVersions with variables that indicate the start of specific # data blocks / time parameters like burnup diff --git a/serpentTools/variables.yaml b/serpentTools/variables.yaml index ea833a9..d92b913 100644 --- a/serpentTools/variables.yaml +++ b/serpentTools/variables.yaml @@ -110,24 +110,42 @@ base: {ALB_SURFACE, ALB_FLIP_DIR, ALB_N_SURF, ALB_IN_CURR, ALB_OUT_CURR, ALB_TOT_ALB, ALB_PART_ALB} -2-1-30: &2-1-30 - n-balance: !!set +2-1-30: + n-balance: !!set &2-1-30-n-balance {BALA_SRC_NEUTRON_SRC, BALA_SRC_NEUTRON_FISS, BALA_SRC_NEUTRON_NXN, BALA_SRC_NEUTRON_VR, BALA_SRC_NEUTRON_TOT, BALA_LOSS_NEUTRON_CAPT, BALA_LOSS_NEUTRON_FISS, BALA_LOSS_NEUTRON_LEAK, BALA_LOSS_NEUTRON_CUT, BALA_LOSS_NEUTRON_TOT, BALA_NEUTRON_DIFF, BALA_LOSS_NEUTRON_ERR} - rad-data: !!set + rad-data: !!set &2-1-30-rad-data {TOT_ACTIVITY, TOT_DECAY_HEAT, TOT_SF_RATE, ACTINIDE_ACTIVITY, INHALATION_TOXICITY, INGENSTION_TOXICITY, ACTINIDE_INH_TOX, ACTINIDE_ING_TOX, FISSION_PRODUCT_INH_TOX, FISSION_PRODUCT_ING_TOX, ACTINIDE_DECAY_HEAT, FISSION_PRODUCT_ACTIVITY, FISSION_PRODUCT_DECAY_HEAT, SR90_ACTIVITY, TE132_ACTIVITY, CS134_ACTIVITY, PHOTON_DECAY_SOURCE, NEUTRON_DECAY_SOURCE, ALPHA_DECAY_SOURCE, ELECTRON_DECAY_SOURCE} - optimization: !!set + optimization: !!set &2-1-30-optimization {OPTIMIZATION_MODE, RECONSTRUCT_MICROXS, RECONSTRUCT_MACROXS, MG_MAJORANT_MODE, SPECTRUM_COLLAPSE, DOUBLE_INDEXING} - six-ff: !!set + six-ff: !!set &2-1-30-six-ff {SIX_FF_EPSILON, SIX_FF_ETA, SIX_FF_F, SIX_FF_KEFF, SIX_FF_KINF, SIX_FF_LT, SIX_FF_LF, SIX_FF_P} -2-1-31: 2-1-30 +2-1-31: + n-balance: 2-1-30-n-balance + rad-data: 2-1-30-rad-data + optimization: 2-1-30-optimization + six-ff: 2-1-30-six-ff + burnup-coeff: !!set + {BURN_MATERIALS, BURN_MODE, BURN_STEP, BURNUP, BURN_DAYS, COEF_IDX, + COEF_BRANCH, COEF_BU_STEP, BURN_RANDOMIZE_DATA} + +2-1-32: + n-balance: 2-1-30-n-balance + rad-data: 2-1-30-rad-data + optimization: 2-1-30-optimization + six-ff: *2-1-30-six-ff + energy-deposition: !!set + {EDEP_MODE, EDEP_DELAYED, EDEP_KEFF_CORR, EDEP_LOCAL_EGD, EDEP_COMP, EDEP_CAPT_E} + burnup-coeff: !!set + {BURN_MATERIALS, BURN_MODE, BURN_STEP, BURNUP, BURN_DAYS, COEF_IDX, + COEF_BRANCH, COEF_BU_STEP, BURN_RANDOMIZE_DATA, FIMA} \ No newline at end of file	ENH Support Serpent 2.1.32 Is your feature request related to a problem? Please describe. Warning messages like ``` WARN SERPENT Serpent 2.1.32 found in godiva_res.m, but version 2.1.31 is defined in settings Attemping to read anyway. Please report strange behaviors/failures to developers. ``` (also a typo) come through. But things seem to work fine. Describe the solution you'd like Determine what new arguments exist in the result file that should be added to https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/develop/serpentTools/variables.yaml even if we just do something like https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/f61cd104bd997aa80429f1059bbc3669adc18654/serpentTools/variables.yaml#L133 I thought I saw there are also new entries added to the depletion file but none seem to show up Describe alternatives you've considered Carry on regardless. Per https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/f61cd104bd997aa80429f1059bbc3669adc18654/serpentTools/settings.py#L108-L116 should check if https://github.com/CORE-GATECH-GROUP/serpent-tools/blob/f61cd104bd997aa80429f1059bbc3669adc18654/serpentTools/parsers/results.py#L43-L55 needs updating or we can copy over	CORE-GATECH-GROUP/serpent-tools	diff --git a/tests/test_results.py b/tests/test_results.py index 057debd..e562cc3 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -1,6 +1,9 @@ import re import os +from os.path import join as pjoin +from tempfile import TemporaryDirectory +import numpy import pytest import serpentTools @@ -38,3 +41,52 @@ def test_multipleGcuNoBu(multipleGcuNoBu): assert key.step == 0 assert key.burnup == 0 assert key.days == 0 + + [email protected](scope="module") +def tdir(): + with TemporaryDirectory() as temp: + yield temp + + [email protected](scope="module") +def fake2132File(tdir): + basefile = serpentTools.data.getFile("InnerAssembly_res.m") + newfile = pjoin(tdir, "InnerAssembly_2132_res.m") + with open(newfile, "w") as ostream, open(basefile, "r") as istream: + for line in istream: + if "2.1.31" in line: + ostream.write(line.replace("2.1.31", "2.1.32")) + else: + ostream.write(line) + if line.startswith("BURN_STEP"): + ostream.write( + "BURN_RANDOMIZE_DATA (idx, [1: 3]) = [ 0 0 0 ];\n" + ) + elif line.startswith("BURN_DAYS"): + ostream.write( + "FIMA (idx, [1: 3]) = [ 0.00000E+00 0.00000E+00 1.00000E+25 ];\n" + ) + yield newfile + os.remove(newfile) + + +def test_2132_nofilter(fake2132File): + with serpentTools.settings.rc as rc: + rc["serpentVersion"] = "2.1.32" + reader = serpentTools.read(fake2132File) + singleFima = numpy.array([0, 0, 1e25]) + assert "fima" in reader.resdata + assert (reader.resdata["fima"] == singleFima).all(), reader.resdata["fima"] + assert not reader.resdata["burnRandomizeData"].any() + + +def test_2132_filterburnup(fake2132File): + with serpentTools.settings.rc as rc: + rc["serpentVersion"] = "2.1.32" + rc["xs.variableGroups"] = ["burnup-coeff", "eig"] + reader = serpentTools.read(fake2132File) + assert "fima" in reader.resdata + assert "burnDays" in reader.resdata + assert "absKeff" in reader.resdata + assert "pop" not in reader.metadata	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 3 }	0.9	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.8", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	cycler==0.12.1 exceptiongroup==1.2.2 iniconfig==2.1.0 kiwisolver==1.4.7 matplotlib==3.3.4 numpy==1.24.4 packaging==24.2 pillow==10.4.0 pluggy==1.5.0 pyparsing==3.1.4 pytest==8.3.5 python-dateutil==2.9.0.post0 PyYAML==6.0.2 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@f7eb3e1c843f7d2506fe7ffa378584d7602a3bb9#egg=serpentTools six==1.17.0 tomli==2.2.1	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=24.2=py38h06a4308_0 - python=3.8.20=he870216_0 - readline=8.2=h5eee18b_0 - setuptools=75.1.0=py38h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.44.0=py38h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - cycler==0.12.1 - exceptiongroup==1.2.2 - iniconfig==2.1.0 - kiwisolver==1.4.7 - matplotlib==3.3.4 - numpy==1.24.4 - packaging==24.2 - pillow==10.4.0 - pluggy==1.5.0 - pyparsing==3.1.4 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pyyaml==6.0.2 - six==1.17.0 - tomli==2.2.1 prefix: /opt/conda/envs/serpent-tools	[ "tests/test_results.py::test_2132_nofilter", "tests/test_results.py::test_2132_filterburnup" ]	[]	[ "tests/test_results.py::test_multipleGcuNoBu" ]	[]	MIT License	swerebench/sweb.eval.x86_64.core-gatech-group_1776_serpent-tools-447
CORE-GATECH-GROUP__serpent-tools-456	e8cd0943cafd7f1cbc5f5a74fabf652097a12777	2021-09-08 21:50:31	d8f02d1f064b40e23db4c5ad6bf0bb31b9e8a6da		diff --git a/docs/changelog.rst b/docs/changelog.rst index 5f652a4..5da1ed1 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -16,6 +16,10 @@ Changelog .. _v0.9.4: +0.9.4rc1 +======== +* Support for reading ``pspec`` fields in xsplot files + :release-tag:`0.9.4rc0` ==================== diff --git a/serpentTools/data/plut_xs0.m b/serpentTools/data/plut_xs0.m index f7f5610..c06b1b2 100644 --- a/serpentTools/data/plut_xs0.m +++ b/serpentTools/data/plut_xs0.m @@ -277,3 +277,35 @@ majorant_xs = [ 2.93887E-01 ]; +i94239_03c_pspec = [ +1.36082E-02 1.47639E-02 +3.27184E-03 5.25321E-03 +1.64140E-02 3.43399E-03 +1.37229E-02 1.50625E-03 +1.29750E-02 3.69478E-04 +5.16240E-02 2.72200E-04 +3.86610E-02 1.04400E-04 +1.29296E-01 6.31000E-05 +9.89280E-02 5.69788E-05 +3.75054E-01 1.55400E-05 +4.13713E-01 1.46600E-05 +9.87800E-02 1.46500E-05 +1.11820E-01 1.29767E-05 +5.68280E-02 1.15200E-05 +1.10928E-01 6.61565E-06 +2.03550E-01 5.69000E-06 +9.92700E-01 2.70000E-10 +8.16000E-01 2.40000E-10 +7.63600E-01 2.20000E-10 +9.86900E-01 2.10000E-10 +6.93200E-01 2.00000E-10 +7.92900E-01 2.00000E-10 +4.12300E-01 1.80000E-10 +1.00570E+00 1.80000E-10 +7.62600E-01 1.00000E-10 +6.70800E-01 9.00000E-11 +6.70990E-01 9.00000E-11 +9.18700E-01 8.40000E-11 +1.72560E-01 3.00000E-11 +1.23228E-01 1.60000E-11 +]; diff --git a/serpentTools/objects/xsdata.py b/serpentTools/objects/xsdata.py index 96cad3b..ea1429a 100644 --- a/serpentTools/objects/xsdata.py +++ b/serpentTools/objects/xsdata.py @@ -44,6 +44,9 @@ class XSData(NamedObject): metadata : dict File-wide metadata from the reader. Alias for accessing :attr:`energies`. Will be removed in the future + misc : dict of str to numpy.ndarray + Other arrays that do not have a dedicated attribute, e.g., + ``pspec`` for photon line spectrum """ @@ -89,6 +92,8 @@ class XSData(NamedObject): # whether nu data present for fissionables self.hasNuData = False + self.misc = {} + def __len__(self): """Number of reactions stored""" return len(self.MT) diff --git a/serpentTools/parsers/xsplot.py b/serpentTools/parsers/xsplot.py index 62927ad..3b5b30c 100644 --- a/serpentTools/parsers/xsplot.py +++ b/serpentTools/parsers/xsplot.py @@ -1,4 +1,7 @@ """Parser responsible for reading the ``*_xsplot.m`` files""" +import warnings +import re + from numpy import array, float64 from serpentTools.messages import error, warning @@ -44,6 +47,7 @@ class XSPlotReader(BaseReader): likely be removed in the future """ + _misc = {"pspec", } def __init__(self, filePath): super().__init__(filePath, 'xsplot') @@ -146,7 +150,22 @@ class XSPlotReader(BaseReader): self.xsections[xsname].setData(chunk) else: - raise ValueError("Unidentifiable entry\n{}".format(chunk)) + self._processMisc(lead, data) + + def _processMisc(self, lead, data): + variable = lead.split()[0] + for key in self._misc: + start = variable.find(key) + if start == -1: + continue + xsname = re.sub("_$", "", variable[:start]) + cleaned = list(map(str.split, data)) + self.xsections[xsname].misc[key] = array(cleaned, dtype=float) + return + + warnings.warn( + "Unidentifiable entry in {}: {}".format(self.filePath, lead) + ) def _precheck(self): """do a quick scan to ensure this looks like a xsplot file."""	Can't Parse Cross Section Data From Serpent 2.1.32 ## Summary of issue I am getting `ValueError: Unidentifiable entry` when trying to load my `_xs0.m` file, because `serpentTools` doesn't know what to do with entries suffixed by `pspec`. These appear to be new to Serpent 2.1.32, and the code runs fine when I manually remove all entries suffixed by `pspec`. However, the code still exits non-zero when I turn off raising errors and attempt to skip the pre-check with the `pspec` entries still there. ## Code for reproducing the issue Obviously, the path is different, and the file I've taken straight from Serpent is [main.input.inp_xs0.m.txt](https://github.com/CORE-GATECH-GROUP/serpent-tools/files/6780212/main.input.inp_xs0.m.txt). Also, I had to add the `.txt` extension so that GitHub would allow me to upload. ``` import serpentTools serpentTools.settings.rc["sampler.raiseErrors"] = False serpentTools.settings.rc["sampler.skipPrecheck"] = True serpentTools.settings.rc["serpentVersion"] = '2.1.32' xsreader = serpentTools.read('/home/ec2-user/PyCharm_KACEGEN/test/serpent/endfb71_mcnp/main.input.inp_xs0.m') ``` ## Actual outcome including console output and error traceback if applicable ``` Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/serpentTools-0.9.4rc0-py3.7.egg/serpentTools/parsers/xsplot.py", line 149, in _read raise ValueError("Unidentifiable entry\n{}".format(chunk)) ValueError: Unidentifiable entry ['i36085_82c_pspec = [\n', '5.13997E-01 4.34000E-03\n', '1.33539E-02 1.02414E-05\n', '1.32919E-02 5.32352E-06\n', '1.49217E-02 1.48786E-06\n', '1.49123E-02 7.63269E-07\n', '1.68737E-03 4.61757E-07\n', '1.75360E-03 2.45265E-07\n', '1.51392E-02 1.60258E-07\n', '1.50285E-03 1.33284E-07\n', '1.51383E-02 8.31602E-08\n', '1.51180E-01 2.17000E-08\n', '3.62810E-01 2.17000E-08\n', '1.94166E-03 4.43158E-09\n', '1.50414E-02 3.50630E-09\n', '1.50398E-02 2.45196E-09\n', '1.29810E-01 2.17000E-09\n'] ``` ## Expected outcome I expected to be able to follow [the tutorial](https://serpent-tools.readthedocs.io/en/master/examples/XSPlot.html). ## Versions * Serpent 2.1.32 * serpentTools 0.9.4rc0 * Python 3.7.6	CORE-GATECH-GROUP/serpent-tools	diff --git a/tests/test_xsplot.py b/tests/test_xsplot.py index b87a39c..1ae5a6c 100644 --- a/tests/test_xsplot.py +++ b/tests/test_xsplot.py @@ -1,12 +1,14 @@ """Test the xsplot reader.""" import unittest from numpy import ndarray, array, newaxis - from numpy.testing import assert_array_equal +import pytest from serpentTools.parsers.xsplot import XSPlotReader from serpentTools.data import getFile +DATA_FILE = getFile("plut_xs0.m") + def findDiff(d1, d2, path=""): """ Diffs two dictionaries with nested structure @@ -48,8 +50,7 @@ class XSPlotTester(unittest.TestCase): @classmethod def setUpClass(cls): - cls.file = getFile('plut_xs0.m') - cls.reader = XSPlotReader(cls.file) + cls.reader = XSPlotReader(DATA_FILE) cls.reader.read() def test1_allXS(self): @@ -782,3 +783,59 @@ class XSPlotTester(unittest.TestCase): """ actual = self.reader.xsections['mfissile'].showMT(retstring=True) self.assertEqual(refString.rstrip(), actual.rstrip()) + + +def test_goodMisc(): + reader = XSPlotReader(DATA_FILE) + reader.read() + pu = reader["i94239_03c"] + assert "pspec" in pu.misc + actualPspec = array( + [ + [1.36082E-02, 1.47639E-02], + [3.27184E-03, 5.25321E-03], + [1.64140E-02, 3.43399E-03], + [1.37229E-02, 1.50625E-03], + [1.29750E-02, 3.69478E-04], + [5.16240E-02, 2.72200E-04], + [3.86610E-02, 1.04400E-04], + [1.29296E-01, 6.31000E-05], + [9.89280E-02, 5.69788E-05], + [3.75054E-01, 1.55400E-05], + [4.13713E-01, 1.46600E-05], + [9.87800E-02, 1.46500E-05], + [1.11820E-01, 1.29767E-05], + [5.68280E-02, 1.15200E-05], + [1.10928E-01, 6.61565E-06], + [2.03550E-01, 5.69000E-06], + [9.92700E-01, 2.70000E-10], + [8.16000E-01, 2.40000E-10], + [7.63600E-01, 2.20000E-10], + [9.86900E-01, 2.10000E-10], + [6.93200E-01, 2.00000E-10], + [7.92900E-01, 2.00000E-10], + [4.12300E-01, 1.80000E-10], + [1.00570E+00, 1.80000E-10], + [7.62600E-01, 1.00000E-10], + [6.70800E-01, 9.00000E-11], + [6.70990E-01, 9.00000E-11], + [9.18700E-01, 8.40000E-11], + [1.72560E-01, 3.00000E-11], + [1.23228E-01, 1.60000E-11], + ] + ) + assert pu.misc["pspec"] == pytest.approx(actualPspec, rel=0, abs=0) + + [email protected] +def xsreaderNoPspec(): + reader = XSPlotReader(DATA_FILE) + reader._misc.difference_update({"pspec", }) + return reader + + +def test_missingPspec(xsreaderNoPspec): + with pytest.warns(UserWarning, match="pspec"): + xsreaderNoPspec.read() + pu = xsreaderNoPspec["i94239_03c"] + assert "pspec" not in pu.misc	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 1 }, "num_modified_files": 4 }	0.9	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.7", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	certifi @ file:///croot/certifi_1671487769961/work/certifi cycler==0.11.0 exceptiongroup==1.2.2 fonttools==4.38.0 importlib-metadata==6.7.0 iniconfig==2.0.0 kiwisolver==1.4.5 matplotlib==3.5.3 numpy==1.21.6 packaging==24.0 Pillow==9.5.0 pluggy==1.2.0 pyparsing==3.1.4 pytest==7.4.4 python-dateutil==2.9.0.post0 PyYAML==6.0.1 -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@e8cd0943cafd7f1cbc5f5a74fabf652097a12777#egg=serpentTools six==1.17.0 tomli==2.0.1 typing_extensions==4.7.1 zipp==3.15.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - cycler==0.11.0 - exceptiongroup==1.2.2 - fonttools==4.38.0 - importlib-metadata==6.7.0 - iniconfig==2.0.0 - kiwisolver==1.4.5 - matplotlib==3.5.3 - numpy==1.21.6 - packaging==24.0 - pillow==9.5.0 - pluggy==1.2.0 - pyparsing==3.1.4 - pytest==7.4.4 - python-dateutil==2.9.0.post0 - pyyaml==6.0.1 - six==1.17.0 - tomli==2.0.1 - typing-extensions==4.7.1 - zipp==3.15.0 prefix: /opt/conda/envs/serpent-tools	[ "tests/test_xsplot.py::test_goodMisc", "tests/test_xsplot.py::test_missingPspec" ]	[]	[ "tests/test_xsplot.py::XSPlotTester::test1_allXS", "tests/test_xsplot.py::XSPlotTester::test2_testPrintMT" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-72	efd13f38b728415f603bef6ab68ae3afd4694983	2017-12-19 15:46:17	0f11460f4d97775d096a6cdb8e1c2b94549c9f4e		diff --git a/README.rst b/README.rst index 5ab1e01..c7b11fb 100644 --- a/README.rst +++ b/README.rst @@ -44,9 +44,6 @@ References ---------- The Annals of Nuclear Energy article should be cited for all work -using ``SERPENT``. If you wish to cite this project, please cite as - -.. code:: bibtex using ``SERPENT``. If you wish to cite this project, please cite as:: url{@serpentTools diff --git a/docs/about.rst b/docs/about.rst deleted file mode 100644 index 7887051..0000000 --- a/docs/about.rst +++ /dev/null @@ -1,31 +0,0 @@ - -.. include:: ../README.rst - -Installation ------------- - -The ``serpentTools`` package can be downloaded either as a git repository or -as a zipped file. Both can be obtained through the ``Clone or download`` option -at the -`serpent-tools GitHub <https://github.com/CORE-GATECH-GROUP/serpent-tools>`_. - -Once the repository has been downloaded or extracted from zip, the package -can be installed with:: - - cd serpentTools - python setup.py install - python setup.py test - -Installing with `setuptools <https://pypi.python.org/pypi/setuptools/38.2.4>`_ -is preferred over the standard ``distutils`` module. ``setuptools`` can be -installed with ``pip`` as:: - - pip install -U setuptools - -Installing in this manner ensures that the supporting packages, -like ``numpy`` are installed and up to date. - -License -------- - -.. include:: ../LICENSE.rst diff --git a/docs/api/index.rst b/docs/api/index.rst index 2b3c86a..a1e4e1f 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -1,3 +1,4 @@ +.. _api: API === diff --git a/docs/conf.py b/docs/conf.py index 0fcd4ae..12a4177 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -161,4 +161,4 @@ texinfo_documents = [ ] # -- Options for auto documentation -------------------------------------- -autodoc_default_flags = ['members', 'inherited-members'] +autodoc_default_flags = ['members'] diff --git a/docs/contributing/index.rst b/docs/contributing/index.rst index a38d11c..dd142de 100644 --- a/docs/contributing/index.rst +++ b/docs/contributing/index.rst @@ -1,3 +1,4 @@ +.. _contributing: Contributing ============ diff --git a/docs/examples/Settings.rst b/docs/examples/Settings.rst index 5e7ba9d..76ab757 100644 --- a/docs/examples/Settings.rst +++ b/docs/examples/Settings.rst @@ -25,9 +25,9 @@ Below are the default values for each setting available .. code:: ipython3 >>> for setting in sorted(defaultSettings.keys()): - >>> print(setting) - >>> for key in defaultSettings[setting]: - >>> print('\t', key, '-', defaultSettings[setting][key]) + ... print(setting) + ... for key in defaultSettings[setting]: + ... print('\t', key, '-', defaultSettings[setting][key]) depletion.materialVariables default - [] description - Names of variables to store. Empty list -> all variables. @@ -83,14 +83,14 @@ correct type, and is an allowable option, if given. .. code:: ipython3 >>> try: - >>> rc['depletion.metadataKeys'] = False - >>> except TypeError as te: - >>> print(te) + ... rc['depletion.metadataKeys'] = False + ... except TypeError as te: + ... print(te) Setting depletion.metadataKeys should be of type <class 'list'>, not <class 'bool'> >>> try: - >>> rc['serpentVersion'] = '1.2.3' - >>> except KeyError as ke: - >>> print(ke) + ... rc['serpentVersion'] = '1.2.3' + ... except KeyError as ke: + ... print(ke) "Setting serpentVersion is 1.2.3 and not one of the allowed options: @@ -102,7 +102,7 @@ changes. .. code:: ipython3 >>> with rc: - >>> rc['depletion.metadataKeys'] = ['ZAI', 'BU'] + ... rc['depletion.metadataKeys'] = ['ZAI', 'BU'] >>> >>> rc['depletion.metadataKeys'] >>> rc['verbosity'] = 'info' @@ -168,3 +168,28 @@ to obtain all the data present in their respective files. See the :ref:`branching-ex` example for more information on using these settings to control scraped data. + +.. _conf-files: + +Configuration Files +------------------- + +As of version 0.1.2, the ``rc`` object allows for settings to be updated +from a yaml configuration file using the +:py:meth:`~serpentTools.settings.UserSettingsLoader.loadYaml` method. +The file is structured with the names of settings as keys and the +desired setting value as the values. +The loader also attempts to expand nested settings, like reader-specific +settings, that may be lumped in a second level. + +.. code:: yaml + + verbosity: warning + xs.getInfXS: False + branching: + areUncsPresent: False + floatVariables: [Fhi, Blo] + depletion: + materials: [fuel] + materialVariables: + [ADENS, MDENS, VOLUME] diff --git a/docs/welcome/about.rst b/docs/welcome/about.rst index 4d2c74b..e09c9c5 100644 --- a/docs/welcome/about.rst +++ b/docs/welcome/about.rst @@ -1,2 +1,71 @@ +============= +serpent-tools +============= -.. include:: ../../README.rst +.. image:: https://travis-ci.org/CORE-GATECH-GROUP/serpent-tools.svg?branch=master + :target: https://travis-ci.org/CORE-GATECH-GROUP/serpent-tools + +A suite of parsers designed to make interacting with +``SERPENT`` [1]_ output files simple and flawless. + +The ``SERPENT`` Monte Carlo code +is developed by VTT Technical Research Centre of Finland, Ltd. +More information, including distribution and licensing of ``SERPENT`` can be +found at `<montecarlo.vtt.fi>`_ + +Installation +------------ + +The ``serpentTools`` package can be downloaded either as a git repository or +as a zipped file. Both can be obtained through the ``Clone or download`` option +at the +`serpent-tools GitHub <https://github.com/CORE-GATECH-GROUP/serpent-tools>`_. + +Once the repository has been downloaded or extracted from zip, the package +can be installed with:: + + cd serpentTools + python setup.py install + python setup.py test + +Installing with `setuptools <https://pypi.python.org/pypi/setuptools/38.2.4>`_ +is preferred over the standard ``distutils`` module. ``setuptools`` can be +installed with ``pip`` as:: + + pip install -U setuptools + +Installing in this manner ensures that the supporting packages, +like ``numpy`` are installed and up to date. + +Issues +------ + +If you have issues installing the project, find a bug, or want to add a feature, +the `GitHub issue page <https://github.com/CORE-GATECH-GROUP/serpent-tools/issues>`_ +is the best place to do that. + +Contributors +------------ + +Here are all the wonderful people that helped make this project happen + + `Andrew Johnson <https://github.com/drewejohnson>`_ +* `Dr. Dan Kotlyar <https://github.com/CORE-GATECH>`_ +* `Stefano Terlizzi <https://github.com/sallustius>`_ + +References +---------- + +The Annals of Nuclear Energy article should be cited for all work +using ``SERPENT``. If you wish to cite this project, please cite as:: + + url{@serpentTools + author = {Andrew Johnson and Dan Kotlyar}, + title = {serpentTools: A suite of parsers designed to make interacting with SERPENT outputs simple and flawless}, + url = {https://github.com/CORE-GATECH-GROUP/serpent-tools}, + year = {2017} + } + +.. [1] Leppanen, J. et al. (2015) "The Serpent Monte Carlo code: Status, + development and applications in 2013." Ann. Nucl. Energy, `82 (2015) 142-150 + <http://www.sciencedirect.com/science/article/pii/S0306454914004095>`_ diff --git a/examples/Settings.ipynb b/examples/Settings.ipynb index d321137..05064f6 100644 --- a/examples/Settings.ipynb +++ b/examples/Settings.ipynb @@ -25,9 +25,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO : serpentTools: Using version 1.0b0+34.gce072dd.dirty\n" + ] + } + ], "source": [ "import serpentTools\n", "from serpentTools.settings import rc, defaultSettings" @@ -42,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -51,60 +59,60 @@ "text": [ "branching.areUncsPresent\n", "\t default - False\n", - "\t type - <class 'bool'>\n", "\t description - True if the values in the .coe file contain uncertainties\n", + "\t type - <class 'bool'>\n", "branching.floatVariables\n", "\t default - []\n", - "\t description - Names of state data variables to convert to floats for each branch\n", "\t type - <class 'list'>\n", + "\t description - Names of state data variables to convert to floats for each branch\n", "branching.intVariables\n", "\t default - []\n", - "\t description - Name of state data variables to convert to integers for each branch\n", "\t type - <class 'list'>\n", + "\t description - Name of state data variables to convert to integers for each branch\n", "depletion.materialVariables\n", "\t default - []\n", - "\t description - Names of variables to store. Empty list -> all variables.\n", "\t type - <class 'list'>\n", + "\t description - Names of variables to store. Empty list -> all variables.\n", "depletion.materials\n", "\t default - []\n", - "\t description - Names of materials to store. Empty list -> all materials.\n", "\t type - <class 'list'>\n", + "\t description - Names of materials to store. Empty list -> all materials.\n", "depletion.metadataKeys\n", - "\t default - ['ZAI', 'NAMES', 'DAYS', 'BU']\n", "\t options - default\n", - "\t description - Non-material data to store, i.e. zai, isotope names, burnup schedule, etc.\n", + "\t default - ['ZAI', 'NAMES', 'DAYS', 'BU']\n", "\t type - <class 'list'>\n", + "\t description - Non-material data to store, i.e. zai, isotope names, burnup schedule, etc.\n", "depletion.processTotal\n", "\t default - True\n", - "\t description - Option to store the depletion data from the TOT block\n", "\t type - <class 'bool'>\n", + "\t description - Option to store the depletion data from the TOT block\n", "serpentVersion\n", - "\t default - 2.1.29\n", "\t options - ['2.1.29']\n", - "\t description - Version of SERPENT\n", + "\t default - 2.1.29\n", "\t type - <class 'str'>\n", + "\t description - Version of SERPENT\n", "verbosity\n", - "\t default - info\n", "\t options - ['critical', 'error', 'warning', 'info', 'debug']\n", - "\t type - <class 'str'>\n", + "\t default - info\n", + "\t updater - <function updateLevel at 0x7f32b2e87048>\n", "\t description - Set the level of errors to be shown.\n", - "\t updater - <function updateLevel at 0x00000251B54FD0D0>\n", + "\t type - <class 'str'>\n", "xs.getB1XS\n", "\t default - True\n", - "\t description - If true, store the critical leakage cross sections.\n", "\t type - <class 'bool'>\n", + "\t description - If true, store the critical leakage cross sections.\n", "xs.getInfXS\n", "\t default - True\n", - "\t description - If true, store the infinite medium cross sections.\n", "\t type - <class 'bool'>\n", + "\t description - If true, store the infinite medium cross sections.\n", "xs.variableExtras\n", "\t default - []\n", - "\t description - Full SERPENT name of variables to be read\n", "\t type - <class 'list'>\n", + "\t description - Full SERPENT name of variables to be read\n", "xs.variableGroups\n", "\t default - []\n", - "\t description - Name of variable groups from variables.yaml to be expanded into SERPENT variable to be stored\n", - "\t type - <class 'list'>\n" + "\t type - <class 'list'>\n", + "\t description - Name of variable groups from variables.yaml to be expanded into SERPENT variable to be stored\n" ] } ], @@ -124,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -148,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -168,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -195,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -236,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -245,7 +253,7 @@ "'2.1.29'" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -256,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -283,10 +291,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "assert 'INF_SCATT3' not in varSet" @@ -305,6 +311,50 @@ "source": [ "See the `BrancingReader` example for more information on using these settings to control scraped data." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration files\n", + "As of version 0.1.2, the `rc` object allows for settings to be updated from a yaml configuration file using the `loadYaml` method. The file contains setting names as keys with the desired variables as values, as\n", + "```\n", + "verbosity: warning\n", + "xs.getInfXS: False\n", + "```\n", + "However, the loader can also expand a nested dictionary structure, as\n", + "```\n", + "branching:\n", + " areUncsPresent: False\n", + " floatVariables: [Fhi, Blo]\n", + "depletion:\n", + " materials: [fuel]\n", + " materialVariables:\n", + " [ADENS, MDENS, VOLUME]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myConf = 'myConfig.yaml'\n", + "rc.loadYaml(myConf)\n", + "rc['branching.areUncsPresent']" + ] } ], "metadata": { @@ -323,7 +373,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/examples/myConfig.yaml b/examples/myConfig.yaml new file mode 100644 index 0000000..8953d66 --- /dev/null +++ b/examples/myConfig.yaml @@ -0,0 +1,12 @@ +xs.getInfXS: False +xs.getB1XS: True +xs.variableGroups: [gc-meta, kinetics, xs] +branching: + areUncsPresent: False + floatVariables: [Fhi, Blo] +depletion: + materials: [fuel] + metadataKeys: [NAMES, BU] + materialVariables: + [ADENS, MDENS, VOLUME] +serpentVersion: 2.1.29 diff --git a/serpentTools/settings.py b/serpentTools/settings.py index 0f2ae8f..a8d9ae5 100644 --- a/serpentTools/settings.py +++ b/serpentTools/settings.py @@ -1,5 +1,6 @@ """Settings to yield control to the user.""" import os +import six import yaml @@ -151,7 +152,7 @@ class DefaultSettingsLoader(dict): for name, value in defaultSettings.items(): if 'options' in value: options = (value['default'] if value['options'] == 'default' - else value['options']) + else value['options']) else: options = None settingsOptions = {'name': name, @@ -272,7 +273,7 @@ class UserSettingsLoader(dict): """ settings = {} settingsPreffix = ([settingsPreffix] if isinstance(settingsPreffix, str) - else settingsPreffix) + else settingsPreffix) for setting, value in self.items(): settingPath = setting.split('.') if settingPath[0] in settingsPreffix: @@ -314,5 +315,60 @@ class UserSettingsLoader(dict): variables.update(baseGroups[key]) return variables + def loadYaml(self, filePath, strict=True): + """ + Update the settings based on the contents of the yaml file + + .. versionadded:: 0.2.0 + + Parameters + ---------- + filePath: str + Path to config file + strict: bool + Fail at the first incorrect setting. If false, failed settings + will not be loaded and alerts will be raised + + Raises + ------ + KeyError or TypeError + If settings found in the config file are not + valid + FileNotFound or OSError + If the file does not exist + + """ + messages.debug('Attempting to read from {}'.format(filePath)) + with open(filePath) as yFile: + l = yaml.safe_load(yFile) + messages.info('Loading settings onto object with strict:{}' + .format(strict)) + + for key, value in six.iteritems(l): + if isinstance(value, dict): + self.__recursiveLoad(value, strict, key) + else: + self.__safeLoad(key, value, strict) + messages.info('Done') + + def __recursiveLoad(self, curLevel, strict, preset): + for nextLevelKey, nextLevel in six.iteritems(curLevel): + newSettingName = preset + '.' + nextLevelKey + if isinstance(nextLevel, dict): + self.__recursiveLoad(nextLevel, strict, newSettingName) + else: + self.__safeLoad(newSettingName, nextLevel, strict) + + def __safeLoad(self, key, value, strict): + messages.debug('Attempting to set setting {} to {}' + .format(key, value)) + try: + self.setValue(key, value) + except (KeyError, TypeError) as error: + if strict: + raise error + else: + messages.error(str(error)) + rc = UserSettingsLoader()	Update settings from config file Currently, the settings loader has to take settings one at a time. For the sake of consistency and ease, it would be nice to have the `rc` settings loader take a file path argument and load all settings from there. I think a `yaml` file would be pretty straight forward, since we already use the `pyyaml` module. The loader could be setup to read in a strict, full name manner, ``` depletion.metadataKeys: ['ZAI', 'NAMES'] depletion.materials: ['fuel', 'bp1'] verbosity: 'error' ... ``` or with nested levels ``` depletion: metadataKeys: ['ZAI', 'NAMES'] materials: ['fuel', 'bp1'] verbosity: 'error' ... ``` The two implementations should have the same result, just that in the second case there is some reconstructing that has to be done behind the scenes.	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_settings.py b/serpentTools/tests/test_settings.py index de3b43d..b6f8e50 100644 --- a/serpentTools/tests/test_settings.py +++ b/serpentTools/tests/test_settings.py @@ -1,133 +1,204 @@ -"""Tests for the settings loaders.""" -import warnings -import unittest - -from serpentTools import settings -from serpentTools.messages import deprecated, willChange - -class DefaultSettingsTester(unittest.TestCase): - """Class to test the functionality of the master loader.""" - - @classmethod - def setUpClass(cls): - cls.defaultLoader = settings.DefaultSettingsLoader() - cls.testSetting = 'depletion.metadataKeys' - cls.testSettingExpected = ['ZAI', 'NAMES', 'DAYS', 'BU'] - cls.testSettingMethod = cls.assertListEqual - - def test_getDefault(self): - """Verify the default settings loader properly retrives defaults.""" - self.testSettingMethod(self._getLoaderSetting(self.testSetting), - self.testSettingExpected) - - def test_cannotChangeDefaults(self): - """Verify the default settings loader is locked after creation.""" - with self.assertRaises(KeyError): - self.defaultLoader['this'] = 'should fail' - - def _getLoaderSetting(self, setting): - return self.defaultLoader[setting].default - - -class RCTester(unittest.TestCase): - """Class to test the functionality of the scriptable settings manager.""" - - @classmethod - def setUpClass(cls): - cls.rc = settings.UserSettingsLoader() - cls.rc['depletion.metadataKeys'] = ['ZAI'] - - def test_failAtNonexistentSetting(self): - """Verify that the loader will not load a nonexistent setting.""" - with self.assertRaises(KeyError): - self.rc['bad setting'] = False - - def test_failAtBadSetting_options(self): - """Verify that the loader will raise an error for bad options.""" - with self.assertRaises(KeyError): - self.rc['depletion.metadata'] = ['this should fail'] - - def test_failAtBadSettings_type(self): - """Verify that the loader will raise an error for bad type.""" - with self.assertRaises(TypeError): - self.rc['depletion.processTotal'] = 'this should fail' - - def test_returnReaderSettings(self): - """Verify the correct reader settings can be retrieved.""" - readerName = 'depletion' - expected = { - 'metadataKeys': ['ZAI'], - 'materialVariables': [], - 'materials': [], - 'processTotal': True, - } - actual = self.rc.getReaderSettings(readerName) - self.assertDictEqual(expected, actual) - - def test_readerWithUpdatedSettings(self): - """Verify the settings passed to the reader reflect the update.""" - from serpentTools.parsers.depletion import DepletionReader - with settings.rc as tempRC: - tempRC['depletion.metadataKeys'] = ['ZAI'] - reader = DepletionReader('None') - self.assertTrue(reader.settings['metadataKeys'] == ['ZAI']) - - def test_expandExtras(self): - """Verify that a set of extras is given if that is the only argument.""" - extras = ['hello', 'testing'] - with self.rc as tempRC: - tempRC['xs.variableExtras'] = extras - expected = set(extras) - actual = tempRC.expandVariables() - self.assertSetEqual(expected, actual) - - def test_fullExtend(self): - """Verify that the variable expansion return the correct variables""" - groupNames = ['times', 'diffusion'] - extras = ['hello'] - expected = {'CMM_TRANSPXS', 'CMM_TRANSPXS_X', 'CMM_TRANSPXS_Y', - 'CMM_TRANSPXS_Z', 'CMM_DIFFCOEF', 'CMM_DIFFCOEF_X', - 'CMM_DIFFCOEF_Y', 'CMM_DIFFCOEF_Z', 'hello', - 'TOT_CPU_TIME', 'RUNNING_TIME', 'INIT_TIME', 'PROCESS_TIME', - 'TRANSPORT_CYCLE_TIME', 'BURNUP_CYCLE_TIME', - 'BATEMAN_SOLUTION_TIME', 'MPI_OVERHEAD_TIME', - 'ESTIMATED_RUNNING_TIME', 'CPU_USAGE', - 'TRANSPORT_CPU_USAGE', 'OMP_PARALLEL_FRAC'} - with self.rc as tempRC: - tempRC['xs.variableExtras'] = extras - tempRC['xs.variableGroups'] = groupNames - actual = tempRC.expandVariables() - self.assertSetEqual(expected, actual) - - -class MessagingTester(unittest.TestCase): - """Class to test the messaging framework.""" - - def test_futureDecorator(self): - """Verify that the future decorator doesn't break""" - - @willChange('This function will be updated in the future, ' - 'but will still exist') - def demoFuture(x, val=5): - return x + val - - with warnings.catch_warnings(record=True) as record: - self.assertEqual(7, demoFuture(2)) - self.assertEqual(7, demoFuture(2, 5)) - self.assertEquals(len(record), 2, 'Did not catch two warnings::willChange') - - def test_depreciatedDecorator(self): - """Verify that the depreciated decorator doesn't break things""" - - @deprecated('this nonexistent function') - def demoFunction(x, val=5): - return x + val - - with warnings.catch_warnings(record=True) as record: - self.assertEqual(7, demoFunction(2)) - self.assertEqual(7, demoFunction(2, 5)) - self.assertEquals(len(record), 2, 'Did not catch two warnings::deprecation') - - -if __name__ == '__main__': - unittest.main() +"""Tests for the settings loaders.""" +from os.path import join +from os import remove +import warnings +import unittest + +import yaml +import six + +from serpentTools import settings +from serpentTools.messages import deprecated, willChange +from serpentTools.tests import TEST_ROOT + + +class DefaultSettingsTester(unittest.TestCase): + """Class to test the functionality of the master loader.""" + + @classmethod + def setUpClass(cls): + cls.defaultLoader = settings.DefaultSettingsLoader() + cls.testSetting = 'depletion.metadataKeys' + cls.testSettingExpected = ['ZAI', 'NAMES', 'DAYS', 'BU'] + cls.testSettingMethod = cls.assertListEqual + + def test_getDefault(self): + """Verify the default settings loader properly retrives defaults.""" + self.testSettingMethod(self._getLoaderSetting(self.testSetting), + self.testSettingExpected) + + def test_cannotChangeDefaults(self): + """Verify the default settings loader is locked after creation.""" + with self.assertRaises(KeyError): + self.defaultLoader['this'] = 'should fail' + + def _getLoaderSetting(self, setting): + return self.defaultLoader[setting].default + + +class RCTester(unittest.TestCase): + """Class to test the functionality of the scriptable settings manager.""" + + @classmethod + def setUpClass(cls): + cls.rc = settings.UserSettingsLoader() + cls.rc['depletion.metadataKeys'] = ['ZAI'] + + def test_failAtNonexistentSetting(self): + """Verify that the loader will not load a nonexistent setting.""" + with self.assertRaises(KeyError): + self.rc['bad setting'] = False + + def test_failAtBadSetting_options(self): + """Verify that the loader will raise an error for bad options.""" + with self.assertRaises(KeyError): + self.rc['depletion.metadata'] = ['this should fail'] + + def test_failAtBadSettings_type(self): + """Verify that the loader will raise an error for bad type.""" + with self.assertRaises(TypeError): + self.rc['depletion.processTotal'] = 'this should fail' + + def test_returnReaderSettings(self): + """Verify the correct reader settings can be retrieved.""" + readerName = 'depletion' + expected = { + 'metadataKeys': ['ZAI'], + 'materialVariables': [], + 'materials': [], + 'processTotal': True, + } + actual = self.rc.getReaderSettings(readerName) + self.assertDictEqual(expected, actual) + + def test_readerWithUpdatedSettings(self): + """Verify the settings passed to the reader reflect the update.""" + from serpentTools.parsers.depletion import DepletionReader + with settings.rc as tempRC: + tempRC['depletion.metadataKeys'] = ['ZAI'] + reader = DepletionReader('None') + self.assertTrue(reader.settings['metadataKeys'] == ['ZAI']) + + def test_expandExtras(self): + """Verify that a set of extras is given if that is the only argument.""" + extras = ['hello', 'testing'] + with self.rc as tempRC: + tempRC['xs.variableExtras'] = extras + expected = set(extras) + actual = tempRC.expandVariables() + self.assertSetEqual(expected, actual) + + def test_fullExtend(self): + """Verify that the variable expansion return the correct variables""" + groupNames = ['times', 'diffusion'] + extras = ['hello'] + expected = {'CMM_TRANSPXS', 'CMM_TRANSPXS_X', 'CMM_TRANSPXS_Y', + 'CMM_TRANSPXS_Z', 'CMM_DIFFCOEF', 'CMM_DIFFCOEF_X', + 'CMM_DIFFCOEF_Y', 'CMM_DIFFCOEF_Z', 'hello', + 'TOT_CPU_TIME', 'RUNNING_TIME', 'INIT_TIME', 'PROCESS_TIME', + 'TRANSPORT_CYCLE_TIME', 'BURNUP_CYCLE_TIME', + 'BATEMAN_SOLUTION_TIME', 'MPI_OVERHEAD_TIME', + 'ESTIMATED_RUNNING_TIME', 'CPU_USAGE', + 'TRANSPORT_CPU_USAGE', 'OMP_PARALLEL_FRAC'} + with self.rc as tempRC: + tempRC['xs.variableExtras'] = extras + tempRC['xs.variableGroups'] = groupNames + actual = tempRC.expandVariables() + self.assertSetEqual(expected, actual) + + +class ConfigLoaderTester(unittest.TestCase): + """Class to test loading multiple setttings at once, i.e. config files""" + + @classmethod + def setUpClass(cls): + cls.configSettings = { + 'branching.areUncsPresent': True, + 'branching.floatVariables': ['Bhi', 'Tlo'], + 'verbosity': 'warning', + 'depletion.materials': ['fuel*'], + 'depletion.materialVariables': ['ADENS', 'MDENS'] + } + cls.nestedSettings = { + 'branching': { + 'areUncsPresent': + cls.configSettings['branching.areUncsPresent'], + 'floatVariables': + cls.configSettings['branching.floatVariables'] + }, + 'depletion': { + 'materials': cls.configSettings['depletion.materials'], + 'materialVariables': + cls.configSettings['depletion.materialVariables'] + }, + 'verbosity': cls.configSettings['verbosity'] + } + cls.files = {'singleLevel': join(TEST_ROOT, 'singleLevelConf.yaml'), + 'nested': join(TEST_ROOT, 'nestedConf.yaml'), + 'badNested': join(TEST_ROOT, 'badNestedConf.yaml')} + cls.rc = settings.UserSettingsLoader() + + def _writeTestRemoveConfFile(self, settings, filePath, expected, strict): + with open(filePath, 'w') as out: + yaml.dump(settings, out) + with self.rc: + self.rc.loadYaml(filePath, strict) + for key, value in six.iteritems(expected): + if isinstance(value, list): + self.assertListEqual(value, self.rc[key]) + else: + self.assertEqual(value, self.rc[key]) + remove(filePath) + + def test_loadSingleLevelConfig(self): + """Test loading settings from a non-nested config file""" + self._writeTestRemoveConfFile(self.configSettings, + self.files['singleLevel'], +self.configSettings, True) + + def test_loadNestedConfig(self): + """Test loading settings from a nested config file""" + self._writeTestRemoveConfFile(self.nestedSettings, self.files['nested'], + self.configSettings, True) + + def test_loadNestedNonStrict(self): + """Test loading settings with errors but non-strict error handling""" + badSettings = {'bad setting': False} + badSettings.update(self.nestedSettings) + self._writeTestRemoveConfFile(badSettings, self.files['nested'], + self.configSettings, False) + + +class MessagingTester(unittest.TestCase): + """Class to test the messaging framework.""" + + def test_futureDecorator(self): + """Verify that the future decorator doesn't break""" + + @willChange('This function will be updated in the future, ' + 'but will still exist') + def demoFuture(x, val=5): + return x + val + + with warnings.catch_warnings(record=True) as record: + self.assertEqual(7, demoFuture(2)) + self.assertEqual(7, demoFuture(2, 5)) + self.assertEquals(len(record), 2, + 'Did not catch two warnings::willChange') + + def test_depreciatedDecorator(self): + """Verify that the depreciated decorator doesn't break things""" + + @deprecated('this nonexistent function') + def demoFunction(x, val=5): + return x + val + + with warnings.catch_warnings(record=True) as record: + self.assertEqual(7, demoFunction(2)) + self.assertEqual(7, demoFunction(2, 5)) + self.assertEquals(len(record), 2, + 'Did not catch two warnings::deprecation') + + +if __name__ == '__main__': + unittest.main()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_added_files", "has_removed_files", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 8 }	0.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "numpy>=1.11.1 matplotlib>=1.5.0 pyyaml>=3.08 scipy six", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.6", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver @ file:///tmp/build/80754af9/kiwisolver_1612282412546/work matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1613407855456/work numpy @ file:///tmp/build/80754af9/numpy_and_numpy_base_1603483703303/work olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work packaging==21.3 Pillow @ file:///tmp/build/80754af9/pillow_1625670622947/work pluggy==1.0.0 py==1.11.0 pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work pytest==7.0.1 python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work PyYAML==5.4.1 scipy @ file:///tmp/build/80754af9/scipy_1597686635649/work -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@efd13f38b728415f603bef6ab68ae3afd4694983#egg=serpentTools six @ file:///tmp/build/80754af9/six_1644875935023/work tomli==1.2.3 tornado @ file:///tmp/build/80754af9/tornado_1606942266872/work typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - blas=1.0=openblas - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - cycler=0.11.0=pyhd3eb1b0_0 - dbus=1.13.18=hb2f20db_0 - expat=2.6.4=h6a678d5_0 - fontconfig=2.14.1=h52c9d5c_1 - freetype=2.12.1=h4a9f257_0 - giflib=5.2.2=h5eee18b_0 - glib=2.69.1=h4ff587b_1 - gst-plugins-base=1.14.1=h6a678d5_1 - gstreamer=1.14.1=h5eee18b_1 - icu=58.2=he6710b0_3 - jpeg=9e=h5eee18b_3 - kiwisolver=1.3.1=py36h2531618_0 - lcms2=2.16=hb9589c4_0 - ld_impl_linux-64=2.40=h12ee557_0 - lerc=4.0.0=h6a678d5_0 - libdeflate=1.22=h5eee18b_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgfortran-ng=7.5.0=ha8ba4b0_17 - libgfortran4=7.5.0=ha8ba4b0_17 - libgomp=11.2.0=h1234567_1 - libopenblas=0.3.18=hf726d26_0 - libpng=1.6.39=h5eee18b_0 - libstdcxx-ng=11.2.0=h1234567_1 - libtiff=4.5.1=hffd6297_1 - libuuid=1.41.5=h5eee18b_0 - libwebp=1.2.4=h11a3e52_1 - libwebp-base=1.2.4=h5eee18b_1 - libxcb=1.15=h7f8727e_0 - libxml2=2.9.14=h74e7548_0 - lz4-c=1.9.4=h6a678d5_1 - matplotlib=3.3.4=py36h06a4308_0 - matplotlib-base=3.3.4=py36h62a2d02_0 - ncurses=6.4=h6a678d5_0 - numpy=1.19.2=py36h6163131_0 - numpy-base=1.19.2=py36h75fe3a5_0 - olefile=0.46=pyhd3eb1b0_0 - openssl=1.1.1w=h7f8727e_0 - pcre=8.45=h295c915_0 - pillow=8.3.1=py36h5aabda8_0 - pip=21.2.2=py36h06a4308_0 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyqt=5.9.2=py36h05f1152_2 - python=3.6.13=h12debd9_1 - python-dateutil=2.8.2=pyhd3eb1b0_0 - pyyaml=5.4.1=py36h27cfd23_1 - qt=5.9.7=h5867ecd_1 - readline=8.2=h5eee18b_0 - scipy=1.5.2=py36habc2bb6_0 - setuptools=58.0.4=py36h06a4308_0 - sip=4.19.8=py36hf484d3e_0 - six=1.16.0=pyhd3eb1b0_1 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tornado=6.1=py36h27cfd23_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - yaml=0.2.5=h7b6447c_0 - zlib=1.2.13=h5eee18b_1 - zstd=1.5.6=hc292b87_0 - pip: - attrs==22.2.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - packaging==21.3 - pluggy==1.0.0 - py==1.11.0 - pytest==7.0.1 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedConfig", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadNestedNonStrict", "serpentTools/tests/test_settings.py::ConfigLoaderTester::test_loadSingleLevelConfig" ]	[]	[ "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_cannotChangeDefaults", "serpentTools/tests/test_settings.py::DefaultSettingsTester::test_getDefault", "serpentTools/tests/test_settings.py::RCTester::test_expandExtras", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSetting_options", "serpentTools/tests/test_settings.py::RCTester::test_failAtBadSettings_type", "serpentTools/tests/test_settings.py::RCTester::test_failAtNonexistentSetting", "serpentTools/tests/test_settings.py::RCTester::test_fullExtend", "serpentTools/tests/test_settings.py::RCTester::test_readerWithUpdatedSettings", "serpentTools/tests/test_settings.py::RCTester::test_returnReaderSettings", "serpentTools/tests/test_settings.py::MessagingTester::test_depreciatedDecorator", "serpentTools/tests/test_settings.py::MessagingTester::test_futureDecorator" ]	[]	MIT License	null
CORE-GATECH-GROUP__serpent-tools-85	d46f0e5a22b6ac257b7ce5f83222eba0f26c3895	2018-02-05 18:59:30	1d475a4dd8982532d097286468b2d616345c8ab8		diff --git a/serpentTools/__init__.py b/serpentTools/__init__.py index 8933bae..6343059 100644 --- a/serpentTools/__init__.py +++ b/serpentTools/__init__.py @@ -4,16 +4,6 @@ ROOT_DIR = os.path.dirname(__file__) from serpentTools.parsers import read from serpentTools import messages - -# List TODOS/feature requests here for now -# Compatibility -# TODO: Test compatibility with earlier numpy releases -# Usage/scripting -# TODO: Update rc with dictionary -# TODO: Update rc with yaml file into dictionary -# TODO: Capture materials with underscores for depletion -# TODO: Find a way to capture some or all of log messages for testing - from ._version import get_versions __version__ = get_versions()['version'] del get_versions diff --git a/serpentTools/messages.py b/serpentTools/messages.py index 8321264..643f994 100644 --- a/serpentTools/messages.py +++ b/serpentTools/messages.py @@ -58,7 +58,7 @@ def info(message): def warning(message): - """Log a warning that something that could go wrong or should be avoided.""" + """Log a warning that something could go wrong or should be avoided.""" __logger__.warning('%s', message) diff --git a/serpentTools/objects/__init__.py b/serpentTools/objects/__init__.py index 11bf9ec..fc528bc 100644 --- a/serpentTools/objects/__init__.py +++ b/serpentTools/objects/__init__.py @@ -1,44 +1,24 @@ """Objects used to support the parsing.""" -class SupportingObject(object): - """ - Base supporting object. - - Parameters - ---------- - container: Some parser from serpentTools.parsers - Container that created this object - - """ - - def __init__(self, container): - self.filePath = container.filePath - - def __str__(self): - return '<{} from {}>'.format(self.__class__.__name__, self.filePath) - - @staticmethod - def _convertVariableName(variable): - """Converta a SERPENT variable to camelCase.""" - lowerSplits = [item.lower() for item in variable.split('_')] - if len(lowerSplits) == 1: - return lowerSplits[0] - else: - return lowerSplits[0] + ''.join([item.capitalize() - for item in lowerSplits[1:]]) - - -class NamedObject(SupportingObject): +class NamedObject(object): """Class for named objects like materials and detectors.""" - def __init__(self, container, name): - SupportingObject.__init__(self, container) + def __init__(self, name): self.name = name def __str__(self): - return '<{} {} from {}>'.format(self.__class__.__name__, - self.name, self.filePath) + return '<{} {}>'.format(self.__class__.__name__, self.name) + + +def convertVariableName(variable): + """Convert a SERPENT variable to camelCase""" + lowerSplits = [item.lower() for item in variable.split('_')] + if len(lowerSplits) == 1: + return lowerSplits[0] + else: + return lowerSplits[0] + ''.join([item.capitalize() + for item in lowerSplits[1:]]) def splitItems(items): diff --git a/serpentTools/objects/containers.py b/serpentTools/objects/containers.py index afe5756..c8ed16b 100644 --- a/serpentTools/objects/containers.py +++ b/serpentTools/objects/containers.py @@ -10,12 +10,12 @@ Contents """ from collections import OrderedDict -from numpy import array, arange, unique, log, divide, ones_like - from matplotlib import pyplot +from numpy import array, arange, unique, log, divide, ones_like + from serpentTools.plot import cartMeshPlot -from serpentTools.objects import SupportingObject, NamedObject +from serpentTools.objects import NamedObject, convertVariableName from serpentTools.messages import warning, SerpentToolsException, debug DET_COLS = ('value', 'energy', 'universe', 'cell', 'material', 'lattice', @@ -23,15 +23,12 @@ DET_COLS = ('value', 'energy', 'universe', 'cell', 'material', 'lattice', """Name of the columns of the data""" -class HomogUniv(SupportingObject): +class HomogUniv(NamedObject): """ Class for storing homogenized universe specifications and retrieving them Parameters ---------- - container: serpentTools.objects.readers.BaseReader or - serpentTools.objects.containers.BranchContainer - Object to which this universe is attached name: str name of the universe bu: float @@ -63,9 +60,8 @@ class HomogUniv(SupportingObject): Other values that do not not conform to inf/b1 dictionaries """ - def __init__(self, container, name, bu, step, day): - SupportingObject.__init__(self, container) - self.name = name + def __init__(self, name, bu, step, day): + NamedObject.__init__(self, name) self.bu = bu self.step = step self.day = day @@ -81,6 +77,7 @@ class HomogUniv(SupportingObject): Sets the value of the variable and, optionally, the associate s.d. .. warning:: + This method will overwrite data for variables that already exist Parameters @@ -101,10 +98,10 @@ class HomogUniv(SupportingObject): """ # 1. Check the input type - variableName = SupportingObject._convertVariableName(variableName) + variableName = convertVariableName(variableName) if not isinstance(uncertainty, bool): - raise TypeError('The variable uncertainty has type %s.\n ...' - 'It should be boolean.', type(uncertainty)) + raise TypeError('The variable uncertainty has type {}, ' + 'should be boolean.'.format(type(uncertainty))) # 2. Pointer to the proper dictionary setter = self._lookup(variableName, uncertainty) # 3. Check if variable is already present. Then set the variable. @@ -186,8 +183,6 @@ class Detector(NamedObject): Parameters ---------- - parser: :py:class:`~serpentTools.parsers.detector.DetectorReader` - Detector reader that created this detector name: str Name of this detector @@ -207,8 +202,8 @@ class Detector(NamedObject): Collection of unique indexes for each requested bin """ - def __init__(self, parser, name): - NamedObject.__init__(self, parser, name) + def __init__(self, name): + NamedObject.__init__(self, name) self.bins = None self.tallies = None self.errors = None @@ -223,9 +218,6 @@ class Detector(NamedObject): return self.bins.shape[0] return 0 - def __str__(self): - return 'Detector {} from {}'.format(self.name, self.filePath) - def addTallyData(self, bins): """Add tally data to this detector""" self.__reshaped = False @@ -312,13 +304,13 @@ class Detector(NamedObject): 'Slicing requires detector to be reshaped') if data not in self._map: raise KeyError( - 'Slicing function only works with the following data arguments:' - '\n{}'.format(', '.join(self._map.keys()))) + 'Data argument {} not in allowed options' + '\n{}'.format(', '.join(data, self._map.keys()))) work = self._map[data] if work is None: raise SerpentToolsException( '{} data for detector {} is None. Cannot perform slicing' - .format(data, self.name)) + .format(data, self.name)) return work[self._getSlices(fixed)] def _getSlices(self, fixed): @@ -397,7 +389,7 @@ class Detector(NamedObject): if not len(slicedTallies.shape) == 1: raise SerpentToolsException( 'Sliced data must be one-dimensional for spectrum plot, not {}' - .format(slicedTallies.shape) + .format(slicedTallies.shape) ) if normalize: lethBins = log( @@ -609,7 +601,7 @@ class Detector(NamedObject): return ax -class BranchContainer(SupportingObject): +class BranchContainer(object): """ Class that stores data for a single branch. @@ -621,8 +613,8 @@ class BranchContainer(SupportingObject): Parameters ---------- - parser: serpentTools.objects.readers.BaseReader - Parser that read the file that created this object + filePath: str + Path to input file from which this container was connected branchID: int Index for the run for this branch branchNames: tuple @@ -641,8 +633,8 @@ class BranchContainer(SupportingObject): ``(universeID, burnup, burnIndex)`` """ - def __init__(self, parser, branchID, branchNames, stateData): - SupportingObject.__init__(self, parser) + def __init__(self, filePath, branchID, branchNames, stateData): + self.filePath = filePath self.branchID = branchID self.stateData = stateData self.universes = {} @@ -696,7 +688,7 @@ class BranchContainer(SupportingObject): ------- newUniv: serpentTools.objects.containers.HomogUniv """ - newUniv = HomogUniv(self, univID, burnup, burnIndex, burnDays) + newUniv = HomogUniv(univID, burnup, burnIndex, burnDays) key = tuple( [univID, burnup, burnIndex] + ([burnDays] if burnDays else [])) if key in self.__keys: @@ -747,16 +739,3 @@ class BranchContainer(SupportingObject): raise KeyError( 'Could not find a universe that matched requested universe {} and ' '{} {}'.format(univID, searchName, searchValue)) - - -if __name__ == '__main__': - import os - from matplotlib import pyplot - - from serpentTools import ROOT_DIR, read - - bwrF = os.path.join(ROOT_DIR, '..', 'examples', 'bwr_det0.m') - bwr = read(bwrF) - s = bwr.detectors['spectrum'] - s.meshPlot('e', 'reaction', xscale='log') - pyplot.show() diff --git a/serpentTools/objects/materials.py b/serpentTools/objects/materials.py index f5706d1..77abf43 100644 --- a/serpentTools/objects/materials.py +++ b/serpentTools/objects/materials.py @@ -4,7 +4,7 @@ import numpy from matplotlib import pyplot from serpentTools import messages -from serpentTools.objects import NamedObject +from serpentTools.objects import NamedObject, convertVariableName class DepletedMaterial(NamedObject): @@ -26,7 +26,7 @@ class DepletedMaterial(NamedObject): names: numpy.array or None Names of isotopes days: numpy.array or None - Days overwhich the material was depleted + Days over which the material was depleted adens: numpy.array or None Atomic density over time for each nuclide mdens: numpy.array or None @@ -37,14 +37,15 @@ class DepletedMaterial(NamedObject): """ def __init__(self, parser, name): - NamedObject.__init__(self, parser, name) + NamedObject.__init__(self, name) self.data = {} self.zai = parser.metadata.get('zai', None) self.names = parser.metadata.get('names', None) self.days = parser.metadata.get('days', None) - self.__burnup__ = None - self.__adens__ = None - self.__mdens__ = None + self.filePath = parser.filePath + self.__burnup = None + self.__adens = None + self.__mdens = None def __getitem__(self, item): if item not in self.data: @@ -57,27 +58,27 @@ class DepletedMaterial(NamedObject): if 'burnup' not in self.data: raise AttributeError('Burnup for material {} has not been loaded' .format(self.name)) - if self.__burnup__ is None: - self.__burnup__ = self.data['burnup'] - return self.__burnup__ + if self.__burnup is None: + self.__burnup = self.data['burnup'] + return self.__burnup @property def adens(self): if 'adens' not in self.data: raise AttributeError('Atomic densities for material {} have not ' 'been loaded'.format(self.name)) - if self.__adens__ is None: - self.__adens__ = self.data['adens'] - return self.__adens__ + if self.__adens is None: + self.__adens = self.data['adens'] + return self.__adens @property def mdens(self): if 'mdens' not in self.data: raise AttributeError('Mass densities for material {} has not been ' 'loaded'.format(self.name)) - if self.__mdens__ is None: - self.__mdens__ = self.data['mdens'] - return self.__mdens__ + if self.__mdens is None: + self.__mdens = self.data['mdens'] + return self.__mdens def addData(self, variable, rawData): """ @@ -90,7 +91,7 @@ class DepletedMaterial(NamedObject): rawData: list List of strings corresponding to the raw data from the file """ - newName = self._convertVariableName(variable) + newName = convertVariableName(variable) messages.debug('Adding {} data to {}'.format(newName, self.name)) if isinstance(rawData, str): scratch = [float(item) for item in rawData.split()] @@ -148,8 +149,8 @@ class DepletedMaterial(NamedObject): if timePoints is not None: timeCheck = self._checkTimePoints(xUnits, timePoints) if any(timeCheck): - raise KeyError('The following times were not present in file {}' - '\n{}'.format(self.filePath, + raise KeyError('The following times were not present in file' + '{}\n{}'.format(self.filePath, ', '.join(timeCheck))) if names and self.names is None: raise AttributeError( diff --git a/serpentTools/parsers/__init__.py b/serpentTools/parsers/__init__.py index 978afc9..fb980bf 100644 --- a/serpentTools/parsers/__init__.py +++ b/serpentTools/parsers/__init__.py @@ -70,7 +70,8 @@ def inferReader(filePath): for reg, reader in six.iteritems(REGEXES): match = re.match(reg, filePath) if match and match.group() == filePath: - info('Inferred reader for {}: {}'.format(filePath, reader.__name__)) + info('Inferred reader for {}: {}' + .format(filePath, reader.__name__)) return reader raise SerpentToolsException( 'Failed to infer filetype and thus accurate reader from' @@ -140,7 +141,8 @@ def read(filePath, reader='infer'): 'Reader type {} not supported'.format(reader) ) else: - assert callable(reader), 'Reader {} is not callable'.format(str(reader)) + assert callable(reader), ( + 'Reader {} is not callable'.format(str(reader))) loader = reader returnedFromLoader = loader(filePath) returnedFromLoader.read() @@ -202,7 +204,8 @@ def depmtx(fileP): if not nMatch: raise SerpentToolsException(failMsg + line) - n0Storage, line, numIso = _parseIsoBlock(f, {}, nMatch, line, nDensRegex) + n0Storage, line, numIso = _parseIsoBlock(f, {}, nMatch, line, + nDensRegex) debug('Found {} isotopes for file {}'.format(numIso, fileP)) n0 = empty((numIso, 1), dtype=longfloat) for indx, v in six.iteritems(n0Storage): diff --git a/serpentTools/parsers/branching.py b/serpentTools/parsers/branching.py index 3daadf8..3d92a86 100644 --- a/serpentTools/parsers/branching.py +++ b/serpentTools/parsers/branching.py @@ -74,7 +74,8 @@ class BranchingReader(XSReader): if branchNames not in self.branches: branchState = self._processBranchStateData() self.branches[branchNames] = ( - BranchContainer(self, coefIndx, branchNames, branchState)) + BranchContainer(self.filePath, coefIndx, branchNames, + branchState)) else: self._advance() return self.branches[branchNames], int(totUniv) @@ -98,7 +99,8 @@ class BranchingReader(XSReader): unvID, numVariables = [int(xx) for xx in self._advance()] univ = branch.addUniverse(unvID, burnup, burnupIndex) for step in range(numVariables): - splitList = self._advance(possibleEndOfFile=step == numVariables-1) + splitList = self._advance( + possibleEndOfFile=step == numVariables - 1) varName = splitList[0] varValues = [float(xx) for xx in splitList[2:]] if self._checkAddVariable(varName): diff --git a/serpentTools/parsers/detector.py b/serpentTools/parsers/detector.py index 9b08305..a2570aa 100644 --- a/serpentTools/parsers/detector.py +++ b/serpentTools/parsers/detector.py @@ -1,6 +1,5 @@ """Parser responsible for reading the ``*det<n>.m`` files""" -import six import numpy from serpentTools.engines import KeywordParser @@ -60,7 +59,7 @@ class DetectorReader(BaseReader): data[indx] = [float(xx) for xx in line.split()] if detName not in self.detectors: # new detector, this data is the tallies - detector = Detector(self, detName) + detector = Detector(detName) detector.addTallyData(data) self.detectors[detName] = detector messages.debug('Adding detector {}'.format(detName)) @@ -70,17 +69,3 @@ class DetectorReader(BaseReader): detector.grids[binType] = data messages.debug('Added bin data {} to detector {}' .format(binType, detName)) - - -if __name__ == '__main__': - from os.path import join - from matplotlib import pyplot - import serpentTools - - det = DetectorReader(join(serpentTools.ROOT_DIR, 'tests', 'ref_det0.m')) - det.read() - xy = det.detectors['xyFissionCapt'] - xy.plot(fixed={'reaction': 1, 'ymesh': 2}) - pyplot.show() - xy.plot(fixed={'reaction': 1, 'ymesh': 2}, sigma=1) - pyplot.show() diff --git a/serpentTools/settings.py b/serpentTools/settings.py index c49c1dc..788f674 100644 --- a/serpentTools/settings.py +++ b/serpentTools/settings.py @@ -158,7 +158,7 @@ class DefaultSettingsLoader(dict): for name, value in defaultSettings.items(): if 'options' in value: options = (value['default'] if value['options'] == 'default' - else value['options']) + else value['options']) else: options = None settingsOptions = {'name': name, @@ -278,8 +278,9 @@ class UserSettingsLoader(dict): dictionary """ settings = {} - settingsPreffix = ([settingsPreffix] if isinstance(settingsPreffix, str) - else settingsPreffix) + settingsPreffix = ( + [settingsPreffix] if isinstance(settingsPreffix, str) + else settingsPreffix) for setting, value in self.items(): settingPath = setting.split('.') if settingPath[0] in settingsPreffix: @@ -346,11 +347,11 @@ class UserSettingsLoader(dict): """ messages.debug('Attempting to read from {}'.format(filePath)) with open(filePath) as yFile: - l = yaml.safe_load(yFile) + loaded = yaml.safe_load(yFile) messages.info('Loading settings onto object with strict:{}' .format(strict)) - for key, value in six.iteritems(l): + for key, value in six.iteritems(loaded): if isinstance(value, dict): self.__recursiveLoad(value, strict, key) else:	Clean up the code There are some debug codes, ```if __name__ == '__main__'```, in some of the scripts. They should be removed. Test scripts, e.g. unittests, still need those to run the tests. - [x] `detector.py` - [x] todos in the root __init__.py	CORE-GATECH-GROUP/serpent-tools	diff --git a/serpentTools/tests/test_container.py b/serpentTools/tests/test_container.py index bba2a3a..fbed4dc 100644 --- a/serpentTools/tests/test_container.py +++ b/serpentTools/tests/test_container.py @@ -10,7 +10,7 @@ class HomogenizedUniverseTester(unittest.TestCase): @classmethod def setUpClass(cls): - cls.univ = containers.HomogUniv(DepletionReader(None), 'dummy', 0, 0, 0) + cls.univ = containers.HomogUniv('dummy', 0, 0, 0) cls.Exp = {} cls.Unc = {} # Data definition	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 3, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 9 }	0.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "numpy>=1.11.1 matplotlib>=1.5.0 pyyaml>=3.08 scipy", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==22.2.0 certifi==2021.5.30 cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work importlib-metadata==4.8.3 iniconfig==1.1.1 kiwisolver @ file:///tmp/build/80754af9/kiwisolver_1612282412546/work matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1613407855456/work numpy @ file:///tmp/build/80754af9/numpy_and_numpy_base_1603483703303/work olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work packaging==21.3 Pillow @ file:///tmp/build/80754af9/pillow_1625670622947/work pluggy==1.0.0 py==1.11.0 pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work pytest==7.0.1 python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work PyYAML==5.4.1 scipy @ file:///tmp/build/80754af9/scipy_1597686635649/work -e git+https://github.com/CORE-GATECH-GROUP/serpent-tools.git@d46f0e5a22b6ac257b7ce5f83222eba0f26c3895#egg=serpentTools six @ file:///tmp/build/80754af9/six_1644875935023/work tomli==1.2.3 tornado @ file:///tmp/build/80754af9/tornado_1606942266872/work typing_extensions==4.1.1 zipp==3.6.0	name: serpent-tools channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - blas=1.0=openblas - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - cycler=0.11.0=pyhd3eb1b0_0 - dbus=1.13.18=hb2f20db_0 - expat=2.6.4=h6a678d5_0 - fontconfig=2.14.1=h52c9d5c_1 - freetype=2.12.1=h4a9f257_0 - giflib=5.2.2=h5eee18b_0 - glib=2.69.1=h4ff587b_1 - gst-plugins-base=1.14.1=h6a678d5_1 - gstreamer=1.14.1=h5eee18b_1 - icu=58.2=he6710b0_3 - jpeg=9e=h5eee18b_3 - kiwisolver=1.3.1=py36h2531618_0 - lcms2=2.16=hb9589c4_0 - ld_impl_linux-64=2.40=h12ee557_0 - lerc=4.0.0=h6a678d5_0 - libdeflate=1.22=h5eee18b_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgfortran-ng=7.5.0=ha8ba4b0_17 - libgfortran4=7.5.0=ha8ba4b0_17 - libgomp=11.2.0=h1234567_1 - libopenblas=0.3.18=hf726d26_0 - libpng=1.6.39=h5eee18b_0 - libstdcxx-ng=11.2.0=h1234567_1 - libtiff=4.5.1=hffd6297_1 - libuuid=1.41.5=h5eee18b_0 - libwebp=1.2.4=h11a3e52_1 - libwebp-base=1.2.4=h5eee18b_1 - libxcb=1.15=h7f8727e_0 - libxml2=2.9.14=h74e7548_0 - lz4-c=1.9.4=h6a678d5_1 - matplotlib=3.3.4=py36h06a4308_0 - matplotlib-base=3.3.4=py36h62a2d02_0 - ncurses=6.4=h6a678d5_0 - numpy=1.19.2=py36h6163131_0 - numpy-base=1.19.2=py36h75fe3a5_0 - olefile=0.46=pyhd3eb1b0_0 - openssl=1.1.1w=h7f8727e_0 - pcre=8.45=h295c915_0 - pillow=8.3.1=py36h5aabda8_0 - pip=21.2.2=py36h06a4308_0 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyqt=5.9.2=py36h05f1152_2 - python=3.6.13=h12debd9_1 - python-dateutil=2.8.2=pyhd3eb1b0_0 - pyyaml=5.4.1=py36h27cfd23_1 - qt=5.9.7=h5867ecd_1 - readline=8.2=h5eee18b_0 - scipy=1.5.2=py36habc2bb6_0 - setuptools=58.0.4=py36h06a4308_0 - sip=4.19.8=py36hf484d3e_0 - six=1.16.0=pyhd3eb1b0_1 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tornado=6.1=py36h27cfd23_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - yaml=0.2.5=h7b6447c_0 - zlib=1.2.13=h5eee18b_1 - zstd=1.5.6=hc292b87_0 - pip: - attrs==22.2.0 - importlib-metadata==4.8.3 - iniconfig==1.1.1 - packaging==21.3 - pluggy==1.0.0 - py==1.11.0 - pytest==7.0.1 - tomli==1.2.3 - typing-extensions==4.1.1 - zipp==3.6.0 prefix: /opt/conda/envs/serpent-tools	[ "serpentTools/tests/test_container.py::HomogenizedUniverseTester::test_getB1Exp", "serpentTools/tests/test_container.py::HomogenizedUniverseTester::test_getB1Unc", "serpentTools/tests/test_container.py::HomogenizedUniverseTester::test_getInfExp", "serpentTools/tests/test_container.py::HomogenizedUniverseTester::test_getInfUnc", "serpentTools/tests/test_container.py::HomogenizedUniverseTester::test_getMeta" ]	[]	[]	[]	MIT License	null
CS-SI__eodag-1108	e619c88de826047b8f46f4485ca5537a58e60678	2024-04-23 15:45:29	0a6208a6e306461fca3f66c1653a41ffda54dfb6	github-actions[bot]: ## Test Results 1 files - 3 1 suites - 3 1m 3s :stopwatch: - 4m 31s 506 tests + 1 496 :white_check_mark: - 6 3 :zzz: ± 0 7 :x: +7 506 runs - 1 514 496 :white_check_mark: - 1 436 3 :zzz: - 85 7 :x: +7 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/24161355224). Results for commit 84cd12a1. ± Comparison against base commit 0dc0ba05. <details> <summary>This pull request <b>removes</b> 1 and <b>adds</b> 2 tests. <i>Note that renamed tests count towards both.</i></summary> ``` tests.test_requirements.TestRequirements ‑ test_requirements ``` ``` tests.test_requirements.TestRequirements ‑ test_all_requirements tests.test_requirements.TestRequirements ‑ test_plugins_extras ``` </details> [test-results]:data:application/gzip;base64,H4sIAFzYJ2YC/1WMyw6DIBBFf8Ww7gIqgtOfMTJAMqmPBmFl+u8dbap2ec7NPauINIRFPCp1q8RSKB/gS+ozzROjqZl5ydvWSPOjbimIrDRc1JNerI5HF3saWNhDhJTmxEaySWU6mxv8J7/mLO58Ce587eE8jpQZRKvRq3uvdOM0WGsD2rZx6KSD2vhYe4TQAgTx/gAHs/PJBAEAAA== github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports/data/extra-reqs-refactor/./badge.svg) ## Code Coverage (Ubuntu) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ __init__.py 11 0 100.00% cli.py 300 47 84.33% 61, 647-686, 788-839, 843 config.py 318 27 91.51% 80-82, 91, 99, 103-105, 178, 190, 391-393, 457-460, 507-508, 517-518, 597, 666-671, 673 crunch.py 6 6 0.00% 18-24 api/__init__.py 1 0 100.00% api/core.py 739 76 89.72% 88-97, 370, 597, 641-644, 682, 787, 791-796, 822, 892, 969, 1082, 1172-1184, 1224, 1226, 1230, 1253, 1257-1268, 1281-1287, 1377-1380, 1409-1429, 1486, 1503-1506, 1518-1521, 1889, 1922-1928, 2193, 2197-2200, 2214-2216, 2251 api/search_result.py 44 6 86.36% 33-35, 70, 79, 86, 100 api/product/__init__.py 6 0 100.00% api/product/_assets.py 44 5 88.64% 27-29, 79, 129 api/product/_product.py 211 32 84.83% 53-60, 64-66, 170-177, 261-262, 352, 388, 449, 463-466, 479, 503-506, 549-555 api/product/metadata_mapping.py 649 82 87.37% 67-69, 130-132, 233, 265-266, 318-330, 332, 343, 349-361, 402-405, 442, 463-466, 489, 497-498, 571-572, 596-597, 603-606, 621-622, 771, 817, 934-939, 1070, 1084-1104, 1124, 1129, 1239, 1261, 1275, 1288-1307, 1346, 1398, 1436-1440, 1459 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 8 2 75.00% 23, 41 plugins/__init__.py 1 0 100.00% plugins/base.py 23 3 86.96% 25, 48, 55 plugins/manager.py 132 14 89.39% 49-51, 105-110, 156, 195, 215, 241, 280-281 plugins/apis/__init__.py 1 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 99 15 84.85% 47-55, 156-158, 205-206, 232-234 plugins/apis/usgs.py 172 36 79.07% 59-64, 128, 201, 235, 270-272, 277, 303-304, 309, 339-346, 357-362, 384-390, 392-398, 421 plugins/authentication/__init__.py 7 1 85.71% 31 plugins/authentication/aws_auth.py 20 2 90.00% 25-27 plugins/authentication/base.py 19 3 84.21% 26, 34, 47 plugins/authentication/generic.py 16 3 81.25% 28, 40, 50 plugins/authentication/header.py 17 1 94.12% 27 plugins/authentication/keycloak.py 88 17 80.68% 32-34, 159-160, 190-212, 238-243 plugins/authentication/oauth.py 15 8 46.67% 25, 32-34, 38-41 plugins/authentication/openid_connect.py 103 64 37.86% 39-41, 138-150, 154-172, 180-222, 228-237, 246-286, 291-299, 304-305 plugins/authentication/qsauth.py 36 2 94.44% 32, 83 plugins/authentication/sas_auth.py 49 2 95.92% 32, 76 plugins/authentication/token.py 81 13 83.95% 35-37, 86, 99, 101, 114-117, 165-168 plugins/crunch/__init__.py 1 0 100.00% plugins/crunch/base.py 10 2 80.00% 25, 38 plugins/crunch/filter_date.py 62 15 75.81% 30, 53-58, 72, 81, 90, 93, 105-107, 116-118, 125 plugins/crunch/filter_latest_intersect.py 50 10 80.00% 32-34, 51-52, 71, 80-83, 85, 92-95 plugins/crunch/filter_latest_tpl_name.py 33 2 93.94% 28, 86 plugins/crunch/filter_overlap.py 68 17 75.00% 28-30, 33, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 33 8 75.76% 29, 60-65, 68-69, 85-89 plugins/download/__init__.py 1 0 100.00% plugins/download/aws.py 491 165 66.40% 77-83, 272, 285, 352-355, 369-373, 419-421, 425, 458-459, 465-469, 502, 537, 541, 548, 578-586, 590, 628-636, 643-645, 686-760, 778-839, 850-855, 871-884, 913, 928-930, 933, 943-951, 959-972, 982-1001, 1008-1020, 1061, 1087, 1132-1134, 1354 plugins/download/base.py 261 57 78.16% 58-64, 145, 180, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 538-542, 572-573, 581-598, 605-613, 615-619, 666, 689, 711, 719 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 458 94 79.48% 82-88, 122, 196-208, 211, 292-297, 321, 323, 387, 414-416, 426, 432, 434, 458, 497-501, 558, 644-700, 718, 751-760, 786-787, 814, 838, 846-851, 873-874, 881, 942-948, 1003-1004, 1010, 1020, 1086, 1090, 1104-1120 plugins/download/s3rest.py 117 27 76.92% 55-58, 124, 165, 199, 229-236, 239-241, 245, 258-264, 272-273, 276-280, 303, 324-327 plugins/search/__init__.py 1 0 100.00% plugins/search/base.py 127 9 92.91% 49-54, 108, 112, 275, 295, 378 plugins/search/build_search_result.py 179 22 87.71% 62, 97, 142-143, 149, 160, 290-293, 327, 384-396, 458, 461, 471, 488, 516, 518 plugins/search/creodias_s3.py 54 3 94.44% 55, 73, 107 plugins/search/csw.py 107 83 22.43% 43-45, 57-58, 62-63, 74-122, 128-141, 149-181, 199-240 plugins/search/data_request_search.py 198 65 67.17% 52, 89-92, 108, 120, 124-125, 139, 144, 149, 156, 169-172, 226-227, 231, 241-247, 252, 280-283, 291-302, 319, 321, 328-329, 331-332, 350-354, 387, 394, 405, 418, 424-436, 441 plugins/search/qssearch.py 548 54 90.15% 89, 361-367, 375-376, 483-489, 544-547, 620-621, 662, 680, 695, 748, 769, 772-773, 782, 793, 802, 825, 885-890, 894-895, 923, 994, 1041, 1117-1121, 1187-1188, 1209, 1234, 1240, 1271-1272, 1282-1288, 1331, 1345, 1365, 1455 plugins/search/static_stac_search.py 47 3 93.62% 39-40, 82 rest/__init__.py 5 2 60.00% 21-22 rest/core.py 199 15 92.46% 71-77, 261, 588, 590, 593-595, 667, 674-678 rest/server.py 299 54 81.94% 80-81, 107, 130-131, 242-244, 260, 300-301, 313-329, 415-420, 451, 612-619, 651, 694-695, 783-785, 802-807, 837, 839, 843-844, 848-849 rest/stac.py 436 95 78.21% 59-61, 229-231, 273, 286-295, 314-320, 365, 402-404, 427, 462-463, 549-594, 639, 659-660, 840, 905-907, 1126, 1136-1148, 1161-1183, 1197-1242, 1401-1402 rest/types/__init__.py 1 0 100.00% rest/types/eodag_search.py 185 9 95.14% 52-55, 231-235, 288, 291, 359 rest/types/stac_queryables.py 30 2 93.33% 28, 114 rest/types/stac_search.py 122 10 91.80% 48-51, 167, 182-184, 192, 196 rest/utils/__init__.py 118 14 88.14% 53, 108-109, 128-130, 183, 193-207, 239 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 31 3 90.32% 78, 90, 92 types/__init__.py 76 6 92.11% 53, 87, 154, 174, 179, 187 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 104 types/whoosh.py 15 0 100.00% utils/__init__.py 496 43 91.33% 83, 88, 109-111, 189-190, 199-226, 229, 243, 325-329, 405-409, 430-432, 514, 519, 529, 567-568, 964-967, 975-976, 1017-1018, 1098, 1182, 1200, 1375 utils/constraints.py 123 42 65.85% 31, 84-93, 134, 139, 143, 154, 177-178, 189-197, 206, 220-236, 245-256 utils/exceptions.py 37 2 94.59% 23, 93 utils/import_system.py 30 20 33.33% 27, 67-81, 93-103 utils/logging.py 29 1 96.55% 123 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/stac_reader.py 91 28 69.23% 55-56, 63-86, 93-95, 99, 141, 155-158 TOTAL 8707 1523 82.51% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover -------------------------- ------- ------ ------- api/core.py +4 0 +0.06% plugins/manager.py +5 0 +0.41% plugins/search/qssearch.py -13 -11 +1.74% rest/__init__.py +4 +2 -40.00% TOTAL 0 -9 +0.11% ``` Results for commit: e87fc113047baa5e36e3c2a1c108e2f6d7492066 _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-ubuntu-latest/coverage.xml --> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports_win/data/extra-reqs-refactor/./badge.svg) ## Code Coverage (Windows) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 11 0 100.00% cli.py 300 199 33.67% 61, 93-99, 112-114, 120, 261-382, 408-472, 488-503, 527-586, 647-686, 788-839, 843 config.py 318 57 82.08% 80-82, 91, 99, 103-105, 172, 174, 178, 190, 206, 345, 392-393, 457-460, 505-512, 517-518, 560-567, 579-601, 631, 660-671, 673, 678 crunch.py 6 6 0.00% 18-24 api/__init__.py 1 0 100.00% api/core.py 739 134 81.87% 88-97, 131-144, 351-355, 370, 597, 641-644, 682, 742-761, 784, 787, 791-796, 822, 849, 892, 916, 969, 1082, 1172-1184, 1224, 1226, 1230, 1253, 1257-1268, 1281-1287, 1377-1380, 1409-1429, 1486, 1503-1506, 1518-1521, 1859-1860, 1889, 1922-1928, 1941-1950, 1995-2013, 2028-2030, 2041-2042, 2053-2062, 2185-2193, 2196-2200, 2214-2216, 2251 api/search_result.py 44 11 75.00% 33-35, 70, 79, 86, 100, 126, 146, 153, 162, 170 api/product/__init__.py 6 0 100.00% api/product/_assets.py 44 5 88.64% 27-29, 79, 129 api/product/_product.py 211 32 84.83% 53-60, 64-66, 170-177, 261-262, 352, 388, 449, 463-466, 479, 503-506, 549-555 api/product/metadata_mapping.py 649 133 79.51% 67-69, 130-132, 233, 265-266, 283-284, 318-330, 332, 343, 349-361, 367, 373, 402-405, 442, 463-466, 489, 497-502, 539, 571-572, 596-597, 603-606, 621-622, 771, 817, 890-891, 918, 934-939, 1033, 1045, 1065-1126, 1129, 1137-1148, 1194, 1212, 1239, 1248, 1261, 1275, 1288-1307, 1346, 1373-1398, 1417-1421, 1436-1440, 1458-1469 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 8 2 75.00% 23, 41 plugins/__init__.py 1 0 100.00% plugins/base.py 23 4 82.61% 25, 48, 55, 68 plugins/manager.py 132 23 82.58% 49-51, 106-110, 124-128, 147-152, 156, 195, 215, 237-244, 280-281 plugins/apis/__init__.py 1 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 99 70 29.29% 47-55, 89, 103-129, 141-160, 172-249, 264, 276 plugins/apis/usgs.py 172 116 32.56% 59-64, 102-111, 128, 141, 152-156, 178-213, 228-237, 250-406, 421 plugins/authentication/__init__.py 7 1 85.71% 31 plugins/authentication/aws_auth.py 20 2 90.00% 25-27 plugins/authentication/base.py 19 6 68.42% 26, 34, 41-42, 47, 57 plugins/authentication/generic.py 16 3 81.25% 28, 40, 50 plugins/authentication/header.py 17 7 58.82% 27, 65-70, 77, 81-82 plugins/authentication/keycloak.py 88 57 35.23% 32-34, 103-107, 116-119, 126-160, 163-217, 220-244 plugins/authentication/oauth.py 15 8 46.67% 25, 32-34, 38-41 plugins/authentication/openid_connect.py 103 76 26.21% 39-41, 138-150, 154-172, 180-222, 228-237, 246-286, 291-299, 304-305, 318-320, 324-334 plugins/authentication/qsauth.py 36 21 41.67% 32, 65-87, 94, 98-104 plugins/authentication/sas_auth.py 49 32 34.69% 32, 48-52, 58-84, 93, 97-108 plugins/authentication/token.py 81 56 30.86% 35-37, 53-65, 71-103, 110-136, 154-157, 161-182 plugins/crunch/__init__.py 1 0 100.00% plugins/crunch/base.py 10 2 80.00% 25, 38 plugins/crunch/filter_date.py 62 15 75.81% 30, 53-58, 72, 81, 90, 93, 105-107, 116-118, 125 plugins/crunch/filter_latest_intersect.py 50 35 30.00% 32-34, 48-53, 69-114 plugins/crunch/filter_latest_tpl_name.py 33 2 93.94% 28, 86 plugins/crunch/filter_overlap.py 68 17 75.00% 28-30, 33, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 33 8 75.76% 29, 60-65, 68-69, 85-89 plugins/download/__init__.py 1 0 100.00% plugins/download/aws.py 491 419 14.66% 77-83, 269-393, 415-429, 439-469, 491-519, 536-591, 616-639, 643-645, 686-760, 778-839, 850-855, 871-884, 903-933, 943-951, 958-974, 982-1001, 1008-1020, 1034-1050, 1054-1074, 1078-1134, 1144-1339, 1354 plugins/download/base.py 261 114 56.32% 58-64, 145, 180, 211-214, 235, 250-252, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 515-623, 666, 674-694, 707-711, 717-726 plugins/download/creodias_s3.py 17 10 41.18% 34, 44-58 plugins/download/http.py 458 109 76.20% 82-88, 122, 196-208, 211, 292-297, 321, 323, 387, 414-416, 426, 432, 434, 458, 497-501, 558, 589, 644-700, 707-718, 730-760, 786-787, 814, 837-842, 846-851, 873-874, 881, 942-948, 1003-1004, 1010, 1020, 1086, 1090, 1104-1120, 1202 plugins/download/s3rest.py 117 97 17.09% 55-58, 91-92, 123-344 plugins/search/__init__.py 1 0 100.00% plugins/search/base.py 127 15 88.19% 49-54, 108, 112, 140-146, 275, 295, 378 plugins/search/build_search_result.py 179 124 30.73% 62, 97, 121, 135-231, 290-293, 307, 319-321, 327, 334-345, 365-368, 381-443, 456-542 plugins/search/creodias_s3.py 54 34 37.04% 50-55, 59-110, 126-136 plugins/search/csw.py 107 83 22.43% 43-45, 57-58, 62-63, 74-122, 128-141, 149-181, 199-240 plugins/search/data_request_search.py 198 170 14.14% 52, 68-112, 120, 124-125, 138-255, 262-288, 291-302, 305-335, 340-354, 364-411, 414-419, 424-436, 440-443 plugins/search/qssearch.py 548 141 74.27% 89, 361-367, 375-376, 394, 436-439, 483-489, 506-519, 544-547, 620-621, 662, 680, 695, 736-773, 782, 793, 802, 813-814, 816, 825, 833, 885-890, 894-895, 923, 951, 994, 1026-1053, 1059, 1082-1087, 1117-1121, 1132, 1160-1195, 1209, 1234, 1240, 1271-1272, 1282-1288, 1331, 1343, 1345, 1355, 1365, 1372, 1375, 1377, 1415-1484 plugins/search/static_stac_search.py 47 3 93.62% 39-40, 82 rest/__init__.py 5 2 60.00% 21-22 rest/core.py 199 88 55.78% 71-77, 158, 160, 162, 168-169, 186-194, 200-206, 253-302, 315-342, 492-519, 537, 587-626, 667, 674-678 rest/server.py 299 299 0.00% 18-862 rest/stac.py 436 154 64.68% 59-61, 214, 229-231, 273, 286-295, 314-320, 365, 402-404, 427, 462-463, 549-594, 639, 647-648, 652-660, 782, 840, 905-907, 924-926, 934-936, 949-951, 965-982, 992-1013, 1023-1045, 1053-1070, 1093-1116, 1126, 1136-1148, 1161-1183, 1197-1242, 1395-1421 rest/types/__init__.py 1 0 100.00% rest/types/eodag_search.py 185 18 90.27% 52-55, 231-235, 268-270, 288, 291, 297, 301, 359, 371-374 rest/types/stac_queryables.py 30 6 80.00% 28, 53-58, 114 rest/types/stac_search.py 122 50 59.02% 48-51, 127, 129, 131, 139, 166-172, 179-198, 204-229, 240, 243, 253-261 rest/utils/__init__.py 118 32 72.88% 53, 79-85, 105, 108-109, 128-130, 143, 150, 176-184, 191-212, 239 rest/utils/cql_evaluate.py 48 27 43.75% 36, 41, 46, 51, 68-105, 110-114, 119 rest/utils/rfc3339.py 31 22 29.03% 43-50, 72-94 types/__init__.py 76 52 31.58% 52-57, 71-87, 113-132, 153-199 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 2 97.53% 46-51 types/search_args.py 70 33 52.86% 60-64, 71-88, 103-134 types/whoosh.py 15 0 100.00% utils/__init__.py 496 51 89.72% 83, 88, 109-111, 189-190, 199-226, 229, 243, 325-329, 367, 405-409, 430-432, 514, 519, 529, 567-568, 903, 964-967, 975-976, 1017-1018, 1071-1072, 1098, 1127, 1141, 1159, 1181-1182, 1200, 1375 utils/constraints.py 123 108 12.20% 31, 56-158, 173-209, 218-237, 245-256 utils/exceptions.py 37 2 94.59% 23, 93 utils/import_system.py 30 20 33.33% 27, 67-81, 93-103 utils/logging.py 29 12 58.62% 43, 58-123, 152, 154-158 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/stac_reader.py 91 31 65.93% 55-59, 63-86, 93-95, 99, 141, 155-158 TOTAL 8707 3510 59.69% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ---------------------------------------- ------- ------ ------- cli.py 0 +152 -50.66% config.py 0 +29 -9.11% api/core.py +4 +58 -7.79% api/search_result.py 0 +5 -11.36% api/product/metadata_mapping.py 0 +50 -7.70% plugins/manager.py +5 +9 -6.40% plugins/apis/ecmwf.py 0 +55 -55.56% plugins/apis/usgs.py 0 +80 -46.51% plugins/authentication/base.py 0 +3 -15.79% plugins/authentication/header.py 0 +6 -35.30% plugins/authentication/keycloak.py 0 +40 -45.45% plugins/authentication/openid_connect.py 0 +12 -11.65% plugins/authentication/qsauth.py 0 +19 -52.77% plugins/authentication/sas_auth.py 0 +30 -61.23% plugins/authentication/token.py 0 +43 -53.09% plugins/download/aws.py 0 +254 -51.74% plugins/download/base.py 0 +55 -21.07% plugins/download/creodias_s3.py 0 +1 -5.88% plugins/download/http.py 0 +15 -3.28% plugins/download/s3rest.py 0 +70 -59.83% plugins/search/base.py 0 +6 -4.72% plugins/search/build_search_result.py 0 +95 -53.07% plugins/search/creodias_s3.py 0 +31 -57.40% plugins/search/data_request_search.py 0 +105 -53.03% plugins/search/qssearch.py -13 +52 -9.87% rest/__init__.py +4 +2 -40.00% rest/types/stac_search.py 0 +38 -31.14% rest/utils/cql_evaluate.py 0 +22 -45.83% rest/utils/rfc3339.py 0 +17 -54.84% types/__init__.py 0 +41 -53.95% types/queryables.py 0 +2 -2.47% types/search_args.py 0 +15 -21.43% utils/__init__.py 0 +7 -1.41% utils/constraints.py 0 +66 -53.65% utils/logging.py 0 +11 -37.93% utils/stac_reader.py 0 +3 -3.30% TOTAL 0 +1499 -17.21% ``` Results for commit: 4869d2e6f8e5256b913555af6da782281cb21e7f _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-windows-latest/coverage.xml -->	diff --git a/README.rst b/README.rst index 0eff4243..441dd1c6 100644 --- a/README.rst +++ b/README.rst @@ -84,6 +84,9 @@ And with ``conda`` from the `conda-forge channel <https://anaconda.org/conda-for conda install -c conda-forge eodag +Please note that EODAG comes with a minimal set of dependencies. If you want more features, please install using one of +the `available extras <https://eodag.readthedocs.io/en/latest/getting_started_guide/install.html#optional-dependencies>`_. + Usage ===== diff --git a/docs/getting_started_guide/install.rst b/docs/getting_started_guide/install.rst index e751e8d8..a8c7a461 100644 --- a/docs/getting_started_guide/install.rst +++ b/docs/getting_started_guide/install.rst @@ -20,6 +20,20 @@ Or with ``conda`` from the conda-forge channel: conda install -c conda-forge eodag +Optional dependencies +^^^^^^^^^^^^^^^^^^^^^ + +Since ``v3.0``, EODAG comes with a minimal set of dependencies. If you want more features, please install using one of +the following extras: + +* ``eodag[all]``, includes everything that would be needed to run EODAG and associated tutorials with all features +* ``eodag[all-providers]``, includes dependencies required to have all providers available +* ``eodag[aws]``, includes dependencies for plugins using Amazon S3 +* ``eodag[csw]``, includes dependencies for plugins using CSW +* ``eodag[ecmwf]``, includes dependencies for :class:`~eodag.plugins.apis.ecmwf.EcmwfApi` (`ecmwf` provider) +* ``eodag[usgs]``, includes dependencies for :class:`~eodag.plugins.apis.usgs.UsgsApi` (`usgs` provider) +* ``eodag[server]``, includes dependencies for server-mode + .. _install_notebooks: Run the notebooks locally @@ -30,7 +44,7 @@ that can be run locally: 1. Install the extras dependencies it requires by executing this command (preferably in a virtual environment):: - python -m pip install eodag[tutorials] + python -m pip install "eodag[tutorials]" 2. Clone ``eodag`` 's repository with git:: diff --git a/docs/plugins_reference/auth.rst b/docs/plugins_reference/auth.rst index 1a6cb2de..baade611 100644 --- a/docs/plugins_reference/auth.rst +++ b/docs/plugins_reference/auth.rst @@ -22,3 +22,4 @@ This table lists all the authentication plugins currently available: eodag.plugins.authentication.openid_connect.OIDCAuthorizationCodeFlowAuth eodag.plugins.authentication.keycloak.KeycloakOIDCPasswordAuth eodag.plugins.authentication.qsauth.HttpQueryStringAuth + eodag.plugins.authentication.sas_auth.SASAuth diff --git a/docs/plugins_reference/download.rst b/docs/plugins_reference/download.rst index 7d9c2205..a76af586 100644 --- a/docs/plugins_reference/download.rst +++ b/docs/plugins_reference/download.rst @@ -17,6 +17,7 @@ This table lists all the download plugins currently available: eodag.plugins.download.http.HTTPDownload eodag.plugins.download.aws.AwsDownload eodag.plugins.download.s3rest.S3RestDownload + eodag.plugins.download.creodias_s3.CreodiasS3Download --------------------------- Download methods call graph diff --git a/docs/plugins_reference/search.rst b/docs/plugins_reference/search.rst index f314e95b..ba01ff45 100644 --- a/docs/plugins_reference/search.rst +++ b/docs/plugins_reference/search.rst @@ -15,11 +15,12 @@ This table lists all the search plugins currently available: :toctree: generated/ eodag.plugins.search.qssearch.QueryStringSearch - eodag.plugins.search.qssearch.AwsSearch eodag.plugins.search.qssearch.ODataV4Search eodag.plugins.search.qssearch.PostJsonSearch eodag.plugins.search.qssearch.StacSearch eodag.plugins.search.static_stac_search.StaticStacSearch + eodag.plugins.search.creodias_s3.CreodiasS3Search eodag.plugins.search.build_search_result.BuildSearchResult eodag.plugins.search.build_search_result.BuildPostSearchResult + eodag.plugins.search.data_request_search.DataRequestSearch eodag.plugins.search.csw.CSWSearch diff --git a/eodag/api/core.py b/eodag/api/core.py index fbe4d14e..081b56db 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -38,6 +38,7 @@ from whoosh.qparser import QueryParser from eodag.api.product.metadata_mapping import mtd_cfg_as_conversion_and_querypath from eodag.api.search_result import SearchResult from eodag.config import ( + PluginConfig, SimpleYamlProxyConfig, get_ext_product_types_conf, load_default_config, @@ -403,6 +404,20 @@ class EODataAccessGateway: for provider in list(self.providers_config.keys()): conf = self.providers_config[provider] + # remove providers using skipped plugins + if [ + v + for v in conf.__dict__.values() + if isinstance(v, PluginConfig) + and getattr(v, "type", None) in self._plugins_manager.skipped_plugins + ]: + self.providers_config.pop(provider) + logger.debug( + f"{provider}: provider needing unavailable plugin has been removed" + ) + continue + + # check authentication if hasattr(conf, "api") and getattr(conf.api, "need_auth", False): credentials_exist = any( [ diff --git a/eodag/plugins/manager.py b/eodag/plugins/manager.py index aec49234..3e94a8f3 100644 --- a/eodag/plugins/manager.py +++ b/eodag/plugins/manager.py @@ -75,7 +75,10 @@ class PluginManager: product_type_to_provider_config_map: Dict[str, List[ProviderConfig]] + skipped_plugins: List[str] + def __init__(self, providers_config: Dict[str, ProviderConfig]) -> None: + self.skipped_plugins = [] self.providers_config = providers_config # Load all the plugins. This will make all plugin classes of a particular # type to be available in the base plugin class's 'plugins' attribute. @@ -92,6 +95,13 @@ class PluginManager: ): try: entry_point.load() + except pkg_resources.DistributionNotFound: + logger.debug( + "%s plugin skipped, eodag[%s] or eodag[all] needed", + entry_point.name, + ",".join(entry_point.extras), + ) + self.skipped_plugins.append(entry_point.name) except ImportError: import traceback as tb diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index 9e8c5a2d..33ef0ef0 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -1002,34 +1002,6 @@ class QueryStringSearch(Search): return response -class AwsSearch(QueryStringSearch): - """A specialisation of RestoSearch that modifies the way the EOProducts are built - from the search results""" - - def normalize_results( - self, results: List[Dict[str, Any]], kwargs: Any - ) -> List[EOProduct]: - """Transform metadata from provider representation to eodag representation""" - normalized: List[EOProduct] = [] - logger.debug("Adapting plugin results to eodag product representation") - for result in results: - ref = result["properties"]["title"].split("_")[5] - year = result["properties"]["completionDate"][0:4] - month = str(int(result["properties"]["completionDate"][5:7])) - day = str(int(result["properties"]["completionDate"][8:10])) - - properties = QueryStringSearch.extract_properties[self.config.result_type]( - result, self.get_metadata_mapping(kwargs.get("productType")) - ) - - properties["downloadLink"] = ( - "s3://tiles/{ref[1]}{ref[2]}/{ref[3]}/{ref[4]}{ref[5]}/{year}/" - "{month}/{day}/0/" - ).format(locals()) - normalized.append(EOProduct(self.provider, properties, kwargs)) - return normalized - - class ODataV4Search(QueryStringSearch): """A specialisation of a QueryStringSearch that does a two step search to retrieve all products metadata""" diff --git a/eodag/resources/ext_product_types.json b/eodag/resources/ext_product_types.json index 989fd46a..98b5bc51 100644 --- a/eodag/resources/ext_product_types.json +++ b/eodag/resources/ext_product_types.json @@ -1,1 +1,1 @@ -{"astraea_eod": {"providers_config": {"landsat8_c2l1t1": {"productType": "landsat8_c2l1t1"}, "mcd43a4": {"productType": "mcd43a4"}, "mod11a1": {"productType": "mod11a1"}, "mod13a1": {"productType": "mod13a1"}, "myd11a1": {"productType": "myd11a1"}, "myd13a1": {"productType": "myd13a1"}, "maxar_open_data": {"productType": "maxar_open_data"}, "naip": {"productType": "naip"}, "sentinel1_l1c_grd": {"productType": "sentinel1_l1c_grd"}, "sentinel2_l1c": {"productType": "sentinel2_l1c"}, "sentinel2_l2a": {"productType": "sentinel2_l2a"}, "spacenet7": {"productType": "spacenet7"}, "umbra_open_data": {"productType": "umbra_open_data"}}, "product_types_config": {"landsat8_c2l1t1": {"abstract": "Landsat 8 Collection 2 Tier 1 Precision Terrain from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat8-c2l1t1", "license": "PDDL-1.0", "title": "Landsat 8 - Level 1", "missionStartDate": "2013-03-18T15:59:02.333Z"}, "mcd43a4": {"abstract": "MCD43A4: MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mcd43a4", "license": "CC-PDDC", "title": "MCD43A4 NBAR", "missionStartDate": "2000-02-16T00:00:00.000Z"}, "mod11a1": {"abstract": "MOD11A1: MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod11a1", "license": "CC-PDDC", "title": "MOD11A1 LST", "missionStartDate": "2000-02-24T00:00:00.000Z"}, "mod13a1": {"abstract": "MOD13A1: MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod13a1", "license": "CC-PDDC", "title": "MOD13A1 VI", "missionStartDate": "2000-02-18T00:00:00.000Z"}, "myd11a1": {"abstract": "MYD11A1: MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd11a1", "license": "CC-PDDC", "title": "MYD11A1 LST", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "myd13a1": {"abstract": "MYD13A1: MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd13a1", "license": "CC-PDDC", "title": "MYD13A1 VI", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "maxar_open_data": {"abstract": "Maxar Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "maxar-open-data", "license": "CC-BY-NC-4.0", "title": "Maxar Open Data", "missionStartDate": "2008-01-15T00:00:00.000Z"}, "naip": {"abstract": "National Agriculture Imagery Program aerial imagery", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "naip", "license": "CC-PDDC", "title": "NAIP", "missionStartDate": "2012-04-23T12:00:00.000Z"}, "sentinel1_l1c_grd": {"abstract": "Sentinel-1 Level-1 Ground Range Detected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel1-l1c-grd", "license": "CC-BY-SA-3.0", "title": "Sentinel-1 L1C GRD", "missionStartDate": "2017-09-27T14:19:16.000"}, "sentinel2_l1c": {"abstract": "Sentinel-2 Level-1C top of atmosphere", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l1c", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L1C", "missionStartDate": "2015-06-27T10:25:31.456Z"}, "sentinel2_l2a": {"abstract": "Sentinel-2 Level-2A atmospherically corrected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l2a", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L2A", "missionStartDate": "2018-04-01T07:02:22.463Z"}, "spacenet7": {"abstract": "SpaceNet 7 Imagery and Labels", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "spacenet7", "license": "CC-BY-SA-4.0", "title": "SpaceNet 7", "missionStartDate": "2018-01-01T00:00:00.000Z"}, "umbra_open_data": {"abstract": "Umbra Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "umbra-open-data", "license": "proprietary", "title": "Umbra Open Data", "missionStartDate": null}}}, "creodias": {"providers_config": {"Sentinel1": {"collection": "Sentinel1"}, "Sentinel1RTC": {"collection": "Sentinel1RTC"}, "Sentinel2": {"collection": "Sentinel2"}, "Sentinel3": {"collection": "Sentinel3"}, "Sentinel5P": {"collection": "Sentinel5P"}, "Sentinel6": {"collection": "Sentinel6"}, "Landsat5": {"collection": "Landsat5"}, "Landsat7": {"collection": "Landsat7"}, "Landsat8": {"collection": "Landsat8"}, "Envisat": {"collection": "Envisat"}, "SMOS": {"collection": "SMOS"}, "S2GLC": {"collection": "S2GLC"}, "CopDem": {"collection": "CopDem"}}, "product_types_config": {"Sentinel1": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel1", "license": null, "title": null, "missionStartDate": null}, "Sentinel1RTC": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel1rtc", "license": null, "title": null, "missionStartDate": null}, "Sentinel2": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2", "license": null, "title": null, "missionStartDate": null}, "Sentinel3": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel3", "license": null, "title": null, "missionStartDate": null}, "Sentinel5P": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel5p", "license": null, "title": null, "missionStartDate": null}, "Sentinel6": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel6", "license": null, "title": null, "missionStartDate": null}, "Landsat5": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat5", "license": null, "title": null, "missionStartDate": null}, "Landsat7": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat7", "license": null, "title": null, "missionStartDate": null}, "Landsat8": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat8", "license": null, "title": null, "missionStartDate": null}, "Envisat": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "envisat", "license": null, "title": null, "missionStartDate": null}, "SMOS": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "smos", "license": null, "title": null, "missionStartDate": null}, "S2GLC": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "s2glc", "license": null, "title": null, "missionStartDate": null}, "CopDem": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "copdem", "license": null, "title": null, "missionStartDate": null}}}, "earth_search": {"providers_config": {"cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "naip": {"productType": "naip"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-2-l1c": {"productType": "sentinel-2-l1c"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-2-c1-l2a": {"productType": "sentinel-2-c1-l2a"}}, "product_types_config": {"cop-dem-glo-30": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme.", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-30", "missionStartDate": "2021-04-22T00:00:00Z"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program", "missionStartDate": "2010-01-01T00:00:00Z"}, "sentinel-2-l2a": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level 2A", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "sentinel-2-l1c": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l1c,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level 1C", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters.", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-90", "missionStartDate": "2021-04-22T00:00:00Z"}, "landsat-c2-l2": {"abstract": "Atmospherically corrected global Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.", "instrument": "tm,etm+,oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-2", "missionStartDate": "1982-08-22T00:00:00Z"}, "sentinel-1-grd": {"abstract": "Sentinel-1 is a pair of Synthetic Aperture Radar (SAR) imaging satellites launched in 2014 and 2016 by the European Space Agency (ESA). Their 6 day revisit cycle and ability to observe through clouds makes this dataset perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.", "instrument": null, "platform": "sentinel-1", "platformSerialIdentifier": "sentinel-1a,sentinel-1b", "processingLevel": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "title": "Sentinel-1 Level 1C Ground Range Detected (GRD)", "missionStartDate": "2014-10-10T00:28:21Z"}, "sentinel-2-c1-l2a": {"abstract": "Sentinel-2 Collection 1 L2A, data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Collection 1 Level-2A", "missionStartDate": "2015-06-27T10:25:31.456000Z"}}}, "earth_search_cog": null, "earth_search_gcs": null, "planetary_computer": {"providers_config": {"daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "3dep-seamless": {"productType": "3dep-seamless"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "fia": {"productType": "fia"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "gridmet": {"productType": "gridmet"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "hgb": {"productType": "hgb"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "goes-cmi": {"productType": "goes-cmi"}, "terraclimate": {"productType": "terraclimate"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "io-lulc-annual-v02": {"productType": "io-lulc-annual-v02"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "mtbs": {"productType": "mtbs"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "mobi": {"productType": "mobi"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "era5-pds": {"productType": "era5-pds"}, "chloris-biomass": {"productType": "chloris-biomass"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "gap": {"productType": "gap"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "gbif": {"productType": "gbif"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "alos-dem": {"productType": "alos-dem"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "us-census": {"productType": "us-census"}, "jrc-gsw": {"productType": "jrc-gsw"}, "deltares-floods": {"productType": "deltares-floods"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "hrea": {"productType": "hrea"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "nasadem": {"productType": "nasadem"}, "io-lulc": {"productType": "io-lulc"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "drcog-lulc": {"productType": "drcog-lulc"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "goes-glm": {"productType": "goes-glm"}, "usda-cdl": {"productType": "usda-cdl"}, "eclipse": {"productType": "eclipse"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "fws-nwi": {"productType": "fws-nwi"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "aster-l1t": {"productType": "aster-l1t"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "naip": {"productType": "naip"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-biodiversity": {"productType": "io-biodiversity"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "ms-buildings": {"productType": "ms-buildings"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}}, "product_types_config": {"daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Puerto Rico", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Hawaii", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "title": "USGS 3DEP Seamless DEMs", "missionStartDate": "1925-01-01T00:00:00Z"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Surface Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "title": "Forest Inventory and Analysis", "missionStartDate": "2020-06-01T00:00:00Z"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)", "missionStartDate": "2014-10-10T00:28:21Z"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "title": "gridMET", "missionStartDate": "1979-01-01T00:00:00Z"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual North America", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly North America", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Hawaii", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Hawaii", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Puerto Rico", "missionStartDate": "1980-01-16T12:00:00Z"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Tables", "missionStartDate": "2020-07-01T00:00:00Z"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "title": "HGB: Harmonized Global Biomass for 2010", "missionStartDate": "2010-12-31T00:00:00Z"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-30", "missionStartDate": "2021-04-22T00:00:00Z"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-90", "missionStartDate": "2021-04-22T00:00:00Z"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "title": "GOES-R Cloud & Moisture Imagery", "missionStartDate": "2017-02-28T00:16:52Z"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "title": "TerraClimate", "missionStartDate": "1958-01-01T00:00:00Z"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- model is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- scenario is one of \"historical\", \"ssp245\" or \"ssp585\".\n- variable is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- year** depends on the value of scenario. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some experimental reference files as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)", "missionStartDate": "1950-01-01T00:00:00Z"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "title": "GPM IMERG", "missionStartDate": "2000-06-01T00:00:00Z"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Rasters", "missionStartDate": "2020-07-01T00:00:00Z"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Height above Ground", "missionStartDate": "2012-01-01T00:00:00Z"}, "io-lulc-annual-v02": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201cBare\u201d and any of the vegetative classes \u201cTrees,\u201d \u201cCrops,\u201d \u201cFlooded Vegetation,\u201d and \u201cRangeland\u201d. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc-annual-v02,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class) V2", "missionStartDate": "2017-01-01T00:00:00Z"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Intensity", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Source", "missionStartDate": "2012-01-01T00:00:00Z"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "title": "MTBS: Monitoring Trends in Burn Severity", "missionStartDate": "1984-12-31T00:00:00Z"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "title": "C-CAP Regional Land Cover and Change", "missionStartDate": "1975-01-01T00:00:00Z"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Cloud", "missionStartDate": "2012-01-01T00:00:00Z"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Burned Area Monthly", "missionStartDate": "2000-11-01T00:00:00Z"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "title": "ALOS Forest/Non-Forest Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Returns", "missionStartDate": "2012-01-01T00:00:00Z"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "title": "MoBI: Map of Biodiversity Importance", "missionStartDate": "2020-04-14T00:00:00Z"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-2", "missionStartDate": "1982-08-22T00:00:00Z"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An analysis, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A forecast starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "title": "ERA5 - PDS", "missionStartDate": "1979-01-01T00:00:00Z"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "title": "Chloris Biomass", "missionStartDate": "2003-07-31T00:00:00Z"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "title": "HydroForecast - Kwando & Upper Zambezi Rivers", "missionStartDate": "2022-01-01T00:00:00Z"}, "planet-nicfi-analytic": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Analytic)", "missionStartDate": "2015-12-01T00:00:00Z"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Puerto Rico", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model (Native)", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Classification", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "title": "USGS Gap Land Cover", "missionStartDate": "1999-01-01T00:00:00Z"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "planet-nicfi-visual": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Visual)", "missionStartDate": "2015-12-01T00:00:00Z"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "title": "Global Biodiversity Information Facility (GBIF)", "missionStartDate": "2021-04-13T00:00:00Z"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Primary Production Yearly Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "title": "ALOS World 3D-30m", "missionStartDate": "2016-12-07T00:00:00Z"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "title": "ALOS PALSAR Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n\| Name \| Scale \| Period \| Number of basins \|\n\|--------\|--------------------------\|-----------\|------------------\|\n\| ERA5 \| Global \| 1967-2020 \| 3236 \|\n\| CHIRPS \| Global (+/- 50 latitude) \| 1981-2020 \| 2951 \|\n\| EOBS \| Europe/North Africa \| 1979-2020 \| 682 \|\n\| NLDAS \| USA \| 1979-2020 \| 1090 \|\n\| BOM \| Australia \| 1979-2020 \| 116 \|\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Water Availability", "missionStartDate": "1970-01-01T00:00:00Z"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Evapotranspiration Yearly Gap-Filled", "missionStartDate": "2001-01-01T00:00:00Z"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day", "missionStartDate": "2000-02-16T00:00:00Z"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "title": "US Census", "missionStartDate": "2021-08-01T00:00:00Z"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "title": "JRC Global Surface Water", "missionStartDate": "1984-03-01T00:00:00Z"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Flood Maps", "missionStartDate": "2018-01-01T00:00:00Z"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily", "missionStartDate": "2000-02-16T00:00:00Z"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire Daily", "missionStartDate": "2000-02-18T00:00:00Z"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. Access: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. Reliability: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. Usage: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. Nighttime Lights: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "title": "HREA: High Resolution Electricity Access", "missionStartDate": "2012-12-31T00:00:00Z"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level-2A", "missionStartDate": "2015-06-27T10:25:31Z"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 8-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 4-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily North America", "missionStartDate": "1980-01-01T12:00:00Z"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "title": "Land Cover of Canada", "missionStartDate": "2015-01-01T00:00:00Z"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover 8-day", "missionStartDate": "2000-02-18T00:00:00Z"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows forecast times in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* stream is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* type is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* reference time is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the reference time changes and `step` as the forecast time changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast was made, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "title": "ECMWF Open Data (real-time)", "missionStartDate": null}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 24-Hour Pass 2 sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 24-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)", "missionStartDate": "2014-10-10T00:28:21Z"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "title": "NASADEM HGT v001", "missionStartDate": "2000-02-20T00:00:00Z"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "Esri 10-Meter Land Cover (10-class)", "missionStartDate": "2017-01-01T00:00:00Z"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-1", "missionStartDate": "1972-07-25T00:00:00Z"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "title": "Denver Regional Council of Governments Land Use Land Cover", "missionStartDate": "2018-01-01T00:00:00Z"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (7-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (13-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "title": "Chesapeake Land Use", "missionStartDate": "2013-01-01T00:00:00Z"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 1 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 1", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 2 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains Monthly data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nNote: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)", "missionStartDate": "1895-01-01T00:00:00Z"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "title": "GOES-R Lightning Detection", "missionStartDate": "2018-02-13T16:10:00Z"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "title": "USDA Cropland Data Layers (CDLs)", "missionStartDate": "2008-01-01T00:00:00Z"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "title": "Urban Innovation Eclipse Sensor Data", "missionStartDate": "2021-01-01T00:00:00Z"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\nNOTE: Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "title": "FWS National Wetlands Inventory", "missionStartDate": "2022-10-01T00:00:00Z"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "title": "USGS LCMAP CONUS Collection 1.3", "missionStartDate": "1985-01-01T00:00:00Z"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "title": "USGS LCMAP Hawaii Collection 1.0", "missionStartDate": "2000-01-01T00:00:00Z"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "title": "NOAA US Tabular Climate Normals", "missionStartDate": "1981-01-01T00:00:00Z"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (NetCDF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n\| Abbreviation \| Description \|\n\| ------------ \| ---------------------------------------- \|\n\| prcp \| Precipitation over the time period \|\n\| tavg \| Mean temperature over the time period \|\n\| tmax \| Maximum temperature over the time period \|\n\| tmin \| Minimum temperature over the time period \|\n\nAnd an \"aggregation\":\n\n\| Abbreviation \| Description \|\n\| ------------ \| ------------------------------------------------------------------------------ \|\n\| max \| Maximum of the variable over the time period \|\n\| min \| Minimum of the variable over the time period \|\n\| std \| Standard deviation of the value over the time period \|\n\| flag \| An count of the number of inputs (months, years, etc.) to calculate the normal \|\n\| norm \| The normal for the variable over the time period \|\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "title": "ASTER L1T", "missionStartDate": "2000-03-04T12:00:00Z"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40] \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40] \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-SA-40] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program", "missionStartDate": "2010-01-01T00:00:00Z"}, "io-lulc-9-class": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class) V1", "missionStartDate": "2017-01-01T00:00:00Z"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "title": "Biodiversity Intactness", "missionStartDate": "2017-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR", "missionStartDate": "1972-03-01T00:00:00Z"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR NetCDFs", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - Optimum Interpolation CDR", "missionStartDate": "1981-09-01T00:00:00Z"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "title": "Sentinel-5P Level-2", "missionStartDate": "2018-04-30T00:18:50Z"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "title": "Sentinel-3 Water (Full Resolution)", "missionStartDate": "2017-11-01T00:07:01.738487Z"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR NetCDFs", "missionStartDate": "1972-03-01T00:00:00Z"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Global Aerosol", "missionStartDate": "2020-04-16T19:36:28.012367Z"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-09-27T11:17:21Z"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "title": "Sentinel-3 Land (Full Resolution)", "missionStartDate": "2016-04-25T11:33:47.368562Z"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Land Radar Altimetry", "missionStartDate": "2016-03-01T14:07:51.632846Z"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Land Surface Temperature", "missionStartDate": "2016-04-19T01:35:17.188500Z"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Sea Surface Temperature", "missionStartDate": "2017-10-31T23:59:57.451604Z"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Ocean Radar Altimetry", "missionStartDate": "2017-01-28T00:59:14.149496Z"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\nStage 1: Semantic Segmentation\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\nStage 2: Polygonization\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are not spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "title": "Microsoft Building Footprints", "missionStartDate": "2014-01-01T00:00:00Z"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Fire Radiative Power", "missionStartDate": "2020-08-08T23:11:15.617203Z"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Land Surface Reflectance and Aerosol", "missionStartDate": "2018-09-22T16:51:00.001276Z"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)", "missionStartDate": "2018-10-08T08:09:40.491227Z"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-10-04T23:17:21Z"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "title": "ESA WorldCover", "missionStartDate": "2020-01-01T00:00:00Z"}}}, "usgs_satapi_aws": {"providers_config": {"landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}, "landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}}, "product_types_config": {"landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-1 Product", "missionStartDate": "1972-07-25T00:00:00.000Z"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}}}} +{"astraea_eod": {"providers_config": {"landsat8_c2l1t1": {"productType": "landsat8_c2l1t1"}, "mcd43a4": {"productType": "mcd43a4"}, "mod11a1": {"productType": "mod11a1"}, "mod13a1": {"productType": "mod13a1"}, "myd11a1": {"productType": "myd11a1"}, "myd13a1": {"productType": "myd13a1"}, "maxar_open_data": {"productType": "maxar_open_data"}, "naip": {"productType": "naip"}, "sentinel1_l1c_grd": {"productType": "sentinel1_l1c_grd"}, "sentinel2_l1c": {"productType": "sentinel2_l1c"}, "sentinel2_l2a": {"productType": "sentinel2_l2a"}, "spacenet7": {"productType": "spacenet7"}, "umbra_open_data": {"productType": "umbra_open_data"}}, "product_types_config": {"landsat8_c2l1t1": {"abstract": "Landsat 8 Collection 2 Tier 1 Precision Terrain from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat8-c2l1t1", "license": "PDDL-1.0", "title": "Landsat 8 - Level 1", "missionStartDate": "2013-03-18T15:59:02.333Z"}, "mcd43a4": {"abstract": "MCD43A4: MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mcd43a4", "license": "CC-PDDC", "title": "MCD43A4 NBAR", "missionStartDate": "2000-02-16T00:00:00.000Z"}, "mod11a1": {"abstract": "MOD11A1: MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod11a1", "license": "CC-PDDC", "title": "MOD11A1 LST", "missionStartDate": "2000-02-24T00:00:00.000Z"}, "mod13a1": {"abstract": "MOD13A1: MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod13a1", "license": "CC-PDDC", "title": "MOD13A1 VI", "missionStartDate": "2000-02-18T00:00:00.000Z"}, "myd11a1": {"abstract": "MYD11A1: MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd11a1", "license": "CC-PDDC", "title": "MYD11A1 LST", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "myd13a1": {"abstract": "MYD13A1: MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd13a1", "license": "CC-PDDC", "title": "MYD13A1 VI", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "maxar_open_data": {"abstract": "Maxar Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "maxar-open-data", "license": "CC-BY-NC-4.0", "title": "Maxar Open Data", "missionStartDate": "2008-01-15T00:00:00.000Z"}, "naip": {"abstract": "National Agriculture Imagery Program aerial imagery", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "naip", "license": "CC-PDDC", "title": "NAIP", "missionStartDate": "2012-04-23T12:00:00.000Z"}, "sentinel1_l1c_grd": {"abstract": "Sentinel-1 Level-1 Ground Range Detected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel1-l1c-grd", "license": "CC-BY-SA-3.0", "title": "Sentinel-1 L1C GRD", "missionStartDate": "2017-09-27T14:19:16.000"}, "sentinel2_l1c": {"abstract": "Sentinel-2 Level-1C top of atmosphere", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l1c", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L1C", "missionStartDate": "2015-06-27T10:25:31.456Z"}, "sentinel2_l2a": {"abstract": "Sentinel-2 Level-2A atmospherically corrected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l2a", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L2A", "missionStartDate": "2018-04-01T07:02:22.463Z"}, "spacenet7": {"abstract": "SpaceNet 7 Imagery and Labels", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "spacenet7", "license": "CC-BY-SA-4.0", "title": "SpaceNet 7", "missionStartDate": "2018-01-01T00:00:00.000Z"}, "umbra_open_data": {"abstract": "Umbra Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "umbra-open-data", "license": "proprietary", "title": "Umbra Open Data", "missionStartDate": null}}}, "creodias": null, "earth_search": {"providers_config": {"cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "naip": {"productType": "naip"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-2-l1c": {"productType": "sentinel-2-l1c"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-2-c1-l2a": {"productType": "sentinel-2-c1-l2a"}}, "product_types_config": {"cop-dem-glo-30": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme.", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-30", "missionStartDate": "2021-04-22T00:00:00Z"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program", "missionStartDate": "2010-01-01T00:00:00Z"}, "sentinel-2-l2a": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level 2A", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "sentinel-2-l1c": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l1c,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level 1C", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters.", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-90", "missionStartDate": "2021-04-22T00:00:00Z"}, "landsat-c2-l2": {"abstract": "Atmospherically corrected global Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.", "instrument": "tm,etm+,oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-2", "missionStartDate": "1982-08-22T00:00:00Z"}, "sentinel-1-grd": {"abstract": "Sentinel-1 is a pair of Synthetic Aperture Radar (SAR) imaging satellites launched in 2014 and 2016 by the European Space Agency (ESA). Their 6 day revisit cycle and ability to observe through clouds makes this dataset perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.", "instrument": null, "platform": "sentinel-1", "platformSerialIdentifier": "sentinel-1a,sentinel-1b", "processingLevel": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "title": "Sentinel-1 Level 1C Ground Range Detected (GRD)", "missionStartDate": "2014-10-10T00:28:21Z"}, "sentinel-2-c1-l2a": {"abstract": "Sentinel-2 Collection 1 L2A, data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Collection 1 Level-2A", "missionStartDate": "2015-06-27T10:25:31.456000Z"}}}, "earth_search_cog": null, "earth_search_gcs": null, "planetary_computer": {"providers_config": {"daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "3dep-seamless": {"productType": "3dep-seamless"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "fia": {"productType": "fia"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "gridmet": {"productType": "gridmet"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "hgb": {"productType": "hgb"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "goes-cmi": {"productType": "goes-cmi"}, "terraclimate": {"productType": "terraclimate"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "io-lulc-annual-v02": {"productType": "io-lulc-annual-v02"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "mtbs": {"productType": "mtbs"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "mobi": {"productType": "mobi"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "era5-pds": {"productType": "era5-pds"}, "chloris-biomass": {"productType": "chloris-biomass"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "gap": {"productType": "gap"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "gbif": {"productType": "gbif"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "alos-dem": {"productType": "alos-dem"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "us-census": {"productType": "us-census"}, "jrc-gsw": {"productType": "jrc-gsw"}, "deltares-floods": {"productType": "deltares-floods"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "hrea": {"productType": "hrea"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "nasadem": {"productType": "nasadem"}, "io-lulc": {"productType": "io-lulc"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "drcog-lulc": {"productType": "drcog-lulc"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "goes-glm": {"productType": "goes-glm"}, "usda-cdl": {"productType": "usda-cdl"}, "eclipse": {"productType": "eclipse"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "fws-nwi": {"productType": "fws-nwi"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "aster-l1t": {"productType": "aster-l1t"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "naip": {"productType": "naip"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-biodiversity": {"productType": "io-biodiversity"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "ms-buildings": {"productType": "ms-buildings"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}}, "product_types_config": {"daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Puerto Rico", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Hawaii", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "title": "USGS 3DEP Seamless DEMs", "missionStartDate": "1925-01-01T00:00:00Z"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Surface Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "title": "Forest Inventory and Analysis", "missionStartDate": "2020-06-01T00:00:00Z"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)", "missionStartDate": "2014-10-10T00:28:21Z"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "title": "gridMET", "missionStartDate": "1979-01-01T00:00:00Z"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual North America", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly North America", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Hawaii", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Hawaii", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Puerto Rico", "missionStartDate": "1980-01-16T12:00:00Z"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Tables", "missionStartDate": "2020-07-01T00:00:00Z"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "title": "HGB: Harmonized Global Biomass for 2010", "missionStartDate": "2010-12-31T00:00:00Z"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-30", "missionStartDate": "2021-04-22T00:00:00Z"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-90", "missionStartDate": "2021-04-22T00:00:00Z"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "title": "GOES-R Cloud & Moisture Imagery", "missionStartDate": "2017-02-28T00:16:52Z"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "title": "TerraClimate", "missionStartDate": "1958-01-01T00:00:00Z"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- model is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- scenario is one of \"historical\", \"ssp245\" or \"ssp585\".\n- variable is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- year depends on the value of scenario. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some experimental reference files as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)", "missionStartDate": "1950-01-01T00:00:00Z"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "title": "GPM IMERG", "missionStartDate": "2000-06-01T00:00:00Z"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Rasters", "missionStartDate": "2020-07-01T00:00:00Z"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Height above Ground", "missionStartDate": "2012-01-01T00:00:00Z"}, "io-lulc-annual-v02": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201cBare\u201d and any of the vegetative classes \u201cTrees,\u201d \u201cCrops,\u201d \u201cFlooded Vegetation,\u201d and \u201cRangeland\u201d. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc-annual-v02,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class) V2", "missionStartDate": "2017-01-01T00:00:00Z"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Intensity", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Source", "missionStartDate": "2012-01-01T00:00:00Z"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "title": "MTBS: Monitoring Trends in Burn Severity", "missionStartDate": "1984-12-31T00:00:00Z"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "title": "C-CAP Regional Land Cover and Change", "missionStartDate": "1975-01-01T00:00:00Z"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Cloud", "missionStartDate": "2012-01-01T00:00:00Z"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Burned Area Monthly", "missionStartDate": "2000-11-01T00:00:00Z"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "title": "ALOS Forest/Non-Forest Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Returns", "missionStartDate": "2012-01-01T00:00:00Z"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "title": "MoBI: Map of Biodiversity Importance", "missionStartDate": "2020-04-14T00:00:00Z"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-2", "missionStartDate": "1982-08-22T00:00:00Z"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An analysis, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A forecast starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "title": "ERA5 - PDS", "missionStartDate": "1979-01-01T00:00:00Z"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "title": "Chloris Biomass", "missionStartDate": "2003-07-31T00:00:00Z"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "title": "HydroForecast - Kwando & Upper Zambezi Rivers", "missionStartDate": "2022-01-01T00:00:00Z"}, "planet-nicfi-analytic": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Analytic)", "missionStartDate": "2015-12-01T00:00:00Z"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Puerto Rico", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model (Native)", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Classification", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "title": "USGS Gap Land Cover", "missionStartDate": "1999-01-01T00:00:00Z"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "planet-nicfi-visual": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Visual)", "missionStartDate": "2015-12-01T00:00:00Z"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "title": "Global Biodiversity Information Facility (GBIF)", "missionStartDate": "2021-04-13T00:00:00Z"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Primary Production Yearly Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "title": "ALOS World 3D-30m", "missionStartDate": "2016-12-07T00:00:00Z"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "title": "ALOS PALSAR Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n\| Name \| Scale \| Period \| Number of basins \|\n\|--------\|--------------------------\|-----------\|------------------\|\n\| ERA5 \| Global \| 1967-2020 \| 3236 \|\n\| CHIRPS \| Global (+/- 50 latitude) \| 1981-2020 \| 2951 \|\n\| EOBS \| Europe/North Africa \| 1979-2020 \| 682 \|\n\| NLDAS \| USA \| 1979-2020 \| 1090 \|\n\| BOM \| Australia \| 1979-2020 \| 116 \|\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Water Availability", "missionStartDate": "1970-01-01T00:00:00Z"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Evapotranspiration Yearly Gap-Filled", "missionStartDate": "2001-01-01T00:00:00Z"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day", "missionStartDate": "2000-02-16T00:00:00Z"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "title": "US Census", "missionStartDate": "2021-08-01T00:00:00Z"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "title": "JRC Global Surface Water", "missionStartDate": "1984-03-01T00:00:00Z"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Flood Maps", "missionStartDate": "2018-01-01T00:00:00Z"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily", "missionStartDate": "2000-02-16T00:00:00Z"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire Daily", "missionStartDate": "2000-02-18T00:00:00Z"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. Access: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. Reliability: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. Usage: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. Nighttime Lights: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "title": "HREA: High Resolution Electricity Access", "missionStartDate": "2012-12-31T00:00:00Z"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level-2A", "missionStartDate": "2015-06-27T10:25:31Z"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 8-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 4-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily North America", "missionStartDate": "1980-01-01T12:00:00Z"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "title": "Land Cover of Canada", "missionStartDate": "2015-01-01T00:00:00Z"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover 8-day", "missionStartDate": "2000-02-18T00:00:00Z"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows forecast times in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* stream is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* type is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* reference time is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the reference time changes and `step` as the forecast time changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast was made, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "title": "ECMWF Open Data (real-time)", "missionStartDate": null}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 24-Hour Pass 2 sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 24-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)", "missionStartDate": "2014-10-10T00:28:21Z"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "title": "NASADEM HGT v001", "missionStartDate": "2000-02-20T00:00:00Z"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "Esri 10-Meter Land Cover (10-class)", "missionStartDate": "2017-01-01T00:00:00Z"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-1", "missionStartDate": "1972-07-25T00:00:00Z"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "title": "Denver Regional Council of Governments Land Use Land Cover", "missionStartDate": "2018-01-01T00:00:00Z"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (7-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (13-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "title": "Chesapeake Land Use", "missionStartDate": "2013-01-01T00:00:00Z"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 1 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 1", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 2 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains Monthly data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nNote: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)", "missionStartDate": "1895-01-01T00:00:00Z"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "title": "GOES-R Lightning Detection", "missionStartDate": "2018-02-13T16:10:00Z"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "title": "USDA Cropland Data Layers (CDLs)", "missionStartDate": "2008-01-01T00:00:00Z"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "title": "Urban Innovation Eclipse Sensor Data", "missionStartDate": "2021-01-01T00:00:00Z"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\nNOTE: Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "title": "FWS National Wetlands Inventory", "missionStartDate": "2022-10-01T00:00:00Z"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "title": "USGS LCMAP CONUS Collection 1.3", "missionStartDate": "1985-01-01T00:00:00Z"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "title": "USGS LCMAP Hawaii Collection 1.0", "missionStartDate": "2000-01-01T00:00:00Z"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "title": "NOAA US Tabular Climate Normals", "missionStartDate": "1981-01-01T00:00:00Z"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (NetCDF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n\| Abbreviation \| Description \|\n\| ------------ \| ---------------------------------------- \|\n\| prcp \| Precipitation over the time period \|\n\| tavg \| Mean temperature over the time period \|\n\| tmax \| Maximum temperature over the time period \|\n\| tmin \| Minimum temperature over the time period \|\n\nAnd an \"aggregation\":\n\n\| Abbreviation \| Description \|\n\| ------------ \| ------------------------------------------------------------------------------ \|\n\| max \| Maximum of the variable over the time period \|\n\| min \| Minimum of the variable over the time period \|\n\| std \| Standard deviation of the value over the time period \|\n\| flag \| An count of the number of inputs (months, years, etc.) to calculate the normal \|\n\| norm \| The normal for the variable over the time period \|\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "title": "ASTER L1T", "missionStartDate": "2000-03-04T12:00:00Z"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40] \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40] \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-SA-40] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program", "missionStartDate": "2010-01-01T00:00:00Z"}, "io-lulc-9-class": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class) V1", "missionStartDate": "2017-01-01T00:00:00Z"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "title": "Biodiversity Intactness", "missionStartDate": "2017-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR", "missionStartDate": "1972-03-01T00:00:00Z"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR NetCDFs", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - Optimum Interpolation CDR", "missionStartDate": "1981-09-01T00:00:00Z"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "title": "Sentinel-5P Level-2", "missionStartDate": "2018-04-30T00:18:50Z"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "title": "Sentinel-3 Water (Full Resolution)", "missionStartDate": "2017-11-01T00:07:01.738487Z"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR NetCDFs", "missionStartDate": "1972-03-01T00:00:00Z"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Global Aerosol", "missionStartDate": "2020-04-16T19:36:28.012367Z"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-09-27T11:17:21Z"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "title": "Sentinel-3 Land (Full Resolution)", "missionStartDate": "2016-04-25T11:33:47.368562Z"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Land Radar Altimetry", "missionStartDate": "2016-03-01T14:07:51.632846Z"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Land Surface Temperature", "missionStartDate": "2016-04-19T01:35:17.188500Z"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Sea Surface Temperature", "missionStartDate": "2017-10-31T23:59:57.451604Z"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Ocean Radar Altimetry", "missionStartDate": "2017-01-28T00:59:14.149496Z"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\nStage 1: Semantic Segmentation\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\nStage 2: Polygonization\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are not spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "title": "Microsoft Building Footprints", "missionStartDate": "2014-01-01T00:00:00Z"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Fire Radiative Power", "missionStartDate": "2020-08-08T23:11:15.617203Z"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Land Surface Reflectance and Aerosol", "missionStartDate": "2018-09-22T16:51:00.001276Z"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)", "missionStartDate": "2018-10-08T08:09:40.491227Z"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-10-04T23:17:21Z"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "title": "ESA WorldCover", "missionStartDate": "2020-01-01T00:00:00Z"}}}, "usgs_satapi_aws": {"providers_config": {"landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}, "landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}}, "product_types_config": {"landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-1 Product", "missionStartDate": "1972-07-25T00:00:00.000Z"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}}}} diff --git a/eodag/rest/__init__.py b/eodag/rest/__init__.py index 7c2da36d..4a740d4f 100644 --- a/eodag/rest/__init__.py +++ b/eodag/rest/__init__.py @@ -16,3 +16,9 @@ # See the License for the specific language governing permissions and # limitations under the License. """EODAG REST API""" +try: + from fastapi import __version__ # noqa: F401 +except ImportError: + raise ImportError( + f"{__name__} not available, please install eodag[server] or eodag[all]" + ) diff --git a/setup.cfg b/setup.cfg index 6c46acde..db9c8d68 100644 --- a/setup.cfg +++ b/setup.cfg @@ -37,40 +37,64 @@ packages = find: include_package_data = True python_requires = >=3.6 install_requires = + annotated-types click - requests - urllib3 - python-dateutil - PyYAML - tqdm - shapely - pyshp - OWSLib >=0.27.1 + geojson + jsonpath-ng < 1.6.0 + lxml orjson < 3.10.0;python_version>='3.12' and platform_system=='Windows' orjson;python_version<'3.12' and platform_system!='Windows' - geojson + pydantic >= 2.1.0 + pydantic_core pyproj >= 2.1.0 - usgs >= 0.3.1 + pyshp + pystac >= 1.0.0b1 + python-dateutil + PyYAML + requests + setuptools + shapely + stream-zip + tqdm + typing_extensions + urllib3 + Whoosh + +[options.extras_require] +all = + eodag[all-providers,csw,server,tutorials] +all-providers = + eodag[aws,ecmwf,usgs] +aws = boto3 botocore +csw = + OWSLib >=0.27.1 +ecmwf = + ecmwf-api-client +usgs = + usgs >= 0.3.1 +server = fastapi >= 0.93.0 + pygeofilter starlette uvicorn - jsonpath-ng < 1.6.0 - lxml - Whoosh - pystac >= 1.0.0b1 - ecmwf-api-client - stream-zip - pydantic >= 2.1.0 - pydantic_core - typing_extensions - annotated-types - setuptools - pygeofilter -[options.extras_require] +notebook = tqdm[notebook] +tutorials = + eodag[notebook] + eodag-cube >= 0.2.0 + jupyter + ipyleaflet >= 0.10.0 + ipywidgets + matplotlib + folium + imageio + rasterio + netcdf4 + dev = + eodag[all-providers,csw,server] pytest pytest-cov # pytest-html max version set and py requirement added @@ -92,19 +116,8 @@ dev = stdlib-list boto3-stubs[essential] types-lxml - -notebook = tqdm[notebook] -tutorials = - eodag-cube >= 0.2.0 - jupyter - ipyleaflet >= 0.10.0 - ipywidgets - matplotlib - folium - imageio - rasterio - netcdf4 docs = + eodag[all] sphinx sphinx-book-theme sphinx-copybutton @@ -127,8 +140,8 @@ exclude = console_scripts = eodag = eodag.cli:eodag eodag.plugins.api = - UsgsApi = eodag.plugins.apis.usgs:UsgsApi - EcmwfApi = eodag.plugins.apis.ecmwf:EcmwfApi + UsgsApi = eodag.plugins.apis.usgs:UsgsApi [usgs] + EcmwfApi = eodag.plugins.apis.ecmwf:EcmwfApi [ecmwf] eodag.plugins.auth = GenericAuth = eodag.plugins.authentication.generic:GenericAuth HTTPHeaderAuth = eodag.plugins.authentication.header:HTTPHeaderAuth @@ -146,14 +159,13 @@ eodag.plugins.crunch = FilterProperty = eodag.plugins.crunch.filter_property:FilterProperty FilterDate = eodag.plugins.crunch.filter_date:FilterDate eodag.plugins.download = - AwsDownload = eodag.plugins.download.aws:AwsDownload + AwsDownload = eodag.plugins.download.aws:AwsDownload [aws] HTTPDownload = eodag.plugins.download.http:HTTPDownload S3RestDownload = eodag.plugins.download.s3rest:S3RestDownload - CreodiasS3Download = eodag.plugins.download.creodias_s3:CreodiasS3Download + CreodiasS3Download = eodag.plugins.download.creodias_s3:CreodiasS3Download [aws] eodag.plugins.search = - CSWSearch = eodag.plugins.search.csw:CSWSearch + CSWSearch = eodag.plugins.search.csw:CSWSearch [csw] QueryStringSearch = eodag.plugins.search.qssearch:QueryStringSearch - AwsSearch = eodag.plugins.search.qssearch:AwsSearch ODataV4Search = eodag.plugins.search.qssearch:ODataV4Search PostJsonSearch = eodag.plugins.search.qssearch:PostJsonSearch StacSearch = eodag.plugins.search.qssearch:StacSearch @@ -161,7 +173,7 @@ eodag.plugins.search = BuildSearchResult = eodag.plugins.search.build_search_result:BuildSearchResult BuildPostSearchResult = eodag.plugins.search.build_search_result:BuildPostSearchResult DataRequestSearch = eodag.plugins.search.data_request_search:DataRequestSearch - CreodiasS3Search = eodag.plugins.search.creodias_s3:CreodiasS3Search + CreodiasS3Search = eodag.plugins.search.creodias_s3:CreodiasS3Search [aws] [flake8] ignore = E203, W503	move external python apis and server to extras_require Get external python apis out of `install_requires` (`usgs`, `ecmwf-api-client`), in order to keep `eodag` core as light as possible. Also move server mode the same way. Raise a well formatted error message if the external api is needed: ``` Access to ECMWF data requires the installation of an additional library. Please install it via pip install ecmwf-api-client`. ``` And add the "complete" `extras_require`, giving the ability to install these external python apis with `pip install eodag[complete]` (also available in `eodag[dev]`)	CS-SI/eodag	diff --git a/tests/test_requirements.py b/tests/test_requirements.py index 86e15515..9ef2dc9b 100644 --- a/tests/test_requirements.py +++ b/tests/test_requirements.py @@ -19,12 +19,15 @@ import ast import configparser import os +import re import unittest +from typing import Any, Dict, Iterator, Set import importlib_metadata from packaging.requirements import Requirement from stdlib_list import stdlib_list +from eodag.config import PluginConfig, load_default_config from tests.context import MisconfiguredError project_path = "./eodag" @@ -32,7 +35,7 @@ setup_cfg_path = "./setup.cfg" allowed_missing_imports = ["eodag"] -def get_imports(filepath): +def get_imports(filepath: str) -> Iterator[Any]: """Get python imports from the given file path""" with open(filepath, "r") as file: try: @@ -55,7 +58,7 @@ def get_imports(filepath): yield node.module.split(".")[0] -def get_project_imports(project_path): +def get_project_imports(project_path: str) -> Set[str]: """Get python imports from the project path""" imports = set() for dirpath, dirs, files in os.walk(project_path): @@ -66,7 +69,7 @@ def get_project_imports(project_path): return imports -def get_setup_requires(setup_cfg_path): +def get_setup_requires(setup_cfg_path: str): """Get requirements from the given setup.cfg file path""" config = configparser.ConfigParser() config.read(setup_cfg_path) @@ -79,12 +82,59 @@ def get_setup_requires(setup_cfg_path): ) +def get_optional_dependencies(setup_cfg_path: str, extra: str) -> Set[str]: + """Get extra requirements from the given setup.cfg file path""" + config = configparser.ConfigParser() + config.read(setup_cfg_path) + deps = set() + for req in config["options.extras_require"][extra].split("\n"): + if req.startswith("eodag["): + for found_extra in re.findall(r"([\w-]+)[,\]]", req): + deps.update(get_optional_dependencies(setup_cfg_path, found_extra)) + elif req: + deps.add(Requirement(req).name) + + return deps + + +def get_resulting_extras(setup_cfg_path: str, extra: str) -> Set[str]: + """Get resulting extras for a single extra from the given setup.cfg file path""" + config = configparser.ConfigParser() + config.read(setup_cfg_path) + extras = set() + for req in config["options.extras_require"][extra].split("\n"): + if req.startswith("eodag["): + extras.update(re.findall(r"([\w-]+)[,\]]", req)) + return extras + + +def get_entrypoints_extras(setup_cfg_path: str) -> Dict[str, str]: + """Get entrypoints and associated extra from the given setup.cfg file path""" + config = configparser.ConfigParser() + config.read(setup_cfg_path) + plugins_extras_dict = dict() + for group in config["options.entry_points"].keys(): + for ep in config["options.entry_points"][group].split("\n"): + # plugin entrypoint with associated extra + match = re.search(r"^(\w+) = [\w\.:]+ \[(\w+)\]$", ep) + if match: + plugins_extras_dict[match.group(1)] = match.group(2) + continue + # plugin entrypoint without extra + match = re.search(r"^(\w+) = [\w\.:]+$", ep) + if match: + plugins_extras_dict[match.group(1)] = None + + return plugins_extras_dict + + class TestRequirements(unittest.TestCase): - def test_requirements(self): + def test_all_requirements(self): """Needed libraries must be in project requirements""" project_imports = get_project_imports(project_path) setup_requires = get_setup_requires(setup_cfg_path) + setup_requires.update(get_optional_dependencies(setup_cfg_path, "all")) import_required_dict = importlib_metadata.packages_distributions() default_libs = stdlib_list() @@ -102,3 +152,24 @@ class TestRequirements(unittest.TestCase): 0, f"The following libraries were not found in project requirements: {missing_imports}", ) + + def test_plugins_extras(self): + """All optional dependencies needed by providers must be resolved with all-providers extra""" + + plugins_extras_dict = get_entrypoints_extras(setup_cfg_path) + all_providers_extras = get_resulting_extras(setup_cfg_path, "all-providers") + + providers_config = load_default_config() + plugins = set() + for provider_conf in providers_config.values(): + plugins.update( + [ + getattr(provider_conf, x).type + for x in dir(provider_conf) + if isinstance(getattr(provider_conf, x), PluginConfig) + ] + ) + + for plugin in plugins: + if extra := plugins_extras_dict.get(plugin): + self.assertIn(extra, all_providers_extras) diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 9d12a2ed..8931105f 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -29,7 +29,7 @@ from pathlib import Path from tempfile import TemporaryDirectory from unittest.mock import Mock -from pkg_resources import resource_filename +from pkg_resources import DistributionNotFound, resource_filename from shapely import wkt from shapely.geometry import LineString, MultiPolygon, Polygon @@ -976,6 +976,27 @@ class TestCore(TestCoreBase): os.environ.pop("EODAG__PEPS__SEARCH__NEED_AUTH", None) os.environ.pop("EODAG__PEPS__AUTH__CREDENTIALS__USERNAME", None) + @mock.patch("eodag.plugins.manager.pkg_resources.iter_entry_points", autospec=True) + def test_prune_providers_list_skipped_plugin(self, mock_iter_ep): + """Providers needing skipped plugin must be pruned on init""" + empty_conf_file = resource_filename( + "eodag", os.path.join("resources", "user_conf_template.yml") + ) + + def skip_qssearch(topic): + ep = mock.MagicMock() + if topic == "eodag.plugins.search": + ep.name = "QueryStringSearch" + ep.load = mock.MagicMock(side_effect=DistributionNotFound()) + return [ep] + + mock_iter_ep.side_effect = skip_qssearch + + dag = EODataAccessGateway(user_conf_file_path=empty_conf_file) + self.assertNotIn("peps", dag.available_providers()) + self.assertEqual(dag._plugins_manager.skipped_plugins, ["QueryStringSearch"]) + dag._plugins_manager.skipped_plugins = [] + def test_prune_providers_list_for_search_without_auth(self): """Providers needing auth for search but without auth plugin must be pruned on init""" empty_conf_file = resource_filename(	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": -1, "issue_text_score": 0, "test_score": -1 }, "num_modified_files": 11 }	2.12	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.23 boto3-stubs==1.37.23 botocore==1.37.23 botocore-stubs==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@e619c88de826047b8f46f4485ca5537a58e60678#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.16 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.0 mypy-boto3-sqs==1.37.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.24.2 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-PyYAML==6.0.12.20250326 types-s3transfer==0.11.4 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.30.0 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.23 - boto3-stubs==1.37.23 - botocore==1.37.23 - botocore-stubs==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.12.2.dev49+ge619c88d - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.16 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.0 - mypy-boto3-sqs==1.37.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.24.2 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-pyyaml==6.0.12.20250326 - types-s3transfer==0.11.4 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.30.0 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_requirements.py::TestRequirements::test_all_requirements", "tests/test_requirements.py::TestRequirements::test_plugins_extras", "tests/units/test_core.py::TestCore::test_prune_providers_list_skipped_plugin" ]	[]	[ "tests/units/test_core.py::TestCore::test_available_sortables", "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_guess_product_type_with_filter", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_list_queryables", "tests/units/test_core.py::TestCore::test_list_queryables_with_constraints", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_read_only_home_dir", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available_with_alias", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreSearch::test_search_sort_by", "tests/units/test_core.py::TestCoreSearch::test_search_sort_by_raise_errors", "tests/units/test_core.py::TestCoreDownload::test_download_local_product", "tests/units/test_core.py::TestCoreProductAlias::test_get_alias_from_product_type", "tests/units/test_core.py::TestCoreProductAlias::test_get_product_type_from_alias" ]	[]	Apache License 2.0	null
CS-SI__eodag-1262	f7efdd09236091864c16b46822730cf1b97317c9	2024-07-15 09:56:06	2473ac169a3d05f04f83d4f227c2fb63689ae2d6	github-actions[bot]: ## Test Results 4 files ±0 4 suites ±0 6m 8s :stopwatch: -6s 552 tests +1 549 :white_check_mark: +1 3 :zzz: ±0 0 :x: ±0 2 208 runs +4 2 106 :white_check_mark: +4 102 :zzz: ±0 0 :x: ±0 Results for commit 250c04bd. ± Comparison against base commit 1e422214. [test-results]:data:application/gzip;base64,H4sIADXzlGYC/03MSQ7DIAyF4atErLswDlN6mSphkFAzVARWUe9eaAPN8v8sv4M4P9ud3Dt268iefGxhUhij39acvVAZ8imWI+dY67EnrQux4U9P/yo/Ddzo5wzQwIawhVNCWssmIqiz6iZSEI1+mxSwymX029dNvS2LjzkIctDAJqORSSvRyZ4aZ6fRMjn0CM4IpFZNSN4fNEhkxwkBAAA= github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports/data/usgs_search_id/./badge.svg) ## Code Coverage (Ubuntu) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 305 51 83.28% 60, 654-700, 802-853, 857 config.py 351 27 92.31% 81-83, 92, 100, 104-106, 183, 195, 460-462, 526-529, 576-577, 586-587, 666, 735-740, 742 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 762 84 88.98% 91-100, 373, 598, 642-645, 683, 720, 750, 798, 802-807, 833, 929, 1012, 1149, 1239-1251, 1291, 1293, 1321, 1325-1336, 1349-1355, 1445-1448, 1481-1501, 1557, 1574-1577, 1589-1592, 1614-1621, 1947, 1980-1986, 2253, 2257-2260, 2274-2276, 2311 api/search_result.py 54 6 88.89% 33-35, 75, 84, 91, 105 api/product/__init__.py 6 0 100.00% api/product/_assets.py 52 8 84.62% 28-30, 80, 157, 167, 170-174 api/product/_product.py 195 27 86.15% 59-66, 70-72, 242-243, 325, 354, 415, 429-432, 445, 469-472, 515-521 api/product/metadata_mapping.py 678 83 87.76% 67-69, 131-133, 234, 266-267, 313-314, 324-336, 338, 349, 355-367, 412-413, 450, 471-474, 497, 505-506, 582-583, 607-608, 614-617, 632-633, 782, 828, 1002-1007, 1138, 1152-1172, 1192, 1197, 1307, 1329, 1343, 1356-1375, 1414, 1466, 1504-1508, 1527 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 8 2 75.00% 23, 41 plugins/__init__.py 0 0 100.00% plugins/base.py 23 3 86.96% 25, 48, 55 plugins/manager.py 134 15 88.81% 49-51, 105-110, 156, 197, 219, 223, 249, 288-289 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 99 15 84.85% 46-54, 150-152, 199-200, 226-228 plugins/apis/usgs.py 186 36 80.65% 60-65, 132, 233, 267, 302-304, 309, 335-336, 341, 371-378, 389-394, 416-422, 424-430, 453 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 20 2 90.00% 25-27 plugins/authentication/base.py 19 3 84.21% 26, 34, 47 plugins/authentication/generic.py 16 3 81.25% 28, 40, 50 plugins/authentication/header.py 21 1 95.24% 28 plugins/authentication/keycloak.py 49 6 87.76% 33-35, 132, 156-161 plugins/authentication/oauth.py 15 8 46.67% 25, 32-34, 38-41 plugins/authentication/openid_connect.py 185 19 89.73% 41-43, 119, 133-158, 166, 320-323, 347 plugins/authentication/qsauth.py 36 2 94.44% 32, 83 plugins/authentication/sas_auth.py 49 2 95.92% 32, 76 plugins/authentication/token.py 90 17 81.11% 35-37, 79, 107, 109, 131-143, 198-201 plugins/authentication/token_exchange.py 35 19 45.71% 74-80, 92-120 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 12 2 83.33% 26, 40 plugins/crunch/filter_date.py 61 15 75.41% 30, 51-56, 70, 79, 88, 91, 103-105, 114-116, 123 plugins/crunch/filter_latest_intersect.py 50 10 80.00% 32-34, 51-52, 71, 80-83, 85, 92-95 plugins/crunch/filter_latest_tpl_name.py 33 2 93.94% 28, 87 plugins/crunch/filter_overlap.py 68 19 72.06% 28-30, 33, 72-75, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 32 8 75.00% 29, 58-63, 66-67, 83-87 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 492 165 66.46% 77-83, 272, 285, 352-355, 369-373, 419-421, 425, 459-460, 466-470, 503, 538, 542, 549, 579-587, 591, 629-637, 644-646, 687-761, 779-840, 851-856, 872-885, 914, 929-931, 934, 944-952, 960-973, 983-1002, 1009-1021, 1062, 1088, 1133-1135, 1355 plugins/download/base.py 260 57 78.08% 58-64, 145, 180, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 538-542, 572-573, 581-598, 605-613, 615-619, 666, 688, 710, 718 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 535 132 75.33% 86-92, 203-215, 217-218, 253-256, 320-323, 325-326, 333-338, 356-371, 388-390, 402, 450, 457-463, 481, 495, 509, 517-519, 535-540, 551, 569, 611-615, 637, 677, 722, 736-742, 778-842, 860, 893-902, 928-929, 956-961, 967, 970, 986, 1003-1004, 1031-1032, 1039, 1101-1107, 1162-1163, 1169, 1179, 1213, 1249, 1267-1283 plugins/download/s3rest.py 119 27 77.31% 55-58, 121, 162, 197, 227-234, 237-239, 243, 256-262, 270-271, 274-278, 301, 322-325 plugins/search/__init__.py 26 3 88.46% 27-31 plugins/search/base.py 133 13 90.23% 48-53, 104, 108, 121, 271, 291, 350-351, 371, 380 plugins/search/build_search_result.py 183 24 86.89% 62, 97, 141-142, 148, 159, 295-298, 327, 384-401, 463, 466, 476, 493, 521, 523 plugins/search/cop_marine.py 200 49 75.50% 43-44, 55, 63-65, 71-72, 88, 90, 93, 128-130, 142-143, 183-192, 196, 199, 203, 221, 251, 255, 270, 274, 278, 282, 286-290, 296-299, 302-316, 333, 356, 359, 365 plugins/search/creodias_s3.py 55 3 94.55% 56, 74, 108 plugins/search/csw.py 108 83 23.15% 44-46, 58-59, 63-64, 72-120, 126-139, 147-179, 197-238 plugins/search/data_request_search.py 202 68 66.34% 53, 90-93, 109, 121, 125-126, 137, 142, 147, 154, 167-170, 224-225, 229, 239-245, 250, 276-279, 287-298, 315, 317, 324-325, 327-328, 346-350, 383, 393, 404, 417, 423-438, 443 plugins/search/qssearch.py 655 77 88.24% 103, 387, 391-397, 405-406, 512-518, 568, 584, 594, 621, 623, 666-669, 743-744, 792, 811, 826, 884, 905, 908-909, 918-919, 928-929, 938-939, 966, 1037-1042, 1046-1055, 1090, 1164, 1213, 1287-1291, 1357-1358, 1379, 1406-1418, 1425, 1457-1459, 1469-1475, 1519, 1534, 1556, 1665-1675 plugins/search/static_stac_search.py 75 12 84.00% 37-38, 101-128, 144, 157 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 7 78.79% 35-37, 53-55, 59, 68 rest/config.py 26 0 100.00% rest/constants.py 7 0 100.00% rest/core.py 238 30 87.39% 83-87, 281, 365, 475, 702, 709-757 rest/server.py 286 55 80.77% 81-82, 108, 131-133, 246-248, 304-305, 317-333, 425-430, 458, 626-633, 662, 706-707, 730, 803-805, 822-827, 856, 858, 862-863, 867-868 rest/stac.py 466 123 73.61% 65-67, 320, 342, 390-393, 417-443, 448-454, 477-479, 502, 537-538, 566, 579, 624-664, 692-708, 787-800, 807, 863-864, 931, 999-1001, 1220, 1230-1242, 1255-1277, 1291-1336, 1498-1499 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 184 9 95.11% 52-55, 235-239, 292, 295, 363 rest/types/queryables.py 58 2 96.55% 38, 172 rest/types/stac_search.py 131 11 91.60% 51-54, 133, 179, 194-196, 204, 208 rest/utils/__init__.py 95 13 86.32% 51, 108-109, 128-130, 182, 192-206 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 3 86.96% 49, 61, 63 types/__init__.py 110 11 90.00% 53, 70, 129-132, 199, 214-218, 245, 258 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 104 types/whoosh.py 15 0 100.00% utils/__init__.py 503 39 92.25% 84, 89, 110-112, 193-194, 203-230, 233, 247, 329-333, 409-413, 434-436, 518, 533, 571-572, 968-971, 979-980, 1021-1022, 1204 utils/constraints.py 119 37 68.91% 94-103, 144, 149, 153, 164, 190-192, 202, 216-232, 241-252 utils/exceptions.py 37 2 94.59% 23, 93 utils/import_system.py 30 20 33.33% 27, 67-81, 93-103 utils/logging.py 29 1 96.55% 123 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 69, 96, 98, 100, 102, 104, 123, 128-130, 138 utils/rest.py 36 1 97.22% 57 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 143, 159-162, 215-224, 234-264 TOTAL 9667 1731 82.09% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover -------------------- ------- ------ ------- plugins/apis/usgs.py +11 0 +1.22% TOTAL +11 0 +0.02% ``` Results for commit: 250c04bdc247e72f731dfebae479320fd621e8b2 _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-ubuntu-latest/coverage.xml --> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports_win/data/usgs_search_id/./badge.svg) ## Code Coverage (Windows) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ __init__.py 8 0 100.00% cli.py 305 51 83.28% 60, 654-700, 802-853, 857 config.py 351 28 92.02% 81-83, 92, 100, 104-106, 183, 195, 460-462, 526-529, 576-577, 586-587, 666, 700, 735-740, 742 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 762 84 88.98% 91-100, 373, 598, 642-645, 683, 720, 750, 798, 802-807, 833, 929, 1012, 1149, 1239-1251, 1291, 1293, 1321, 1325-1336, 1349-1355, 1445-1448, 1481-1501, 1557, 1574-1577, 1589-1592, 1614-1621, 1947, 1980-1986, 2253, 2257-2260, 2274-2276, 2311 api/search_result.py 54 6 88.89% 33-35, 75, 84, 91, 105 api/product/__init__.py 6 0 100.00% api/product/_assets.py 52 8 84.62% 28-30, 80, 157, 167, 170-174 api/product/_product.py 195 27 86.15% 59-66, 70-72, 242-243, 325, 354, 415, 429-432, 445, 469-472, 515-521 api/product/metadata_mapping.py 678 83 87.76% 67-69, 131-133, 234, 266-267, 313-314, 324-336, 338, 349, 355-367, 412-413, 450, 471-474, 497, 505-506, 582-583, 607-608, 614-617, 632-633, 782, 828, 1002-1007, 1138, 1152-1172, 1192, 1197, 1307, 1329, 1343, 1356-1375, 1414, 1466, 1504-1508, 1527 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 8 2 75.00% 23, 41 plugins/__init__.py 0 0 100.00% plugins/base.py 23 4 82.61% 25, 48, 55, 68 plugins/manager.py 134 15 88.81% 49-51, 105-110, 156, 197, 219, 223, 249, 288-289 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 99 15 84.85% 46-54, 150-152, 199-200, 226-228 plugins/apis/usgs.py 186 36 80.65% 60-65, 132, 233, 267, 302-304, 309, 335-336, 341, 371-378, 389-394, 416-422, 424-430, 453 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 20 2 90.00% 25-27 plugins/authentication/base.py 19 3 84.21% 26, 34, 47 plugins/authentication/generic.py 16 3 81.25% 28, 40, 50 plugins/authentication/header.py 21 1 95.24% 28 plugins/authentication/keycloak.py 49 6 87.76% 33-35, 132, 156-161 plugins/authentication/oauth.py 15 8 46.67% 25, 32-34, 38-41 plugins/authentication/openid_connect.py 185 19 89.73% 41-43, 119, 133-158, 166, 320-323, 347 plugins/authentication/qsauth.py 36 2 94.44% 32, 83 plugins/authentication/sas_auth.py 49 2 95.92% 32, 76 plugins/authentication/token.py 90 17 81.11% 35-37, 79, 107, 109, 131-143, 198-201 plugins/authentication/token_exchange.py 35 19 45.71% 74-80, 92-120 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 12 2 83.33% 26, 40 plugins/crunch/filter_date.py 61 15 75.41% 30, 51-56, 70, 79, 88, 91, 103-105, 114-116, 123 plugins/crunch/filter_latest_intersect.py 50 35 30.00% 32-34, 48-53, 69-114 plugins/crunch/filter_latest_tpl_name.py 33 2 93.94% 28, 87 plugins/crunch/filter_overlap.py 68 19 72.06% 28-30, 33, 72-75, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 32 8 75.00% 29, 58-63, 66-67, 83-87 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 492 165 66.46% 77-83, 272, 285, 352-355, 369-373, 419-421, 425, 459-460, 466-470, 503, 538, 542, 549, 579-587, 591, 629-637, 644-646, 687-761, 779-840, 851-856, 872-885, 914, 929-931, 934, 944-952, 960-973, 983-1002, 1009-1021, 1062, 1088, 1133-1135, 1355 plugins/download/base.py 260 59 77.31% 58-64, 145, 180, 250-252, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 538-542, 572-573, 581-598, 605-613, 615-619, 666, 688, 710, 718 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 535 132 75.33% 86-92, 203-215, 217-218, 253-256, 320-323, 325-326, 333-338, 356-371, 388-390, 402, 450, 457-463, 481, 495, 509, 517-519, 535-540, 551, 569, 611-615, 637, 677, 722, 736-742, 778-842, 860, 893-902, 928-929, 956-961, 967, 970, 986, 1003-1004, 1031-1032, 1039, 1101-1107, 1162-1163, 1169, 1179, 1213, 1249, 1267-1283 plugins/download/s3rest.py 119 27 77.31% 55-58, 121, 162, 197, 227-234, 237-239, 243, 256-262, 270-271, 274-278, 301, 322-325 plugins/search/__init__.py 26 3 88.46% 27-31 plugins/search/base.py 133 18 86.47% 48-53, 104, 108, 121, 271, 291, 350-351, 371, 374-382, 384 plugins/search/build_search_result.py 183 31 83.06% 62, 97, 141-142, 148, 159, 295-298, 327, 384-401, 463, 466, 476, 493, 513-528 plugins/search/cop_marine.py 200 49 75.50% 43-44, 55, 63-65, 71-72, 88, 90, 93, 128-130, 142-143, 183-192, 196, 199, 203, 221, 251, 255, 270, 274, 278, 282, 286-290, 296-299, 302-316, 333, 356, 359, 365 plugins/search/creodias_s3.py 55 3 94.55% 56, 74, 108 plugins/search/csw.py 108 83 23.15% 44-46, 58-59, 63-64, 72-120, 126-139, 147-179, 197-238 plugins/search/data_request_search.py 202 68 66.34% 53, 90-93, 109, 121, 125-126, 137, 142, 147, 154, 167-170, 224-225, 229, 239-245, 250, 276-279, 287-298, 315, 317, 324-325, 327-328, 346-350, 383, 393, 404, 417, 423-438, 443 plugins/search/qssearch.py 655 105 83.97% 103, 387, 391-397, 405-406, 512-518, 568, 571, 584, 594, 613-628, 666-669, 743-744, 792, 811, 826, 884, 905, 908-909, 918-919, 928-929, 938-939, 966, 1037-1042, 1046-1055, 1090, 1164, 1213, 1287-1291, 1357-1358, 1379, 1406-1418, 1425, 1457-1459, 1469-1475, 1519, 1534, 1556, 1624-1695 plugins/search/static_stac_search.py 75 12 84.00% 37-38, 101-128, 144, 157 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 22 33.33% 35-37, 44-70 rest/config.py 26 4 84.62% 34-36, 68 rest/constants.py 7 0 100.00% rest/core.py 238 153 35.71% 83-87, 167-248, 272-326, 339-375, 412-444, 464-480, 503-514, 523-559, 578, 624-663, 702, 709-757 rest/server.py 286 286 0.00% 18-879 rest/stac.py 466 394 15.45% 65-67, 128-134, 142-165, 196-201, 233, 253-376, 386-455, 473-512, 528-581, 591-614, 624-664, 692-708, 718, 734-737, 756-826, 844-880, 909-934, 942-958, 966-971, 981-1008, 1018-1020, 1028-1030, 1043-1045, 1059-1076, 1086-1107, 1117-1139, 1147-1164, 1187-1210, 1220, 1230-1242, 1255-1277, 1291-1336, 1344-1361, 1371-1529 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 184 19 89.67% 52-55, 125, 235-239, 272-274, 292, 295, 301, 305, 363, 375-378 rest/types/queryables.py 58 14 75.86% 38, 51-52, 58-59, 65-66, 95-100, 109-110, 172 rest/types/stac_search.py 131 22 83.21% 51-54, 131-133, 157-158, 163-164, 179, 194-196, 204, 208, 255-260 rest/utils/__init__.py 95 31 67.37% 51, 79-85, 105, 108-109, 128-130, 143, 150, 175-183, 190-211 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 6 73.91% 41, 44-45, 49, 61, 63 types/__init__.py 110 36 67.27% 53, 66-70, 81-93, 120-122, 129-132, 172, 199, 209-221, 226, 245, 250, 258, 268 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 104 types/whoosh.py 15 0 100.00% utils/__init__.py 503 41 91.85% 84, 89, 110-112, 193-194, 203-230, 233, 247, 329-333, 409-413, 434-436, 518, 533, 571-572, 968-971, 979-980, 1021-1022, 1055, 1204, 1380 utils/constraints.py 119 37 68.91% 94-103, 144, 149, 153, 164, 190-192, 202, 216-232, 241-252 utils/exceptions.py 37 2 94.59% 23, 93 utils/import_system.py 30 20 33.33% 27, 67-81, 93-103 utils/logging.py 29 1 96.55% 123 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 69, 96, 98, 100, 102, 104, 123, 128-130, 138 utils/rest.py 36 1 97.22% 57 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 143, 159-162, 215-224, 234-264 TOTAL 9667 2525 73.88% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover -------------------- ------- ------ ------- plugins/apis/usgs.py +11 0 +1.22% TOTAL +11 0 +0.03% ``` Results for commit: 250c04bdc247e72f731dfebae479320fd621e8b2 _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-windows-latest/coverage.xml -->	diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 2e552f0b..d4ecf61f 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -172,11 +172,39 @@ class UsgsApi(Api): max_results=items_per_page, starting_number=(1 + (page - 1) * items_per_page), ) - logger.info( - f"Sending search request for {usgs_dataset} with {api_search_kwargs}" - ) - results = api.scene_search(usgs_dataset, api_search_kwargs) + # search by id + if searched_id := kwargs.get("id"): + dataset_filters = api.dataset_filters(usgs_dataset) + # ip pattern set as parameter queryable (first element of param conf list) + id_pattern = self.config.metadata_mapping["id"][0] + # loop on matching dataset_filters until one returns expected results + for dataset_filter in dataset_filters["data"]: + if id_pattern in dataset_filter["searchSql"]: + logger.debug( + f"Try using {dataset_filter['searchSql']} dataset filter to search by id on {usgs_dataset}" + ) + full_api_search_kwargs = { + "where": { + "filter_id": dataset_filter["id"], + "value": searched_id, + }, + api_search_kwargs, + } + logger.info( + f"Sending search request for {usgs_dataset} with {full_api_search_kwargs}" + ) + results = api.scene_search( + usgs_dataset, full_api_search_kwargs + ) + if len(results["data"]["results"]) == 1: + # search by id using this dataset_filter succeeded + break + else: + logger.info( + f"Sending search request for {usgs_dataset} with {api_search_kwargs}" + ) + results = api.scene_search(usgs_dataset, api_search_kwargs) # update results with storage info from download_options() results_by_entity_id = { diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 51fc63f2..b2013f83 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -31,7 +31,9 @@ max_items_per_page: 5000 total_items_nb_key_path: '$.totalHits' metadata_mapping: - id: '$.displayId' + id: + - '_ID' + - '$.displayId' geometry: '$.spatialBounds' productType: '$.productType' title: '$.displayId'	USGS Search by ID lists many products instead of just the matching one When I try to use `eodag.search` method with setting the `id` parameter, it lists many (millions) of products instead of just the product matching the ID, and the only work around I found is to iterate through all the pages and look for the matching product manually, but it takes a lot of time to load all these results. Code To Reproduce ```py from eodag import EODataAccessGateway from eodag.utils.logging import setup_logging setup_logging(verbose=3) dag = EODataAccessGateway() dag.search(id="LC09_L2SP_016035_20240605_20240611_02_T1", provider="usgs", productType="LANDSAT_C2L2") ``` Output ``` 2024-07-08 22:46:20,166 eodag.config [DEBUG ] (tid=140046769037376) Loading configuration from /home/hamed/Desktop/playground/env/lib/python3.11/site-packages/eodag/resources/providers.yml 2024-07-08 22:46:21,432 eodag.config [INFO ] (tid=140046769037376) Loading user configuration from: /home/hamed/.config/eodag/eodag.yml 2024-07-08 22:46:21,452 eodag.core [INFO ] (tid=140046769037376) aws_eos: provider needing auth for search has been pruned because no crendentials could be found 2024-07-08 22:46:21,452 eodag.core [INFO ] (tid=140046769037376) meteoblue: provider needing auth for search has been pruned because no crendentials could be found 2024-07-08 22:46:21,452 eodag.core [INFO ] (tid=140046769037376) hydroweb_next: provider needing auth for search has been pruned because no crendentials could be found 2024-07-08 22:46:21,452 eodag.core [INFO ] (tid=140046769037376) wekeo: provider needing auth for search has been pruned because no crendentials could be found 2024-07-08 22:46:21,452 eodag.core [INFO ] (tid=140046769037376) creodias_s3: provider needing auth for search has been pruned because no crendentials could be found 2024-07-08 22:46:21,460 eodag.core [DEBUG ] (tid=140046769037376) Opening product types index in /home/hamed/.config/eodag/.index 2024-07-08 22:46:21,473 eodag.core [INFO ] (tid=140046769037376) Locations configuration loaded from /home/hamed/.config/eodag/locations.yml 2024-07-08 22:46:21,474 eodag.core [INFO ] (tid=140046769037376) Searching product type 'LANDSAT_C2L2' on provider: usgs 2024-07-08 22:46:21,474 eodag.core [INFO ] (tid=140046769037376) Searching product with id 'LC09_L2SP_016035_20240605_20240611_02_T1' on provider: usgs 2024-07-08 22:46:21,474 eodag.core [DEBUG ] (tid=140046769037376) Using plugin class for search: UsgsApi 2024-07-08 22:46:22,680 eodag.apis.usgs [INFO ] (tid=140046769037376) Sending search request for landsat_ot_c2_l2 with {'start_date': None, 'end_date': None, 'll': None, 'ur': None, 'max_results': 2, 'starting_number': 1} 2024-07-08 22:46:24,927 eodag.apis.usgs [DEBUG ] (tid=140046769037376) Adapting 2 plugin results to eodag product representation 2024-07-08 22:46:27,095 eodag.core [INFO ] (tid=140046769037376) Found 3127730 result(s) on provider 'usgs' 2024-07-08 22:46:27,095 eodag.core [INFO ] (tid=140046769037376) Several products found for this id ([EOProduct(id=LC09_L2SP_016001_20240707_20240708_02_T1, provider=usgs), EOProduct(id=LC09_L2SP_016002_20240707_20240708_02_T1, provider=usgs)]). You may try searching using more selective criteria. ``` Environment: - Python version: `3.11.2` - EODAG version: `2.12.1` Additional context Here is the actual product that corresponds to the "ID" in the search: https://earthexplorer.usgs.gov/scene/metadata/full/5e83d14f2fc39685/LC90160352024157LGN00/	CS-SI/eodag	diff --git a/tests/units/test_apis_plugins.py b/tests/units/test_apis_plugins.py index 30cfbb41..7f3d184a 100644 --- a/tests/units/test_apis_plugins.py +++ b/tests/units/test_apis_plugins.py @@ -392,6 +392,65 @@ class TestApisPluginEcmwfApi(BaseApisPluginTest): class TestApisPluginUsgsApi(BaseApisPluginTest): + SCENE_SEARCH_RETURN = { + "data": { + "results": [ + { + "browse": [ + { + "browsePath": "https://path/to/quicklook.jpg", + "thumbnailPath": "https://path/to/thumbnail.jpg", + }, + {}, + {}, + ], + "cloudCover": "77.46", + "entityId": "LC81780382020041LGN00", + "displayId": "LC08_L1GT_178038_20200210_20200224_01_T2", + "spatialBounds": { + "type": "Polygon", + "coordinates": [ + [ + [28.03905, 30.68073], + [28.03905, 32.79057], + [30.46294, 32.79057], + [30.46294, 30.68073], + [28.03905, 30.68073], + ] + ], + }, + "temporalCoverage": { + "endDate": "2020-02-10 00:00:00", + "startDate": "2020-02-10 00:00:00", + }, + "publishDate": "2020-02-10 08:24:46", + }, + ], + "recordsReturned": 5, + "totalHits": 139, + "startingNumber": 6, + "nextRecord": 11, + } + } + + DOWNLOAD_OPTION_RETURN = { + "data": [ + { + "id": "5e83d0b8e7f6734c", + "entityId": "LC81780382020041LGN00", + "available": True, + "filesize": 9186067, + "productName": "LandsatLook Natural Color Image", + "downloadSystem": "dds", + }, + { + "entityId": "LC81780382020041LGN00", + "available": False, + "downloadSystem": "wms", + }, + ] + } + def setUp(self): self.provider = "usgs" self.api_plugin = self.get_search_plugin(provider=self.provider) @@ -450,67 +509,12 @@ class TestApisPluginUsgsApi(BaseApisPluginTest): @mock.patch( "usgs.api.scene_search", autospec=True, - return_value={ - "data": { - "results": [ - { - "browse": [ - { - "browsePath": "https://path/to/quicklook.jpg", - "thumbnailPath": "https://path/to/thumbnail.jpg", - }, - {}, - {}, - ], - "cloudCover": "77.46", - "entityId": "LC81780382020041LGN00", - "displayId": "LC08_L1GT_178038_20200210_20200224_01_T2", - "spatialBounds": { - "type": "Polygon", - "coordinates": [ - [ - [28.03905, 30.68073], - [28.03905, 32.79057], - [30.46294, 32.79057], - [30.46294, 30.68073], - [28.03905, 30.68073], - ] - ], - }, - "temporalCoverage": { - "endDate": "2020-02-10 00:00:00", - "startDate": "2020-02-10 00:00:00", - }, - "publishDate": "2020-02-10 08:24:46", - }, - ], - "recordsReturned": 5, - "totalHits": 139, - "startingNumber": 6, - "nextRecord": 11, - } - }, + return_value=SCENE_SEARCH_RETURN, ) @mock.patch( "usgs.api.download_options", autospec=True, - return_value={ - "data": [ - { - "id": "5e83d0b8e7f6734c", - "entityId": "LC81780382020041LGN00", - "available": True, - "filesize": 9186067, - "productName": "LandsatLook Natural Color Image", - "downloadSystem": "dds", - }, - { - "entityId": "LC81780382020041LGN00", - "available": False, - "downloadSystem": "wms", - }, - ] - }, + return_value=DOWNLOAD_OPTION_RETURN, ) def test_plugins_apis_usgs_query( self, @@ -566,6 +570,61 @@ class TestApisPluginUsgsApi(BaseApisPluginTest): total_count, mock_api_scene_search.return_value["data"]["totalHits"] ) + @mock.patch("usgs.api.login", autospec=True) + @mock.patch("usgs.api.logout", autospec=True) + @mock.patch( + "usgs.api.scene_search", + autospec=True, + return_value=SCENE_SEARCH_RETURN, + ) + @mock.patch( + "usgs.api.download_options", + autospec=True, + return_value=DOWNLOAD_OPTION_RETURN, + ) + @mock.patch( + "usgs.api.dataset_filters", + autospec=True, + return_value={ + "data": [ + {"id": "foo_id", "searchSql": "DONT_USE_THIS !"}, + {"id": "bar_id", "searchSql": "USE_THIS_ID !"}, + ] + }, + ) + def test_plugins_apis_usgs_query_by_id( + self, + mock_dataset_filters, + mock_api_download_options, + mock_api_scene_search, + mock_api_logout, + mock_api_login, + ): + """UsgsApi.query by id must search using usgs api""" + + search_kwargs = { + "productType": "LANDSAT_C2L1", + "id": "SOME_PRODUCT_ID", + "prep": PreparedSearch( + items_per_page=500, + page=1, + ), + } + search_results, total_count = self.api_plugin.query(**search_kwargs) + mock_api_scene_search.assert_called_once_with( + "landsat_ot_c2_l1", + where={"filter_id": "bar_id", "value": "SOME_PRODUCT_ID"}, + start_date=None, + end_date=None, + ll=None, + ur=None, + max_results=500, + starting_number=1, + ) + self.assertEqual(search_results[0].provider, "usgs") + self.assertEqual(search_results[0].product_type, "LANDSAT_C2L1") + self.assertEqual(len(search_results), 1) + @mock.patch("usgs.api.login", autospec=True) @mock.patch("usgs.api.logout", autospec=True) @mock.patch("usgs.api.download_request", autospec=True)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 0 }, "num_modified_files": 2 }	3.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.23 boto3-stubs==1.37.23 botocore==1.37.23 botocore-stubs==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@f7efdd09236091864c16b46822730cf1b97317c9#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.16 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.0 mypy-boto3-sqs==1.37.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.24.2 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-PyYAML==6.0.12.20250326 types-s3transfer==0.11.4 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.23 - boto3-stubs==1.37.23 - botocore==1.37.23 - botocore-stubs==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==3.0.0b3.dev11+gf7efdd09 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.16 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.0 - mypy-boto3-sqs==1.37.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.24.2 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-pyyaml==6.0.12.20250326 - types-s3transfer==0.11.4 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_query_by_id" ]	[]	[ "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download_all", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_dates_missing", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_custom_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_without_producttype", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_download", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_query" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-1262
CS-SI__eodag-1271	97b7b6f881714b4c40029a174c2539085cd50868	2024-07-24 16:34:33	2473ac169a3d05f04f83d4f227c2fb63689ae2d6	github-actions[bot]: ## Test Results 2 files - 2 2 suites - 2 1m 34s :stopwatch: - 4m 43s 553 tests ± 0 549 :white_check_mark: - 1 3 :zzz: ± 0 1 :x: +1 553 runs - 1 659 549 :white_check_mark: - 1 561 3 :zzz: - 99 1 :x: +1 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/27870645132). Results for commit 860b476c. ± Comparison against base commit a456422e. [test-results]:data:application/gzip;base64,H4sIAOwtoWYC/1WMyw7CIBQFf6Vh7QIo0OLPGLhCQmyL4bEy/ruXGktdzpyTeREfFpfJdeCXgeQaygH3mkwJcUPUAhmX0jYpxx/dcgVoSuiuHuGJqn+8CQsKdgiXUkxoKJpUt95s8J/8ml7c+RTc+dyDuK6hIJBZUSsmBVwZKSSMTFgzOQaUM668HPkMVmnuyfsD3pyDlwQBAAA= github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports/data/rename-to-camelcase/./badge.svg) ## Code Coverage (Ubuntu) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 303 50 83.50% 654-700, 802-853, 857 config.py 356 27 92.42% 81-83, 92, 100, 104-106, 183, 195, 468-470, 534-537, 584-585, 594-595, 674, 743-748, 750 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 752 75 90.03% 373, 598, 642-645, 683, 720, 750, 798, 802-807, 833, 929, 1012, 1149, 1239-1251, 1291, 1293, 1321, 1325-1336, 1349-1355, 1445-1448, 1481-1501, 1557, 1574-1577, 1589-1592, 1614-1621, 1947, 1980-1986, 2253, 2257-2260, 2274-2276, 2311 api/search_result.py 51 4 92.16% 75, 84, 91, 105 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 80, 157, 167, 170-174 api/product/_product.py 187 20 89.30% 70-72, 242-243, 325, 354, 415, 429-432, 445, 469-472, 515-521 api/product/metadata_mapping.py 675 81 88.00% 131-133, 234, 266-267, 313-314, 324-336, 338, 349, 355-367, 412-413, 450, 471-474, 497, 505-506, 582-583, 607-608, 614-617, 632-633, 782, 828, 1002-1007, 1138, 1152-1172, 1192, 1197, 1307, 1329, 1343, 1356-1375, 1414, 1466, 1504-1508, 1527 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 6 1 83.33% 41 plugins/__init__.py 0 0 100.00% plugins/base.py 21 2 90.48% 48, 55 plugins/manager.py 130 12 90.77% 105-110, 160, 201, 223, 227, 253, 292-293 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 91 8 91.21% 150-152, 199-200, 226-228 plugins/apis/usgs.py 180 31 82.78% 132, 234, 268, 304-306, 311, 337-338, 343, 373-380, 391-396, 418-424, 426-432, 455 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 19 0 100.00% plugins/authentication/base.py 17 2 88.24% 34, 47 plugins/authentication/generic.py 14 2 85.71% 40, 50 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 46 4 91.30% 132, 156-161 plugins/authentication/oauth.py 13 7 46.15% 32-34, 38-41 plugins/authentication/openid_connect.py 183 17 90.71% 119, 133-158, 166, 320-323, 347 plugins/authentication/qsauth.py 34 1 97.06% 83 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 88 16 81.82% 79, 107, 109, 131-143, 198-202 plugins/authentication/token_exchange.py 35 19 45.71% 74-80, 92-120 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 40 plugins/crunch/filter_date.py 59 14 76.27% 51-56, 70, 79, 88, 91, 103-105, 114-116, 123 plugins/crunch/filter_latest_intersect.py 47 8 82.98% 51-52, 71, 80-83, 85, 92-95 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 87 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 72-75, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 30 7 76.67% 58-63, 66-67, 83-87 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 489 163 66.67% 273, 286, 353-356, 370-374, 420-422, 426, 460-461, 467-471, 504, 539, 543, 550, 580-588, 592, 630-638, 645-647, 688-762, 780-841, 852-857, 873-886, 915, 930-932, 935, 945-953, 961-974, 984-1015, 1022-1034, 1075, 1101, 1146-1148, 1368 plugins/download/base.py 253 51 79.84% 145, 180, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 538-542, 572-573, 581-598, 605-613, 615-619, 666, 688, 710, 718 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 534 129 75.84% 203-215, 217-218, 253-256, 320-323, 325-326, 333-338, 356-371, 388-390, 402, 450, 457-463, 481, 495, 509, 517-519, 535-540, 551, 569, 611-615, 637, 677, 722, 736-742, 778-842, 860, 893-902, 928-929, 956-961, 967, 970, 986, 1003-1004, 1034-1035, 1042, 1103-1109, 1164-1165, 1171, 1181, 1217, 1253, 1271-1287, 1313-1315 plugins/download/s3rest.py 116 24 79.31% 121, 157, 164, 199, 229-236, 239-241, 245, 258-264, 272-273, 276-280, 303, 324-327 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 128 9 92.97% 104, 108, 121, 271, 291, 350-351, 371, 380 plugins/search/build_search_result.py 181 23 87.29% 97, 141-142, 148, 159, 295-298, 327, 384-401, 463, 466, 476, 493, 521, 523 plugins/search/cop_marine.py 197 47 76.14% 55, 63-65, 71-72, 88, 90, 93, 128-130, 142-143, 183-192, 196, 199, 203, 221, 251, 255, 270, 274, 278, 282, 286-290, 296-299, 302-316, 333, 356, 359, 365 plugins/search/creodias_s3.py 55 3 94.55% 56, 74, 108 plugins/search/csw.py 105 81 22.86% 58-59, 63-64, 72-120, 126-139, 147-179, 197-238 plugins/search/data_request_search.py 200 67 66.50% 90-93, 109, 121, 125-126, 137, 142, 147, 154, 167-170, 224-225, 229, 239-245, 250, 276-279, 287-298, 315, 317, 324-325, 327-328, 346-350, 383, 393, 404, 417, 423-438, 443 plugins/search/qssearch.py 664 80 87.95% 391, 395-401, 409-410, 516-522, 572, 588, 598, 625, 627, 670-673, 747-748, 796, 815, 830, 888, 909, 912-913, 922-923, 932-933, 942-943, 970, 1041-1046, 1050-1059, 1093, 1115, 1175, 1224, 1298-1302, 1362, 1365, 1371-1372, 1393, 1420-1432, 1439, 1471-1473, 1483-1489, 1519, 1542, 1557, 1579, 1688-1698 plugins/search/static_stac_search.py 72 10 86.11% 101-128, 144, 157 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 7 78.79% 35-37, 53-55, 59, 68 rest/config.py 26 0 100.00% rest/constants.py 7 0 100.00% rest/core.py 234 26 88.89% 281, 367, 477, 704, 711-759 rest/server.py 283 53 81.27% 108, 131-133, 246-248, 304-305, 317-333, 425-430, 458, 626-633, 662, 706-707, 730, 803-805, 822-827, 856, 858, 862-863, 867-868 rest/stac.py 464 120 74.14% 322, 344, 392-395, 419-445, 450-456, 479-481, 504, 539-540, 568, 581, 626-666, 694-710, 789-802, 809, 865-866, 933, 1001-1003, 1222, 1232-1244, 1257-1279, 1293-1338, 1500-1501 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 179 5 97.21% 232-236, 289, 292, 360 rest/types/queryables.py 56 1 98.21% 174 rest/types/stac_search.py 126 7 94.44% 131, 177, 192-194, 202, 206 rest/utils/__init__.py 93 12 87.10% 108-109, 128-130, 182, 192-206 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 3 86.96% 49, 61, 63 types/__init__.py 114 14 87.72% 53, 70, 129-132, 199, 213-222, 232, 253, 266 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 105 types/whoosh.py 15 0 100.00% utils/__init__.py 500 37 92.60% 85, 90, 194-195, 204-231, 234, 248, 330-334, 410-414, 435-437, 519, 534, 572-573, 969-972, 980-981, 1022-1023, 1205 utils/constraints.py 119 37 68.91% 94-103, 144, 149, 153, 164, 190-192, 202, 216-232, 241-252 utils/exceptions.py 35 1 97.14% 93 utils/import_system.py 28 19 32.14% 67-81, 93-103 utils/logging.py 29 1 96.55% 123 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 69, 96, 98, 100, 102, 104, 123, 131-133, 141 utils/rest.py 36 1 97.22% 57 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 143, 159-162, 215-224, 234-264 TOTAL 9534 1624 82.97% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ---------- ------- ------ -------- TOTAL 0 0 +100.00% ``` Results for commit: 0fe60d74d01ee1938ce3ee913c7a52f64605800f _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-ubuntu-latest/coverage.xml --> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports_win/data/rename-to-camelcase/./badge.svg) ## Code Coverage (Windows) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 303 50 83.50% 654-700, 802-853, 857 config.py 356 28 92.13% 81-83, 92, 100, 104-106, 183, 195, 468-470, 534-537, 584-585, 594-595, 674, 708, 743-748, 750 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 752 75 90.03% 373, 598, 642-645, 683, 720, 750, 798, 802-807, 833, 929, 1012, 1149, 1239-1251, 1291, 1293, 1321, 1325-1336, 1349-1355, 1445-1448, 1481-1501, 1557, 1574-1577, 1589-1592, 1614-1621, 1947, 1980-1986, 2253, 2257-2260, 2274-2276, 2311 api/search_result.py 51 4 92.16% 75, 84, 91, 105 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 80, 157, 167, 170-174 api/product/_product.py 187 20 89.30% 70-72, 242-243, 325, 354, 415, 429-432, 445, 469-472, 515-521 api/product/metadata_mapping.py 675 81 88.00% 131-133, 234, 266-267, 313-314, 324-336, 338, 349, 355-367, 412-413, 450, 471-474, 497, 505-506, 582-583, 607-608, 614-617, 632-633, 782, 828, 1002-1007, 1138, 1152-1172, 1192, 1197, 1307, 1329, 1343, 1356-1375, 1414, 1466, 1504-1508, 1527 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 6 1 83.33% 41 plugins/__init__.py 0 0 100.00% plugins/base.py 21 3 85.71% 48, 55, 68 plugins/manager.py 130 12 90.77% 105-110, 160, 201, 223, 227, 253, 292-293 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 91 8 91.21% 150-152, 199-200, 226-228 plugins/apis/usgs.py 180 31 82.78% 132, 234, 268, 304-306, 311, 337-338, 343, 373-380, 391-396, 418-424, 426-432, 455 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 19 0 100.00% plugins/authentication/base.py 17 2 88.24% 34, 47 plugins/authentication/generic.py 14 2 85.71% 40, 50 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 46 4 91.30% 132, 156-161 plugins/authentication/oauth.py 13 7 46.15% 32-34, 38-41 plugins/authentication/openid_connect.py 183 17 90.71% 119, 133-158, 166, 320-323, 347 plugins/authentication/qsauth.py 34 1 97.06% 83 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 88 16 81.82% 79, 107, 109, 131-143, 198-202 plugins/authentication/token_exchange.py 35 19 45.71% 74-80, 92-120 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 40 plugins/crunch/filter_date.py 59 14 76.27% 51-56, 70, 79, 88, 91, 103-105, 114-116, 123 plugins/crunch/filter_latest_intersect.py 47 33 29.79% 48-53, 69-114 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 87 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 72-75, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 30 7 76.67% 58-63, 66-67, 83-87 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 489 163 66.67% 273, 286, 353-356, 370-374, 420-422, 426, 460-461, 467-471, 504, 539, 543, 550, 580-588, 592, 630-638, 645-647, 688-762, 780-841, 852-857, 873-886, 915, 930-932, 935, 945-953, 961-974, 984-1015, 1022-1034, 1075, 1101, 1146-1148, 1368 plugins/download/base.py 253 53 79.05% 145, 180, 250-252, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 538-542, 572-573, 581-598, 605-613, 615-619, 666, 688, 710, 718 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 534 130 75.66% 203-215, 217-218, 253-256, 320-323, 325-326, 333-338, 356-371, 388-390, 402, 450, 457-463, 481, 495, 509, 517-519, 535-540, 551, 569, 611-615, 637, 677, 722, 736-742, 778-842, 860, 893-902, 928-929, 956-961, 967, 970, 986, 1003-1004, 1017, 1034-1035, 1042, 1103-1109, 1164-1165, 1171, 1181, 1217, 1253, 1271-1287, 1313-1315 plugins/download/s3rest.py 116 24 79.31% 121, 157, 164, 199, 229-236, 239-241, 245, 258-264, 272-273, 276-280, 303, 324-327 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 128 14 89.06% 104, 108, 121, 271, 291, 350-351, 371, 374-382, 384 plugins/search/build_search_result.py 181 30 83.43% 97, 141-142, 148, 159, 295-298, 327, 384-401, 463, 466, 476, 493, 513-528 plugins/search/cop_marine.py 197 47 76.14% 55, 63-65, 71-72, 88, 90, 93, 128-130, 142-143, 183-192, 196, 199, 203, 221, 251, 255, 270, 274, 278, 282, 286-290, 296-299, 302-316, 333, 356, 359, 365 plugins/search/creodias_s3.py 55 3 94.55% 56, 74, 108 plugins/search/csw.py 105 81 22.86% 58-59, 63-64, 72-120, 126-139, 147-179, 197-238 plugins/search/data_request_search.py 200 67 66.50% 90-93, 109, 121, 125-126, 137, 142, 147, 154, 167-170, 224-225, 229, 239-245, 250, 276-279, 287-298, 315, 317, 324-325, 327-328, 346-350, 383, 393, 404, 417, 423-438, 443 plugins/search/qssearch.py 664 108 83.73% 391, 395-401, 409-410, 516-522, 572, 575, 588, 598, 617-632, 670-673, 747-748, 796, 815, 830, 888, 909, 912-913, 922-923, 932-933, 942-943, 970, 1041-1046, 1050-1059, 1093, 1115, 1175, 1224, 1298-1302, 1362, 1365, 1371-1372, 1393, 1420-1432, 1439, 1471-1473, 1483-1489, 1519, 1542, 1557, 1579, 1647-1718 plugins/search/static_stac_search.py 72 10 86.11% 101-128, 144, 157 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 22 33.33% 35-37, 44-70 rest/config.py 26 4 84.62% 34-36, 68 rest/constants.py 7 0 100.00% rest/core.py 234 149 36.32% 167-248, 272-326, 339-377, 414-446, 466-482, 505-516, 525-561, 580, 626-665, 704, 711-759 rest/server.py 283 283 0.00% 18-879 rest/stac.py 464 393 15.30% 128-134, 142-167, 198-203, 235, 255-378, 388-457, 475-514, 530-583, 593-616, 626-666, 694-710, 720, 736-739, 758-828, 846-882, 911-936, 944-960, 968-973, 983-1010, 1020-1022, 1030-1032, 1045-1047, 1061-1078, 1088-1109, 1119-1141, 1149-1166, 1189-1212, 1222, 1232-1244, 1257-1279, 1293-1338, 1346-1363, 1373-1531 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 179 15 91.62% 122, 232-236, 269-271, 289, 292, 298, 302, 360, 372-375 rest/types/queryables.py 56 13 76.79% 51-52, 59-60, 67-68, 97-102, 111-112, 174 rest/types/stac_search.py 126 18 85.71% 129-131, 155-156, 161-162, 177, 192-194, 202, 206, 253-258 rest/utils/__init__.py 93 30 67.74% 79-85, 105, 108-109, 128-130, 143, 150, 175-183, 190-211 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 6 73.91% 41, 44-45, 49, 61, 63 types/__init__.py 114 39 65.79% 53, 66-70, 81-93, 120-122, 129-132, 172, 199, 209-225, 230, 232, 253, 258, 266, 276 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 105 types/whoosh.py 15 0 100.00% utils/__init__.py 500 39 92.20% 85, 90, 194-195, 204-231, 234, 248, 330-334, 410-414, 435-437, 519, 534, 572-573, 969-972, 980-981, 1022-1023, 1056, 1205, 1381 utils/constraints.py 119 37 68.91% 94-103, 144, 149, 153, 164, 190-192, 202, 216-232, 241-252 utils/exceptions.py 35 1 97.14% 93 utils/import_system.py 28 19 32.14% 67-81, 93-103 utils/logging.py 29 1 96.55% 123 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 69, 96, 98, 100, 102, 104, 123, 131-133, 141 utils/rest.py 36 1 97.22% 57 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 143, 159-162, 215-224, 234-264 TOTAL 9534 2420 74.62% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ---------- ------- ------ -------- TOTAL 0 0 +100.00% ``` Results for commit: 0fe60d74d01ee1938ce3ee913c7a52f64605800f _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-windows-latest/coverage.xml -->	diff --git a/docs/notebooks/api_user_guide/4_search.ipynb b/docs/notebooks/api_user_guide/4_search.ipynb index c131f344..ac416c2b 100644 --- a/docs/notebooks/api_user_guide/4_search.ipynb +++ b/docs/notebooks/api_user_guide/4_search.ipynb @@ -2140,14 +2140,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### `sortBy`" + "#### `sort_by`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "EO products can be sorted by metadata that the provider used supports as sorting parameters (see the [available_sortables()](../../api_reference/core.rst#eodag.api.core.EODataAccessGateway.available_sortables) method) in the wanted sorting order (`ASC`\|`DESC`) by passing the `sortBy` kwarg. If `sortBy` is not passed but the provider has a default sorting parameter, the sort is realized with it. If the number of sorting parameters exceeds the maximum allowed for the provider or if the provider does not support the sorting feature or at least one sorting parameter, an error is returned." + "EO products can be sorted by metadata that the provider used supports as sorting parameters (see the [available_sortables()](../../api_reference/core.rst#eodag.api.core.EODataAccessGateway.available_sortables) method) in the wanted sorting order (`ASC`\|`DESC`) by passing the `sort_by` kwarg. If `sort_by` is not passed but the provider has a default sorting parameter, the sort is realized with it. If the number of sorting parameters exceeds the maximum allowed for the provider or if the provider does not support the sorting feature or at least one sorting parameter, an error is returned." ] }, { @@ -2183,7 +2183,7 @@ ")\n", "sorted_by_start_date_in_desc_order_products = dag.search(\n", " provider=\"cop_dataspace\",\n", - " sortBy=[(\"startTimeFromAscendingNode\", \"DESC\")],\n", + " sort_by=[(\"startTimeFromAscendingNode\", \"DESC\")],\n", " default_search_criteria\n", ")\n", "print(\n", diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index d4ecf61f..d15407b2 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -128,13 +128,14 @@ class UsgsApi(Api): raise NoMatchingProductType( "Cannot search on USGS without productType specified" ) - if kwargs.get("sortBy"): + if kwargs.get("sort_by"): raise ValidationError("USGS does not support sorting feature") self.authenticate() product_type_def_params = self.config.products.get( # type: ignore - product_type, self.config.products[GENERIC_PRODUCT_TYPE] # type: ignore + product_type, + self.config.products[GENERIC_PRODUCT_TYPE], # type: ignore ) usgs_dataset = format_dict_items(product_type_def_params, kwargs)["dataset"] start_date = kwargs.pop("startTimeFromAscendingNode", None) @@ -288,7 +289,8 @@ class UsgsApi(Api): outputs_extension = cast( str, self.config.products.get( # type: ignore - product.product_type, self.config.products[GENERIC_PRODUCT_TYPE] # type: ignore + product.product_type, + self.config.products[GENERIC_PRODUCT_TYPE], # type: ignore ).get("outputs_extension", ".tar.gz"), ) kwargs["outputs_extension"] = kwargs.get("outputs_extension", outputs_extension) diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 51bd67a3..e7cd1510 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -122,7 +122,7 @@ class HTTPDownload(Download): def __init__(self, provider: str, config: PluginConfig) -> None: super(HTTPDownload, self).__init__(provider, config) - def orderDownload( + def order_download( self, product: EOProduct, auth: Optional[AuthBase] = None, @@ -257,7 +257,7 @@ class HTTPDownload(Download): return json_response - def orderDownloadStatus( + def order_download_status( self, product: EOProduct, auth: Optional[AuthBase] = None, @@ -934,13 +934,13 @@ class HTTPDownload(Download): and product.properties.get("storageStatus") == OFFLINE_STATUS and not product.properties.get("orderStatus") ): - self.orderDownload(product=product, auth=auth) + self.order_download(product=product, auth=auth) if ( product.properties.get("orderStatusLink", None) and product.properties.get("storageStatus") != ONLINE_STATUS ): - self.orderDownloadStatus(product=product, auth=auth) + self.order_download_status(product=product, auth=auth) params = kwargs.pop("dl_url_params", None) or getattr( self.config, "dl_url_params", {} @@ -1083,7 +1083,6 @@ class HTTPDownload(Download): # loop for assets download for asset in assets_values: - if not asset["href"] or asset["href"].startswith("file:"): logger.info( f"Local asset detected. Download skipped for {asset['href']}" diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index f3349661..39517b20 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -133,7 +133,7 @@ class S3RestDownload(Download): and "storageStatus" in product.properties and product.properties["storageStatus"] != ONLINE_STATUS ): - self.http_download_plugin.orderDownload(product=product, auth=auth) + self.http_download_plugin.order_download(product=product, auth=auth) @self._download_retry(product, wait, timeout) def download_request( @@ -145,7 +145,7 @@ class S3RestDownload(Download): ): # check order status if product.properties.get("orderStatusLink", None): - self.http_download_plugin.orderDownloadStatus( + self.http_download_plugin.order_download_status( product=product, auth=auth ) diff --git a/eodag/plugins/search/base.py b/eodag/plugins/search/base.py index d0318a88..1ac9dd00 100644 --- a/eodag/plugins/search/base.py +++ b/eodag/plugins/search/base.py @@ -206,16 +206,16 @@ class Search(PluginTopic): return self.config.metadata_mapping def get_sort_by_arg(self, kwargs: Dict[str, Any]) -> Optional[SortByList]: - """Extract the "sortBy" argument from the kwargs or the provider default sort configuration + """Extract the "sort_by" argument from the kwargs or the provider default sort configuration :param kwargs: Search arguments :type kwargs: Dict[str, Any] - :returns: The "sortBy" argument from the kwargs or the provider default sort configuration + :returns: The "sort_by" argument from the kwargs or the provider default sort configuration :rtype: :class:`~eodag.types.search_args.SortByList` """ - # remove "sortBy" from search args if exists because it is not part of metadata mapping, + # remove "sort_by" from search args if exists because it is not part of metadata mapping, # it will complete the query string or body once metadata mapping will be done - sort_by_arg_tmp = kwargs.pop("sortBy", None) + sort_by_arg_tmp = kwargs.pop("sort_by", None) sort_by_arg = sort_by_arg_tmp or getattr(self.config, "sort", {}).get( "sort_by_default", None ) @@ -230,11 +230,11 @@ class Search(PluginTopic): self, sort_by_arg: SortByList ) -> Tuple[str, Dict[str, List[Dict[str, str]]]]: """Build the sorting part of the query string or body by transforming - the "sortBy" argument into a provider-specific string or dictionnary + the "sort_by" argument into a provider-specific string or dictionnary - :param sort_by_arg: the "sortBy" argument in EODAG format + :param sort_by_arg: the "sort_by" argument in EODAG format :type sort_by_arg: :class:`~eodag.types.search_args.SortByList` - :returns: The "sortBy" argument in provider-specific format + :returns: The "sort_by" argument in provider-specific format :rtype: Union[str, Dict[str, List[Dict[str, str]]]] """ if not hasattr(self.config, "sort"): diff --git a/eodag/plugins/search/data_request_search.py b/eodag/plugins/search/data_request_search.py index 2c990b1f..1ba34563 100644 --- a/eodag/plugins/search/data_request_search.py +++ b/eodag/plugins/search/data_request_search.py @@ -133,7 +133,7 @@ class DataRequestSearch(Search): """ performs the search for a provider where several steps are required to fetch the data """ - if kwargs.get("sortBy"): + if kwargs.get("sort_by"): raise ValidationError(f"{self.provider} does not support sorting feature") product_type = kwargs.get("productType", None) diff --git a/eodag/rest/core.py b/eodag/rest/core.py index 656fb593..6fb159ed 100644 --- a/eodag/rest/core.py +++ b/eodag/rest/core.py @@ -350,11 +350,13 @@ def _order_and_update( if ( product.properties.get("storageStatus") != ONLINE_STATUS and NOT_AVAILABLE in product.properties.get("orderStatusLink", "") - and hasattr(product.downloader, "orderDownload") + and hasattr(product.downloader, "order_download") ): # first order logger.debug("Order product") - order_status_dict = product.downloader.orderDownload(product=product, auth=auth) + order_status_dict = product.downloader.order_download( + product=product, auth=auth + ) query_args.update(order_status_dict or {}) if ( @@ -365,11 +367,11 @@ def _order_and_update( product.properties["storageStatus"] = STAGING_STATUS if product.properties.get("storageStatus") == STAGING_STATUS and hasattr( - product.downloader, "orderDownloadStatus" + product.downloader, "order_download_status" ): # check order status if needed logger.debug("Checking product order status") - product.downloader.orderDownloadStatus(product=product, auth=auth) + product.downloader.order_download_status(product=product, auth=auth) if product.properties.get("storageStatus") != ONLINE_STATUS: raise NotAvailableError("Product is not available yet") diff --git a/eodag/rest/types/eodag_search.py b/eodag/rest/types/eodag_search.py index 2557d83b..38089cbf 100644 --- a/eodag/rest/types/eodag_search.py +++ b/eodag/rest/types/eodag_search.py @@ -110,7 +110,7 @@ class EODAGSearch(BaseModel): illuminationAzimuthAngle: Optional[float] = Field(None, alias="view:sun_azimuth") page: Optional[int] = Field(1) items_per_page: int = Field(DEFAULT_ITEMS_PER_PAGE, alias="limit") - sortBy: Optional[List[Tuple[str, str]]] = Field(None, alias="sortby") + sort_by: Optional[List[Tuple[str, str]]] = Field(None, alias="sortby") raise_errors: bool = False _to_eodag_map: Dict[str, str] @@ -311,14 +311,14 @@ class EODAGSearch(BaseModel): return ",".join(v) return v - @field_validator("sortBy", mode="before") + @field_validator("sort_by", mode="before") @classmethod def parse_sortby( cls, sortby_post_params: List[Dict[str, str]], ) -> List[Tuple[str, str]]: """ - Convert STAC POST sortby to EODAG sortby + Convert STAC POST sortby to EODAG sort_by """ special_fields = { "start": "startTimeFromAscendingNode", diff --git a/eodag/rest/types/stac_search.py b/eodag/rest/types/stac_search.py index 6f04da23..2675bacb 100644 --- a/eodag/rest/types/stac_search.py +++ b/eodag/rest/types/stac_search.py @@ -16,6 +16,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """Model describing a STAC search POST request""" + from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union @@ -71,7 +72,7 @@ Geometry = Union[ Direction = Annotated[Literal["asc", "desc"], StringConstraints(min_length=1)] -class Sortby(BaseModel): +class SortBy(BaseModel): """ A class representing a parameter with which we want to sort results and its sorting order in a POST search @@ -117,7 +118,7 @@ class SearchPostRequest(BaseModel): description="The language used for filtering.", validate_default=True, ) - sortby: Optional[List[Sortby]] = None + sortby: Optional[List[SortBy]] = None crunch: Optional[str] = None @field_serializer("intersects") @@ -259,16 +260,16 @@ class SearchPostRequest(BaseModel): def sortby2list( v: Optional[str], -) -> Optional[List[Sortby]]: +) -> Optional[List[SortBy]]: """ Convert sortby filter parameter GET syntax to POST syntax """ if not v: return None - sortby: List[Sortby] = [] + sortby: List[SortBy] = [] for sortby_param in v.split(","): sortby_param = sortby_param.strip() direction: Direction = "desc" if sortby_param.startswith("-") else "asc" field = sortby_param.lstrip("+-") - sortby.append(Sortby(field=field, direction=direction)) + sortby.append(SortBy(field=field, direction=direction)) return sortby diff --git a/eodag/types/search_args.py b/eodag/types/search_args.py index e3f5510c..0ebc6e7d 100644 --- a/eodag/types/search_args.py +++ b/eodag/types/search_args.py @@ -51,7 +51,7 @@ class SearchArgs(BaseModel): locations: Optional[Dict[str, str]] = Field(None) page: Optional[int] = Field(DEFAULT_PAGE, gt=0) # type: ignore items_per_page: Optional[PositiveInt] = Field(DEFAULT_ITEMS_PER_PAGE) # type: ignore - sortBy: Optional[SortByList] = Field(None) # type: ignore + sort_by: Optional[SortByList] = Field(None) # type: ignore @field_validator("start", "end", mode="before") @classmethod @@ -87,16 +87,17 @@ class SearchArgs(BaseModel): raise TypeError(f"Invalid geometry type: {type(v)}") - @field_validator("sortBy", mode="before") + @field_validator("sort_by", mode="before") @classmethod def check_sort_by_arg( - cls, sort_by_arg: Optional[SortByList] # type: ignore + cls, + sort_by_arg: Optional[SortByList], # type: ignore ) -> Optional[SortByList]: # type: ignore - """Check if the sortBy argument is correct + """Check if the sort_by argument is correct - :param sort_by_arg: The sortBy argument + :param sort_by_arg: The sort_by argument :type sort_by_arg: str - :returns: The sortBy argument with sorting order parsed (whitespace(s) are + :returns: The sort_by argument with sorting order parsed (whitespace(s) are removed and only the 3 first letters in uppercase are kept) :rtype: str """	rename camelCase parameters and methods Rename: - `sortBy` to `sortby` - `orderDownload` to `order_download` - `orderDownloadStatus` to `order_download_status`	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 9fc9cdd9..63749da5 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -2669,7 +2669,7 @@ class TestCoreSearch(TestCoreBase): mock_postjsonsearch__request, ): """search must sort results by sorting parameter(s) in their sorting order - from the "sortBy" argument or by default sorting parameter if exists""" + from the "sort_by" argument or by default sorting parameter if exists""" mock_qssearch__request.return_value.json.return_value = { "properties": {"totalResults": 2}, "features": [], @@ -2720,7 +2720,7 @@ class TestCoreSearch(TestCoreBase): dag.search( provider="dummy_provider", productType="S2_MSI_L1C", - sortBy=[("eodagSortParam", "DESC")], + sort_by=[("eodagSortParam", "DESC")], ) # a provider-specific string has been created to sort by @@ -2739,7 +2739,7 @@ class TestCoreSearch(TestCoreBase): next_page_query_obj: '{{"limit":{items_per_page},"page":{page}}}' total_items_nb_key_path: '$.meta.found' sort: - sort_by_tpl: '{{"sortby": [ {{"field": "{sort_param}", "direction": "{sort_order}" }} ] }}' + sort_by_tpl: '{{"sort_by": [ {{"field": "{sort_param}", "direction": "{sort_order}" }} ] }}' sort_param_mapping: eodagSortParam: providerSortParam sort_order_mapping: @@ -2755,22 +2755,22 @@ class TestCoreSearch(TestCoreBase): dag.search( provider="other_dummy_provider", productType="S2_MSI_L1C", - sortBy=[("eodagSortParam", "DESC")], + sort_by=[("eodagSortParam", "DESC")], ) # a provider-specific dictionnary has been created to sort by self.assertIn( - "sortby", mock_postjsonsearch__request.call_args[0][1].query_params.keys() + "sort_by", mock_postjsonsearch__request.call_args[0][1].query_params.keys() ) self.assertEqual( [{"field": "providerSortParam", "direction": "desc"}], - mock_postjsonsearch__request.call_args[0][1].query_params["sortby"], + mock_postjsonsearch__request.call_args[0][1].query_params["sort_by"], ) # TODO: sort by default sorting parameter and sorting order def test_search_sort_by_raise_errors(self): - """search used with "sortBy" argument must raise errors if the argument is incorrect or if the provider does + """search used with "sort_by" argument must raise errors if the argument is incorrect or if the provider does not support a maximum number of sorting parameter, one sorting parameter or the sorting feature """ dag = EODataAccessGateway() @@ -2794,7 +2794,7 @@ class TestCoreSearch(TestCoreBase): dag.search( provider="dummy_provider", productType="S2_MSI_L1C", - sortBy=[("eodagSortParam", "ASC")], + sort_by=[("eodagSortParam", "ASC")], ) self.assertIn( "dummy_provider does not support sorting feature", str(cm_logs.output) @@ -2827,7 +2827,7 @@ class TestCoreSearch(TestCoreBase): dag.search( provider="dummy_provider", productType="S2_MSI_L1C", - sortBy=[("otherEodagSortParam", "ASC")], + sort_by=[("otherEodagSortParam", "ASC")], ) self.assertIn( "\\'otherEodagSortParam\\' parameter is not sortable with dummy_provider. " @@ -2864,7 +2864,7 @@ class TestCoreSearch(TestCoreBase): dag.search( provider="dummy_provider", productType="S2_MSI_L1C", - sortBy=[("eodagSortParam", "ASC"), ("otherEodagSortParam", "ASC")], + sort_by=[("eodagSortParam", "ASC"), ("otherEodagSortParam", "ASC")], ) self.assertIn( "Search results can be sorted by only 1 parameter(s) with dummy_provider", diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index e8e0574e..2e4f49cb 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -1122,7 +1122,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): @mock.patch("eodag.plugins.download.http.requests.request", autospec=True) def test_plugins_download_http_order_get(self, mock_request): - """HTTPDownload.orderDownload() must request using orderLink and GET protocol""" + """HTTPDownload.order_download() must request using orderLink and GET protocol""" plugin = self.get_download_plugin(self.product) self.product.properties["orderLink"] = "http://somewhere/order" self.product.properties["storageStatus"] = OFFLINE_STATUS @@ -1135,7 +1135,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): auth_plugin.config.credentials = {"username": "foo", "password": "bar"} auth = auth_plugin.authenticate() - plugin.orderDownload(self.product, auth=auth) + plugin.order_download(self.product, auth=auth) mock_request.assert_called_once_with( method="GET", @@ -1153,7 +1153,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): @mock.patch("eodag.plugins.download.http.requests.request", autospec=True) def test_plugins_download_http_order_post(self, mock_request): - """HTTPDownload.orderDownload() must request using orderLink and POST protocol""" + """HTTPDownload.order_download() must request using orderLink and POST protocol""" plugin = self.get_download_plugin(self.product) self.product.properties["storageStatus"] = OFFLINE_STATUS plugin.config.order_method = "POST" @@ -1164,7 +1164,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): # orderLink without query query args self.product.properties["orderLink"] = "http://somewhere/order" - plugin.orderDownload(self.product, auth=auth) + plugin.order_download(self.product, auth=auth) mock_request.assert_called_once_with( method="POST", url=self.product.properties["orderLink"], @@ -1176,7 +1176,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): # orderLink with query query args mock_request.reset_mock() self.product.properties["orderLink"] = "http://somewhere/order?foo=bar" - plugin.orderDownload(self.product, auth=auth) + plugin.order_download(self.product, auth=auth) mock_request.assert_called_once_with( method="POST", url="http://somewhere/order", @@ -1192,7 +1192,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): self.product.properties[ "orderLink" ] = 'http://somewhere/order?{"location": "dataset_id=lorem&data_version=202211", "cacheable": "true"}' - plugin.orderDownload(self.product, auth=auth) + plugin.order_download(self.product, auth=auth) mock_request.assert_called_once_with( method="POST", url="http://somewhere/order", @@ -1207,7 +1207,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): ) def test_plugins_download_http_order_status(self): - """HTTPDownload.orderDownloadStatus() must request status using orderStatusLink""" + """HTTPDownload.order_download_status() must request status using orderStatusLink""" plugin = self.get_download_plugin(self.product) plugin.config.order_status = { "metadata_mapping": { @@ -1233,7 +1233,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): ) with self.assertRaises(DownloadError): - plugin.orderDownloadStatus(self.product, auth=auth) + plugin.order_download_status(self.product, auth=auth) self.assertIn( list(USER_AGENT.items())[0], responses.calls[0].request.headers.items() @@ -1243,7 +1243,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): run() def test_plugins_download_http_order_status_search_again(self): - """HTTPDownload.orderDownloadStatus() must search again after success if needed""" + """HTTPDownload.order_download_status() must search again after success if needed""" plugin = self.get_download_plugin(self.product) plugin.config.order_status = { "metadata_mapping": {"status": "$.json.status"}, @@ -1284,7 +1284,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): ), ) - plugin.orderDownloadStatus(self.product, auth=auth) + plugin.order_download_status(self.product, auth=auth) self.assertEqual( self.product.properties["downloadLink"], "http://new-download-link" @@ -1793,9 +1793,11 @@ class TestDownloadPluginS3Rest(BaseDownloadPluginTest): ) @mock.patch( - "eodag.plugins.download.http.HTTPDownload.orderDownloadStatus", autospec=True + "eodag.plugins.download.http.HTTPDownload.order_download_status", autospec=True + ) + @mock.patch( + "eodag.plugins.download.http.HTTPDownload.order_download", autospec=True ) - @mock.patch("eodag.plugins.download.http.HTTPDownload.orderDownload", autospec=True) def test_plugins_download_s3rest_online(self, mock_order, mock_order_status): """S3RestDownload.download() must create outputfiles""" @@ -1863,9 +1865,11 @@ class TestDownloadPluginS3Rest(BaseDownloadPluginTest): mock_order_status.assert_not_called() @mock.patch( - "eodag.plugins.download.http.HTTPDownload.orderDownloadStatus", autospec=True + "eodag.plugins.download.http.HTTPDownload.order_download_status", autospec=True + ) + @mock.patch( + "eodag.plugins.download.http.HTTPDownload.order_download", autospec=True ) - @mock.patch("eodag.plugins.download.http.HTTPDownload.orderDownload", autospec=True) def test_plugins_download_s3rest_offline(self, mock_order, mock_order_status): """S3RestDownload.download() must order offline products""" diff --git a/tests/units/test_http_server.py b/tests/units/test_http_server.py index f47acbdb..92343125 100644 --- a/tests/units/test_http_server.py +++ b/tests/units/test_http_server.py @@ -1242,8 +1242,8 @@ class RequestTestCase(unittest.TestCase): product = two_results[0] mock_search.return_value = SearchResult([product], 1) product.downloader_auth = MagicMock() - product.downloader.orderDownload = MagicMock(return_value={"status": "foo"}) - product.downloader.orderDownloadStatus = MagicMock() + product.downloader.order_download = MagicMock(return_value={"status": "foo"}) + product.downloader.order_download_status = MagicMock() product.downloader.order_response_process = MagicMock() product.downloader._stream_download_dict = MagicMock( side_effect=NotAvailableError("Product offline. Try again later.") @@ -1257,8 +1257,8 @@ class RequestTestCase(unittest.TestCase): self._request_not_found( f"catalogs/{self.tested_product_type}/items/foo/download" ) - product.downloader.orderDownload.assert_not_called() - product.downloader.orderDownloadStatus.assert_not_called() + product.downloader.order_download.assert_not_called() + product.downloader.order_download_status.assert_not_called() product.downloader.order_response_process.assert_not_called() product.downloader._stream_download_dict.assert_called_once() product.downloader._stream_download_dict.reset_mock() @@ -1269,9 +1269,9 @@ class RequestTestCase(unittest.TestCase): resp_json = self._request_accepted( f"catalogs/{self.tested_product_type}/items/foo/download" ) - product.downloader.orderDownload.assert_called_once() - product.downloader.orderDownload.reset_mock() - product.downloader.orderDownloadStatus.assert_not_called() + product.downloader.order_download.assert_called_once() + product.downloader.order_download.reset_mock() + product.downloader.order_download_status.assert_not_called() product.downloader.order_response_process.assert_called() product.downloader.order_response_process.reset_mock() product.downloader._stream_download_dict.assert_not_called() @@ -1284,8 +1284,8 @@ class RequestTestCase(unittest.TestCase): resp_json = self._request_accepted( f"catalogs/{self.tested_product_type}/items/foo/download" ) - product.downloader.orderDownload.assert_not_called() - product.downloader.orderDownloadStatus.assert_not_called() + product.downloader.order_download.assert_not_called() + product.downloader.order_download_status.assert_not_called() product.downloader.order_response_process.assert_not_called() product.downloader._stream_download_dict.assert_called_once() product.downloader._stream_download_dict.reset_mock() @@ -1298,9 +1298,9 @@ class RequestTestCase(unittest.TestCase): self._request_accepted( f"catalogs/{self.tested_product_type}/items/foo/download" ) - product.downloader.orderDownload.assert_not_called() - product.downloader.orderDownloadStatus.assert_called_once() - product.downloader.orderDownloadStatus.reset_mock() + product.downloader.order_download.assert_not_called() + product.downloader.order_download_status.assert_called_once() + product.downloader.order_download_status.reset_mock() product.downloader.order_response_process.assert_called() product.downloader.order_response_process.reset_mock() product.downloader._stream_download_dict.assert_not_called() diff --git a/tests/units/test_search_types.py b/tests/units/test_search_types.py index 747abd83..1453424a 100644 --- a/tests/units/test_search_types.py +++ b/tests/units/test_search_types.py @@ -26,24 +26,27 @@ from eodag.utils.exceptions import ValidationError as EodagValidationError class TestStacSearch(unittest.TestCase): def test_search_sort_by_arg(self): - """search used with "sortBy" argument must not raise errors if the argument is correct""" - # "sortBy" argument must be a list of tuples of two elements and the second element must be "ASC" or "DESC" + """search used with "sort_by" argument must not raise errors if the argument is correct""" + # "sort_by" argument must be a list of tuples of two elements and the second element must be "ASC" or "DESC" search_args.SearchArgs.model_validate( - {"productType": "dummy_product_type", "sortBy": [("eodagSortParam", "ASC")]} + { + "productType": "dummy_product_type", + "sort_by": [("eodagSortParam", "ASC")], + } ) search_args.SearchArgs.model_validate( { "productType": "dummy_product_type", - "sortBy": [("eodagSortParam", "DESC")], + "sort_by": [("eodagSortParam", "DESC")], } ) def test_search_sort_by_arg_with_errors(self): - """search used with "sortBy" argument must raise errors if the argument is incorrect""" + """search used with "sort_by" argument must raise errors if the argument is incorrect""" # raise a Pydantic error with an empty list with self.assertRaises(ValidationError) as context: search_args.SearchArgs.model_validate( - {"productType": "dummy_product_type", "sortBy": []} + {"productType": "dummy_product_type", "sort_by": []} ) self.assertIn( "List should have at least 1 item after validation, not 0", @@ -52,7 +55,7 @@ class TestStacSearch(unittest.TestCase): # raise a Pydantic error with syntax errors with self.assertRaises(ValidationError) as context: search_args.SearchArgs.model_validate( - {"productType": "dummy_product_type", "sortBy": "eodagSortParam ASC"} + {"productType": "dummy_product_type", "sort_by": "eodagSortParam ASC"} ) self.assertIn( "Sort argument must be a list of tuple(s), got a '<class 'str'>' instead", @@ -60,7 +63,7 @@ class TestStacSearch(unittest.TestCase): ) with self.assertRaises(ValidationError) as context: search_args.SearchArgs.model_validate( - {"productType": "dummy_product_type", "sortBy": ["eodagSortParam ASC"]} + {"productType": "dummy_product_type", "sort_by": ["eodagSortParam ASC"]} ) self.assertIn( "Sort argument must be a list of tuple(s), got a list of '<class 'str'>' instead", @@ -71,7 +74,7 @@ class TestStacSearch(unittest.TestCase): search_args.SearchArgs.model_validate( { "productType": "dummy_product_type", - "sortBy": [("eodagSortParam", " wrong_order ")], + "sort_by": [("eodagSortParam", " wrong_order ")], } ) self.assertIn( @@ -84,7 +87,7 @@ class TestStacSearch(unittest.TestCase): search_args.SearchArgs.model_validate( { "productType": "dummy_product_type", - "sortBy": [("eodagSortParam", "ASC"), ("eodagSortParam", "DESC")], + "sort_by": [("eodagSortParam", "ASC"), ("eodagSortParam", "DESC")], } ) self.assertIn( diff --git a/tests/units/test_stac_types.py b/tests/units/test_stac_types.py index fa011285..fb056ad7 100644 --- a/tests/units/test_stac_types.py +++ b/tests/units/test_stac_types.py @@ -9,18 +9,18 @@ from eodag.rest.types import eodag_search, stac_search class TestStacSearch(unittest.TestCase): def test_sortby(self): # Test with valid field and direction - sortby = stac_search.Sortby(field="test", direction="asc") + sortby = stac_search.SortBy(field="test", direction="asc") self.assertEqual(sortby.field, "test") self.assertEqual(sortby.direction, "asc") # Test with invalid direction with self.assertRaises(ValidationError) as context: - stac_search.Sortby(field="test", direction="invalid") + stac_search.SortBy(field="test", direction="invalid") self.assertTrue("Input should be 'asc' or 'desc'" in str(context.exception)) # Test with empty field with self.assertRaises(ValidationError) as context: - stac_search.Sortby(field="", direction="asc") + stac_search.SortBy(field="", direction="asc") def test_sortby2list(self): # Test with no input @@ -28,6 +28,7 @@ class TestStacSearch(unittest.TestCase): # Test with valid input sortby_list = stac_search.sortby2list("test,+test2,-test3") + self.assertIsNotNone(sortby_list) self.assertEqual(len(sortby_list), 3) self.assertEqual(sortby_list[0].field, "test") self.assertEqual(sortby_list[0].direction, "asc") @@ -404,7 +405,7 @@ class TestEODAGSearch(unittest.TestCase): exclude_none=True ), { - "sortBy": [("test", "desc")], + "sort_by": [("test", "desc")], "productType": "test_collection", "items_per_page": 20, "page": 1,	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 0, "test_score": 3 }, "num_modified_files": 10 }	3.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.23 boto3-stubs==1.37.23 botocore==1.37.23 botocore-stubs==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@97b7b6f881714b4c40029a174c2539085cd50868#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy==1.15.0 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.16 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.0 mypy-boto3-sqs==1.37.0 mypy-extensions==1.0.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.24.2 types-cachetools==5.5.0.20240820 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-python-dateutil==2.9.0.20241206 types-PyYAML==6.0.12.20250326 types-requests==2.31.0.6 types-s3transfer==0.11.4 types-setuptools==78.1.0.20250329 types-tqdm==4.67.0.20250319 types-urllib3==1.26.25.14 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.23 - boto3-stubs==1.37.23 - botocore==1.37.23 - botocore-stubs==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==3.0.0b3.dev19+g97b7b6f8 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy==1.15.0 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.16 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.0 - mypy-boto3-sqs==1.37.0 - mypy-extensions==1.0.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.24.2 - types-cachetools==5.5.0.20240820 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-python-dateutil==2.9.0.20241206 - types-pyyaml==6.0.12.20250326 - types-requests==2.31.0.6 - types-s3transfer==0.11.4 - types-setuptools==78.1.0.20250329 - types-tqdm==4.67.0.20250319 - types-urllib3==1.26.25.14 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCoreSearch::test_search_sort_by", "tests/units/test_core.py::TestCoreSearch::test_search_sort_by_raise_errors", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status_search_again", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_offline", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_online", "tests/units/test_http_server.py::RequestTestCase::test_download_offline_item_from_catalog", "tests/units/test_search_types.py::TestStacSearch::test_search_sort_by_arg_with_errors", "tests/units/test_stac_types.py::TestStacSearch::test_sortby", "tests/units/test_stac_types.py::TestEODAGSearch::test_convert_stac_to_eodag_sortby" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/units/test_core.py::TestCore::test_available_sortables", "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_guess_product_type_with_filter", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_list_queryables", "tests/units/test_core.py::TestCore::test_list_queryables_with_constraints", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_skipped_plugin", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_read_only_home_dir", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available_with_alias", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreDownload::test_download_local_product", "tests/units/test_core.py::TestCoreProductAlias::test_get_alias_from_product_type", "tests/units/test_core.py::TestCoreProductAlias::test_get_product_type_from_alias", "tests/units/test_core.py::TestCoreProviderGroup::test_available_providers_by_group", "tests/units/test_core.py::TestCoreProviderGroup::test_get_search_plugins", "tests/units/test_core.py::TestCoreProviderGroup::test_list_product_types", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_asset_filter", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_resume", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_file_without_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets_without_ssl", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_nonzip_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_download_cop_ads", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_tar_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_zip_file_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets", "tests/units/test_download_plugins.py::TestDownloadPluginCreodiasS3::test_plugins_download_creodias_s3", "tests/units/test_http_server.py::RequestTestCase::test_assets_alt_url_blacklist", "tests/units/test_http_server.py::RequestTestCase::test_auth_error", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse_date_search", "tests/units/test_http_server.py::RequestTestCase::test_cloud_cover_post_search", "tests/units/test_http_server.py::RequestTestCase::test_collection", "tests/units/test_http_server.py::RequestTestCase::test_collection_free_text_search", "tests/units/test_http_server.py::RequestTestCase::test_conformance", "tests/units/test_http_server.py::RequestTestCase::test_cql_post_search", "tests/units/test_http_server.py::RequestTestCase::test_date_post_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_catalog_items", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_catalog_items_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_items", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_catalog_stream", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_collection_no_stream", "tests/units/test_http_server.py::RequestTestCase::test_filter", "tests/units/test_http_server.py::RequestTestCase::test_forward", "tests/units/test_http_server.py::RequestTestCase::test_ids_post_search", "tests/units/test_http_server.py::RequestTestCase::test_intersects_post_search", "tests/units/test_http_server.py::RequestTestCase::test_items_response", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_nok", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_ok", "tests/units/test_http_server.py::RequestTestCase::test_not_found", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_error", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_from_constraints", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_queryables", "tests/units/test_http_server.py::RequestTestCase::test_queryables_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_queryables_with_provider_error", "tests/units/test_http_server.py::RequestTestCase::test_request_params", "tests/units/test_http_server.py::RequestTestCase::test_root", "tests/units/test_http_server.py::RequestTestCase::test_route", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_collection", "tests/units/test_http_server.py::RequestTestCase::test_search_provider_in_downloadlink", "tests/units/test_http_server.py::RequestTestCase::test_search_response_contains_pagination_info", "tests/units/test_http_server.py::RequestTestCase::test_service_desc", "tests/units/test_http_server.py::RequestTestCase::test_service_doc", "tests/units/test_http_server.py::RequestTestCase::test_stac_extension_oseo", "tests/units/test_http_server.py::RequestTestCase::test_stac_queryables_type", "tests/units/test_http_server.py::RequestTestCase::test_timeout_error", "tests/units/test_search_types.py::TestStacSearch::test_search_sort_by_arg", "tests/units/test_stac_types.py::TestStacSearch::test_sortby2list", "tests/units/test_stac_types.py::TestSearchPostRequest::test_check_filter_lang", "tests/units/test_stac_types.py::TestSearchPostRequest::test_limit", "tests/units/test_stac_types.py::TestSearchPostRequest::test_page", "tests/units/test_stac_types.py::TestSearchPostRequest::test_str_to_str_list", "tests/units/test_stac_types.py::TestSearchPostRequest::test_validate_bbox", "tests/units/test_stac_types.py::TestSearchPostRequest::test_validate_datetime", "tests/units/test_stac_types.py::TestSearchPostRequest::test_validate_spatial", "tests/units/test_stac_types.py::TestEODAGSearch::test_alias_to_property", "tests/units/test_stac_types.py::TestEODAGSearch::test_assemble_geom", "tests/units/test_stac_types.py::TestEODAGSearch::test_cleanup_dates", "tests/units/test_stac_types.py::TestEODAGSearch::test_convert_collections_to_product_type", "tests/units/test_stac_types.py::TestEODAGSearch::test_convert_query_to_dict", "tests/units/test_stac_types.py::TestEODAGSearch::test_join_instruments", "tests/units/test_stac_types.py::TestEODAGSearch::test_parse_cql", "tests/units/test_stac_types.py::TestEODAGSearch::test_remove_custom_extensions", "tests/units/test_stac_types.py::TestEODAGSearch::test_remove_keys", "tests/units/test_stac_types.py::TestEODAGSearch::test_verify_producttype_is_present" ]	[]	Apache License 2.0	null
CS-SI__eodag-1338	1a1a40f4c4ec7d7672aa917fa0d6a28326df25da	2024-10-10 12:51:06	2473ac169a3d05f04f83d4f227c2fb63689ae2d6	github-actions[bot]: ## Test Results 4 files ±0 4 suites ±0 6m 6s :stopwatch: -1s 567 tests +2 564 :white_check_mark: +2 3 :zzz: ±0 0 :x: ±0 2 268 runs +8 2 172 :white_check_mark: +6 96 :zzz: +2 0 :x: ±0 Results for commit f2bfdcc0. ± Comparison against base commit 1a1a40f4. [test-results]:data:application/gzip;base64,H4sIANzOB2cC/03MTQ6DIBCG4asY1l3giEPpZRoYICH1p0FYmd69aJW6fJ/JfCvzYXALezTi1rAlh1TD5qhTmKeSHWKBckrbsUd51nPJRDuJP73Ce/up4HUYCvAKLsY5HhLztG0C4P2ocxNaCZV+mwpPuGzufZ2keRxDKsE8GG+JuFCoySFvveAapDHCdsoiKGtd10tiny/ERgdHCAEAAA== github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports/data/1337-raise-error-when-request-to-backend-fails/./badge.svg) ## Code Coverage (Ubuntu) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 314 59 81.21% 657-716, 818-869, 873 config.py 384 27 92.97% 83-85, 94, 102, 106-108, 179, 190, 581-583, 696-699, 742-743, 752-753, 858, 921-926, 928 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 767 69 91.00% 363, 653, 697-700, 738, 782, 816, 861-866, 892, 983, 1051, 1189, 1274-1286, 1322, 1324, 1352, 1356-1367, 1380-1386, 1469-1472, 1505-1525, 1577, 1594-1598, 1610-1613, 1969, 1993-1999, 2250, 2254-2258, 2267-2269, 2301 api/search_result.py 59 4 93.22% 83, 92, 99, 113 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 75, 147, 155, 158-162 api/product/_product.py 187 20 89.30% 70-72, 237-238, 313, 342, 399, 413-416, 429, 453-456, 499-505 api/product/metadata_mapping.py 680 68 90.00% 130-132, 227, 259-260, 306-307, 317-329, 331, 342, 348-360, 405-406, 443, 464-467, 490, 498-499, 575-576, 600-601, 607-610, 625-626, 775, 821, 992-997, 1124, 1138-1158, 1178, 1183, 1312, 1326, 1397, 1449, 1489-1493, 1508 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 6 1 83.33% 38 plugins/__init__.py 0 0 100.00% plugins/base.py 21 2 90.48% 48, 55 plugins/manager.py 173 15 91.33% 116-121, 171, 209, 231, 235, 259, 281, 390-393, 405-406 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 91 8 91.21% 149-151, 198-199, 225-227 plugins/apis/usgs.py 180 31 82.78% 132, 234, 268, 303-305, 310, 336-337, 342, 372-379, 390-395, 417-423, 425-431, 454 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 19 0 100.00% plugins/authentication/base.py 17 2 88.24% 43, 56 plugins/authentication/generic.py 14 2 85.71% 40, 50 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 48 4 91.67% 134, 160-165 plugins/authentication/oauth.py 13 7 46.15% 32-34, 38-41 plugins/authentication/openid_connect.py 187 17 90.91% 114, 128-154, 162, 316-319, 345 plugins/authentication/qsauth.py 34 1 97.06% 83 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 89 16 82.02% 79, 108, 110, 133-146, 202-206 plugins/authentication/token_exchange.py 36 20 44.44% 74-80, 92-122 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 40 plugins/crunch/filter_date.py 59 14 76.27% 49-54, 66, 75, 84, 87, 99-101, 110-112, 119 plugins/crunch/filter_latest_intersect.py 47 8 82.98% 51-52, 68, 77-80, 82, 89-92 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 83 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 68-71, 78-81, 87, 95, 106-122 plugins/crunch/filter_property.py 30 7 76.67% 54-59, 62-63, 79-83 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 491 163 66.80% 266, 279, 346-349, 363-367, 409-411, 415, 447-448, 454-458, 487, 519, 523, 530, 560-568, 572, 604-612, 623-625, 656-730, 748-808, 819-824, 836-849, 874, 889-891, 894, 904-912, 920-933, 943-974, 981-993, 1031, 1057, 1102-1104, 1324 plugins/download/base.py 253 51 79.84% 136, 164, 296-297, 314-320, 351-355, 361-362, 404, 407-421, 433, 437, 501-505, 535-536, 544-561, 568-576, 578-582, 625, 647, 669, 677 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 533 120 77.49% 204, 300-303, 305-306, 313-318, 336-351, 368-370, 382, 430, 437-443, 461, 475, 489, 497-499, 515-520, 531, 549, 591-595, 617, 657, 702, 716-722, 751-815, 833, 863-872, 894-895, 922-927, 933, 936, 952, 969-970, 1000-1001, 1008, 1069-1075, 1130-1131, 1137, 1147, 1183, 1219, 1237-1250, 1276-1278 plugins/download/s3rest.py 116 24 79.31% 113, 149, 156, 191, 218-225, 228-230, 234, 245-251, 259-260, 263-267, 290, 311-314 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 132 13 90.15% 102, 106, 117, 276, 296, 352-353, 373, 376-384 plugins/search/build_search_result.py 189 24 87.30% 96, 137-138, 144, 155, 289-292, 321, 356-373, 422, 451, 454, 464, 481, 509, 511 plugins/search/cop_marine.py 236 50 78.81% 55, 63-65, 75-76, 81, 86-87, 103, 105, 108, 143-145, 157-158, 200, 206, 210, 214, 227, 238-239, 247, 275, 279, 294, 298, 302, 306, 310-314, 320-323, 326-340, 357, 406-410, 415, 427 plugins/search/creodias_s3.py 55 3 94.55% 58, 76, 110 plugins/search/csw.py 105 81 22.86% 58-59, 63-64, 72-120, 126-139, 147-179, 197-238 plugins/search/data_request_search.py 202 69 65.84% 90-93, 109, 120, 124-125, 136, 141, 146, 153, 166-169, 223-224, 228, 238-244, 249, 275-278, 286-297, 314, 316, 323-326, 328-329, 347-351, 384, 394, 405, 418, 424-439, 444 plugins/search/qssearch.py 718 94 86.91% 390, 394-400, 515-527, 571, 587, 597, 616-631, 668-671, 742-743, 791, 810, 817, 829, 886, 907, 910-911, 920-921, 930-931, 940-941, 968, 1039-1044, 1048-1057, 1091, 1113, 1173, 1222, 1286, 1289-1290, 1372-1376, 1438, 1441, 1447-1448, 1469, 1496-1508, 1515, 1547-1549, 1559-1565, 1595, 1618, 1633, 1649, 1724-1727, 1732-1735, 1762-1763, 1778 plugins/search/static_stac_search.py 72 10 86.11% 98-125, 141, 154 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 7 78.79% 35-37, 53-55, 59, 68 rest/config.py 26 0 100.00% rest/constants.py 6 0 100.00% rest/core.py 251 63 74.90% 216-217, 273, 281, 299-316, 331-369, 460, 502-533, 680, 687-735 rest/errors.py 69 5 92.75% 106, 116, 127, 143-144 rest/server.py 188 24 87.23% 89, 112-114, 277-282, 310, 495-497, 514-519, 548, 550, 554-555, 559-560 rest/stac.py 319 63 80.25% 306, 328, 380-383, 410-437, 468-470, 493, 525-526, 608-648, 670-686, 778-782, 789, 843-844, 905, 995-997 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 176 5 97.16% 225-229, 282, 285, 353 rest/types/queryables.py 56 1 98.21% 164 rest/types/stac_search.py 126 7 94.44% 129, 175, 190-192, 200, 204 rest/utils/__init__.py 93 12 87.10% 108-109, 128-130, 182, 192-206 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 3 86.96% 48, 60, 62 types/__init__.py 114 14 87.72% 53, 70, 129-132, 199, 213-222, 232, 253, 266 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 103 types/whoosh.py 15 0 100.00% utils/__init__.py 502 37 92.63% 85, 90, 194-195, 204-231, 234, 248, 328-332, 406-410, 429-431, 510, 525, 561-562, 926-929, 937-938, 976-977, 1148 utils/constraints.py 119 38 68.07% 62, 89-98, 139, 144, 148, 159, 182-184, 194, 208-224, 233-244 utils/exceptions.py 40 2 95.00% 98-99 utils/import_system.py 28 19 32.14% 64-78, 89-99 utils/logging.py 28 1 96.43% 41 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 64, 86, 88, 90, 92, 94, 110, 118-120, 128 utils/rest.py 36 1 97.22% 55 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 137, 153-156, 203-212, 222-252 TOTAL 9608 1591 83.44% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ------------------------ ------- ------ ------- plugins/download/http.py -1 -7 +1.27% rest/core.py 0 -1 +0.40% rest/errors.py +2 0 +0.21% TOTAL +1 -8 +0.08% ``` Results for commit: f2bfdcc0496ace601f40a27bb4d39d629dde357c _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-ubuntu-latest/coverage.xml --> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports_win/data/1337-raise-error-when-request-to-backend-fails/./badge.svg) ## Code Coverage (Windows) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 314 59 81.21% 657-716, 818-869, 873 config.py 384 28 92.71% 83-85, 94, 102, 106-108, 179, 190, 581-583, 696-699, 742-743, 752-753, 858, 889, 921-926, 928 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 767 69 91.00% 363, 653, 697-700, 738, 782, 816, 861-866, 892, 983, 1051, 1189, 1274-1286, 1322, 1324, 1352, 1356-1367, 1380-1386, 1469-1472, 1505-1525, 1577, 1594-1598, 1610-1613, 1969, 1993-1999, 2250, 2254-2258, 2267-2269, 2301 api/search_result.py 59 4 93.22% 83, 92, 99, 113 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 75, 147, 155, 158-162 api/product/_product.py 187 20 89.30% 70-72, 237-238, 313, 342, 399, 413-416, 429, 453-456, 499-505 api/product/metadata_mapping.py 680 68 90.00% 130-132, 227, 259-260, 306-307, 317-329, 331, 342, 348-360, 405-406, 443, 464-467, 490, 498-499, 575-576, 600-601, 607-610, 625-626, 775, 821, 992-997, 1124, 1138-1158, 1178, 1183, 1312, 1326, 1397, 1449, 1489-1493, 1508 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 6 1 83.33% 38 plugins/__init__.py 0 0 100.00% plugins/base.py 21 3 85.71% 48, 55, 68 plugins/manager.py 173 15 91.33% 116-121, 171, 209, 231, 235, 259, 281, 390-393, 405-406 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 91 8 91.21% 149-151, 198-199, 225-227 plugins/apis/usgs.py 180 31 82.78% 132, 234, 268, 303-305, 310, 336-337, 342, 372-379, 390-395, 417-423, 425-431, 454 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 19 0 100.00% plugins/authentication/base.py 17 2 88.24% 43, 56 plugins/authentication/generic.py 14 2 85.71% 40, 50 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 48 4 91.67% 134, 160-165 plugins/authentication/oauth.py 13 7 46.15% 32-34, 38-41 plugins/authentication/openid_connect.py 187 17 90.91% 114, 128-154, 162, 316-319, 345 plugins/authentication/qsauth.py 34 1 97.06% 83 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 89 16 82.02% 79, 108, 110, 133-146, 202-206 plugins/authentication/token_exchange.py 36 20 44.44% 74-80, 92-122 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 40 plugins/crunch/filter_date.py 59 14 76.27% 49-54, 66, 75, 84, 87, 99-101, 110-112, 119 plugins/crunch/filter_latest_intersect.py 47 33 29.79% 48-53, 66-111 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 83 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 68-71, 78-81, 87, 95, 106-122 plugins/crunch/filter_property.py 30 7 76.67% 54-59, 62-63, 79-83 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 491 163 66.80% 266, 279, 346-349, 363-367, 409-411, 415, 447-448, 454-458, 487, 519, 523, 530, 560-568, 572, 604-612, 623-625, 656-730, 748-808, 819-824, 836-849, 874, 889-891, 894, 904-912, 920-933, 943-974, 981-993, 1031, 1057, 1102-1104, 1324 plugins/download/base.py 253 53 79.05% 136, 164, 231-233, 296-297, 314-320, 351-355, 361-362, 404, 407-421, 433, 437, 501-505, 535-536, 544-561, 568-576, 578-582, 625, 647, 669, 677 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 533 121 77.30% 204, 300-303, 305-306, 313-318, 336-351, 368-370, 382, 430, 437-443, 461, 475, 489, 497-499, 515-520, 531, 549, 591-595, 617, 657, 702, 716-722, 751-815, 833, 863-872, 894-895, 922-927, 933, 936, 952, 969-970, 983, 1000-1001, 1008, 1069-1075, 1130-1131, 1137, 1147, 1183, 1219, 1237-1250, 1276-1278 plugins/download/s3rest.py 116 24 79.31% 113, 149, 156, 191, 218-225, 228-230, 234, 245-251, 259-260, 263-267, 290, 311-314 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 132 14 89.39% 102, 106, 117, 276, 296, 352-353, 373, 376-384, 386 plugins/search/build_search_result.py 189 31 83.60% 96, 137-138, 144, 155, 289-292, 321, 356-373, 422, 451, 454, 464, 481, 501-516 plugins/search/cop_marine.py 236 50 78.81% 55, 63-65, 75-76, 81, 86-87, 103, 105, 108, 143-145, 157-158, 200, 206, 210, 214, 227, 238-239, 247, 275, 279, 294, 298, 302, 306, 310-314, 320-323, 326-340, 357, 406-410, 415, 427 plugins/search/creodias_s3.py 55 3 94.55% 58, 76, 110 plugins/search/csw.py 105 81 22.86% 58-59, 63-64, 72-120, 126-139, 147-179, 197-238 plugins/search/data_request_search.py 202 69 65.84% 90-93, 109, 120, 124-125, 136, 141, 146, 153, 166-169, 223-224, 228, 238-244, 249, 275-278, 286-297, 314, 316, 323-326, 328-329, 347-351, 384, 394, 405, 418, 424-439, 444 plugins/search/qssearch.py 718 123 82.87% 390, 394-400, 515-527, 571, 574, 587, 597, 616-631, 668-671, 742-743, 791, 810, 817, 829, 886, 907, 910-911, 920-921, 930-931, 940-941, 968, 1039-1044, 1048-1057, 1091, 1113, 1173, 1222, 1286, 1289-1290, 1372-1376, 1438, 1441, 1447-1448, 1469, 1496-1508, 1515, 1547-1549, 1559-1565, 1595, 1618, 1633, 1649, 1707-1807 plugins/search/static_stac_search.py 72 10 86.11% 98-125, 141, 154 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 22 33.33% 35-37, 44-70 rest/config.py 26 1 96.15% 36 rest/constants.py 6 0 100.00% rest/core.py 251 139 44.62% 162, 164, 166, 169-170, 184-194, 206-207, 209-210, 216-217, 220, 223, 264-318, 331-369, 400-434, 449-465, 481-490, 502-533, 550, 592-641, 680, 687-735 rest/errors.py 69 49 28.99% 60, 65-100, 105-108, 115-118, 126-147, 155-160, 175-181 rest/server.py 188 188 0.00% 18-573 rest/stac.py 319 68 78.68% 240, 306, 328, 380-383, 410-437, 468-470, 493, 525-526, 608-648, 670-686, 713, 778-782, 789, 843-844, 850, 905, 943, 976, 995-997 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 176 16 90.91% 225-229, 262-264, 282, 285, 291, 295, 353, 370-380 rest/types/queryables.py 56 13 76.79% 51-52, 59-60, 67-68, 94-99, 108-109, 164 rest/types/stac_search.py 126 11 91.27% 127-129, 175, 190-192, 200, 204, 252, 255 rest/utils/__init__.py 93 30 67.74% 79-85, 105, 108-109, 128-130, 143, 150, 175-183, 190-211 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 5 78.26% 43-44, 48, 60, 62 types/__init__.py 114 39 65.79% 53, 66-70, 81-93, 120-122, 129-132, 172, 199, 209-225, 230, 232, 253, 258, 266, 276 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 103 types/whoosh.py 15 0 100.00% utils/__init__.py 502 37 92.63% 85, 90, 194-195, 204-231, 234, 248, 328-332, 406-410, 429-431, 510, 525, 561-562, 926-929, 937-938, 976-977, 1148 utils/constraints.py 119 38 68.07% 62, 89-98, 139, 144, 148, 159, 182-184, 194, 208-224, 233-244 utils/exceptions.py 40 2 95.00% 98-99 utils/import_system.py 28 19 32.14% 64-78, 89-99 utils/logging.py 28 1 96.43% 41 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 64, 86, 88, 90, 92, 94, 110, 118-120, 128 utils/rest.py 36 1 97.22% 55 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 137, 153-156, 203-212, 222-252 TOTAL 9608 2035 78.82% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ------------------------ ------- ------ ------- plugins/download/http.py -1 -7 +1.27% rest/errors.py +2 +2 -0.86% TOTAL +1 -5 +0.05% ``` Results for commit: f2bfdcc0496ace601f40a27bb4d39d629dde357c _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-windows-latest/coverage.xml -->	diff --git a/docs/plugins_reference/auth.rst b/docs/plugins_reference/auth.rst index 8f4b0206..fbad669f 100644 --- a/docs/plugins_reference/auth.rst +++ b/docs/plugins_reference/auth.rst @@ -9,6 +9,8 @@ Multiple authentication plugins can be defined per provider under ``auth``, ``se using matching settings :attr:`~eodag.config.PluginConfig.matching_url` and :attr:`~eodag.config.PluginConfig.matching_conf` as configuration parameters. Credentials are automatically shared between plugins having the same matching settings. +Authentication plugins without matching settings configured will not be shared and will automatically match their +provider. Authentication plugins must inherit the following class and implement :meth:`authenticate`: diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 83926fba..2302c53b 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -79,6 +79,7 @@ from eodag.utils.exceptions import ( DownloadError, MisconfiguredError, NotAvailableError, + RequestError, TimeOutError, ) @@ -193,18 +194,11 @@ class HTTPDownload(Download): logger.debug(ordered_message) product.properties["storageStatus"] = STAGING_STATUS except RequestException as e: - if hasattr(e, "response") and ( - content := getattr(e.response, "content", None) - ): - error_message = f"{content.decode('utf-8')} - {e}" - else: - error_message = str(e) - logger.warning( - "%s could not be ordered, request returned %s", - product.properties["title"], - error_message, - ) self._check_auth_exception(e) + title = product.properties["title"] + message = f"{title} could not be ordered" + raise RequestError.from_error(e, message) from e + return self.order_response_process(response, product) except requests.exceptions.Timeout as exc: raise TimeOutError(exc, timeout=timeout) from exc diff --git a/eodag/plugins/manager.py b/eodag/plugins/manager.py index 32335d87..671868c7 100644 --- a/eodag/plugins/manager.py +++ b/eodag/plugins/manager.py @@ -335,12 +335,8 @@ class PluginManager: ): # url matches return True - if not plugin_matching_conf and not plugin_matching_url: - # plugin without match criteria - return True - else: - # no match - return False + # no match + return False # providers configs with given provider at first sorted_providers_config = deepcopy(self.providers_config) @@ -354,7 +350,20 @@ class PluginManager: auth_conf = getattr(provider_conf, key, None) if auth_conf is None: continue - if _is_auth_plugin_matching(auth_conf, matching_url, matching_conf): + # plugin without configured match criteria: only works for given provider + unconfigured_match = ( + True + if ( + not getattr(auth_conf, "matching_conf", {}) + and not getattr(auth_conf, "matching_url", None) + and provider == plugin_provider + ) + else False + ) + + if unconfigured_match or _is_auth_plugin_matching( + auth_conf, matching_url, matching_conf + ): auth_conf.priority = provider_conf.priority plugin = cast( Authentication, diff --git a/eodag/resources/product_types.yml b/eodag/resources/product_types.yml index b54de976..08bb7428 100644 --- a/eodag/resources/product_types.yml +++ b/eodag/resources/product_types.yml @@ -2866,7 +2866,7 @@ ERA5_LAND: sensorType: ATMOSPHERIC license: proprietary title: ERA5-Land hourly data from 1950 to present - missionStartDate: "1950-01-01T00:00:00Z" + missionStartDate: "1950-01-01T01:00:00Z" ERA5_LAND_MONTHLY: abstract: \| diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 097ff545..849094cc 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1567,7 +1567,6 @@ Content-Type: application/json auth: !plugin type: GenericAuth - matching_url: https://catalogue.onda-dias.eu --- !provider # MARK: astraea_eod @@ -4189,36 +4188,12 @@ variable: - '{{"variable": "{variable}"}}' - '$.null' - leadtime_hour: - - '{{"leadtime_hour": {leadtime_hour}}}' - - '$.null' - leadtime_month: - - '{{"leadtime_month": {leadtime_month}}}' - - '$.null' - origin: - - '{{"origin": "{origin}"}}' - - '$.null' system: - '{{"system": "{system}"}}' - '$.null' - format: - - '{{"format": "{format}"}}' - - '$.null' - pressure_level: - - '{{"pressure_level": {pressure_level}}}' - - '$.null' - model_level: - - '{{"model_level": {model_level}}}' - - '$.null' - sensor_and_algorithm: - - '{{"sensor_and_algorithm": "{sensor_and_algorithm}"}}' - - '$.null' version: - '{{"version": {version}}}' - '$.null' - time: - - '{{"time": {time}}}' - - '$.null' region: - '{{"region": {region}}}' - '$.null' @@ -4228,72 +4203,18 @@ source: - '{{"source": {source}}}' - '$.null' - quantity: - - '{{"quantity": "{quantity}"}}' - - '$.null' - input_observations: - - '{{"input_observations": "{input_observations}"}}' - - '$.null' - aggregation: - - '{{"aggregation": "{aggregation}"}}' - - '$.null' model: - '{{"model": {model}}}' - '$.null' level: - '{{"level": {level}}}' - '$.null' - forcing_type: - - '{{"forcing_type": "{forcing_type}"}}' - - '$.null' - sky_type: - - '{{"sky_type": {sky_type}}}' - - '$.null' - band: - - '{{"band": {band}}}' - - '$.null' - aerosol_type: - - '{{"aerosol_type": {aerosol_type}}}' - - '$.null' step: - '{{"step": {step}}}' - '$.null' - longitude: - - '{{"longitude": "{longitude}"}}' - - '$.null' - latitude: - - '{{"latitude": "{latitude}"}}' - - '$.null' - altitude: - - '{{"altitude": "{altitude}"}}' - - '$.null' - time_reference: - - '{{"time_reference": "{time_reference}"}}' - - '$.null' - grid: - - '{{"grid": "{grid}"}}' - - '$.null' - soil_level: - - '{{"soil_level": {soil_level}}}' - - '$.null' - year: - - '{{"year": {year}}}' - - '$.null' - month: - - '{{"month": {month}}}' - - '$.null' - day: - - '{{"day": {day}}}' - - '$.null' satellite: - '{{"satellite": {satellite}}}' - '$.null' - cdr_type: - - '{{"cdr_type": "{cdr_type}"}}' - - '$.null' - statistic: - - '{{"statistic": {statistic}}}' - - '$.null' sensor: - '{{"sensor": "{sensor}"}}' - '$.null' @@ -4785,12 +4706,6 @@ providerProductType: - '{{"productType": "{providerProductType}"}}' - '$.null' - timeliness: - - '{{"timeliness": "{timeliness}"}}' - - '$.null' - orbitDirection: - - '{{"orbitDirection": "{orbitDirection}"}}' - - '$.null' variable: - '{{"variable": {variable}}}' - '$.null' @@ -5397,6 +5312,9 @@ format: grib metadata_mapping: id: '$.id' + month: + - '{{"month": "{month}"}}' + - '$.null' startTimeFromAscendingNode: - \| {{ diff --git a/eodag/rest/errors.py b/eodag/rest/errors.py index 6ff067f4..574a3e37 100644 --- a/eodag/rest/errors.py +++ b/eodag/rest/errors.py @@ -126,7 +126,10 @@ async def eodag_errors_handler(request: Request, exc: Exception) -> ORJSONRespon if not isinstance(exc, EodagError): return starlette_exception_handler(request, exc) - code = EODAG_DEFAULT_STATUS_CODES.get(type(exc), getattr(exc, "status_code", 500)) + exception_status_code = getattr(exc, "status_code", None) + default_status_code = exception_status_code or 500 + code = EODAG_DEFAULT_STATUS_CODES.get(type(exc), default_status_code) + detail = f"{type(exc).__name__}: {str(exc)}" if type(exc) in (MisconfiguredError, AuthenticationError, TimeOutError):	Raise an error when a request to a backend provider fails When a request to a provider such as cop_ads fails because of a malformed request (e.g. providing a date range with an end date before the start date), eodag logs the error, which may be unnotified by the user. Instead it should raise an error.	CS-SI/eodag	diff --git a/tests/integration/test_core_search.py b/tests/integration/test_core_search.py index e5ae64b2..46b83844 100644 --- a/tests/integration/test_core_search.py +++ b/tests/integration/test_core_search.py @@ -495,6 +495,23 @@ class TestCoreSearch(unittest.TestCase): }, ) + # auth plugin without match configuration + self.dag.add_provider( + "provider_without_match_configured", + "https://foo.bar/baz/search", + search={"need_auth": True}, + auth={ + "type": "GenericAuth", + "credentials": { + "username": "some-username", + "password": "some-password", + }, + }, + ) + self.dag.search(provider="provider_without_match_configured") + self.assertEqual(mock_query.call_args[0][1].auth.username, "some-username") + mock_query.reset_mock() + # search endpoint matching self.dag.add_provider( "provider_matching_search_api", diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index cda5f43c..d51f3454 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -28,12 +28,14 @@ from tempfile import NamedTemporaryFile, TemporaryDirectory, gettempdir from typing import Any, Callable, Dict from unittest import mock +import pytest import responses import yaml +from requests import RequestException from eodag.api.product.metadata_mapping import DEFAULT_METADATA_MAPPING from eodag.utils import ProgressCallback -from eodag.utils.exceptions import DownloadError, NoMatchingProductType +from eodag.utils.exceptions import DownloadError, NoMatchingProductType, RequestError from tests import TEST_RESOURCES_PATH from tests.context import ( DEFAULT_STREAM_REQUESTS_TIMEOUT, @@ -1157,6 +1159,38 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): else: del plugin.config.timeout + @mock.patch("eodag.plugins.download.http.requests.request", autospec=True) + def test_plugins_download_http_order_get_raises_if_request_failed( + self, mock_request + ): + """HTTPDownload.order_download() must raise an error if request to backend + provider failed""" + + # Configure mock to raise an error + mock_response = mock_request.return_value.__enter__.return_value + mock_response.raise_for_status.side_effect = RequestException("Some error msg") + + plugin = self.get_download_plugin(self.product) + self.product.properties["downloadLink"] = "https://peps.cnes.fr/dummy" + self.product.properties["orderLink"] = "http://somewhere/order" + + auth_plugin = self.get_auth_plugin(plugin, self.product) + auth_plugin.config.credentials = {"username": "foo", "password": "bar"} + auth = auth_plugin.authenticate() + + # Verify that a RequestError is raised + with pytest.raises(RequestError): + plugin.order_download(self.product, auth=auth) + + mock_request.assert_called_once_with( + method="GET", + url=self.product.properties["orderLink"], + auth=auth, + headers=USER_AGENT, + timeout=5, + verify=True, + ) + @mock.patch("eodag.plugins.download.http.requests.request", autospec=True) def test_plugins_download_http_order_post(self, mock_request): """HTTPDownload.order_download() must request using orderLink and POST protocol""" diff --git a/tests/units/test_http_server.py b/tests/units/test_http_server.py index 0e16f846..863578eb 100644 --- a/tests/units/test_http_server.py +++ b/tests/units/test_http_server.py @@ -323,6 +323,7 @@ class RequestTestCase(unittest.TestCase): post_data: Optional[Any] = None, search_call_count: Optional[int] = None, search_result: SearchResult = None, + expected_status_code: int = 200, ) -> httpx.Response: if search_result: mock_search.return_value = search_result @@ -354,7 +355,7 @@ class RequestTestCase(unittest.TestCase): elif expected_search_kwargs is not None: mock_search.assert_called_once_with(**expected_search_kwargs) - self.assertEqual(200, response.status_code, response.text) + self.assertEqual(expected_status_code, response.status_code, response.text) return response @@ -1150,6 +1151,34 @@ class RequestTestCase(unittest.TestCase): expected_file ), f"File {expected_file} should have been deleted" + @mock.patch( + "eodag.plugins.authentication.base.Authentication.authenticate", + autospec=True, + ) + @mock.patch( + "eodag.plugins.download.base.Download._stream_download_dict", + autospec=True, + ) + def test_error_handler_supports_request_exception_with_status_code_none( + self, mock_stream_download: Mock, mock_auth: Mock + ): + """ + A RequestError with a status code set to None (the default value) should not + crash the server. This test ensures that it doesn't crash the server and that a + 500 status code is returned to the user. + """ + # Make _stream_download_dict reaise an exception object with a status_code + # attribute set to None + exception = RequestError() + exception.status_code = None + mock_stream_download.side_effect = exception + + response = self._request_valid_raw( + f"collections/{self.tested_product_type}/items/foo/download?provider=peps", + expected_status_code=500, + ) + self.assertIn("RequestError", response.text) + def test_root(self): """Request to / should return a valid response""" resp_json = self._request_valid("", check_links=False)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 6 }	3.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.23 boto3-stubs==1.37.23 botocore==1.37.23 botocore-stubs==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@1a1a40f4c4ec7d7672aa917fa0d6a28326df25da#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy==1.15.0 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.16 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.0 mypy-boto3-sqs==1.37.0 mypy-extensions==1.0.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.24.2 types-cachetools==5.5.0.20240820 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-python-dateutil==2.9.0.20241206 types-PyYAML==6.0.12.20250326 types-requests==2.31.0.6 types-s3transfer==0.11.4 types-setuptools==78.1.0.20250329 types-tqdm==4.67.0.20250319 types-urllib3==1.26.25.14 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.23 - boto3-stubs==1.37.23 - botocore==1.37.23 - botocore-stubs==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==3.0.1.dev1+g1a1a40f4 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy==1.15.0 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.16 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.0 - mypy-boto3-sqs==1.37.0 - mypy-extensions==1.0.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.24.2 - types-cachetools==5.5.0.20240820 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-python-dateutil==2.9.0.20241206 - types-pyyaml==6.0.12.20250326 - types-requests==2.31.0.6 - types-s3transfer==0.11.4 - types-setuptools==78.1.0.20250329 - types-tqdm==4.67.0.20250319 - types-urllib3==1.26.25.14 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get_raises_if_request_failed", "tests/units/test_http_server.py::RequestTestCase::test_error_handler_supports_request_exception_with_status_code_none" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_auths_matching", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_buildpost", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_odata", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_postjson", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_qssearch", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_stacsearch", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_usgs", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_nothing", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_on_first", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_on_fourth", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_on_fourth_empty_results", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_given_provider", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_raise_errors", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_asset_filter", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_resume", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_file_without_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets_without_ssl", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_nonzip_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_download_cop_ads", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status_search_again", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_tar_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_zip_file_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_matching_product_type", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_offline", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_online", "tests/units/test_download_plugins.py::TestDownloadPluginCreodiasS3::test_plugins_download_creodias_s3", "tests/units/test_http_server.py::RequestTestCase::test_assets_alt_url_blacklist", "tests/units/test_http_server.py::RequestTestCase::test_auth_error", "tests/units/test_http_server.py::RequestTestCase::test_cloud_cover_post_search", "tests/units/test_http_server.py::RequestTestCase::test_collection", "tests/units/test_http_server.py::RequestTestCase::test_collection_free_text_search", "tests/units/test_http_server.py::RequestTestCase::test_conformance", "tests/units/test_http_server.py::RequestTestCase::test_cql_post_search", "tests/units/test_http_server.py::RequestTestCase::test_date_post_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_items", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_collection_no_stream", "tests/units/test_http_server.py::RequestTestCase::test_filter", "tests/units/test_http_server.py::RequestTestCase::test_forward", "tests/units/test_http_server.py::RequestTestCase::test_ids_post_search", "tests/units/test_http_server.py::RequestTestCase::test_intersects_post_search", "tests/units/test_http_server.py::RequestTestCase::test_items_response", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_nok", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_ok", "tests/units/test_http_server.py::RequestTestCase::test_not_found", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_error", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_from_constraints", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_provider_prefix_post_search", "tests/units/test_http_server.py::RequestTestCase::test_queryables", "tests/units/test_http_server.py::RequestTestCase::test_queryables_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_queryables_with_provider_error", "tests/units/test_http_server.py::RequestTestCase::test_request_params", "tests/units/test_http_server.py::RequestTestCase::test_root", "tests/units/test_http_server.py::RequestTestCase::test_route", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_collection", "tests/units/test_http_server.py::RequestTestCase::test_search_no_results_with_errors", "tests/units/test_http_server.py::RequestTestCase::test_search_provider_in_downloadlink", "tests/units/test_http_server.py::RequestTestCase::test_search_response_contains_pagination_info", "tests/units/test_http_server.py::RequestTestCase::test_search_results_with_errors", "tests/units/test_http_server.py::RequestTestCase::test_service_desc", "tests/units/test_http_server.py::RequestTestCase::test_service_doc", "tests/units/test_http_server.py::RequestTestCase::test_stac_extension_oseo", "tests/units/test_http_server.py::RequestTestCase::test_stac_queryables_type", "tests/units/test_http_server.py::RequestTestCase::test_timeout_error" ]	[]	Apache License 2.0	null
CS-SI__eodag-1357	cdee784fa2d227c8e908610422140188df4ed142	2024-10-18 14:22:22	2473ac169a3d05f04f83d4f227c2fb63689ae2d6	github-actions[bot]: ## Test Results 2 files - 2 2 suites - 2 2m 49s :stopwatch: - 2m 51s 568 tests ± 0 561 :white_check_mark: - 4 3 :zzz: ± 0 4 :x: +4 1 136 runs - 1 136 1 126 :white_check_mark: - 1 050 6 :zzz: - 90 4 :x: +4 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/31737278239). Results for commit 64d35774. ± Comparison against base commit 4785e8a1. [test-results]:data:application/gzip;base64,H4sIALZvEmcC/03MSw6DIBSF4a0Yxh1wkYd0Mw0CJqQqDY9R0733ais6/L+TnDeZwuwzuXfs1pFcQ2nhajIlxBUTpEbAqWyjkMNRj1yt3QlOeoYXUt9gMmFG4A18SjGhUJRU1+0ToJf/Oj4B2Em/z9aXy72vjzYuSygYRHLXC6U4ZVJYypmQTo+DNkY7bj13RqCMCsjnC5a5EUAHAQAA github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports/data/geodes/./badge.svg) ## Code Coverage (Ubuntu) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 314 59 81.21% 657-716, 818-869, 873 config.py 396 27 93.18% 83-85, 94, 102, 106-108, 179, 190, 608-610, 723-726, 769-770, 779-780, 885, 948-953, 955 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 767 69 91.00% 374, 664, 708-711, 749, 793, 827, 872-877, 903, 994, 1062, 1200, 1285-1297, 1333, 1335, 1363, 1367-1378, 1391-1397, 1480-1483, 1516-1536, 1588, 1605-1609, 1621-1624, 1980, 2004-2010, 2261, 2265-2269, 2278-2280, 2312 api/search_result.py 58 4 93.10% 92, 101, 108, 122 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 75, 147, 155, 158-162 api/product/_product.py 187 20 89.30% 70-72, 237-238, 313, 342, 399, 413-416, 429, 453-456, 499-505 api/product/metadata_mapping.py 680 68 90.00% 130-132, 227, 259-260, 306-307, 317-329, 331, 342, 348-360, 405-406, 443, 464-467, 490, 498-499, 575-576, 600-601, 607-610, 625-626, 775, 821, 992-997, 1124, 1138-1158, 1178, 1183, 1312, 1326, 1397, 1449, 1489-1493, 1508 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 6 1 83.33% 38 plugins/__init__.py 0 0 100.00% plugins/base.py 21 2 90.48% 48, 55 plugins/manager.py 172 15 91.28% 116-121, 171, 209, 231, 235, 259, 281, 399-402, 414-415 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 91 8 91.21% 149-151, 198-199, 225-227 plugins/apis/usgs.py 180 31 82.78% 132, 234, 268, 303-305, 310, 336-337, 342, 372-379, 390-395, 417-423, 425-431, 454 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 19 0 100.00% plugins/authentication/base.py 17 2 88.24% 43, 56 plugins/authentication/generic.py 14 2 85.71% 40, 50 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 48 4 91.67% 134, 160-165 plugins/authentication/oauth.py 13 7 46.15% 32-34, 38-41 plugins/authentication/openid_connect.py 187 17 90.91% 114, 128-154, 162, 316-319, 345 plugins/authentication/qsauth.py 34 1 97.06% 83 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 89 16 82.02% 79, 108, 110, 133-146, 202-206 plugins/authentication/token_exchange.py 36 20 44.44% 74-80, 92-122 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 40 plugins/crunch/filter_date.py 59 14 76.27% 49-54, 66, 75, 84, 87, 99-101, 110-112, 119 plugins/crunch/filter_latest_intersect.py 47 8 82.98% 51-52, 68, 77-80, 82, 89-92 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 83 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 68-71, 78-81, 87, 95, 106-122 plugins/crunch/filter_property.py 30 7 76.67% 54-59, 62-63, 79-83 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 491 163 66.80% 266, 279, 346-349, 363-367, 409-411, 415, 447-448, 454-458, 487, 519, 523, 530, 560-568, 572, 604-612, 623-625, 656-730, 748-808, 819-824, 836-849, 874, 889-891, 894, 904-912, 920-933, 943-974, 981-993, 1031, 1057, 1102-1104, 1324 plugins/download/base.py 253 51 79.84% 136, 164, 296-297, 314-320, 351-355, 361-362, 404, 407-421, 433, 437, 501-505, 535-536, 544-561, 568-576, 578-582, 625, 647, 669, 677 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 534 120 77.53% 204, 300-303, 305-306, 313-318, 336-351, 368-370, 382, 430, 437-443, 461, 475, 489, 497-499, 515-520, 531, 549, 591-595, 617, 657, 702, 716-722, 751-815, 833, 863-872, 894-895, 924-929, 935, 938, 955, 972-973, 1003-1004, 1011, 1072-1078, 1133-1134, 1140, 1150, 1186, 1222, 1240-1253, 1279-1281 plugins/download/s3rest.py 116 24 79.31% 113, 149, 156, 191, 218-225, 228-230, 234, 245-251, 259-260, 263-267, 290, 311-314 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 132 13 90.15% 100, 104, 115, 274, 294, 350-351, 371, 374-382 plugins/search/build_search_result.py 188 24 87.23% 105, 146-147, 153, 164, 298-301, 330, 365-382, 431, 460, 463, 473, 490, 518, 520 plugins/search/cop_marine.py 236 50 78.81% 55, 63-65, 75-76, 81, 86-87, 103, 105, 108, 143-145, 157-158, 200, 206, 210, 214, 227, 238-239, 247, 275, 279, 294, 298, 302, 306, 310-314, 320-323, 326-340, 357, 406-410, 415, 427 plugins/search/creodias_s3.py 55 3 94.55% 58, 76, 110 plugins/search/csw.py 105 81 22.86% 58-59, 63-64, 72-120, 126-139, 147-179, 197-238 plugins/search/data_request_search.py 202 69 65.84% 90-93, 109, 120, 124-125, 136, 141, 146, 153, 166-169, 223-224, 228, 238-244, 249, 275-278, 286-297, 314, 316, 323-326, 328-329, 347-351, 384, 394, 405, 418, 424-439, 444 plugins/search/qssearch.py 742 107 85.58% 371, 381-410, 440, 444-450, 461-462, 572-584, 628, 644, 654, 673-688, 725-728, 799-800, 848, 867, 874, 886, 943, 964, 967-968, 977-978, 987-988, 997-998, 1025, 1096-1101, 1105-1114, 1148, 1170, 1230, 1279, 1343, 1346-1347, 1429-1433, 1495, 1498, 1504-1505, 1526, 1553-1565, 1572, 1604-1606, 1616-1622, 1652, 1675, 1690, 1706, 1781-1784, 1789-1792, 1819-1820, 1835 plugins/search/static_stac_search.py 72 10 86.11% 98-125, 141, 154 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 7 78.79% 35-37, 53-55, 59, 68 rest/config.py 25 0 100.00% rest/constants.py 6 0 100.00% rest/core.py 251 63 74.90% 216-217, 273, 281, 299-316, 331-369, 460, 502-533, 680, 687-735 rest/errors.py 69 5 92.75% 106, 116, 127, 143-144 rest/server.py 188 24 87.23% 89, 112-114, 277-282, 310, 495-497, 514-519, 548, 550, 554-555, 559-560 rest/stac.py 319 63 80.25% 306, 328, 380-383, 410-437, 468-470, 493, 525-526, 608-648, 670-686, 778-782, 789, 843-844, 905, 995-997 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 176 5 97.16% 225-229, 282, 285, 353 rest/types/queryables.py 55 1 98.18% 163 rest/types/stac_search.py 125 7 94.40% 138, 184, 199-201, 209, 213 rest/utils/__init__.py 93 12 87.10% 108-109, 128-130, 182, 192-206 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 3 86.96% 48, 60, 62 types/__init__.py 114 14 87.72% 65, 82, 141-144, 211, 225-234, 244, 265, 278 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 80 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 103 types/whoosh.py 15 0 100.00% utils/__init__.py 499 36 92.79% 85, 189-190, 199-226, 229, 243, 323-327, 401-405, 424-426, 505, 520, 556-557, 921-924, 932-933, 971-972, 1143 utils/constraints.py 119 38 68.07% 62, 89-98, 139, 144, 148, 159, 182-184, 194, 208-224, 233-244 utils/exceptions.py 41 0 100.00% utils/import_system.py 28 19 32.14% 64-78, 89-99 utils/logging.py 28 1 96.43% 41 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 64, 86, 88, 90, 92, 94, 110, 118-120, 128 utils/rest.py 36 1 97.22% 55 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 137, 153-156, 203-212, 222-252 TOTAL 9636 1601 83.39% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover -------------------------- ------- ------ ------- config.py +12 0 +0.21% plugins/download/http.py +1 0 +0.04% plugins/search/qssearch.py +24 +13 -1.33% TOTAL +37 +13 -0.07% ``` Results for commit: 64d357740265c04256d9b89aa9d4ce4da556db71 _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.13-ubuntu-latest/coverage.xml --> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports_win/data/geodes/./badge.svg) ## Code Coverage (Windows) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 314 59 81.21% 657-716, 818-869, 873 config.py 425 28 93.41% 83-85, 94, 102, 106-108, 179, 190, 684-686, 799-802, 845-846, 855-856, 961, 992, 1024-1029, 1031 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 767 69 91.00% 374, 664, 708-711, 749, 793, 827, 872-877, 903, 994, 1062, 1200, 1285-1297, 1333, 1335, 1363, 1367-1378, 1391-1397, 1480-1483, 1516-1536, 1588, 1605-1609, 1621-1624, 1980, 2004-2010, 2261, 2265-2269, 2278-2280, 2312 api/search_result.py 58 4 93.10% 92, 101, 108, 122 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 75, 147, 155, 158-162 api/product/_product.py 187 20 89.30% 70-72, 237-238, 313, 342, 399, 413-416, 429, 453-456, 499-505 api/product/metadata_mapping.py 680 68 90.00% 130-132, 227, 259-260, 306-307, 317-329, 331, 342, 348-360, 405-406, 443, 464-467, 490, 498-499, 575-576, 600-601, 607-610, 625-626, 775, 821, 992-997, 1124, 1138-1158, 1178, 1183, 1312, 1326, 1397, 1449, 1489-1493, 1508 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 6 1 83.33% 38 plugins/__init__.py 0 0 100.00% plugins/base.py 21 3 85.71% 48, 55, 68 plugins/manager.py 172 15 91.28% 116-121, 171, 209, 231, 235, 259, 281, 399-402, 414-415 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 91 8 91.21% 159-161, 208-209, 235-237 plugins/apis/usgs.py 180 31 82.78% 155, 257, 291, 326-328, 333, 359-360, 365, 395-402, 413-418, 440-446, 448-454, 477 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 19 0 100.00% plugins/authentication/base.py 17 2 88.24% 43, 56 plugins/authentication/generic.py 14 2 85.71% 51, 61 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 48 4 91.67% 153, 179-184 plugins/authentication/oauth.py 13 7 46.15% 42-44, 48-51 plugins/authentication/openid_connect.py 187 17 90.91% 118, 132-158, 166, 300-303, 329 plugins/authentication/qsauth.py 34 1 97.06% 91 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 89 16 82.02% 101, 130, 132, 155-168, 224-228 plugins/authentication/token_exchange.py 36 20 44.44% 72-78, 90-120 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 43 plugins/crunch/filter_date.py 59 14 76.27% 52-57, 69, 78, 87, 90, 102-104, 113-115, 122 plugins/crunch/filter_latest_intersect.py 47 33 29.79% 49-54, 67-112 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 83 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 66-69, 76-79, 85, 93, 104-120 plugins/crunch/filter_property.py 30 7 76.67% 55-60, 63-64, 80-84 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 491 163 66.80% 282, 295, 362-365, 379-383, 425-427, 431, 463-464, 470-474, 503, 535, 539, 546, 576-584, 588, 620-628, 639-641, 672-746, 764-824, 835-840, 852-865, 890, 905-907, 910, 920-928, 936-949, 959-990, 997-1009, 1047, 1073, 1118-1120, 1340 plugins/download/base.py 253 53 79.05% 136, 164, 231-233, 296-297, 314-320, 351-355, 361-362, 404, 407-421, 433, 437, 501-505, 535-536, 544-561, 568-576, 578-582, 625, 647, 669, 677 plugins/download/creodias_s3.py 17 9 47.06% 53-67 plugins/download/http.py 534 121 77.34% 234, 330-333, 335-336, 343-348, 366-381, 398-400, 412, 460, 467-473, 491, 505, 519, 527-529, 545-550, 561, 579, 621-625, 647, 687, 732, 746-752, 781-845, 863, 893-902, 924-925, 954-959, 965, 968, 985, 1002-1003, 1016, 1033-1034, 1041, 1102-1108, 1163-1164, 1170, 1180, 1216, 1252, 1270-1283, 1309-1311 plugins/download/s3rest.py 116 24 79.31% 118, 154, 161, 196, 223-230, 233-235, 239, 250-256, 264-265, 268-272, 295, 316-319 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 132 14 89.39% 100, 104, 115, 274, 294, 350-351, 371, 374-382, 384 plugins/search/build_search_result.py 188 31 83.51% 100, 141-142, 148, 159, 292-295, 324, 359-376, 425, 454, 457, 467, 484, 504-519 plugins/search/cop_marine.py 236 50 78.81% 55, 63-65, 75-76, 81, 86-87, 103, 105, 108, 155-157, 169-170, 212, 218, 222, 226, 239, 250-251, 259, 287, 291, 306, 310, 314, 318, 322-326, 332-335, 338-352, 369, 418-422, 427, 439 plugins/search/creodias_s3.py 55 3 94.55% 58, 76, 110 plugins/search/csw.py 105 81 22.86% 98-99, 103-104, 112-160, 166-179, 187-219, 237-278 plugins/search/data_request_search.py 202 69 65.84% 188-191, 207, 218, 222-223, 234, 239, 244, 251, 264-267, 321-322, 326, 336-342, 347, 373-376, 384-395, 412, 414, 421-424, 426-427, 445-449, 482, 492, 503, 516, 522-537, 542 plugins/search/qssearch.py 756 145 80.82% 444, 454-483, 502, 516, 520-526, 537-538, 554-558, 650-662, 706, 709, 722, 732, 751-766, 803-806, 877-878, 926, 945, 952, 964, 1021, 1042, 1045-1046, 1055-1056, 1065-1066, 1075-1076, 1103, 1174-1179, 1183-1192, 1226, 1248, 1308, 1398, 1485, 1488-1489, 1571-1575, 1637, 1640, 1646-1647, 1668, 1695-1707, 1714, 1746-1748, 1758-1764, 1794, 1817, 1832, 1848, 1916-2022 plugins/search/static_stac_search.py 75 13 82.67% 98-124, 140, 153 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 22 33.33% 35-37, 44-70 rest/config.py 25 1 96.00% 35 rest/constants.py 6 0 100.00% rest/core.py 251 139 44.62% 162, 164, 166, 169-170, 184-194, 206-207, 209-210, 216-217, 220, 223, 264-318, 331-369, 400-434, 449-465, 481-490, 502-533, 550, 592-641, 680, 687-735 rest/errors.py 69 49 28.99% 60, 65-100, 105-108, 115-118, 126-147, 155-160, 175-181 rest/server.py 188 188 0.00% 18-573 rest/stac.py 319 68 78.68% 240, 306, 328, 380-383, 410-437, 468-470, 493, 525-526, 608-648, 670-686, 713, 778-782, 789, 843-844, 850, 905, 943, 976, 995-997 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 176 16 90.91% 225-229, 262-264, 282, 285, 291, 295, 353, 370-380 rest/types/queryables.py 55 13 76.36% 50-51, 58-59, 66-67, 93-98, 107-108, 163 rest/types/stac_search.py 125 11 91.20% 136-138, 184, 199-201, 209, 213, 261, 264 rest/utils/__init__.py 93 30 67.74% 79-85, 105, 108-109, 128-130, 143, 150, 175-183, 190-211 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 5 78.26% 43-44, 48, 60, 62 types/__init__.py 114 39 65.79% 65, 78-82, 93-105, 132-134, 141-144, 184, 211, 221-237, 242, 244, 265, 270, 278, 288 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 80 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 103 types/whoosh.py 15 0 100.00% utils/__init__.py 499 36 92.79% 85, 189-190, 199-226, 229, 244, 324-328, 401-405, 424-426, 505, 520, 560-561, 925-928, 936-937, 975-976, 1147 utils/constraints.py 119 38 68.07% 62, 89-98, 139, 144, 148, 159, 182-184, 194, 208-224, 233-244 utils/exceptions.py 41 0 100.00% utils/import_system.py 28 19 32.14% 64-78, 89-99 utils/logging.py 28 1 96.43% 41 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 30 8 73.33% 36, 38, 42, 76, 94-101 utils/requests.py 55 11 80.00% 64, 86, 88, 90, 92, 94, 110, 118-120, 128 utils/rest.py 36 1 97.22% 55 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 137, 153-156, 203-212, 222-252 TOTAL 9682 2057 78.75% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover -------------------------- ------- ------ ------- config.py +4 0 +0.06% plugins/download/http.py +1 0 +0.04% plugins/search/qssearch.py +25 +13 -1.12% TOTAL +30 +13 -0.07% ``` Results for commit: fb85752aeb6503e88b6a9db0923356cb354f59ba _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.13-windows-latest/coverage.xml -->	diff --git a/docs/getting_started_guide/providers.rst b/docs/getting_started_guide/providers.rst index 55b28631..d3bc844e 100644 --- a/docs/getting_started_guide/providers.rst +++ b/docs/getting_started_guide/providers.rst @@ -23,6 +23,7 @@ Products from the following providers are made avaiable through ``eodag``: Storage download * `ecmwf <https://www.ecmwf.int/>`_: European Centre for Medium-Range Weather Forecasts * `eumetsat_ds <https://data.eumetsat.int>`_: EUMETSAT Data Store (European Organisation for the Exploitation of Meteorological Satellites) +* `geodes <https://geodes.cnes.fr>`_: French National Space Agency (CNES) Earth Observation portal * `hydroweb_next <https://hydroweb.next.theia-land.fr>`_: hydroweb.next thematic hub for hydrology data access * `meteoblue <https://content.meteoblue.com/en/business-solutions/weather-apis/dataset-api>`_: Meteoblue forecast * `onda <https://www.onda-dias.eu/cms/>`_: Serco DIAS diff --git a/docs/getting_started_guide/register.rst b/docs/getting_started_guide/register.rst index cf7a3810..03317eac 100644 --- a/docs/getting_started_guide/register.rst +++ b/docs/getting_started_guide/register.rst @@ -145,6 +145,12 @@ Create an account `here <https://eoportal.eumetsat.int/userMgmt/register.faces>` Then use the consumer key as `username` and the consumer secret as `password` from `here <https://api.eumetsat.int/api-key/>`__ in eodag credentials. +``geodes`` +^^^^^^^^^^ +Go to `https://geodes-portal.cnes.fr <https://https://geodes-portal.cnes.fr>`_, then login or create an account by +clicking on ``Log in`` in the top-right corner. Once logged-in, create an API key in the user settings page, and used it +as apikey in EODAG provider auth credentials. + ``hydroweb_next`` ^^^^^^^^^^^^^^^^^ Go to `https://hydroweb.next.theia-land.fr <https://hydroweb.next.theia-land.fr>`_, then login or create an account by diff --git a/docs/index.rst b/docs/index.rst index 64a6969c..68e9ecc5 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -27,31 +27,32 @@ types (Sentinel 1, Sentinel 2, Sentinel 3, Landsat, etc.) that can be searched a [Growing list of] supported providers `astraea_eod <https://eod-catalog-svc-prod.astraea.earth/api.html>`_, - `aws_eos <https://eos.com/>`_, + `aws_eos <https://eos.com>`_, `cop_ads <https://ads.atmosphere.copernicus.eu>`_, `cop_cds <https://cds.climate.copernicus.eu>`_, - `cop_dataspace <https://dataspace.copernicus.eu/>`_, + `cop_dataspace <https://dataspace.copernicus.eu>`_, `cop_marine <https://marine.copernicus.eu>`_, - `creodias <https://creodias.eu/>`_, - `creodias_s3 <https://creodias.eu/>`_, + `creodias <https://creodias.eu>`_, + `creodias_s3 <https://creodias.eu>`_, `dedl <https://hda.data.destination-earth.eu/ui>`_, `dedt_lumi <https://polytope.lumi.apps.dte.destination-earth.eu/openapi>`_, `earth_search <https://www.element84.com/earth-search/>`_, `earth_search_gcs <https://cloud.google.com/storage/docs/public-datasets>`_, - `ecmwf <https://www.ecmwf.int/>`_, + `ecmwf <https://www.ecmwf.int>`_, + `geodes <https://data.eumetsat.int>`_, `eumetsat_ds <https://data.eumetsat.int>`_, `hydroweb_next <https://hydroweb.next.theia-land.fr>`_, `meteoblue <https://content.meteoblue.com/en/business-solutions/weather-apis/dataset-api>`_, - `onda <https://www.onda-dias.eu/cms/>`_, + `onda <https://www.onda-dias.eu/cms>`_, `peps <https://peps.cnes.fr/rocket/#/home>`_, - `planetary_computer <https://planetarycomputer.microsoft.com/>`_, + `planetary_computer <https://planetarycomputer.microsoft.com>`_, `sara <https://copernicus.nci.org.au>`_, - `theia <https://theia.cnes.fr/atdistrib/rocket/>`_, - `usgs <https://earthexplorer.usgs.gov/>`_, - `usgs_satapi_aws <https://landsatlook.usgs.gov/sat-api/>`_, + `theia <https://theia.cnes.fr/atdistrib/rocket>`_, + `usgs <https://earthexplorer.usgs.gov>`_, + `usgs_satapi_aws <https://geodes.cnes.fr>`_, `wekeo_cmems <https://www.wekeo.eu>`_, - `wekeo_ecmwf <https://www.wekeo.eu/>`_, - `wekeo_main <https://www.wekeo.eu/>`_ + `wekeo_ecmwf <https://www.wekeo.eu>`_, + `wekeo_main <https://www.wekeo.eu>`_ EODAG has the following primary features: diff --git a/docs/notebooks/api_user_guide/1_overview.ipynb b/docs/notebooks/api_user_guide/1_overview.ipynb index b1c4eca4..def0b7b9 100644 --- a/docs/notebooks/api_user_guide/1_overview.ipynb +++ b/docs/notebooks/api_user_guide/1_overview.ipynb @@ -120,8 +120,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-10-17 16:51:54,405 eodag.config [INFO ] Loading user configuration from: /home/anesson/.config/eodag/eodag.yml\n", - "2023-10-17 16:51:54,592 eodag.core [INFO ] Locations configuration loaded from /home/anesson/.config/eodag/locations.yml\n" + "2024-10-22 14:50:41,809 eodag.config [INFO ] Loading user configuration from: /home/sylvain/.config/eodag/eodag.yml\n", + "2024-10-22 14:50:41,859 eodag.core [INFO ] Locations configuration loaded from /home/sylvain/.config/eodag/locations.yml\n" ] } ], @@ -145,7 +145,7 @@ { "data": { "text/plain": [ - "['S1_SAR_GRD', 'S1_SAR_OCN', 'S1_SAR_SLC', 'S2_MSI_L1C', 'S2_MSI_L2A']" + "['S1_SAR_GRD', 'S1_SAR_OCN', 'S1_SAR_SLC', 'S2_MSI_L1C']" ] }, "execution_count": 5, @@ -172,17 +172,20 @@ { "data": { "text/plain": [ - "['astraea_eod',\n", + "['peps',\n", + " 'astraea_eod',\n", " 'aws_eos',\n", " 'cop_dataspace',\n", " 'creodias',\n", + " 'creodias_s3',\n", + " 'dedl',\n", " 'earth_search',\n", " 'earth_search_gcs',\n", + " 'geodes',\n", " 'onda',\n", - " 'peps',\n", " 'sara',\n", " 'usgs',\n", - " 'wekeo']" + " 'wekeo_main']" ] }, "execution_count": 6, diff --git a/docs/notebooks/api_user_guide/2_providers_products_available.ipynb b/docs/notebooks/api_user_guide/2_providers_products_available.ipynb index 64896afc..696ff46d 100644 --- a/docs/notebooks/api_user_guide/2_providers_products_available.ipynb +++ b/docs/notebooks/api_user_guide/2_providers_products_available.ipynb @@ -18,17 +18,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-08-09 09:14:18,559 eodag.config [INFO ] Loading user configuration from: /home/sylvain/workspace/eodag/eodag/resources/user_conf_template.yml\n", - "2024-08-09 09:14:18,611 eodag.core [INFO ] usgs: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,613 eodag.core [INFO ] aws_eos: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,614 eodag.core [INFO ] meteoblue: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,615 eodag.core [INFO ] hydroweb_next: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,618 eodag.core [INFO ] wekeo_main: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,619 eodag.core [INFO ] wekeo_ecmwf: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,621 eodag.core [INFO ] wekeo_cmems: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,622 eodag.core [INFO ] creodias_s3: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,624 eodag.core [INFO ] dedl: provider needing auth for search has been pruned because no credentials could be found\n", - "2024-08-09 09:14:18,658 eodag.core [INFO ] Locations configuration loaded from /home/sylvain/.config/eodag/locations.yml\n" + "2024-10-22 14:59:44,334 eodag.config [INFO ] Loading user configuration from: /home/sylvain/.config/eodag/eodag.yml\n", + "2024-10-22 14:59:44,377 eodag.core [INFO ] Locations configuration loaded from /home/sylvain/.config/eodag/locations.yml\n" ] } ], @@ -63,22 +54,32 @@ "text/plain": [ "['peps',\n", " 'astraea_eod',\n", + " 'aws_eos',\n", " 'cop_ads',\n", " 'cop_cds',\n", " 'cop_dataspace',\n", " 'cop_marine',\n", " 'creodias',\n", + " 'creodias_s3',\n", + " 'dedl',\n", " 'dedt_lumi',\n", " 'earth_search',\n", " 'earth_search_cog',\n", " 'earth_search_gcs',\n", " 'ecmwf',\n", " 'eumetsat_ds',\n", + " 'geodes',\n", + " 'hydroweb_next',\n", + " 'meteoblue',\n", " 'onda',\n", " 'planetary_computer',\n", " 'sara',\n", " 'theia',\n", - " 'usgs_satapi_aws']" + " 'usgs',\n", + " 'usgs_satapi_aws',\n", + " 'wekeo_cmems',\n", + " 'wekeo_ecmwf',\n", + " 'wekeo_main']" ] }, "execution_count": 2, @@ -100,7 +101,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "eodag has 18 providers already configured.\n" + "eodag has 28 providers already configured.\n" ] } ], @@ -125,12 +126,18 @@ "text/plain": [ "['peps',\n", " 'astraea_eod',\n", + " 'aws_eos',\n", " 'cop_dataspace',\n", " 'creodias',\n", + " 'creodias_s3',\n", + " 'dedl',\n", " 'earth_search',\n", " 'earth_search_gcs',\n", + " 'geodes',\n", " 'onda',\n", - " 'sara']" + " 'sara',\n", + " 'usgs',\n", + " 'wekeo_main']" ] }, "execution_count": 4, @@ -178,7 +185,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "EODAG has 216 product types stored in its internal catalog.\n" + "EODAG has 248 product types stored in its internal catalog.\n" ] } ], diff --git a/docs/notebooks/api_user_guide/3_configuration.ipynb b/docs/notebooks/api_user_guide/3_configuration.ipynb index 9b64e66b..34b4a300 100644 --- a/docs/notebooks/api_user_guide/3_configuration.ipynb +++ b/docs/notebooks/api_user_guide/3_configuration.ipynb @@ -46,25 +46,26 @@ " 'cop_dataspace',\n", " 'cop_marine',\n", " 'creodias',\n", + " 'creodias_s3',\n", + " 'dedl',\n", " 'dedt_lumi',\n", " 'earth_search',\n", " 'earth_search_cog',\n", " 'earth_search_gcs',\n", " 'ecmwf',\n", " 'eumetsat_ds',\n", + " 'geodes',\n", " 'hydroweb_next',\n", " 'meteoblue',\n", " 'onda',\n", " 'planetary_computer',\n", " 'sara',\n", " 'theia',\n", + " 'usgs',\n", " 'usgs_satapi_aws',\n", " 'wekeo_cmems',\n", " 'wekeo_ecmwf',\n", - " 'wekeo_main',\n", - " 'creodias_s3',\n", - " 'dedl',\n", - " 'usgs']" + " 'wekeo_main']" ] }, "execution_count": 2, @@ -279,7 +280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "nbsphinx": { "execute": "always" diff --git a/docs/plugins.rst b/docs/plugins.rst index 52abea5e..d7aceaf4 100644 --- a/docs/plugins.rst +++ b/docs/plugins.rst @@ -60,6 +60,8 @@ The providers are implemented with a triplet of Search/Authentication/Download +------------------------+------------------------------------+---------------------------------+------------------+ \| ``eumetsat_ds`` \| \|QueryStringSearch\| \| \|TokenAuth\| \| \|HTTPDownload\| \| +------------------------+------------------------------------+---------------------------------+------------------+ +\| ``geodes`` \| \|StacSearch\| \| \|HTTPHeaderAuth\| \| \|HTTPDownload\| \| ++------------------------+------------------------------------+---------------------------------+------------------+ \| ``hydroweb_next`` \| \|StacSearch\| \| \|HTTPHeaderAuth\| \| \|HTTPDownload\| \| +------------------------+------------------------------------+---------------------------------+------------------+ \| ``meteoblue`` \| \|BuildPostSearchResult\| \| \|HttpQueryStringAuth\| \| \|HTTPDownload\| \| diff --git a/docs/stac.rst b/docs/stac.rst index 35517f32..f840b015 100644 --- a/docs/stac.rst +++ b/docs/stac.rst @@ -11,9 +11,9 @@ STAC client ----------- STAC API providers can be configured to be used for `search` and `download` using EODAG. -Some providers (astraea_eod, earth_search, usgs_satapi_aws) are already implemented -and new providers can be dynamically added by the user. Static catalogs can also be -fetched by EODAG. +Some providers (astraea_eod, dedl, earth_search, geodes, hydroweb_next, planetary_computer, +usgs_satapi_aws) are already implemented and new providers can be dynamically added by the user. +Static catalogs can also be fetched by EODAG. STAC server ----------- diff --git a/eodag/api/core.py b/eodag/api/core.py index fa4b1505..c9918fb3 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -1561,7 +1561,7 @@ class EODataAccessGateway: try: product_type = self.get_product_type_from_alias(product_type) except NoMatchingProductType: - logger.warning("product type %s not found", product_type) + logger.debug("product type %s not found", product_type) get_search_plugins_kwargs = dict(provider=provider, product_type=product_type) search_plugins = self._plugins_manager.get_search_plugins( get_search_plugins_kwargs @@ -1929,7 +1929,7 @@ class EODataAccessGateway: eo_product.product_type ) except NoMatchingProductType: - logger.warning("product type %s not found", eo_product.product_type) + logger.debug("product type %s not found", eo_product.product_type) if eo_product.search_intersection is not None: download_plugin = self._plugins_manager.get_download_plugin( diff --git a/eodag/config.py b/eodag/config.py index f3fd1c82..b0a8b662 100644 --- a/eodag/config.py +++ b/eodag/config.py @@ -268,6 +268,14 @@ class PluginConfig(yaml.YAMLObject): #: URL from which the product types can be fetched fetch_url: Optional[str] + #: HTTP method used to fetch product types + fetch_method: str + #: Request body to fetch product types using POST method + fetch_body: Dict[str, Any] + #: The f-string template for pagination requests. + next_page_url_tpl: str + #: Index of the starting page for pagination requests. + start_page: int #: Type of the provider result result_type: str #: JsonPath to the list of product types diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 3337db49..72b52cad 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -924,6 +924,8 @@ class HTTPDownload(Download): logger.info("Progress bar unavailable, please call product.download()") progress_callback = ProgressCallback(disable=True) + ssl_verify = getattr(self.config, "ssl_verify", True) + ordered_message = "" if ( "orderLink" in product.properties @@ -974,6 +976,7 @@ class HTTPDownload(Download): params=params, headers=USER_AGENT, timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + verify=ssl_verify, req_kwargs, ) as self.stream: try: diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index a3b93f86..15f4b404 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -437,15 +437,70 @@ class QueryStringSearch(Search): def discover_product_types(self, kwargs: Any) -> Optional[Dict[str, Any]]: """Fetch product types list from provider using `discover_product_types` conf + :returns: configuration dict containing fetched product types information + """ + unpaginated_fetch_url = self.config.discover_product_types.get("fetch_url") + if not unpaginated_fetch_url: + return None + + # product types pagination + next_page_url_tpl = self.config.discover_product_types.get("next_page_url_tpl") + page = self.config.discover_product_types.get("start_page", 1) + + if not next_page_url_tpl: + # no pagination + return self.discover_product_types_per_page(kwargs) + + conf_update_dict: Dict[str, Any] = { + "providers_config": {}, + "product_types_config": {}, + } + + while True: + fetch_url = next_page_url_tpl.format(url=unpaginated_fetch_url, page=page) + + conf_update_dict_per_page = self.discover_product_types_per_page( + fetch_url=fetch_url, kwargs + ) + + if ( + not conf_update_dict_per_page + or not conf_update_dict_per_page.get("providers_config") + or conf_update_dict_per_page.items() <= conf_update_dict.items() + ): + # conf_update_dict_per_page is empty or a subset on existing conf + break + else: + conf_update_dict["providers_config"].update( + conf_update_dict_per_page["providers_config"] + ) + conf_update_dict["product_types_config"].update( + conf_update_dict_per_page["product_types_config"] + ) + + page += 1 + + return conf_update_dict + + def discover_product_types_per_page( + self, kwargs: Any + ) -> Optional[Dict[str, Any]]: + """Fetch product types list from provider using `discover_product_types` conf + using paginated ``kwargs["fetch_url"]`` + :returns: configuration dict containing fetched product types information """ try: prep = PreparedSearch() - fetch_url = self.config.discover_product_types.get("fetch_url") + # url from discover_product_types() or conf + fetch_url: Optional[str] = kwargs.get("fetch_url") if fetch_url is None: - return None - prep.url = fetch_url.format(self.config.__dict__) + if fetch_url := self.config.discover_product_types.get("fetch_url"): + fetch_url = fetch_url.format(self.config.__dict__) + else: + return None + prep.url = fetch_url # get auth if available if "auth" in kwargs: @@ -475,7 +530,14 @@ class QueryStringSearch(Search): "Skipping error while fetching product types for " "{} {} instance:" ).format(self.provider, self.__class__.__name__) - response = QueryStringSearch._request(self, prep) + # Query using appropriate method + fetch_method = self.config.discover_product_types.get("fetch_method", "GET") + fetch_body = self.config.discover_product_types.get("fetch_body", {}) + if fetch_method == "POST" and isinstance(self, PostJsonSearch): + prep.query_params = fetch_body + response = self._request(prep) + else: + response = QueryStringSearch._request(self, prep) except (RequestError, KeyError, AttributeError): return None else: diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 19ca0c3e..f939c869 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -7425,3 +7425,130 @@ matching_url: s3:// matching_conf: s3_endpoint: https://s3.waw3-1.cloudferro.com + +--- +!provider # MARK: geodes + name: geodes + priority: 0 + roles: + - host + description: French National Space Agency (CNES) Earth Observation portal + url: https://geodes.cnes.fr + search: !plugin + type: StacSearch + api_endpoint: https://geodes-portal.cnes.fr/api/stac/search + need_auth: false + ssl_verify: false + discover_queryables: + fetch_url: null + product_type_fetch_url: null + discover_product_types: + fetch_url: https://geodes-portal.cnes.fr/api/stac/collections + fetch_method: POST + fetch_body: + limit: 10000 + pagination: + total_items_nb_key_path: '$.context.matched' + # 1000 is ok and 2000 fails + max_items_per_page: 1000 + sort: + sort_by_tpl: '{{"sortBy": [ {{"field": "{sort_param}", "direction": "{sort_order}" }} ] }}' + sort_by_default: + - !!python/tuple [startTimeFromAscendingNode, ASC] + sort_param_mapping: + id: spaceborne:s2TakeId + startTimeFromAscendingNode: temporal:startDate + completionTimeFromAscendingNode: temporal:endDate + platformSerialIdentifier: spaceborne:satellitePlatform + cloudCover: spaceborne:cloudCover + metadata_mapping: + uid: '$.id' + # OpenSearch Parameters for Collection Search (Table 3) + productType: + - '{{"query":{{"dataType":{{"eq":"{productType}"}}}}}}' + - '$.properties."dataType"' + processingLevel: + - '{{"query":{{"spaceborne:productLevel":{{"eq":"{processingLevel}"}}}}}}' + - '$.properties."spaceborne:productLevel"' + platformSerialIdentifier: + - '{{"query":{{"spaceborne:satellitePlatform":{{"eq":"{platformSerialIdentifier}"}}}}}}' + - '$.properties."spaceborne:satellitePlatform"' + instrument: + - '{{"query":{{"spaceborne:satelliteSensor":{{"eq":"{instrument}"}}}}}}' + - '$.properties."spaceborne:satelliteSensor"' + # INSPIRE obligated OpenSearch Parameters for Collection Search (Table 4) + title: '{$.properties."accessService:endpointURL"#replace_str(r"^.\/(\w+)\.zip$",r"\1")}' + keyword: '$.properties."spaceborne:keywords"' + # OpenSearch Parameters for Product Search (Table 5) + orbitNumber: + - '{{"query":{{"spaceborne:absoluteOrbitID":{{"eq":"{orbitNumber}"}}}}}}' + - '$.properties."spaceborne:absoluteOrbitID"' + orbitDirection: + - '{{"query":{{"spaceborne:orbitDirection":{{"eq":"{orbitDirection}"}}}}}}' + - '$.properties."spaceborne:orbitDirection"' + swathIdentifier: + - '{{"query":{{"spaceborne:swath":{{"eq":"{swathIdentifier}"}}}}}}' + - '$.properties."spaceborne:swath"' + cloudCover: + - '{{"query":{{"spaceborne:cloudCover":{{"lte":"{cloudCover}"}}}}}}' + - '$.properties."spaceborne:cloudCover"' + sensorMode: + - '{{"query":{{"spaceborne:sensorMode":{{"eq":"{sensorMode}"}}}}}}' + - '$.properties."spaceborne:sensorMode"' + # OpenSearch Parameters for Acquistion Parameters Search (Table 6) + startTimeFromAscendingNode: + - '{{"query":{{"temporal:endDate":{{"lte":"{startTimeFromAscendingNode#to_iso_utc_datetime}"}}}}}}' + - '$.properties."temporal:startDate"' + completionTimeFromAscendingNode: + - '{{"query":{{"temporal:startDate":{{"gte":"{completionTimeFromAscendingNode#to_iso_utc_datetime}"}}}}}}' + - '$.properties."temporal:endDate"' + polarizationChannels: + - '{{"query":{{"spaceborne:polarization":{{"eq":"{polarizationChannels}"}}}}}}' + - '$.properties."spaceborne:polarization"' + # Custom parameters (not defined in the base document referenced above) + id: + - '{{"query":{{"accessService:endpointURL":{{"contains":"{id}"}}}}}}' + - '{$.properties."accessService:endpointURL"#replace_str(r"^.\/(\w+)\.zip$",r"\1")}' + geometry: + - '{{"intersects":{geometry#to_geojson}}}' + - '($.geometry.`str()`.`sub(/^None$/, POLYGON((180 -90, 180 90, -180 90, -180 -90, 180 -90)))`)\|($.geometry[*])' + providerProductType: + - '{{"query":{{"spaceborne:productType":{{"eq":"{providerProductType}"}}}}}}' + - '$.null' + downloadLink: '$.assets[?(@.roles[0] == "data")].href' + quicklook: '$.assets[?(@.roles[0] == "overview")].href' + thumbnail: '$.assets[?(@.roles[0] == "overview")].href' + # storageStatus set to ONLINE for consistency between providers + storageStatus: '{$.null#replace_str("Not Available","ONLINE")}' + products: + S1_SAR_OCN: + providerProductType: OCN + productType: PEPS_S1_L2 + metadata_mapping: + cloudCover: '$.null' + S1_SAR_GRD: + providerProductType: GRD + productType: PEPS_S1_L1 + metadata_mapping: + cloudCover: '$.null' + S1_SAR_SLC: + providerProductType: SLC + productType: PEPS_S1_L1 + metadata_mapping: + cloudCover: '$.null' + S2_MSI_L1C: + productType: PEPS_S2_L1C + S2_MSI_L2A_MAJA: + productType: MUSCATE_SENTINEL2_SENTINEL2_L2A + GENERIC_PRODUCT_TYPE: + productType: '{productType}' + download: !plugin + type: HTTPDownload + ssl_verify: false + ignore_assets: true + archive_depth: 2 + auth: !plugin + type: HTTPHeaderAuth + matching_url: https://geodes-portal.cnes.fr + headers: + X-API-Key: "{apikey}" diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index 3ae98246..e9729747 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -15,42 +15,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -peps: - priority: # Lower value means lower priority (Default: 1) - search: # Search parameters configuration - download: - extract: # whether to extract the downloaded products, only applies to archived products (true or false, Default: true). - output_dir: # where to store downloaded products, as an absolute file path (Default: local temporary directory) - dl_url_params: # additional parameters to pass over to the download url as an url parameter - delete_archive: # whether to delete the downloaded archives (true or false, Default: true). - auth: - credentials: - username: - password: -theia: +astraea_eod: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration - download: - extract: - output_dir: - dl_url_params: + search: # Search parameters configuration auth: credentials: - ident: - pass: -usgs: - priority: # Lower value means lower priority (Default: 0) - api: - extract: + aws_access_key_id: + aws_secret_access_key: + aws_profile: + download: output_dir: - dl_url_params: - product_location_scheme: - credentials: - username: - password: aws_eos: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration search_auth: credentials: api_key: @@ -61,9 +38,27 @@ aws_eos: aws_profile: download: output_dir: -creodias: +cop_ads: + priority: # Lower value means lower priority (Default: 0) + search: # Search parameters configuration + download: + extract: + output_dir: + auth: + credentials: + apikey: +cop_cds: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration + download: + extract: + output_dir: + auth: + credentials: + apikey: +cop_dataspace: + priority: # Lower value means lower priority (Default: 0) + search: # Search parameters configuration download: extract: output_dir: @@ -71,10 +66,15 @@ creodias: credentials: username: password: - totp: # set totp dynamically, see https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication -onda: +cop_marine: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration + download: + extract: + output_dir: +creodias: + priority: # Lower value means lower priority (Default: 0) + search: # Search parameters configuration download: extract: output_dir: @@ -82,29 +82,37 @@ onda: credentials: username: password: -astraea_eod: + totp: # set totp dynamically, see https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication +creodias_s3: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration + download: + output_dir: auth: credentials: aws_access_key_id: aws_secret_access_key: - aws_profile: +dedl: + priority: # Lower value means lower priority (Default: 0) + search: # Search parameters configuration download: output_dir: -usgs_satapi_aws: - priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration auth: credentials: - aws_access_key_id: - aws_secret_access_key: - aws_profile: + username: + password: +dedt_lumi: + priority: # Lower value means lower priority (Default: 0) + search: download: output_dir: + auth: + credentials: + username: + password: earth_search: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration auth: credentials: aws_access_key_id: @@ -114,12 +122,12 @@ earth_search: output_dir: earth_search_cog: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration download: output_dir: earth_search_gcs: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration auth: credentials: aws_access_key_id: @@ -133,46 +141,44 @@ ecmwf: credentials: username: password: -cop_ads: +eumetsat_ds: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration - download: - extract: + search: # Search parameters configuration output_dir: auth: - credentials: - apikey: -cop_cds: - priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + credentials: + username: + password: download: - extract: output_dir: +geodes: + priority: # Lower value means lower priority (Default: 0) + search: # Search parameters configuration auth: credentials: apikey: -sara: - priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration download: extract: output_dir: - delete_archive: +hydroweb_next: + priority: # Lower value means lower priority (Default: 0) + search: # Search parameters configuration auth: credentials: - username: - password: + apikey: + download: + output_dir: meteoblue: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration auth: credentials: apikey: download: output_dir: -cop_dataspace: +onda: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration download: extract: output_dir: @@ -180,59 +186,69 @@ cop_dataspace: credentials: username: password: -planetary_computer: - priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration +peps: + priority: # Lower value means lower priority (Default: 1) + search: # Search parameters configuration + download: + extract: # whether to extract the downloaded products, only applies to archived products (true or false, Default: true). + output_dir: # where to store downloaded products, as an absolute file path (Default: local temporary directory) + dl_url_params: # additional parameters to pass over to the download url as an url parameter + delete_archive: # whether to delete the downloaded archives (true or false, Default: true). auth: credentials: - apikey: - download: - output_dir: -hydroweb_next: + username: + password: +planetary_computer: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration auth: credentials: apikey: download: output_dir: -wekeo_main: +sara: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration download: + extract: output_dir: + delete_archive: auth: credentials: username: password: -wekeo_ecmwf: +theia: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration download: + extract: output_dir: + dl_url_params: auth: credentials: - username: - password: -wekeo_cmems: + ident: + pass: +usgs: priority: # Lower value means lower priority (Default: 0) - search: - download: + api: + extract: output_dir: - auth: + dl_url_params: + product_location_scheme: credentials: username: password: -creodias_s3: +usgs_satapi_aws: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration - download: - output_dir: + search: # Search parameters configuration auth: credentials: - aws_access_key_id: - aws_secret_access_key: -dedt_lumi: + aws_access_key_id: + aws_secret_access_key: + aws_profile: + download: + output_dir: +wekeo_cmems: priority: # Lower value means lower priority (Default: 0) search: download: @@ -241,28 +257,21 @@ dedt_lumi: credentials: username: password: -dedl: +wekeo_ecmwf: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration download: output_dir: - auth: - credentials: - username: - password: -eumetsat_ds: - priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration - output_dir: auth: credentials: username: password: - download: - output_dir: -cop_marine: +wekeo_main: priority: # Lower value means lower priority (Default: 0) - search: # Search parameters configuration + search: # Search parameters configuration download: - extract: output_dir: + auth: + credentials: + username: + password:	Support geodes provider Support geodes provider : https://geodes.cnes.fr/migration-de-peps-vers-geodes/ Deprecate peps provider Add geodes provider	CS-SI/eodag	diff --git a/tests/integration/test_core_search.py b/tests/integration/test_core_search.py index 46b83844..69d20246 100644 --- a/tests/integration/test_core_search.py +++ b/tests/integration/test_core_search.py @@ -237,6 +237,7 @@ class TestCoreSearch(unittest.TestCase): "peps", "cop_dataspace", "creodias", + "geodes", "onda", "sara", "wekeo_main", @@ -272,6 +273,7 @@ class TestCoreSearch(unittest.TestCase): "peps", "cop_dataspace", "creodias", + "geodes", "onda", "sara", "wekeo_main", @@ -306,6 +308,7 @@ class TestCoreSearch(unittest.TestCase): "peps", "cop_dataspace", "creodias", + "geodes", "onda", "sara", "wekeo_main", @@ -362,6 +365,7 @@ class TestCoreSearch(unittest.TestCase): "peps", "cop_dataspace", "creodias", + "geodes", "onda", "sara", "wekeo_main", @@ -404,6 +408,7 @@ class TestCoreSearch(unittest.TestCase): "peps", "cop_dataspace", "creodias", + "geodes", "onda", "sara", "wekeo_main", @@ -438,6 +443,7 @@ class TestCoreSearch(unittest.TestCase): "peps", "cop_dataspace", "creodias", + "geodes", "onda", "sara", "wekeo_main", diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 282ec539..0670b1c3 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -261,6 +261,7 @@ class TestCore(TestCoreBase): "creodias_s3", "dedl", "earth_search", + "geodes", "onda", "peps", "planetary_computer", @@ -272,6 +273,7 @@ class TestCore(TestCoreBase): "cop_dataspace", "creodias", "creodias_s3", + "geodes", "onda", "peps", "sara", @@ -289,6 +291,7 @@ class TestCore(TestCoreBase): "creodias", "creodias_s3", "dedl", + "geodes", "onda", "peps", "sara", @@ -303,6 +306,7 @@ class TestCore(TestCoreBase): "dedl", "earth_search", "earth_search_gcs", + "geodes", "onda", "peps", "sara", @@ -323,7 +327,7 @@ class TestCore(TestCoreBase): ], "S2_MSI_L2AP": ["wekeo_main"], "S2_MSI_L2A_COG": ["earth_search_cog"], - "S2_MSI_L2A_MAJA": ["theia"], + "S2_MSI_L2A_MAJA": ["geodes", "theia"], "S2_MSI_L2B_MAJA_SNOW": ["theia"], "S2_MSI_L2B_MAJA_WATER": ["theia"], "S2_MSI_L3A_WASP": ["theia"], @@ -592,6 +596,7 @@ class TestCore(TestCoreBase): "earth_search_gcs", "ecmwf", "eumetsat_ds", + "geodes", "hydroweb_next", "meteoblue", "onda", @@ -1596,6 +1601,16 @@ class TestCore(TestCoreBase): "max_sort_params": 1, }, "cop_marine": None, + "geodes": { + "max_sort_params": None, + "sortables": [ + "id", + "startTimeFromAscendingNode", + "completionTimeFromAscendingNode", + "platformSerialIdentifier", + "cloudCover", + ], + }, "hydroweb_next": { "sortables": [ "id", diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index d51f3454..1e005058 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -316,6 +316,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): params={}, headers=USER_AGENT, timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + verify=True, ) @mock.patch("eodag.plugins.download.http.requests.Session.request", autospec=True) @@ -401,6 +402,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): params={}, headers=USER_AGENT, timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + verify=True, ) @mock.patch( diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index c40fb27b..3d18d8b0 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -445,6 +445,109 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): # restore configuration search_plugin.config.discover_product_types["results_entry"] = results_entry + def test_plugins_search_querystringsearch_discover_product_types_paginated(self): + """QueryStringSearch.discover_product_types must handle pagination""" + # One of the providers that has a QueryStringSearch Search plugin and discover_product_types configured + provider = "astraea_eod" + search_plugin = self.get_search_plugin(self.product_type, provider) + + # change onfiguration for this test to filter out some collections + discover_product_types_conf = search_plugin.config.discover_product_types + search_plugin.config.discover_product_types[ + "fetch_url" + ] = "https://foo.bar/collections" + search_plugin.config.discover_product_types[ + "next_page_url_tpl" + ] = "{url}?page={page}" + search_plugin.config.discover_product_types["start_page"] = 0 + + with responses.RequestsMock( + assert_all_requests_are_fired=True + ) as mock_requests_post: + mock_requests_post.add( + responses.GET, + "https://foo.bar/collections?page=0", + json={ + "collections": [ + { + "id": "foo_collection", + "title": "The FOO collection", + "billing": "free", + } + ] + }, + ) + mock_requests_post.add( + responses.GET, + "https://foo.bar/collections?page=1", + json={ + "collections": [ + { + "id": "bar_collection", + "title": "The BAR non-free collection", + "billing": "non-free", + }, + ] + }, + ) + mock_requests_post.add( + responses.GET, + "https://foo.bar/collections?page=2", + json={"collections": []}, + ) + conf_update_dict = search_plugin.discover_product_types() + self.assertIn("foo_collection", conf_update_dict["providers_config"]) + self.assertIn("foo_collection", conf_update_dict["product_types_config"]) + self.assertIn("bar_collection", conf_update_dict["providers_config"]) + self.assertIn("bar_collection", conf_update_dict["product_types_config"]) + self.assertEqual( + conf_update_dict["providers_config"]["foo_collection"]["productType"], + "foo_collection", + ) + self.assertEqual( + conf_update_dict["product_types_config"]["foo_collection"]["title"], + "The FOO collection", + ) + + # restore configuration + search_plugin.config.discover_product_types = discover_product_types_conf + + @mock.patch("eodag.plugins.search.qssearch.PostJsonSearch._request", autospec=True) + def test_plugins_search_querystringsearch_discover_product_types_post( + self, mock__request + ): + """QueryStringSearch.discover_product_types must be able to query using POST requests""" + # One of the providers that has a QueryStringSearch.discover_product_types configured with POST requests + provider = "geodes" + search_plugin = self.get_search_plugin(self.product_type, provider) + + mock__request.return_value = mock.Mock() + mock__request.return_value.json.return_value = { + "collections": [ + { + "id": "foo_collection", + "title": "The FOO collection", + }, + { + "id": "bar_collection", + "title": "The BAR collection", + }, + ] + } + conf_update_dict = search_plugin.discover_product_types() + self.assertIn("foo_collection", conf_update_dict["providers_config"]) + self.assertIn("foo_collection", conf_update_dict["product_types_config"]) + self.assertIn("bar_collection", conf_update_dict["providers_config"]) + self.assertIn("bar_collection", conf_update_dict["product_types_config"]) + self.assertEqual( + conf_update_dict["providers_config"]["foo_collection"]["productType"], + "foo_collection", + ) + self.assertEqual( + conf_update_dict["product_types_config"]["foo_collection"]["title"], + "The FOO collection", + ) + @mock.patch("eodag.plugins.search.qssearch.requests.get", autospec=True) def test_plugins_search_querystringsearch_discover_product_types_with_query_param( self, mock__request	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 3, "test_score": 0 }, "num_modified_files": 14 }	3.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.23 boto3-stubs==1.37.23 botocore==1.37.23 botocore-stubs==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@cdee784fa2d227c8e908610422140188df4ed142#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy==1.15.0 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.16 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.0 mypy-boto3-sqs==1.37.0 mypy-extensions==1.0.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tox-uv==1.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.24.2 types-cachetools==5.5.0.20240820 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-python-dateutil==2.9.0.20241206 types-PyYAML==6.0.12.20250326 types-requests==2.31.0.6 types-s3transfer==0.11.4 types-setuptools==78.1.0.20250329 types-tqdm==4.67.0.20250319 types-urllib3==1.26.25.14 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uv==0.6.11 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.23 - boto3-stubs==1.37.23 - botocore==1.37.23 - botocore-stubs==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==3.0.1.dev13+gcdee784f - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy==1.15.0 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.16 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.0 - mypy-boto3-sqs==1.37.0 - mypy-extensions==1.0.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tox-uv==1.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.24.2 - types-cachetools==5.5.0.20240820 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-python-dateutil==2.9.0.20241206 - types-pyyaml==6.0.12.20250326 - types-requests==2.31.0.6 - types-s3transfer==0.11.4 - types-setuptools==78.1.0.20250329 - types-tqdm==4.67.0.20250319 - types-urllib3==1.26.25.14 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uv==0.6.11 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_nothing", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_on_first", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_on_fourth", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_find_on_fourth_empty_results", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_given_provider", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_fallback_raise_errors", "tests/units/test_core.py::TestCore::test_available_sortables", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_tar_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_zip_file_ok", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_discover_product_types_paginated", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_discover_product_types_post" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_auths_matching", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_buildpost", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_odata", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_postjson", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_qssearch", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_stacsearch", "tests/integration/test_core_search.py::TestCoreSearch::test_core_search_errors_usgs", "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_guess_product_type_with_filter", "tests/units/test_core.py::TestCore::test_guess_product_type_with_mission_dates", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_list_queryables", "tests/units/test_core.py::TestCore::test_list_queryables_with_constraints", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_skipped_plugin", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_read_only_home_dir", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available_with_alias", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreSearch::test_search_sort_by", "tests/units/test_core.py::TestCoreSearch::test_search_sort_by_raise_errors", "tests/units/test_core.py::TestCoreDownload::test_download_local_product", "tests/units/test_core.py::TestCoreProductAlias::test_get_alias_from_product_type", "tests/units/test_core.py::TestCoreProductAlias::test_get_product_type_from_alias", "tests/units/test_core.py::TestCoreProviderGroup::test_available_providers_by_group", "tests/units/test_core.py::TestCoreProviderGroup::test_discover_product_types_grouped_providers", "tests/units/test_core.py::TestCoreProviderGroup::test_fetch_product_types_list_grouped_providers", "tests/units/test_core.py::TestCoreProviderGroup::test_get_search_plugins", "tests/units/test_core.py::TestCoreProviderGroup::test_list_product_types", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_asset_filter", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_resume", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_file_without_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets_without_ssl", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_nonzip_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_download_cop_ads", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get_raises_if_request_failed", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status_search_again", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_matching_product_type", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_offline", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_online", "tests/units/test_download_plugins.py::TestDownloadPluginCreodiasS3::test_plugins_download_creodias_s3", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearchXml::test_plugins_search_querystringsearch_xml_count_and_search_mundi", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearchXml::test_plugins_search_querystringsearch_xml_distinct_product_type_mtd_mapping", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearchXml::test_plugins_search_querystringsearch_xml_no_count_and_search_mundi", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_timeout", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_count_and_search_peps", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_discover_product_types", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_discover_product_types_keywords", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_discover_product_types_with_query_param", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_discover_queryables", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_distinct_product_type_mtd_mapping", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_no_count_and_search_peps", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringsearch_search_cloudcover_peps", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos_s2l2a", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_default_dates", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_distinct_product_type_mtd_mapping", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_query_params_wekeo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_request_auth_error", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_request_error", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_search_cloudcover_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda_per_product_metadata_query", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda_per_product_metadata_query_request_error", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_distinct_product_type_mtd_mapping", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_normalize_results_onda", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_search_cloudcover_onda", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_with_ssl_context", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_default_geometry", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_distinct_product_type_mtd_mapping", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_distinct_product_type_mtd_mapping_astraea_eod", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_mapping_earthsearch", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_opened_time_intervals", "tests/units/test_search_plugins.py::TestSearchPluginBuildPostSearchResult::test_plugins_search_buildpostsearchresult_count_and_search", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_check_request_status", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_create_data_request", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_get_result_data", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_get_result_data_ssl_verify_false", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_search_datareq_dates_required", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_search_datareq_distinct_product_type_mtd_mapping", "tests/units/test_search_plugins.py::TestSearchPluginCreodiasS3Search::test_plugins_search_creodias_s3_client_error", "tests/units/test_search_plugins.py::TestSearchPluginCreodiasS3Search::test_plugins_search_creodias_s3_links", "tests/units/test_search_plugins.py::TestSearchPluginBuildSearchResult::test_plugins_search_buildsearchresult_dates_missing", "tests/units/test_search_plugins.py::TestSearchPluginBuildSearchResult::test_plugins_search_buildsearchresult_discover_queryables", "tests/units/test_search_plugins.py::TestSearchPluginBuildSearchResult::test_plugins_search_buildsearchresult_discover_queryables_with_local_constraints_file", "tests/units/test_search_plugins.py::TestSearchPluginBuildSearchResult::test_plugins_search_buildsearchresult_exclude_end_date", "tests/units/test_search_plugins.py::TestSearchPluginBuildSearchResult::test_plugins_search_buildsearchresult_with_custom_producttype", "tests/units/test_search_plugins.py::TestSearchPluginBuildSearchResult::test_plugins_search_buildsearchresult_with_producttype", "tests/units/test_search_plugins.py::TestSearchPluginBuildSearchResult::test_plugins_search_buildsearchresult_without_producttype", "tests/units/test_search_plugins.py::TestSearchPluginCopMarineSearch::test_plugins_search_cop_marine_query_no_dates_in_id", "tests/units/test_search_plugins.py::TestSearchPluginCopMarineSearch::test_plugins_search_cop_marine_query_with_dates", "tests/units/test_search_plugins.py::TestSearchPluginCopMarineSearch::test_plugins_search_cop_marine_query_with_id", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearchWithStacQueryables::test_plugins_search_postjsonsearch_discover_queryables", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearchWithStacQueryables::test_plugins_search_postjsonsearchwithstacqueryables_search_wekeomain" ]	[]	Apache License 2.0	null
CS-SI__eodag-1536	b7cc06e19025e5bbf14e28a0975f1749e21e9be3	2025-02-21 10:04:30	67f653deb272109dae563f5cc513ca696561bf2c	github-actions[bot]: ## Test Results 2 files - 2 2 suites - 2 3m 14s ⏱️ - 3m 24s 607 tests ± 0 597 ✅ - 7 3 💤 ± 0 7 ❌ + 7 1 214 runs - 1 214 1 194 ✅ - 1 136 6 💤 - 92 14 ❌ +14 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/37595570511). Results for commit 077e0afa. ± Comparison against base commit b7cc06e1. [test-results]:data:application/gzip;base64,H4sIAFtQuGcC/02NSQ7DIAwAvxJx7oElQOlnKoeAhJqEiuUU9e91RLbjjO3xSnyYXCavjj86kmsoJ4w1QQlxQWSmR4Gjsg0V1Qe9c7UWlTQ39QlfVOIUHsKE4tpwKcWEhqJJddmajLN+p6PJ2temWlMdvCevm3vSxnkOBYFQrR0FD5SaXjIQ2o7GgeIDGIHMuHhKOWhFfn8FOcjjCAEAAA== github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports/data/geodes_s3_user_conf/./badge.svg) ## Code Coverage (Ubuntu) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ __init__.py 8 0 100.00% cli.py 323 64 80.19% 62-63, 87, 674-737, 839-890, 894 config.py 430 27 93.72% 80-82, 91, 99, 103-105, 176, 187, 692-694, 807-810, 853-854, 863-864, 969, 1032-1037, 1039 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 783 75 90.42% 317-318, 368, 644, 658, 702-705, 743, 787, 821, 866-871, 897, 988, 1056, 1194, 1279-1291, 1327, 1329, 1357, 1361-1372, 1385-1391, 1474-1477, 1510-1530, 1582, 1599-1603, 1615-1618, 1954, 1978-1984, 2235, 2239-2243, 2252-2254, 2298-2299, 2328-2329 api/search_result.py 58 4 93.10% 82, 91, 98, 112 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 90, 176, 184, 187-191 api/product/_product.py 196 19 90.31% 70-72, 236-237, 312, 341, 398, 412-415, 428, 452-455, 515-518 api/product/metadata_mapping.py 695 64 90.79% 119-121, 218, 250-251, 297-298, 308-320, 322, 333, 398-399, 436, 457-460, 483, 491-492, 578-579, 603-604, 610-613, 628-629, 778, 824, 977, 986-990, 1007-1012, 1139, 1153-1173, 1193, 1198, 1327, 1341, 1366, 1412, 1464, 1487-1488, 1504-1508, 1524, 1532 api/product/drivers/__init__.py 20 2 90.00% 33-36 api/product/drivers/base.py 29 1 96.55% 98 api/product/drivers/generic.py 7 0 100.00% api/product/drivers/sentinel1.py 15 0 100.00% api/product/drivers/sentinel2.py 15 0 100.00% plugins/__init__.py 0 0 100.00% plugins/base.py 21 2 90.48% 48, 55 plugins/manager.py 172 14 91.86% 106-111, 161, 199, 221, 225, 249, 389-392, 404-405 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 97 10 89.69% 171-173, 220-221, 247-249, 302-303 plugins/apis/usgs.py 180 26 85.56% 156, 258, 292, 327-329, 334, 360-361, 366, 396-403, 414-419, 441-447, 478 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 20 0 100.00% plugins/authentication/base.py 17 2 88.24% 45, 58 plugins/authentication/generic.py 14 2 85.71% 51, 61 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 46 7 84.78% 151-154, 175-180 plugins/authentication/oauth.py 13 7 46.15% 43-45, 49-52 plugins/authentication/openid_connect.py 208 28 86.54% 80-81, 93-111, 149, 155-183, 191, 323-326, 352, 386 plugins/authentication/qsauth.py 34 1 97.06% 91 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 100 16 84.00% 141, 170, 172, 207-220, 276-280 plugins/authentication/token_exchange.py 36 14 61.11% 75, 92-120 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 43 plugins/crunch/filter_date.py 59 14 76.27% 52-57, 69, 78, 87, 90, 102-104, 113-115, 122 plugins/crunch/filter_latest_intersect.py 47 8 82.98% 52-53, 69, 78-81, 83, 90-93 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 83 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 66-69, 76-79, 85, 93, 104-120 plugins/crunch/filter_property.py 30 5 83.33% 55-60, 63-64 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 491 152 69.04% 270, 283, 350-353, 367-371, 413-415, 419, 451-452, 458-462, 492, 524, 528, 535, 565-573, 577, 609-617, 628-630, 661-735, 753-811, 822-827, 880, 895-897, 900, 910-918, 926-939, 949-971, 978-990, 1028, 1054, 1099-1101, 1321 plugins/download/base.py 253 42 83.40% 127, 155, 235-238, 291-292, 340-344, 350-351, 393, 396-410, 422, 426, 490-494, 524-525, 550-558, 560-564, 607, 629, 651, 659 plugins/download/creodias_s3.py 25 9 64.00% 55-69 plugins/download/http.py 529 95 82.04% 227, 323-326, 329, 336-341, 359-374, 391, 403, 451, 458-464, 482, 496, 510, 518-520, 536-541, 552, 570, 606-609, 638, 642, 662, 739, 758-769, 777-782, 792-809, 827, 857-866, 902, 927-928, 947-952, 958, 961, 978, 981, 996-997, 1032, 1094, 1109, 1168-1169, 1175, 1185, 1221, 1257, 1277, 1312-1314 plugins/download/s3rest.py 116 24 79.31% 119, 153, 160, 195, 222-229, 232-234, 238, 249-255, 263-264, 267-271, 294, 315-318 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 145 11 92.41% 101, 105, 129-135, 275, 295, 428 plugins/search/build_search_result.py 357 44 87.68% 239, 267-268, 304, 307, 378-381, 470-487, 515, 564, 566, 597, 631-633, 637, 661, 696, 747, 795-810, 853, 878, 881, 889, 1001-1002, 1008, 1019, 1077, 1128 plugins/search/cop_marine.py 244 47 80.74% 56, 64-66, 76-77, 82, 87-88, 104, 106, 109, 175-176, 228, 234, 238, 242, 255, 266-267, 275, 303, 307, 322, 326, 330, 334, 338-342, 348-351, 354-368, 385, 434-438, 443, 455 plugins/search/creodias_s3.py 25 1 96.00% 51 plugins/search/csw.py 105 81 22.86% 98-99, 103-104, 112-160, 166-179, 187-219, 237-278 plugins/search/data_request_search.py 202 69 65.84% 189-192, 208, 219, 223-224, 235, 240, 245, 252, 265-268, 322-323, 327, 337-343, 348, 374-377, 385-396, 413, 415, 422-425, 427-428, 446-450, 483, 493, 504, 517, 523-538, 543 plugins/search/qssearch.py 737 89 87.92% 450, 508, 522, 526-532, 560-564, 677-689, 734-737, 808-809, 857, 876, 883, 895, 952, 973, 976-977, 986-987, 996-997, 1006-1007, 1034, 1116-1121, 1125-1134, 1168, 1190, 1267, 1357, 1442-1443, 1453, 1530-1534, 1596, 1599, 1605-1606, 1627, 1655-1667, 1674, 1706-1708, 1718-1724, 1754, 1777, 1782-1783, 1798, 1814, 1899-1902, 1907-1910, 1919, 1949-1953, 1959 plugins/search/stac_list_assets.py 25 10 60.00% 44-51, 75-85 plugins/search/static_stac_search.py 75 13 82.67% 98-124, 140, 153 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 7 78.79% 35-37, 53-55, 59, 68 rest/config.py 25 0 100.00% rest/constants.py 6 0 100.00% rest/core.py 260 66 74.62% 258, 266, 284-301, 316-352, 446, 483-522, 705, 712-765 rest/errors.py 69 5 92.75% 106, 116, 127, 143-144 rest/server.py 192 24 87.50% 86, 109-111, 285-290, 318, 514-516, 533-538, 567, 569, 573-574, 578-579 rest/stac.py 319 63 80.25% 308, 330, 382-385, 412-439, 470-472, 495, 527-528, 610-650, 672-688, 780-784, 791, 845-846, 907, 997-999 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 180 6 96.67% 225-229, 282, 285, 353, 385 rest/types/queryables.py 57 5 91.23% 93-98, 162 rest/types/stac_search.py 125 7 94.40% 128, 174, 189-191, 199, 203 rest/utils/__init__.py 94 12 87.23% 101-102, 121-123, 175, 185-199 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 3 86.96% 48, 60, 62 types/__init__.py 133 41 69.17% 63, 76-80, 91-103, 131-133, 140-145, 186, 227, 237-253, 258, 260, 282, 287, 295, 305 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 108 1 99.07% 63 types/search_args.py 70 18 74.29% 60-64, 71-88, 103 types/whoosh.py 81 16 80.25% 129-132, 136-143, 155-161, 174-176 utils/__init__.py 538 38 92.94% 81, 198-199, 208-235, 238, 253, 333-337, 410-414, 433-435, 449, 541, 556, 596-597, 626, 1001-1004, 1051-1052, 1099-1100, 1234 utils/exceptions.py 46 0 100.00% utils/import_system.py 28 19 32.14% 64-78, 89-99 utils/logging.py 28 1 96.43% 41 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 38 8 78.95% 51, 53, 57, 98, 122-129 utils/requests.py 55 29 47.27% 51-52, 64, 85-96, 107-124, 128 utils/rest.py 36 1 97.22% 55 utils/s3.py 65 3 95.38% 139-140, 203 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 137, 153-156, 203-212, 222-252 TOTAL 10147 1611 84.12% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ---------------------------------- ------- ------ ------- api/core.py 0 +1 -0.13% plugins/search/stac_list_assets.py 0 -1 +4.00% utils/__init__.py 0 -2 +0.37% TOTAL 0 -2 +0.02% ``` Results for commit: 077e0afa009451a37cd9ea62ba9351a123855b76 _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.13-ubuntu-latest/coverage.xml --> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports_win/data/geodes_s3_user_conf/./badge.svg) ## Code Coverage (Windows) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 8 0 100.00% cli.py 323 64 80.19% 62-63, 87, 674-737, 839-890, 894 config.py 430 28 93.49% 80-82, 91, 99, 103-105, 176, 187, 692-694, 808-811, 854-855, 864-865, 970, 1001, 1033-1038, 1040 crunch.py 5 5 0.00% 20-24 api/__init__.py 0 0 100.00% api/core.py 783 74 90.55% 317-318, 368, 658, 702-705, 743, 787, 821, 866-871, 897, 988, 1056, 1194, 1279-1291, 1327, 1329, 1357, 1361-1372, 1385-1391, 1474-1477, 1510-1530, 1582, 1599-1603, 1615-1618, 1954, 1978-1984, 2235, 2239-2243, 2252-2254, 2298-2299, 2328-2329 api/search_result.py 58 4 93.10% 82, 91, 98, 112 api/product/__init__.py 6 0 100.00% api/product/_assets.py 48 5 89.58% 90, 176, 184, 187-191 api/product/_product.py 196 19 90.31% 70-72, 236-237, 312, 341, 398, 412-415, 428, 452-455, 515-518 api/product/metadata_mapping.py 695 65 90.65% 119-121, 218, 250-251, 297-298, 308-320, 322, 333, 398-399, 436, 457-460, 483, 491-492, 578-579, 603-604, 610-613, 628-629, 778, 824, 977, 986-990, 1007-1012, 1139, 1153-1173, 1193, 1198, 1327, 1341, 1366, 1412, 1464, 1487-1488, 1491, 1504-1508, 1524, 1532 api/product/drivers/__init__.py 20 2 90.00% 33-36 api/product/drivers/base.py 29 1 96.55% 98 api/product/drivers/generic.py 7 0 100.00% api/product/drivers/sentinel1.py 15 0 100.00% api/product/drivers/sentinel2.py 15 0 100.00% plugins/__init__.py 0 0 100.00% plugins/base.py 21 3 85.71% 48, 55, 68 plugins/manager.py 172 14 91.86% 106-111, 161, 199, 221, 225, 249, 389-392, 404-405 plugins/apis/__init__.py 0 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 97 10 89.69% 171-173, 220-221, 247-249, 302-303 plugins/apis/usgs.py 180 26 85.56% 156, 258, 292, 327-329, 334, 360-361, 366, 396-403, 414-419, 441-447, 478 plugins/authentication/__init__.py 6 1 83.33% 31 plugins/authentication/aws_auth.py 20 0 100.00% plugins/authentication/base.py 17 2 88.24% 45, 58 plugins/authentication/generic.py 14 2 85.71% 51, 61 plugins/authentication/header.py 19 0 100.00% plugins/authentication/keycloak.py 46 7 84.78% 151-154, 175-180 plugins/authentication/oauth.py 13 7 46.15% 43-45, 49-52 plugins/authentication/openid_connect.py 208 28 86.54% 80-81, 93-111, 149, 155-183, 191, 323-326, 352, 386 plugins/authentication/qsauth.py 34 1 97.06% 91 plugins/authentication/sas_auth.py 47 1 97.87% 76 plugins/authentication/token.py 100 16 84.00% 141, 170, 172, 207-220, 276-280 plugins/authentication/token_exchange.py 36 14 61.11% 75, 92-120 plugins/crunch/__init__.py 0 0 100.00% plugins/crunch/base.py 10 1 90.00% 43 plugins/crunch/filter_date.py 59 14 76.27% 52-57, 69, 78, 87, 90, 102-104, 113-115, 122 plugins/crunch/filter_latest_intersect.py 47 33 29.79% 49-54, 67-112 plugins/crunch/filter_latest_tpl_name.py 31 1 96.77% 83 plugins/crunch/filter_overlap.py 66 18 72.73% 28-30, 66-69, 76-79, 85, 93, 104-120 plugins/crunch/filter_property.py 30 5 83.33% 55-60, 63-64 plugins/download/__init__.py 0 0 100.00% plugins/download/aws.py 491 152 69.04% 270, 283, 350-353, 367-371, 413-415, 419, 451-452, 458-462, 492, 524, 528, 535, 565-573, 577, 609-617, 628-630, 661-735, 753-811, 822-827, 880, 895-897, 900, 910-918, 926-939, 949-971, 978-990, 1028, 1054, 1099-1101, 1321 plugins/download/base.py 253 44 82.61% 127, 155, 222-224, 235-238, 291-292, 340-344, 350-351, 393, 396-410, 422, 426, 490-494, 524-525, 550-558, 560-564, 607, 629, 651, 659 plugins/download/creodias_s3.py 25 9 64.00% 55-69 plugins/download/http.py 529 95 82.04% 227, 323-326, 329, 336-341, 359-374, 391, 403, 451, 458-464, 482, 496, 510, 518-520, 536-541, 552, 570, 606-609, 638, 642, 662, 739, 758-769, 777-782, 792-809, 827, 857-866, 902, 927-928, 947-952, 958, 961, 978, 981, 996-997, 1032, 1094, 1109, 1168-1169, 1175, 1185, 1221, 1257, 1277, 1312-1314 plugins/download/s3rest.py 116 24 79.31% 119, 153, 160, 195, 222-229, 232-234, 238, 249-255, 263-264, 267-271, 294, 315-318 plugins/search/__init__.py 22 0 100.00% plugins/search/base.py 145 11 92.41% 101, 105, 129-135, 275, 295, 428 plugins/search/build_search_result.py 357 45 87.39% 239, 267-268, 304, 307, 378-381, 470-487, 515, 564, 566, 597, 631-633, 637, 661, 696, 747, 761, 795-810, 853, 878, 881, 889, 1001-1002, 1008, 1019, 1077, 1128 plugins/search/cop_marine.py 244 47 80.74% 56, 64-66, 76-77, 82, 87-88, 104, 106, 109, 175-176, 228, 234, 238, 242, 255, 266-267, 275, 303, 307, 322, 326, 330, 334, 338-342, 348-351, 354-368, 385, 434-438, 443, 455 plugins/search/creodias_s3.py 25 1 96.00% 51 plugins/search/csw.py 105 81 22.86% 98-99, 103-104, 112-160, 166-179, 187-219, 237-278 plugins/search/data_request_search.py 202 69 65.84% 189-192, 208, 219, 223-224, 235, 240, 245, 252, 265-268, 322-323, 327, 337-343, 348, 374-377, 385-396, 413, 415, 422-425, 427-428, 446-450, 483, 493, 504, 517, 523-538, 543 plugins/search/qssearch.py 737 125 83.04% 450, 508, 522, 526-532, 560-564, 677-689, 734-737, 808-809, 857, 876, 883, 895, 952, 973, 976-977, 986-987, 996-997, 1006-1007, 1034, 1116-1121, 1125-1134, 1168, 1190, 1267, 1357, 1442-1443, 1453, 1530-1534, 1596, 1599, 1605-1606, 1627, 1655-1667, 1674, 1706-1708, 1718-1724, 1754, 1777, 1782-1783, 1798, 1814, 1882-1992 plugins/search/stac_list_assets.py 25 10 60.00% 44-51, 75-85 plugins/search/static_stac_search.py 75 13 82.67% 98-124, 140, 153 rest/__init__.py 4 2 50.00% 21-22 rest/cache.py 33 22 33.33% 35-37, 44-70 rest/config.py 25 1 96.00% 35 rest/constants.py 6 0 100.00% rest/core.py 260 153 41.15% 157, 159, 161, 164-165, 179-189, 198-199, 205, 208, 249-303, 316-352, 383-420, 435-451, 467-476, 483-522, 539, 581-666, 705, 712-765 rest/errors.py 69 49 28.99% 60, 65-100, 105-108, 115-118, 126-147, 155-160, 175-181 rest/server.py 192 192 0.00% 18-592 rest/stac.py 319 68 78.68% 242, 308, 330, 382-385, 412-439, 470-472, 495, 527-528, 610-650, 672-688, 715, 780-784, 791, 845-846, 852, 907, 945, 978, 997-999 rest/types/__init__.py 0 0 100.00% rest/types/collections_search.py 13 13 0.00% 18-44 rest/types/eodag_search.py 180 20 88.89% 225-229, 262-264, 282, 285, 291, 295, 353, 371-386 rest/types/queryables.py 57 13 77.19% 50-51, 58-59, 66-67, 93-98, 107-108, 162 rest/types/stac_search.py 125 11 91.20% 126-128, 174, 189-191, 199, 203, 251, 254 rest/utils/__init__.py 94 30 68.09% 72-78, 98, 101-102, 121-123, 136, 143, 168-176, 183-204 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 23 5 78.26% 43-44, 48, 60, 62 types/__init__.py 133 43 67.67% 63, 67, 76-80, 91-103, 131-133, 140-145, 186, 200, 227, 237-253, 258, 260, 282, 287, 295, 305 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 108 1 99.07% 63 types/search_args.py 70 18 74.29% 60-64, 71-88, 103 types/whoosh.py 81 16 80.25% 129-132, 136-143, 155-161, 174-176 utils/__init__.py 538 38 92.94% 81, 198-199, 208-235, 238, 253, 333-337, 410-414, 433-435, 449, 541, 556, 596-597, 626, 1001-1004, 1051-1052, 1099-1100, 1234 utils/exceptions.py 46 0 100.00% utils/import_system.py 28 19 32.14% 64-78, 89-99 utils/logging.py 28 1 96.43% 41 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/repr.py 38 8 78.95% 51, 53, 57, 98, 122-129 utils/requests.py 55 29 47.27% 51-52, 64, 85-96, 107-124, 128 utils/rest.py 36 1 97.22% 55 utils/s3.py 65 3 95.38% 139-140, 203 utils/stac_reader.py 111 45 59.46% 56-57, 63-85, 95-97, 101, 137, 153-156, 203-212, 222-252 TOTAL 10147 2045 79.85% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ---------------------------------- ------- ------ ------- plugins/search/stac_list_assets.py 0 -1 +4.00% utils/__init__.py 0 -2 +0.37% TOTAL 0 -3 +0.03% ``` Results for commit: 312966a46513a082333d46cd7212bdce36aaf769 _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.13-windows-latest/coverage.xml -->	diff --git a/eodag/config.py b/eodag/config.py index 0ceb1f1f..db5ae7a0 100644 --- a/eodag/config.py +++ b/eodag/config.py @@ -779,6 +779,7 @@ def provider_config_init( and provider_config.search.type in [ "StacSearch", + "StacListAssets", "StaticStacSearch", ] ): diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index 0d6c9e24..5221bd2d 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -157,7 +157,7 @@ geodes: download: extract: output_dir: -geodes: +geodes_s3: priority: # Lower value means lower priority (Default: 0) search: # Search parameters configuration auth:	Geodes & Geodes_s3 have the same key in pre release 3.1.0b2 Describe the bug - The latest pre-release adds geodes_s3 for internal access the CNES data lake - The latest provider config uses the same key ``` - geodes: priority: # Lower value means lower priority (Default: 0) search: # Search parameters configuration auth: credentials: apikey: download: extract: output_dir: geodes: priority: # Lower value means lower priority (Default: 0) search: # Search parameters configuration auth: credentials: aws_access_key_id: aws_secret_access_key: aws_session_token: download: extract: output_dir: ``` - This breaks the public Geodes access with error: `MisconfiguredError('The following credentials are missing for provider geodes: aws_access_key_id, aws_secret_access_key, aws_session_token')`	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 3b8faf95..9f5fdfd8 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -305,6 +305,7 @@ class TestCore(TestCoreBase): "dedl", "earth_search", "geodes", + "geodes_s3", "onda", "peps", "planetary_computer", @@ -317,6 +318,7 @@ class TestCore(TestCoreBase): "creodias", "creodias_s3", "geodes", + "geodes_s3", "onda", "peps", "sara", @@ -335,6 +337,7 @@ class TestCore(TestCoreBase): "creodias_s3", "dedl", "geodes", + "geodes_s3", "onda", "peps", "sara", @@ -349,6 +352,7 @@ class TestCore(TestCoreBase): "earth_search", "earth_search_gcs", "geodes", + "geodes_s3", "onda", "peps", "sara", @@ -368,7 +372,7 @@ class TestCore(TestCoreBase): ], "S2_MSI_L2AP": ["wekeo_main"], "S2_MSI_L2A_COG": ["earth_search_cog"], - "S2_MSI_L2A_MAJA": ["geodes", "theia"], + "S2_MSI_L2A_MAJA": ["geodes", "geodes_s3", "theia"], "S2_MSI_L2B_MAJA_SNOW": ["theia"], "S2_MSI_L2B_MAJA_WATER": ["theia"], "S2_MSI_L3A_WASP": ["theia"], @@ -635,6 +639,7 @@ class TestCore(TestCoreBase): "ecmwf", "eumetsat_ds", "geodes", + "geodes_s3", "hydroweb_next", "meteoblue", "onda", @@ -1828,6 +1833,16 @@ class TestCore(TestCoreBase): "cloudCover", ], }, + "geodes_s3": { + "max_sort_params": None, + "sortables": [ + "id", + "startTimeFromAscendingNode", + "completionTimeFromAscendingNode", + "platformSerialIdentifier", + "cloudCover", + ], + }, "hydroweb_next": { "sortables": [ "id",	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 2 }	3.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements-dev.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.27 boto3-stubs==1.37.27 botocore==1.37.27 botocore-stubs==1.37.27 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@b7cc06e19025e5bbf14e28a0975f1749e21e9be3#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy==1.15.0 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.24 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.24 mypy-boto3-sqs==1.37.0 mypy-extensions==1.0.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.2 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.1 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 PyJWT==2.10.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.1.0 pytest-html==4.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tox-uv==1.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.25.6 types-cachetools==5.5.0.20240820 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-python-dateutil==2.9.0.20241206 types-PyYAML==6.0.12.20250402 types-requests==2.31.0.6 types-s3transfer==0.11.4 types-setuptools==78.1.0.20250329 types-tqdm==4.67.0.20250401 types-urllib3==1.26.25.14 typing-inspection==0.4.0 typing_extensions==4.13.1 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uv==0.6.12 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.30.0 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.27 - boto3-stubs==1.37.27 - botocore==1.37.27 - botocore-stubs==1.37.27 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==3.1.0b3.dev12+gb7cc06e1 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy==1.15.0 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.24 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.24 - mypy-boto3-sqs==1.37.0 - mypy-extensions==1.0.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.2 - pydantic-core==2.33.1 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyjwt==2.10.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.1.0 - pytest-html==4.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tox-uv==1.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.25.6 - types-cachetools==5.5.0.20240820 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-python-dateutil==2.9.0.20241206 - types-pyyaml==6.0.12.20250402 - types-requests==2.31.0.6 - types-s3transfer==0.11.4 - types-setuptools==78.1.0.20250329 - types-tqdm==4.67.0.20250401 - types-urllib3==1.26.25.14 - typing-extensions==4.13.1 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uv==0.6.12 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.30.0 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCore::test_available_sortables", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok" ]	[]	[ "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_guess_product_type_with_filter", "tests/units/test_core.py::TestCore::test_guess_product_type_with_mission_dates", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_list_queryables", "tests/units/test_core.py::TestCore::test_list_queryables_additional", "tests/units/test_core.py::TestCore::test_list_queryables_priority_sorted", "tests/units/test_core.py::TestCore::test_list_queryables_with_constraints", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_skipped_plugin", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_queryables_repr", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_read_only_home_dir", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available_with_alias", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreSearch::test_search_sort_by", "tests/units/test_core.py::TestCoreSearch::test_search_sort_by_raise_errors", "tests/units/test_core.py::TestCoreDownload::test_download_local_product", "tests/units/test_core.py::TestCoreProductAlias::test_get_alias_from_product_type", "tests/units/test_core.py::TestCoreProductAlias::test_get_product_type_from_alias", "tests/units/test_core.py::TestCoreProviderGroup::test_available_providers_by_group", "tests/units/test_core.py::TestCoreProviderGroup::test_discover_product_types_grouped_providers", "tests/units/test_core.py::TestCoreProviderGroup::test_fetch_product_types_list_grouped_providers", "tests/units/test_core.py::TestCoreProviderGroup::test_get_search_plugins", "tests/units/test_core.py::TestCoreProviderGroup::test_list_product_types" ]	[]	Apache License 2.0	null
CS-SI__eodag-1551	643cb3c0e6228ebf83ba01bf38a80b05162a7ab4	2025-03-06 17:52:56	67f653deb272109dae563f5cc513ca696561bf2c		diff --git a/docs/_static/params_mapping_extra.csv b/docs/_static/params_mapping_extra.csv index c048a7cf..ba9cdbb3 100644 --- a/docs/_static/params_mapping_extra.csv +++ b/docs/_static/params_mapping_extra.csv @@ -1,17 +1,14 @@ parameter,cop_ads,cop_cds,cop_dataspace,cop_ewds,cop_marine,creodias,dedt_lumi,earth_search,earth_search_cog,earth_search_gcs,ecmwf,eumetsat_ds,geodes,onda,peps,planetary_computer,sara,theia,usgs_satapi_aws -_date,metadata only,metadata only,,metadata only,,,,,,,,,,,,,,, acquisitionInformation,,,,,,,,,,,,metadata only,,,,,,, assets,,,,,,,,metadata only,metadata only,metadata only,,metadata only,metadata only,,,metadata only,,,metadata only awsProductId,,,,,,,,,,,,,,,,,,,metadata only collection,,,:green:`queryable metadata`,,,:green:`queryable metadata`,,,,,,,,,,,,, -defaultGeometry,metadata only,metadata only,,metadata only,metadata only,,,,,,,metadata only,,,,,,, -downloadLink,metadata only,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only -ecmwf:system_version,,,,:green:`queryable metadata`,,,,,,,,,,,,,,, -ecmwf:version,,:green:`queryable metadata`,,,,,,,,,,,,,,,,, +defaultGeometry,,,,,metadata only,,,,,,,metadata only,,,,,,, +downloadLink,,,metadata only,,,metadata only,,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only extraInformation,,,,,,,,,,,,metadata only,,,,,,, -geometry,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` +geometry,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` gridSquare,,,,,,,,:green:`queryable metadata`,,,,,,,,,,, -id,metadata only,metadata only,:green:`queryable metadata`,metadata only,,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` +id,,,:green:`queryable metadata`,,,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` latitudeBand,,,,,,,,:green:`queryable metadata`,,,,,,,,,,, links,,,,,,,,,,,,,,,metadata only,,,, modifiedAfter,,,:green:`queryable metadata`,,,,,,,,,,,,,,,, @@ -29,7 +26,7 @@ quicklook,,,metadata only,,,metadata only,,metadata only,metadata only,metadata relativeOrbitNumber,,,:green:`queryable metadata`,,,,,,,,,,:green:`queryable metadata`,,,,,, services,,,,,,,,,,,,,,,metadata only,,,, size,,,,,,,,,,,,metadata only,,,,,,, -storageStatus,metadata only,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only +storageStatus,,,metadata only,,,metadata only,,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only thumbnail,,,metadata only,,,metadata only,,metadata only,metadata only,metadata only,,,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only tileIdentifier,,,:green:`queryable metadata`,,,:green:`queryable metadata`,,:green:`queryable metadata`,,,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`, type,,,,,,,,,,,,metadata only,,,,,,, diff --git a/docs/_static/params_mapping_offline_infos.json b/docs/_static/params_mapping_offline_infos.json index 8b48e88a..6ac1cd48 100644 --- a/docs/_static/params_mapping_offline_infos.json +++ b/docs/_static/params_mapping_offline_infos.json @@ -1,1 +1,1 @@ -{"_date": {"parameter": "_date", "open-search": "", "class": "", "description": "", "type": ""}, "abstract": {"parameter": "abstract", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Abstract.", "type": "String"}, "accessConstraint": {"parameter": "accessConstraint", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations on obtaining the resource", "type": "String "}, "acquisitionInformation": {"parameter": "acquisitionInformation", "open-search": "", "class": "", "description": "", "type": ""}, "acquisitionStation": {"parameter": "acquisitionStation", "open-search": true, "class": "", "description": "", "type": ""}, "acquisitionSubType": {"parameter": "acquisitionSubType", "open-search": true, "class": "", "description": "", "type": ""}, "acquisitionType": {"parameter": "acquisitionType", "open-search": true, "class": "", "description": "", "type": ""}, "antennaLookDirection": {"parameter": "antennaLookDirection", "open-search": true, "class": "", "description": "", "type": ""}, "archivingCenter": {"parameter": "archivingCenter", "open-search": true, "class": "", "description": "", "type": ""}, "assets": {"parameter": "assets", "open-search": "", "class": "", "description": "", "type": ""}, "availabilityTime": {"parameter": "availabilityTime", "open-search": true, "class": "", "description": "", "type": ""}, "awsProductId": {"parameter": "awsProductId", "open-search": "", "class": "", "description": "", "type": ""}, "cloudCover": {"parameter": "cloudCover", "open-search": true, "class": "", "description": "", "type": ""}, "collection": {"parameter": "collection", "open-search": "", "class": "", "description": "", "type": ""}, "completionTimeFromAscendingNode": {"parameter": "completionTimeFromAscendingNode", "open-search": true, "class": "", "description": "", "type": ""}, "creationDate": {"parameter": "creationDate", "open-search": true, "class": "", "description": "", "type": ""}, "defaultGeometry": {"parameter": "defaultGeometry", "open-search": "", "class": "", "description": "", "type": ""}, "doi": {"parameter": "doi", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "Digital Object Identifier identifying the product (see http://www.doi.org)", "type": "String"}, "dopplerFrequency": {"parameter": "dopplerFrequency", "open-search": true, "class": "", "description": "", "type": ""}, "downloadLink": {"parameter": "downloadLink", "open-search": "", "class": "", "description": "", "type": ""}, "ecmwf:system_version": {"parameter": "ecmwf:system_version", "open-search": "", "class": "", "description": "", "type": ""}, "ecmwf:version": {"parameter": "ecmwf:version", "open-search": "", "class": "", "description": "", "type": ""}, "extraInformation": {"parameter": "extraInformation", "open-search": "", "class": "", "description": "", "type": ""}, "geometry": {"parameter": "geometry", "open-search": "", "class": "", "description": "", "type": ""}, "gridSquare": {"parameter": "gridSquare", "open-search": "", "class": "", "description": "", "type": ""}, "id": {"parameter": "id", "open-search": "", "class": "", "description": "", "type": ""}, "illuminationAzimuthAngle": {"parameter": "illuminationAzimuthAngle", "open-search": true, "class": "", "description": "", "type": ""}, "illuminationElevationAngle": {"parameter": "illuminationElevationAngle", "open-search": true, "class": "", "description": "", "type": ""}, "illuminationZenithAngle": {"parameter": "illuminationZenithAngle", "open-search": true, "class": "", "description": "", "type": ""}, "incidenceAngleVariation": {"parameter": "incidenceAngleVariation", "open-search": true, "class": "", "description": "", "type": ""}, "instrument": {"parameter": "instrument", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the instrument (e.g. MERIS, AATSR, ASAR, HRVIR. SAR).", "type": "String"}, "keyword": {"parameter": "keyword", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Commonly used word(s) or formalised word(s) or phrase(s) used to describe the subject.", "type": "String"}, "latitudeBand": {"parameter": "latitudeBand", "open-search": "", "class": "", "description": "", "type": ""}, "lineage": {"parameter": "lineage", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "General explanation of the data producer\u2019s knowledge about the lineage of a dataset.", "type": "String"}, "links": {"parameter": "links", "open-search": "", "class": "", "description": "", "type": ""}, "maximumIncidenceAngle": {"parameter": "maximumIncidenceAngle", "open-search": true, "class": "", "description": "", "type": ""}, "minimumIncidenceAngle": {"parameter": "minimumIncidenceAngle", "open-search": true, "class": "", "description": "", "type": ""}, "modificationDate": {"parameter": "modificationDate", "open-search": true, "class": "", "description": "", "type": ""}, "modifiedAfter": {"parameter": "modifiedAfter", "open-search": "", "class": "", "description": "", "type": ""}, "modifiedBefore": {"parameter": "modifiedBefore", "open-search": "", "class": "", "description": "", "type": ""}, "orbitDirection": {"parameter": "orbitDirection", "open-search": true, "class": "", "description": "", "type": ""}, "orbitNumber": {"parameter": "orbitNumber", "open-search": true, "class": "", "description": "", "type": ""}, "orderLink": {"parameter": "orderLink", "open-search": "", "class": "", "description": "", "type": ""}, "organisationName": {"parameter": "organisationName", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "A string identifying the name of the organization responsible for the resource", "type": "String"}, "parentIdentifier": {"parameter": "parentIdentifier", "open-search": true, "class": "", "description": "", "type": ""}, "platform": {"parameter": "platform", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string with the platform short name (e.g. Sentinel-1)", "type": "String"}, "platformSerialIdentifier": {"parameter": "platformSerialIdentifier", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string with the Platform serial identifier", "type": "String"}, "polarizationChannels": {"parameter": "polarizationChannels", "open-search": "", "class": "", "description": "", "type": ""}, "polarizationMode": {"parameter": "polarizationMode", "open-search": "", "class": "", "description": "", "type": ""}, "processingCenter": {"parameter": "processingCenter", "open-search": true, "class": "", "description": "", "type": ""}, "processingDate": {"parameter": "processingDate", "open-search": true, "class": "", "description": "", "type": ""}, "processingLevel": {"parameter": "processingLevel", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the processing level applied to the entry", "type": "String"}, "processingMode": {"parameter": "processingMode", "open-search": true, "class": "", "description": "", "type": ""}, "processorName": {"parameter": "processorName", "open-search": true, "class": "", "description": "", "type": ""}, "productIdentifier": {"parameter": "productIdentifier", "open-search": "", "class": "", "description": "", "type": ""}, "productInformation": {"parameter": "productInformation", "open-search": "", "class": "", "description": "", "type": ""}, "productQualityStatus": {"parameter": "productQualityStatus", "open-search": true, "class": "", "description": "", "type": ""}, "productType": {"parameter": "productType", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the entry type (e.g. ER02_SAR_IM__0P, MER_RR__1P, SM_SLC__1S, GES_DISC_AIRH3STD_V005)", "type": "String "}, "productVersion": {"parameter": "productVersion", "open-search": true, "class": "", "description": "", "type": ""}, "providerProductType": {"parameter": "providerProductType", "open-search": "", "class": "", "description": "", "type": ""}, "publicationDate": {"parameter": "publicationDate", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "The date when the resource was issued", "type": "Date time"}, "publishedAfter": {"parameter": "publishedAfter", "open-search": "", "class": "", "description": "", "type": ""}, "publishedBefore": {"parameter": "publishedBefore", "open-search": "", "class": "", "description": "", "type": ""}, "qs": {"parameter": "qs", "open-search": "", "class": "", "description": "", "type": ""}, "quicklook": {"parameter": "quicklook", "open-search": "", "class": "", "description": "", "type": ""}, "relativeOrbitNumber": {"parameter": "relativeOrbitNumber", "open-search": "", "class": "", "description": "", "type": ""}, "resolution": {"parameter": "resolution", "open-search": true, "class": "", "description": "", "type": ""}, "sensorMode": {"parameter": "sensorMode", "open-search": true, "class": "", "description": "", "type": ""}, "sensorType": {"parameter": "sensorType", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the sensor type. Suggested values are: OPTICAL, RADAR, ALTIMETRIC, ATMOSPHERIC, LIMB", "type": "String"}, "services": {"parameter": "services", "open-search": "", "class": "", "description": "", "type": ""}, "size": {"parameter": "size", "open-search": "", "class": "", "description": "", "type": ""}, "snowCover": {"parameter": "snowCover", "open-search": true, "class": "", "description": "", "type": ""}, "startTimeFromAscendingNode": {"parameter": "startTimeFromAscendingNode", "open-search": true, "class": "", "description": "", "type": ""}, "storageStatus": {"parameter": "storageStatus", "open-search": "", "class": "", "description": "", "type": ""}, "swathIdentifier": {"parameter": "swathIdentifier", "open-search": true, "class": "", "description": "", "type": ""}, "thumbnail": {"parameter": "thumbnail", "open-search": "", "class": "", "description": "", "type": ""}, "tileIdentifier": {"parameter": "tileIdentifier", "open-search": "", "class": "", "description": "", "type": ""}, "title": {"parameter": "title", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "A name given to the resource", "type": "String "}, "topicCategory": {"parameter": "topicCategory", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Main theme(s) of the dataset", "type": "String "}, "type": {"parameter": "type", "open-search": "", "class": "", "description": "", "type": ""}, "uid": {"parameter": "uid", "open-search": "", "class": "", "description": "", "type": ""}, "utmZone": {"parameter": "utmZone", "open-search": "", "class": "", "description": "", "type": ""}} +{"abstract": {"parameter": "abstract", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Abstract.", "type": "String"}, "accessConstraint": {"parameter": "accessConstraint", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations on obtaining the resource", "type": "String "}, "acquisitionInformation": {"parameter": "acquisitionInformation", "open-search": "", "class": "", "description": "", "type": ""}, "acquisitionStation": {"parameter": "acquisitionStation", "open-search": true, "class": "", "description": "", "type": ""}, "acquisitionSubType": {"parameter": "acquisitionSubType", "open-search": true, "class": "", "description": "", "type": ""}, "acquisitionType": {"parameter": "acquisitionType", "open-search": true, "class": "", "description": "", "type": ""}, "antennaLookDirection": {"parameter": "antennaLookDirection", "open-search": true, "class": "", "description": "", "type": ""}, "archivingCenter": {"parameter": "archivingCenter", "open-search": true, "class": "", "description": "", "type": ""}, "assets": {"parameter": "assets", "open-search": "", "class": "", "description": "", "type": ""}, "availabilityTime": {"parameter": "availabilityTime", "open-search": true, "class": "", "description": "", "type": ""}, "awsProductId": {"parameter": "awsProductId", "open-search": "", "class": "", "description": "", "type": ""}, "cloudCover": {"parameter": "cloudCover", "open-search": true, "class": "", "description": "", "type": ""}, "collection": {"parameter": "collection", "open-search": "", "class": "", "description": "", "type": ""}, "completionTimeFromAscendingNode": {"parameter": "completionTimeFromAscendingNode", "open-search": true, "class": "", "description": "", "type": ""}, "creationDate": {"parameter": "creationDate", "open-search": true, "class": "", "description": "", "type": ""}, "defaultGeometry": {"parameter": "defaultGeometry", "open-search": "", "class": "", "description": "", "type": ""}, "doi": {"parameter": "doi", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "Digital Object Identifier identifying the product (see http://www.doi.org)", "type": "String"}, "dopplerFrequency": {"parameter": "dopplerFrequency", "open-search": true, "class": "", "description": "", "type": ""}, "downloadLink": {"parameter": "downloadLink", "open-search": "", "class": "", "description": "", "type": ""}, "extraInformation": {"parameter": "extraInformation", "open-search": "", "class": "", "description": "", "type": ""}, "geometry": {"parameter": "geometry", "open-search": "", "class": "", "description": "", "type": ""}, "gridSquare": {"parameter": "gridSquare", "open-search": "", "class": "", "description": "", "type": ""}, "id": {"parameter": "id", "open-search": "", "class": "", "description": "", "type": ""}, "illuminationAzimuthAngle": {"parameter": "illuminationAzimuthAngle", "open-search": true, "class": "", "description": "", "type": ""}, "illuminationElevationAngle": {"parameter": "illuminationElevationAngle", "open-search": true, "class": "", "description": "", "type": ""}, "illuminationZenithAngle": {"parameter": "illuminationZenithAngle", "open-search": true, "class": "", "description": "", "type": ""}, "incidenceAngleVariation": {"parameter": "incidenceAngleVariation", "open-search": true, "class": "", "description": "", "type": ""}, "instrument": {"parameter": "instrument", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the instrument (e.g. MERIS, AATSR, ASAR, HRVIR. SAR).", "type": "String"}, "keyword": {"parameter": "keyword", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Commonly used word(s) or formalised word(s) or phrase(s) used to describe the subject.", "type": "String"}, "latitudeBand": {"parameter": "latitudeBand", "open-search": "", "class": "", "description": "", "type": ""}, "lineage": {"parameter": "lineage", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "General explanation of the data producer\u2019s knowledge about the lineage of a dataset.", "type": "String"}, "links": {"parameter": "links", "open-search": "", "class": "", "description": "", "type": ""}, "maximumIncidenceAngle": {"parameter": "maximumIncidenceAngle", "open-search": true, "class": "", "description": "", "type": ""}, "minimumIncidenceAngle": {"parameter": "minimumIncidenceAngle", "open-search": true, "class": "", "description": "", "type": ""}, "modificationDate": {"parameter": "modificationDate", "open-search": true, "class": "", "description": "", "type": ""}, "modifiedAfter": {"parameter": "modifiedAfter", "open-search": "", "class": "", "description": "", "type": ""}, "modifiedBefore": {"parameter": "modifiedBefore", "open-search": "", "class": "", "description": "", "type": ""}, "orbitDirection": {"parameter": "orbitDirection", "open-search": true, "class": "", "description": "", "type": ""}, "orbitNumber": {"parameter": "orbitNumber", "open-search": true, "class": "", "description": "", "type": ""}, "orderLink": {"parameter": "orderLink", "open-search": "", "class": "", "description": "", "type": ""}, "organisationName": {"parameter": "organisationName", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "A string identifying the name of the organization responsible for the resource", "type": "String"}, "parentIdentifier": {"parameter": "parentIdentifier", "open-search": true, "class": "", "description": "", "type": ""}, "platform": {"parameter": "platform", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string with the platform short name (e.g. Sentinel-1)", "type": "String"}, "platformSerialIdentifier": {"parameter": "platformSerialIdentifier", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string with the Platform serial identifier", "type": "String"}, "polarizationChannels": {"parameter": "polarizationChannels", "open-search": "", "class": "", "description": "", "type": ""}, "polarizationMode": {"parameter": "polarizationMode", "open-search": "", "class": "", "description": "", "type": ""}, "processingCenter": {"parameter": "processingCenter", "open-search": true, "class": "", "description": "", "type": ""}, "processingDate": {"parameter": "processingDate", "open-search": true, "class": "", "description": "", "type": ""}, "processingLevel": {"parameter": "processingLevel", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the processing level applied to the entry", "type": "String"}, "processingMode": {"parameter": "processingMode", "open-search": true, "class": "", "description": "", "type": ""}, "processorName": {"parameter": "processorName", "open-search": true, "class": "", "description": "", "type": ""}, "productIdentifier": {"parameter": "productIdentifier", "open-search": "", "class": "", "description": "", "type": ""}, "productInformation": {"parameter": "productInformation", "open-search": "", "class": "", "description": "", "type": ""}, "productQualityStatus": {"parameter": "productQualityStatus", "open-search": true, "class": "", "description": "", "type": ""}, "productType": {"parameter": "productType", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the entry type (e.g. ER02_SAR_IM__0P, MER_RR__1P, SM_SLC__1S, GES_DISC_AIRH3STD_V005)", "type": "String "}, "productVersion": {"parameter": "productVersion", "open-search": true, "class": "", "description": "", "type": ""}, "providerProductType": {"parameter": "providerProductType", "open-search": "", "class": "", "description": "", "type": ""}, "publicationDate": {"parameter": "publicationDate", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "The date when the resource was issued", "type": "Date time"}, "publishedAfter": {"parameter": "publishedAfter", "open-search": "", "class": "", "description": "", "type": ""}, "publishedBefore": {"parameter": "publishedBefore", "open-search": "", "class": "", "description": "", "type": ""}, "qs": {"parameter": "qs", "open-search": "", "class": "", "description": "", "type": ""}, "quicklook": {"parameter": "quicklook", "open-search": "", "class": "", "description": "", "type": ""}, "relativeOrbitNumber": {"parameter": "relativeOrbitNumber", "open-search": "", "class": "", "description": "", "type": ""}, "resolution": {"parameter": "resolution", "open-search": true, "class": "", "description": "", "type": ""}, "sensorMode": {"parameter": "sensorMode", "open-search": true, "class": "", "description": "", "type": ""}, "sensorType": {"parameter": "sensorType", "open-search": true, "class": "OpenSearch Parameters for Collection Search", "description": "A string identifying the sensor type. Suggested values are: OPTICAL, RADAR, ALTIMETRIC, ATMOSPHERIC, LIMB", "type": "String"}, "services": {"parameter": "services", "open-search": "", "class": "", "description": "", "type": ""}, "size": {"parameter": "size", "open-search": "", "class": "", "description": "", "type": ""}, "snowCover": {"parameter": "snowCover", "open-search": true, "class": "", "description": "", "type": ""}, "startTimeFromAscendingNode": {"parameter": "startTimeFromAscendingNode", "open-search": true, "class": "", "description": "", "type": ""}, "storageStatus": {"parameter": "storageStatus", "open-search": "", "class": "", "description": "", "type": ""}, "swathIdentifier": {"parameter": "swathIdentifier", "open-search": true, "class": "", "description": "", "type": ""}, "thumbnail": {"parameter": "thumbnail", "open-search": "", "class": "", "description": "", "type": ""}, "tileIdentifier": {"parameter": "tileIdentifier", "open-search": "", "class": "", "description": "", "type": ""}, "title": {"parameter": "title", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "A name given to the resource", "type": "String "}, "topicCategory": {"parameter": "topicCategory", "open-search": true, "class": "Additional INSPIRE obligated OpenSearch Parameters for Collection Search", "description": "Main theme(s) of the dataset", "type": "String "}, "type": {"parameter": "type", "open-search": "", "class": "", "description": "", "type": ""}, "uid": {"parameter": "uid", "open-search": "", "class": "", "description": "", "type": ""}, "utmZone": {"parameter": "utmZone", "open-search": "", "class": "", "description": "", "type": ""}} diff --git a/docs/_static/params_mapping_opensearch.csv b/docs/_static/params_mapping_opensearch.csv index 7c52a890..4d33fbd5 100644 --- a/docs/_static/params_mapping_opensearch.csv +++ b/docs/_static/params_mapping_opensearch.csv @@ -34,7 +34,7 @@ processingDate,,,,,,,,,,,,,,metadata only,,,,metadata only, processingMode,,,,,,,,,,,,,,,,,,metadata only, processorName,,,,,,,,,,,,,,,metadata only,,metadata only,, productQualityStatus,,,,,,,,,,,,,,:green:`queryable metadata`,metadata only,,metadata only,, -":abbr:`productType ([OpenSearch Parameters for Collection Search] A string identifying the entry type (e.g. ER02_SAR_IM__0P, MER_RR__1P, SM_SLC__1S, GES_DISC_AIRH3STD_V005) (String ))`",metadata only,metadata only,:green:`queryable metadata`,metadata only,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` +":abbr:`productType ([OpenSearch Parameters for Collection Search] A string identifying the entry type (e.g. ER02_SAR_IM__0P, MER_RR__1P, SM_SLC__1S, GES_DISC_AIRH3STD_V005) (String ))`",metadata only,metadata only,:green:`queryable metadata`,,,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` productVersion,,,,,,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata` :abbr:`publicationDate ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] The date when the resource was issued (Date time))`,,,metadata only,,,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,,:green:`queryable metadata`,,metadata only,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata` resolution,,,:green:`queryable metadata`,,,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` @@ -43,5 +43,5 @@ sensorMode,,,:green:`queryable metadata`,,,:green:`queryable metadata`,,:green:` snowCover,,,,,,,,,,,,,,,:green:`queryable metadata`,,:green:`queryable metadata`,metadata only, startTimeFromAscendingNode,metadata only,metadata only,:green:`queryable metadata`,metadata only,,:green:`queryable metadata`,metadata only,metadata only,metadata only,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only swathIdentifier,,,,,,,,,,,,,:green:`queryable metadata`,,:green:`queryable metadata`,,:green:`queryable metadata`,, -:abbr:`title ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] A name given to the resource (String ))`,metadata only,metadata only,metadata only,metadata only,,metadata only,,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only +:abbr:`title ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] A name given to the resource (String ))`,,,metadata only,,,metadata only,,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only :abbr:`topicCategory ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Main theme(s) of the dataset (String ))`,,,,,,,,,,,,,,:green:`queryable metadata`,metadata only,,metadata only,, diff --git a/docs/_static/product_types_information.csv b/docs/_static/product_types_information.csv index 73b8198c..84aaf7de 100644 --- a/docs/_static/product_types_information.csv +++ b/docs/_static/product_types_information.csv @@ -30,7 +30,7 @@ DT_CLIMATE_ADAPTATION,"The Digital Twin on Climate Change Adaptation support the DT_EXTREMES,The Digital Twin on Weather-Induced and Geophysical Extremes provides capabilities for the assessment and prediction of environmental extremes in support of risk assessment and management. ,,Digital Twin,DT,,"DT,DE,LUMI,Destination-Earth,Digital-Twin,Weather,Geophysical,Extremes",ATMOSPHERIC,other,Weather and Geophysical Extremes Digital Twin (DT),2024-04-04T00:00:00Z,DT_EXTREMES,,,,,,,,,available,available,,,,,,,,,,,,,,,,,,, EEA_DAILY_VI,"Vegetation Indices (VI) comprises four daily vegetation indices (PPI, NDVI, LAI and FAPAR) and quality information, that are part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite. The 10m resolution, daily updated Plant Phenology Index (PPI), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are derived from Copernicus Sentinel-2 satellite observations. They are provided together with a related quality indicator (QFLAG2) that flags clouds, shadows, snow, open water and other areas where the VI retrieval is less reliable. These Vegetation Indices are made available as a set of raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from 2017 until today, with daily updates. The Vegetation Indices are part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). ",,Sentinel-2,"S2A, S2B",,"Land,Plant-phenology-index,Phenology,Vegetation,Sentinel-2,S2A,S2B",RADAR,other,"Vegetation Indices, daily, UTM projection",,EEA_DAILY_VI,,,,,,,,,available,,,,,,,,,,,,,,,,,,,,available EFAS_FORECAST,"This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is sub-daily high-resolution and ensemble forecasts of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis data set was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with meteorological forecasts. The forecasts are initialised twice daily at 00 and 12 UTC with time steps of 6 or 24 hours and lead times between 5 and 15 days depending on the forcing numerical weather prediction model. The forcing meteorological data are high-resolution and ensemble forecasts from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members, high-resolution forecasts from the Deutsches Wetter Dienst (DWD) and the ensemble forecasts from the COSMO Local Ensemble Prediction System (COSMO-LEPS) with 20 ensemble members. The hydrological forecasts are available from 2018-10-10 up until present with a 30-day delay. The real-time data is only available to EFAS partners.\nCompanion datasets, also available through the CDS, are historical simulations which can be used to derive the hydrological climatology and for verification; reforecasts for research, local skill assessment and post-processing; and seasonal forecasts and reforecasts for users looking for longer leadtime forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area ",,CEMS,CEMS,,"ECMWF,CEMS,EFAS,forecast,river,discharge",ATMOSPHERIC,other,River discharge and related forecasted data by the European Flood Awareness System,2018-10-11T00:00:00Z,EFAS_FORECAST,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, -EFAS_HISTORICAL,"This dataset provides gridded modelled daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model with gridded observational data of precipitation and temperature at a 5x5 km resolution across the EFAS domain. The most recent version\nuses a 6-hourly time step, whereas older versions uses a 24-hour time step. It is available from 1991-01-01 up until near-real time, with a delay of 6 days. The real-time data is only available to EFAS partners.\nCompanion datasets, also available through the CDS, are forecasts for users who are looking medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area ",,CEMS,CEMS,,"ECMWF,CEMS,EFAS,historical,river,discharge",ATMOSPHERIC,other,River discharge and related historical data from the European Flood Awareness System,1991-01-01T00:00:00Z,EFAS_HISTORICAL,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, +EFAS_HISTORICAL,"This dataset provides gridded modelled daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model with gridded observational data of precipitation and temperature at a 5x5 km resolution across the EFAS domain. The most recent version\nuses a 6-hourly time step, whereas older versions uses a 24-hour time step. It is available from 1991-01-01 up until near-real time, with a delay of 6 days. The real-time data is only available to EFAS partners.\nCompanion datasets, also available through the CDS, are forecasts for users who are looking medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area ",,CEMS,CEMS,,"ECMWF,CEMS,EFAS,historical,river,discharge",ATMOSPHERIC,other,River discharge and related historical data from the European Flood Awareness System,1991-01-01T06:00:00Z,EFAS_HISTORICAL,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, EFAS_REFORECAST,"This dataset provides gridded modelled hydrological time series forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is 20 years of sub-daily reforecasts initialised twice weekly (Mondays and Thursdays) of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with ensemble meteorological reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF). Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised twice weekly with lead times up to 46 days, at 6-hourly time steps for 20 years. For more specific information on the how the reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations which can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts an historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area ",,CEMS,CEMS,,"ECMWF,CEMS,EFAS,reforecast,river,discharge",ATMOSPHERIC,other,Reforecasts of river discharge and related data by the European Flood Awareness System,1999-01-03T00:00:00Z,EFAS_REFORECAST,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, EFAS_SEASONAL,"This dataset provides gridded modelled daily hydrological time series forced with seasonal meteorological forecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month consisting of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with seasonal meteorological ensemble forecasts. The forecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The meteorological data are seasonal forecasts (SEAS5) from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members. The forecasts are available from November 2020.\nCompanion datasets, also available through the Climate Data Store (CDS), are seasonal reforecasts for research, local skill assessment and post-processing of the seasonal forecasts. There are also medium-range forecasts for users who want to look at shorter time ranges. These are accompanied by historical simulations which can be used to derive the hydrological climatology, and medium-range reforecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area ",,CEMS,CEMS,,"ECMWF,CEMS,EFAS,seasonal,forecast,river,discharge",ATMOSPHERIC,other,Seasonal forecasts of river discharge and related data by the European Flood Awareness System,2020-11-01T00:00:00Z,EFAS_SEASONAL,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, EFAS_SEASONAL_REFORECAST,"This dataset provides modelled daily hydrological time series forced with seasonal meteorological reforecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month over the reforecast period 1991-2020 of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km gridded resolution with seasonal meteorological ensemble reforecasts. Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The forcing meteorological data are seasonal reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF), consisting of 25 ensemble members up until December 2016, and after that 51 members. Hydrometeorological reforecasts are available from 1991-01-01 up until 2020-10-01. \nCompanion datasets, also available through the Climate Data Store (CDS), are seasonal forecasts, for which the seasonal reforecasts can be useful for local skill assessment and post-processing of the seasonal forecasts. For users looking for shorter time ranges there are medium-range forecasts and reforecasts, as well as historical simulations which can be used to derive the hydrological climatology. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area"" ",,CEMS,CEMS,,"ECMWF,CEMS,EFAS,seasonal,reforecast,river,discharge",ATMOSPHERIC,other,Seasonal reforecasts of river discharge and related data by the European Flood Awareness System,1991-01-01T00:00:00Z,EFAS_SEASONAL_REFORECAST,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, @@ -52,7 +52,7 @@ GLOFAS_FORECAST,"This dataset contains global modelled daily data of river disch GLOFAS_HISTORICAL,"This dataset contains global modelled daily data of river discharge from the Global Flood Awareness System (GloFAS), which is part of the Copernicus Emergency Management Service (CEMS). River discharge, or river flow as it is also known, is defined as the amount of water that flows through a river section at a given time. \nThis dataset is simulated by forcing a hydrological modelling chain with inputs from a global reanalysis. Data availability for the historical simulation is from 1979-01-01 up to near real time.\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours\n\nVariables in the dataset/application are:\nUpstream area ",,CEMS,CEMS,,"ECMWF,CEMS,GloFAS,historical,river,discharge",ATMOSPHERIC,other,River discharge and related historical data from the Global Flood Awareness System,1979-01-01T00:00:00Z,GLOFAS_HISTORICAL,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, GLOFAS_REFORECAST,"This dataset provides a gridded modelled time series of river discharge, forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather Forecasts (ECMWF) 11-member ensemble ECMWF-ENS reforecasts. Reforecasts are forecasts run over past dates, and those presented here are used for providing a suitably long time period against which the skill of the 30-day real-time operational forecast can be assessed. The reforecasts are initialised twice weekly with lead times up to 46 days, at 24-hour steps for 20 years in the recent history. For more specific information on the how the reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours\n\nVariables in the dataset/application are:\nUpstream area ",,CEMS,CEMS,,"ECMWF,CEMS,GloFAS,reforecast,river,discharge",ATMOSPHERIC,other,Reforecasts of river discharge and related data by the Global Flood Awareness System,1999-01-03T00:00:00Z,GLOFAS_REFORECAST,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, GLOFAS_SEASONAL,"This dataset provides a gridded modelled time series of river discharge, forced with seasonal range meteorological forecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 0.1° (~11 km at the equator) resolution with downscaled runoff forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF) 51-member ensemble seasonal forecasting system, SEAS5. The forecasts are initialised on the first of each month with a 24-hourly time step, and cover 123 days.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and medium-range and seasonal reforecasts. The latter dataset enables research, local skill assessment and post-processing of the seasonal forecasts. In addition, the seasonal reforecasts are also used to derive a specific range dependent climatology for the seasonal system. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours\n\nVariables in the dataset/application are:\nUpstream area ",,CEMS,CEMS,,"ECMWF,CEMS,GloFAS,seasonal,forecast,river,discharge",ATMOSPHERIC,other,Seasonal forecasts of river discharge and related data by the Global Flood Awareness System,2020-12-01T00:00:00Z,GLOFAS_SEASONAL,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, -GLOFAS_SEASONAL_REFORECAST,"This dataset provides a gridded modelled time series of river discharge forced with seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member ensemble. Reforecasts are forecasts run over past dates, with those presented here used for producing the seasonal river discharge thresholds. In addition, they provide a suitably long time period against which the skill of the seasonal forecast can be assessed. The reforecasts are initialised monthly and run for 123 days, with a 24-hourly time step. For more specific information on the how the seasonal reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), include the seasonal forecasts, for which the dataset provided here can be useful for local skill assessment and post-processing. For users looking for shorter term forecasts there are also medium-range forecasts and reforecasts available, as well as historical simulations that can be used to derive the hydrological climatology. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours\n\nVariables in the dataset/application are:\nUpstream area"" ",,CEMS,CEMS,,"ECMWF,CEMS,GloFAS,seasonal,forecast,river,discharge",ATMOSPHERIC,other,Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System,1981-01-01T00:00:00Z,GLOFAS_SEASONAL_REFORECAST,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, +GLOFAS_SEASONAL_REFORECAST,"This dataset provides a gridded modelled time series of river discharge forced with seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member ensemble. Reforecasts are forecasts run over past dates, with those presented here used for producing the seasonal river discharge thresholds. In addition, they provide a suitably long time period against which the skill of the seasonal forecast can be assessed. The reforecasts are initialised monthly and run for 123 days, with a 24-hourly time step. For more specific information on the how the seasonal reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), include the seasonal forecasts, for which the dataset provided here can be useful for local skill assessment and post-processing. For users looking for shorter term forecasts there are also medium-range forecasts and reforecasts available, as well as historical simulations that can be used to derive the hydrological climatology. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours\n\nVariables in the dataset/application are:\nUpstream area"" ",,CEMS,CEMS,,"ECMWF,CEMS,GloFAS,seasonal,forecast,river,discharge",ATMOSPHERIC,other,Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System,1981-01-27T00:00:00Z,GLOFAS_SEASONAL_REFORECAST,,,,,available,,,,available,,,,,,,,,,,,,,,,,,,, GRIDDED_GLACIERS_MASS_CHANGE,"The dataset provides annual glacier mass changes distributed on a global regular grid at 0.5° resolution (latitude, longitude). Glaciers play a fundamental role in the Earth's water cycles. They are one of the most important freshwater resources for societies and ecosystems and the recent increase in ice melt contributes directly to the rise of ocean levels. Due to this they have been declared as an Essential Climate Variable (ECV) by GCOS, the Global Climate Observing System. Within the Copernicus Services, the global gridded annual glacier mass change dataset provides information on changing glacier resources by combining glacier change observations from the Fluctuations of Glaciers (FoG) database that is brokered from World Glacier Monitoring Service (WGMS). Previous glacier products were provided to the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) as a homogenized state-of-the-art glacier dataset with separated elevation and mass change time series collected by scientists and the national correspondents of each country as provided to the WGMS (see Related data). The new approach combines glacier mass balances from in-situ observations with glacier elevation changes from remote sensing to generate a new gridded product of annual glacier mass changes and related uncertainties for every hydrological year since 1975/76 provided in a 0.5°x0.5° global regular grid. The dataset bridges the gap on spatio-temporal coverage of glacier change observations, providing for the first time in the CDS an annually resolved glacier mass change product using the glacier elevation change sample as calibration. This goal has become feasible at the global scale thanks to a new globally near-complete (96 percent of the world glaciers) dataset of glacier elevation change observations recently ingested by the FoG database. To develop the distributed glacier change product the glacier outlines were used from the Randolph Glacier Inventory 6.0 (see Related data). A glacier is considered to belong to a grid-point when its geometric centroid lies within the grid point. The centroid is obtained from the glacier outlines from the Randolph Glacier Inventory 6.0. The glacier mass changes in the unit Gigatonnes (1 Gt = 1x10^9 tonnes) correspond to the total mass of water lost/gained over the glacier surface during a given year. Note that to propagate to mm/cm/m of water column on the grid cell, the grid cell area needs to be considered. Also note that the data is provided for hydrological years, which vary between the Northern Hemisphere (01 October to 30 September next year) and the Southern Hemisphere (01 April to 31 March next year). This dataset has been produced by researchers at the WGMS on behalf of Copernicus Climate Change Service. Variables in the dataset/application are: Glacier mass change Variables in the dataset/application are: Uncertainty ",,,,,"ECMWF,WGMS,INSITU,CDS,C3S,glacier,randolph,mass,gridded",ATMOSPHERIC,other,Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database,1975-01-01T00:00:00Z,GRIDDED_GLACIERS_MASS_CHANGE,,,available,,,,,,available,,,,,,,,,,,,,,,,,,,available, GSW_CHANGE,"The Global Surface Water Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2021. Both the direction of change and its intensity are documented. ",,GSW,GSW,,"PEKEL, Global Surface Water, Change, Landsat",HYDROLOGICAL,proprietary,Global Surface Water Occurrence Change Intensity 1984-2020,1984-01-01T00:00:00Z,GSW_CHANGE,,,,,,,,,available,,,,,,,,,,,,,,,,,,,, GSW_EXTENT,The Global Surface Water Maximum Water Extent shows all the locations ever detected as water over a 38-year period (1984-2021) ,,GSW,GSW,,"PEKEL, Global Surface Water, Extent, Landsat",HYDROLOGICAL,proprietary,Global Surface Water Maximum Water Extent 1984-2021,1984-01-01T00:00:00Z,GSW_EXTENT,,,,,,,,,available,,,,,,,,,,,,,,,,,,,, @@ -152,8 +152,16 @@ MSG_MFG_GSA_0,Release 2 of the Thematic Climate Data Record (TCDR) of the Meteos MSG_MSG15_RSS,"Rectified (level 1.5) Meteosat SEVIRI Rapid Scan image data. The baseline scan region is a reduced area of the top 1/3 of a nominal repeat cycle, covering a latitude range from approximately 15 degrees to 70 degrees. The service generates repeat cycles at 5-minute intervals (the same as currently used for weather radars). The dissemination of RSS data is similar to the normal dissemination, with image segments based on 464 lines and compatible with the full disk level 1.5 data scans. Epilogue and prologue (L1.5 Header and L1.5 Trailer) have the same structure. Calibration is as in Full Earth Scan. Image rectification is to 9.5 degreesE. The scans start at 00:00, 00:05, 00:10, 00:15 ... etc. (5 min scan). The differences from the nominal Full Earth scan are that for channels 1 - 11, only segments 6 - 8 are disseminated and for the High Resolution Visible Channel only segments 16 - 24 are disseminated. ",SEVIRI,MSG,MSG,L1,"MSG15-RSS,MSG15,MSG,SEVIRI,OPTICAL,OCEAN,ATMOSPHERE,LAND,L1",OPTICAL,other,Rapid Scan High Rate SEVIRI Level 1.5 Image Data - MSG,2008-05-13T00:00:00Z,MSG_MSG15_RSS,,,,,,,,,available,,,,,,available,,,,,,,,,,,,,, MSG_OCA_CDR,"The OCA Release 1 Climate Data Record (CDR) covers the MSG observation period from 2004 up to 2019, providing a homogenous cloud properties time series. It is generated at full Meteosat repeat cycle (15 minutes) fequency. Cloud properties retrieved by OCA are cloud top pressure, cloud optical thickness, and cloud effective radius, together with uncertainties. The OCA algorithm has been slightly adapted for climate data record processing. The adaptation mainly consists in the usage of different inputs, because the one used for Near Real Time (NRT) were not available for the reprocessing (cloud mask, clear sky reflectance map) and also not homogenous (reanalysis) over the complete time period. it extends the NRT data record more than 9 years back in time. This is a Thematic Climate Data Record (TCDR). ",SEVIRI,MSG,MSG,L2,"MSG,L2,SEVIRI,Climate,Clouds,Atmosphere,Observation,Thematic,TCDR,OCA",MSG,other,Optimal Cloud Analysis Climate Data Record Release 1 - MSG - 0 degree,2004-01-19T00:00:00Z,MSG_OCA_CDR,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, MSG_RSS_CLM,"The Rapid Scanning Services (RSS) Cloud Mask product describes the scene type (either 'clear' or 'cloudy') on a pixel level. Each pixel is classified as one of the following four types: clear sky over water, clear sky over land, cloud, or not processed (off Earth disc). Applications & Uses: The main use is in support of Nowcasting applications, where it frequently serves as a basis for other cloud products, and the remote sensing of continental and ocean surfaces. ",SEVIRI,MSG,MSG,L2,"RSS-CLM,MSGCLMK,MSG,SEVIRI,OPTICAL,CLOUDS,ATMOSPHERE,L2",OPTICAL,other,Rapid Scan Cloud Mask - MSG,2013-02-28T00:00:00Z,MSG_RSS_CLM,,,,,,,,,available,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_AMV_BUFR,"The Atmospheric Motion Vector (AMV) product is realised by tracking clouds or water vapour features in consecutive FCI satellite images based on feature tracking between each pair of consecutive repeat cycles, leading to two intermediate AMV products for an image triplet. The final product is then derived from these two intermediate products, and includes information on wind speed, direction, height, and quality. AMVs are extracted from the FCI VIS 0.8, IR 3.8 (night only), IR 10.5, WV 6.3 and WV 7.3 channels. The AMV product is available in BUFR and netCDF format, every 30 minutes. ",FCI,MTG,MTG,L2,"MTG,L2,FCI,AMV,Clouds,BUFR",Imager,other,Atmospheric Motion Vectors (BUFR) - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_AMV_BUFR,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_AMV_NETCDF,"The Atmospheric Motion Vector (AMV) product is realised by tracking clouds or water vapour features in consecutive FCI satellite images based on feature tracking between each pair of consecutive repeat cycles, leading to two intermediate AMV products for an image triplet. The final product is then derived from these two intermediate products, and includes information on wind speed, direction, height, and quality. AMVs are extracted from the FCI VIS 0.8, IR 3.8 (night only), IR 10.5, WV 6.3 and WV 7.3 channels. The AMV product is available in BUFR and netCDF format, every 30 minutes. ",FCI,MTG,MTG,L2,"MTG,L2,FCI,AMV,Clouds,netCDF",Imager,other,Atmospheric Motion Vectors (netCDF) - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_AMV_NETCDF,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_ASR_BUFR,"The All-Sky Radiance (ASR) product is a segmented product that provides FCI Level 1C data statistics within processing segments referred to as Field-of-Regard (FoR). The statistics are computed on the L1C radiances (for all FCI channels), brightness temperatures (for the eight IR channels) and reflectances (for the eight visible and near-infrared channels) and include the mean value, standard deviation, minimum and maximum values within the FoR. The ASR product is available in BUFR and netCDF format, every 10 minutes, at a spatial resolution of 16x16 pixels (IR) and 32x32 pixels (VIS). ",FCI,MTG,MTG,L2,"MTG,L2,FCI,ASR,Radiance,BUFR",Imager,other,All Sky Radiance (BUFR) - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_ASR_BUFR,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_ASR_NETCDF,"The All-Sky Radiance (ASR) product is a segmented product that provides FCI Level 1C data statistics within processing segments referred to as Field-of-Regard (FoR). The statistics are computed on the L1C radiances (for all FCI channels), brightness temperatures (for the eight IR channels) and reflectances (for the eight visible and near-infrared channels) and include the mean value, standard deviation, minimum and maximum values within the FoR. The ASR product is available in BUFR and netCDF format, every 10 minutes, at a spatial resolution of 16x16 pixels (IR) and 32x32 pixels (VIS). ",FCI,MTG,MTG,L2,"MTG,L2,FCI,ASR,Radiance,netCDF",Imager,other,All Sky Radiance (netCDF) - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_ASR_NETCDF,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_CLM,"The central aim of the cloud mask (CLM) product is to identify cloudy and cloud free FCI Level 1c pixels with high confidence. The product also provides information on the presence of snow/sea ice, volcanic ash and dust. This information is crucial both for spatiotemporal analyses of the cloud coverage and for the subsequent retrieval of other meteorological products that are only valid for cloudy (e.g. cloud properties) or clear pixels (e.g. clear sky reflectance maps or global instability indices). The algorithm is based on multispectral threshold techniques applied to each pixel of the image. CLM is available in netCDF and GRIB format, every 10 minutes, at a spatial resolution of 2 km at nadir. ",FCI,MTG,MTG,L2,"MTG,L2,FCI,CLM,Clouds",Imager,other,Cloud Mask - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_CLM,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, MTG_FCI_FDHSI,"The rectified (Level 1c) Meteosat FCI full disc image data in normal spatial (FDHSI) resolution. The FCI instrument consists of 16 imaging spectral channels ranging from 0.4 µm to 13.3 µm with the channel at 3.8 µm having an extended dynamic range dedicated to fire monitoring. The spatial resolution is 1km for visible and near-infrared channels and 2 km for infrared channels. FCI Level 1c rectified radiance dataset consists of a set of files that contain the level 1c science data rectified to a reference grid together with the auxiliary data associated with the processing configuration and the quality assessment of the dataset. Level 1c image data here corresponds to initially geolocated and radiometrically pre-processed image data, without full georeferencing and cal/val in spatial and spectral domains applied. The data are ready for further processing and testing, e.g. value chains and initial tests for extracting meteorological products, however, we generally do not recommend the generation of Level 2 products due to known limitations in the Level 1c data. ",FCI,MTG,MTG,L1,"MTG,L1,FCI,FDHSI,Atmosphere,Ocean,Land",Imager,other,FCI Level 1c Normal Resolution Image Data - MTG - 0 degree,2024-09-24T00:00:00Z,MTG_FCI_FDHSI,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_GII,"The Global Instability Index (GII) product provides information about instability of the atmosphere and thus can identify regions of convective potential. GII is a segmented product that uses an optimal estimation scheme to fit clear-sky vertical profiles of temperature and humidity, constrained by NWP forecast products, to FCI observations in the seven channels WV6.3, WV7.3, IR8.7, IR9.7, IR10.5, IR12.3, and IR13.3. The retrieved profiles are then used to compute atmospheric instability indices: Lifted Index, K Index, Layer Precipitable Water, Total Precipitable Water. The GII product is available in netCDF format, every 10 minutes, in 3x3 pixels (IR channels), leading to a spatial resolution of 6 km at nadir. ",FCI,MTG,MTG,L2,"MTG,L2,FCI,GII,atmosphere",Imager,other,Global Instability Indices - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_GII,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, MTG_FCI_HRFI,"The rectified (Level 1c) Meteosat FCI full disc image data in high spatial (HRFI) resolution.The FCI instrument consists of 16 imaging spectral channels ranging from 0.4 µm to 13.3 µm with the channel at 3.8 µm having an extended dynamic range dedicated to fire monitoring. The high-resolution HRFI dataset has 4 spectral channels at VIS 0.6 µm, NIR 2.2 µm, IR 3.8 µm and IR 13.3 µm with a spatial resolution of 0.5 km for visible and near-infrared channels and 1 km for infrared channels. FCI Level 1c rectified radiance dataset consists of a set of files that contain the level 1c science data rectified to a reference grid together with the auxiliary data associated with the processing configuration and the quality assessment of the dataset. Level 1c image data here corresponds to initially geolocated and radiometrically pre-processed image data, without full georeferencing and cal/val in spatial and spectral domains applied. The data are ready for further processing and testing, e.g. value chains and initial tests for extracting meteorological products, however, we generally do not recommend the generation of Level 2 products due to known limitations in the Level 1c data. A selection of single channel data are visualised in our EUMETView service. ",FCI,MTG,MTG,L1,"MTG,L1,FCI,HRFI,Atmosphere,Ocean,Land",Imager,other,FCI Level 1c High Resolution Image Data - MTG - 0 degree,2024-09-24T00:00:00Z,MTG_FCI_HRFI,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_OCA,"The Optimal Cloud Analysis (OCA) product uses an optimal estimation retrieval scheme to retrieve cloud properties (phase, height and microphysical properties) from visible, near-infrared and thermal infrared FCI channels. The optimal estimation framework aims to ensure that measurements and any prior information may be given appropriate weight in the solution depending on error characteristics whether instrumental or from modelling sources. The product can also contain information on dust and volcanic ash clouds if these are flagged in the corresponding Cloud Analysis Product. The OCA product is available in netCDF format, every 10 minutes, at 2km spatial resolution at nadir. ",FCI,MTG,MTG,L2,"MTG,L2,FCI,OCA,Clouds",Imager,other,Optimal Cloud Analysis - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_OCA,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, +MTG_FCI_OLR,"The Outgoing Longwave Radiation (OLR) product is important for Earth radiation budget studies as well as for weather and climate model validation purposes, since variations in OLR reflect the response of the Earth-atmosphere system to solar diurnal forcing. The product is based on a statistical relationship linking the radiance measured in each FCI infrared channel to the top-of-atmosphere outgoing longwave flux integrated over the full infrared spectrum. The computation is done for each pixel considering the cloud cover characteristics (clear sky, semi-transparent and opaque cloud cover). The OLR product is available in netCDF format, every 10 minutes, at 2 km spatial resolution at nadir. ",FCI,MTG,MTG,L2,"MTG,L2,FCI,OLR,Radiation,LW",Imager,other,Outgoing LW radiation at TOA - MTG - 0 degree,2025-01-22T00:00:00Z,MTG_FCI_OLR,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, MTG_LI_AF,LI Level 2 Accumulated Flashes (AF) complements the LI Level 2 Accumulated Flash Area (AFA) by providing one with the variation of the number of events within those regions reported to have lightning flashes in the Accumulated Flash Area (AFA). Accumulated Flashes provide users with data about the mapping of the number of LI events/detections rather than the mapping of flashes. One should keep in mind that the absolute value within each pixel of the Accumulated Flashes has no real physical meaning; it is rather a proxy for the pixel-by-pixel variation of the number of events. It is worth noting that one can derive the flash rate over a region encompassing a complete lightning feature (not within an FCI grid pixel) in Accumulated Flashes; this stems from the definition in Accumulated (gridded) data. ,LI,MTG,MTG,L2,"MTG,L2,LI,AF,Lightning,Weather,Flashes",Lightning Imager,other,LI Accumulated Flashes - MTG - 0 degree,2024-07-08T00:00:00Z,MTG_LI_AF,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, MTG_LI_AFA,"LI Level 2 Accumulated Flash Area (AFA) provides the user with data about flash mapping by using the area covered by the optical emission of each flash in LI Level 2 Lightning Flashes (LFL). It is important to keep in mind that each flash is treated as a flat (uniform) optical emission in this data. Accumulated Flash Area allows one to monitor the regions within a cloud top from which lightning-related optical emissions over 30 sec are emerging and accumulating and to know the number of flashes that were observed within the FCI grid pixels composing those regions. For example, from the Accumulated Flash Area, one can derive the flash rate for each pixel of the FCI 2km grid. This is a considerable improvement compared to the simple description of the flash using the variable flash_footprint available in Lightning Flashes. ",LI,MTG,MTG,L2,"MTG,L2,LI,AFA,Lightning,Weather,Flashes,Accumulated",Lightning Imager,other,LI Accumulated Flash Area - MTG - 0 degree,2024-07-08T00:00:00Z,MTG_LI_AFA,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, MTG_LI_AFR,LI Level 2 Accumulated Flash Radiance (AFR) is meant to describe the pixel-by-pixel variation of the optical emission accumulated over 30 sec within the FCI 2km grid. This stems from the events contributing to LI Level 2 Accumulated Flashes (AF) (each one contributing with its radiance) and it can be thought of as the 'appearance' of the accumulated optical emissions over 30 sec as seen by LI. ,LI,MTG,MTG,L2,"MTG,L2,LI,AFR,Lightning,Weather,Flashes,Accumulated,Radiance",Lightning Imager,other,LI Accumulated Flash Radiance - MTG - 0 degree,2024-07-08T00:00:00Z,MTG_LI_AFR,,,,,,,,,,,,,,,available,,,,,,,,,,,,,, diff --git a/docs/getting_started_guide/register.rst b/docs/getting_started_guide/register.rst index 70bc5405..56ef3a9f 100644 --- a/docs/getting_started_guide/register.rst +++ b/docs/getting_started_guide/register.rst @@ -47,7 +47,7 @@ You need credentials for both EOS (search) and AWS (download): Go to the `ECMWF homepage <https://www.ecmwf.int/>`__ and create an account by clicking on Log in and then Register. Then log in and go to your user profile on `Atmosphere Data Store <https://ads.atmosphere.copernicus.eu/>`__ and -use your Personal Access Token as apikey in eodag credentials. +use your Personal Access Token as ``apikey`` in eodag credentials. To download data you have to accept the `Licence to use Copernicus Products`. To accept the licence: @@ -60,7 +60,7 @@ To download data you have to accept the `Licence to use Copernicus Products`. To ^^^^^^^^^^^ Go to the `ECMWF homepage <https://www.ecmwf.int/>`__ and create an account by clicking on Log in and then Register. Then log in and go to your user profile on `Climate Data Store <https://cds.climate.copernicus.eu/>`__ and use your -Personal Access Token as apikey in eodag credentials. +Personal Access Token as ``apikey`` in eodag credentials. To download data, you also have to accept certain terms depending on the dataset. Some datasets have a specific licence whereas other licences are valid for a group of datasets. @@ -84,7 +84,7 @@ Create an account `here ^^^^^^^^^^^^ Go to the `ECMWF homepage <https://www.ecmwf.int/>`__ and create an account by clicking on Log in and then Register. Then log in and go to your user profile on `CEMS Early Warning Data Store <https://ewds.climate.copernicus.eu>`__ and use your -Personal Access Token as apikey in eodag credentials. +Personal Access Token as ``apikey`` in eodag credentials. To download data, you also have to accept certain terms depending on the dataset. There are two different licences that have to be accepted to use the CEMS EWDS datasets. Accepting the `CEMS-FLOODS datasets licence` is necessary to use the `GLOFAS` and `EFAS` datasets, @@ -104,7 +104,7 @@ No account is required ``creodias`` ^^^^^^^^^^^^ -Create an account `here <https://portal.creodias.eu/register.php>`__, then use your `username`, `password` in eodag +Create an account `here <https://portal.creodias.eu/register.php>`__, then use your ``username``, ``password`` in eodag credentials. You will also need `totp` in credentials, a temporary 6-digits OTP (One Time Password, see `Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`__) @@ -130,7 +130,7 @@ then click `Authenticate`. Finally click on `Register` to create a new account. ``dedt_lumi`` ^^^^^^^^^^^^^ -Create an account on `DestinE <https://platform.destine.eu/>`__, then use your `username`, `password` in eodag +Create an account on `DestinE <https://platform.destine.eu/>`__, then use your ``username``, ``password`` in eodag credentials. ``earth_search_gcs`` @@ -152,7 +152,7 @@ No authentication needed. ^^^^^^^^^ Create an account `here <https://apps.ecmwf.int/registration/>`__. -Then use email as username and key as password from `here <https://api.ecmwf.int/v1/key/>`__ in eodag credentials. +Then use email as ``username`` and key as ``password`` from `here <https://api.ecmwf.int/v1/key/>`__ in eodag credentials. EODAG can be used to request for public datasets as for operational archive. Please note that for public datasets you might need to accept a license (e.g. for `TIGGE <https://apps.ecmwf.int/datasets/data/tigge/licence/>`__) @@ -160,14 +160,14 @@ might need to accept a license (e.g. for `TIGGE <https://apps.ecmwf.int/datasets ^^^^^^^^^^^^^^^ Create an account `here <https://eoportal.eumetsat.int/userMgmt/register.faces>`__. -Then use the consumer key as `username` and the consumer secret as `password` from `here +Then use the consumer key as ``username`` and the consumer secret as ``password`` from `here <https://api.eumetsat.int/api-key/>`__ in eodag credentials. ``geodes`` ^^^^^^^^^^ Go to `https://geodes-portal.cnes.fr <https://geodes-portal.cnes.fr>`_, then login or create an account by clicking on ``Log in`` in the top-right corner. Once logged-in, create an API key in the user settings page, and used it -as apikey in EODAG provider auth credentials. +as ``apikey`` in EODAG provider auth credentials. ``geodes_s3`` ^^^^^^^^^^^^^ @@ -179,7 +179,7 @@ Get credentials for internal Datalake and use them as ``aws_access_key_id``, ``a ^^^^^^^^^^^^^^^^^ Go to `https://hydroweb.next.theia-land.fr <https://hydroweb.next.theia-land.fr>`_, then login or create an account by clicking on ``Log in`` in the top-right corner. Once logged-in, create an API key in the user settings page, and used it -as apikey in EODAG provider auth credentials. +as ``apikey`` in EODAG provider auth credentials. ``meteoblue`` ^^^^^^^^^^^^^ @@ -195,7 +195,7 @@ Create an account `here: <https://www.onda-dias.eu/cms/>`__ ``peps`` ^^^^^^^^ -create an account `here <https://peps.cnes.fr/rocket/#/register>`__, then use your email as `username` in eodag +create an account `here <https://peps.cnes.fr/rocket/#/register>`__, then use your email as ``username`` in eodag credentials. ``planetary_computer`` @@ -208,7 +208,7 @@ with your Microsoft account `here <https://planetarycomputer.developer.azure-api ``sara`` ^^^^^^^^ -Create an account `here <https://copernicus.nci.org.au/sara.client/#/register>`__, then use your email as `username` in +Create an account `here <https://copernicus.nci.org.au/sara.client/#/register>`__, then use your email as ``username`` in eodag credentials. ``theia`` @@ -217,9 +217,11 @@ Create an account `here <https://sso.theia-land.fr/theia/register/register.xhtml ``usgs`` ^^^^^^^^ -Create an account `here <https://ers.cr.usgs.gov/register/>`__ and then +Create an account `here <https://ers.cr.usgs.gov/register/>`__, and `request an access <https://ers.cr.usgs.gov/profile/access>`_ to the `Machine-to-Machine (M2M) API <https://m2m.cr.usgs.gov/>`_. +Then you will need to `generate an application token <https://ers.cr.usgs.gov/password/appgenerate>`_. Use it as +``password`` in eodag credentials, associated to your ``username``. Product requests can be performed once access to the M2M API has been granted to you. @@ -237,7 +239,7 @@ The registration procedure is the same as for `wekeo_main <getting_started_guide You need an access token to authenticate and to accept terms and conditions with it: * Create an account on `WEkEO <https://www.wekeo.eu/register>`__ -* Add your WEkEO credentials (username, password) to the user configuration file. +* Add your WEkEO credentials (``username``, ``password``) to the user configuration file. * Depending on which data you want to retrieve, you will then need to accept terms and conditions (for once). To do this, follow the `tutorial guidelines <https://eodag.readthedocs.io/en/latest/notebooks/tutos/tuto_wekeo.html#Registration>`__ or run the following commands in your terminal. diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index c786edcb..b30dd1c2 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -128,7 +128,7 @@ class UsgsApi(Api): api.logout() continue except USGSError as e: - if i == 0: + if i == 0 and os.path.isfile(api.TMPFILE): # `.usgs` API file key might be obsolete # Remove it and try again os.remove(api.TMPFILE) diff --git a/eodag/resources/ext_product_types.json b/eodag/resources/ext_product_types.json index e998740e..0eae0a33 100644 --- a/eodag/resources/ext_product_types.json +++ b/eodag/resources/ext_product_types.json @@ -1,1 +1,1 @@ -{"cop_marine": {"product_types_config": {"ANTARCTIC_OMI_SI_extent": {"abstract": "DEFINITION\n\nEstimates of Antarctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Southern Hemisphere (excluding ice lakes) and from 1993 up to real time aiming to:\ni) obtain the Antarctic sea ice extent as expressed in millions of km squared (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss/gain since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013)). For the Southern Hemisphere, these trends are calculated from the annual mean values.\nThe Antarctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\nCONTEXT\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n \nCMEMS KEY FINDINGS\n\nWith quasi regular highs and lows, the annual Antarctic sea ice extent shows large variability until several monthly record high in 2014 and record lows in 2017, 2018 and 2023. Since the year 1993, the Southern Hemisphere annual sea ice extent regularly alternates positive and negative trend. The period 1993-2023 have seen a slight decrease at a rate of -0.28106km2 per decade. This represents a loss amount of -2.4% per decade of Southern Hemisphere sea ice extent during this period; with however large uncertainties. The last quarter of the year 2016 and years 2017 and 2018 experienced unusual losses of ice. The year 2023 is an exceptional year and its average has a strong impact on the whole time series.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00186\n\nReferences:\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00186", "instrument": null, "keywords": "antarctic-omi-si-extent,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Sea Ice Extent from Reanalysis"}, "ANTARCTIC_OMI_SI_extent_obs": {"abstract": "DEFINITION\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the September values (winter ice loss); (iii) from February values (summer ice loss). The annual mean trend is reported on the key figure, the September (maximum extent) and February (minimum extent) values are reported in the text below.\nSIE includes all sea ice, except for lake and river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\nCONTEXT\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019). \n\nCMEMS KEY FINDINGS\n\nSince 1979, there has been an overall slight increase of sea ice extent in the Southern Hemisphere but a sharp decrease was observed after 2016. Over the period 1979-2022, the annual rate amounts to +0.02 +/- 0.05 106 km2 per decade (+0.18% per decade). Winter (September) sea ice extent trend amounts to +0.06 +/- 0.05106 km2 per decade (+0.32% per decade). Summer (February) sea ice extent trend amounts to -0.01+/- 0.05 106 km2 per decade (-0.38% per decade). These trend estimates are hardly significant, which is in agreement with the IPCC SROCC, which has assessed that \u2018Antarctic sea ice extent overall has had no statistically significant trend (1979\u20132018) due to contrasting regional signals and large interannual variability (high confidence).\u2019 (IPCC, 2019). Both June and July 2022 had the lowest average sea ice extent values for these months since 1979. \n\nFigure caption\n\na) The seasonal cycle of Southern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Southern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line on panel b).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00187\n\nReferences:\n\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00187", "instrument": null, "keywords": "antarctic-omi-si-extent-obs,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Monthly Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"abstract": "The operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00003\n\nReferences:\n\n* Ardyna, M., Babin, M., Gosselin, M., Devred, E., B\u00e9langer, S., Matsuoka, A., and Tremblay, J.-\u00c9.: Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences, 10, 4383\u20134404, https://doi.org/10.5194/bg-10-4383-2013, 2013.\n* Yumruktepe, V. \u00c7., Samuelsen, A., and Daewel, U.: ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic, Geosci. Model Dev., 15, 3901\u20133921, https://doi.org/10.5194/gmd-15-3901-2022, 2022.\n", "doi": "10.48670/moi-00003", "instrument": null, "keywords": "arctic-analysisforecast-bgc-002-004,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"abstract": "The operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\nDOI (product):\nhttps://doi.org/10.48670/moi-00001\n\nReferences:\n\n* Sakov, P., Counillon, F., Bertino, L., Lis\u00e6ter, K. A., Oke, P. R. and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8(4), 633\u2013656, doi:10.5194/os-8-633-2012, 2012.\n* Melsom, A., Counillon, F., LaCasce, J. H. and Bertino, L.: Forecasting search areas using ensemble ocean circulation modeling, Ocean Dyn., 62(8), 1245\u20131257, doi:10.1007/s10236-012-0561-5, 2012.\n", "doi": "10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-analysisforecast-phy-002-001,arctic-ocean,coastal-marine-environment,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "2021-07-05T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"abstract": "The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\nDOI (product): \n\nhttps://doi.org/10.48670/moi-00004\n\nReferences:\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/moi-00004", "instrument": null, "keywords": "arctic-analysisforecast-phy-ice-002-011,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"abstract": "The Arctic Ocean Surface Currents Analysis and Forecast system uses the HYCOM model at 3 km resolution forced with tides at its lateral boundaries, surface winds sea level pressure from the ECMWF (European Centre for Medium-Range Weather Forecasts) and wave terms (Stokes-Coriolis drift, stress and parameterisation of mixing by Langmuir cells) from the Arctic wave forecast. HYCOM runs daily providing 10 days forecast. The output variables are the surface currents and sea surface heights, provided at 15 minutes frequency, which therefore include mesoscale signals (though without data assimilation so far), tides and storm surge signals. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00005", "doi": "10.48670/moi-00005", "instrument": null, "keywords": "arctic-analysisforecast-phy-tide-002-015,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-surface-elevation,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Tidal Analysis and Forecast"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"abstract": "The Arctic Ocean Wave Analysis and Forecast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) together with tidal currents and ice from the ARC MFC forecasts (Sea Ice concentration and thickness). WAM runs twice daily providing one hourly 10 days forecast and one hourly 5 days forecast. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00002", "doi": "10.48670/moi-00002", "instrument": null, "keywords": "arctic-analysis-forecast-wav-002-014,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Analysis and Forecast"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"abstract": "The TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00006\n\nReferences:\n\n* Simon, E., Samuelsen, A., Bertino, L. and Mouysset, S.: Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter, J. Mar. Syst., 152, 1\u201317, doi:10.1016/j.jmarsys.2015.07.004, 2015.\n", "doi": "10.48670/moi-00006", "instrument": null, "keywords": "arctic-multiyear-bgc-002-005,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"abstract": "The current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00007", "doi": "10.48670/moi-00007", "instrument": null, "keywords": "arctic-multiyear-phy-002-003,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"abstract": "The Arctic Sea Ice Reanalysis system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, from Bering Strait to the North Atlantic. neXtSIM is forced by reanalyzed surface atmosphere forcings (ERA5) from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4b, the ARC MFC MYP system (002_003). neXtSIM assimilates satellite sea ice concentrations from Passive Microwave satellite sensors, and sea ice thickness from CS2SMOS in winter from October 2010 onwards. The output variables are sea ice concentrations (total, young ice, and multi-year ice), sea ice thickness, sea ice velocity, snow depth on sea ice, sea ice type, sea ice age, sea ice ridge volume fraction and sea ice albedo, provided at daily and monthly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00336\n\nReferences:\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/mds-00336", "instrument": null, "keywords": "arctic-multiyear-phy-ice-002-016,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Reanalysis"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"abstract": "The Arctic Ocean Wave Hindcast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis together with ice from the ARC MFC reanalysis (Sea Ice concentration and thickness). Additionally, in the North Atlantic area, surface winds are used from a 2.5km atmospheric hindcast system. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00008", "doi": "10.48670/moi-00008", "instrument": null, "keywords": "arctic-multiyear-wav-002-013,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Hindcast"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"abstract": "DEFINITION\n\nNet sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79\u00b0N, 20\u00b0W - 10\u00b0E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80\u00b0N, 27\u00b0E - 60\u00b0E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60\u00b0E, 76\u00b0N - 80\u00b0N (positive westward). For further details, see Lien et al. (2021).\n\nCONTEXT\n\nThe Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015).\n\nCMEMS KEY FINDINGS\n\nThe sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00192\n\nReferences:\n\n* Aaboe S, Lind S, Hendricks S, Down E, Lavergne T, Ricker R. 2021. Sea-ice and ocean conditions surprisingly normal in the Svalbard-Barents Sea region after large sea-ice inflows in 2019. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 140-148\n* Aagaard K, Swift JH, Carmack EC. 1985. Thermohaline circulation in the Arctic Mediterranean seas. J Geophys Res. 90(C7), 4833-4846\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Lien VS, Raj RP, Chatterjee S. 2021. Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents seas. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 10-17\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea ice import. Nature Clim Change. doi:10.1038/s41558-018-0205-y\n* Rampal P, Weiss J, Dubois C, Campin J-M. 2011. IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. J Geophys Res. 116, C00D07. https://doi.org/10.1029/2011JC007110\n* Ricker R, Girard-Ardhuin F, Krumpen T, Lique C. 2018. Satellite-derived sea ice export and its impact on Arctic ice mass balance. Cryosphere. 12, 3017-3032\n* Vinje T, Nordlund N, Kvambekk \u00c5. 1998. Monitoring ice thickness in Fram Strait. J Geophys Res. 103(C5), 10437-10449\n", "doi": "10.48670/moi-00192", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-transport-nordicseas,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis"}, "ARCTIC_OMI_SI_extent": {"abstract": "DEFINITION\n\nEstimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to:\ni) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss).\nThe Arctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\nCONTEXT\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n\nCMEMS KEY FINDINGS\n\nSince the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00190\n\nReferences:\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00190", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea Ice Extent from Reanalysis"}, "ARCTIC_OMI_SI_extent_obs": {"abstract": "DEFINITION\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean that is covered with sea ice. It is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The annual mean trend is reported on the key figure, the March and September values are reported in the text below.\nSIE includes all sea ice, but not lake or river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\nCONTEXT\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea ice cover is changing is essential for monitoring the health of the Earth. Sea ice has a significant impact on ecosystems and Arctic communities, as well as economic activities such as fisheries, tourism, and transport (Meredith et al. 2019).\n\nCMEMS KEY FINDINGS\n\nSince 1979, the Northern Hemisphere sea ice extent has decreased at an annual rate of -0.51 +/- 0.03106 km2 per decade (-4.41% per decade). Loss of sea ice extent during summer exceeds the loss observed during winter periods: Summer (September) sea ice extent loss amounts to -0.81 +/- 0.06 106 km2 per decade (-12.73% per decade). Winter (March) sea ice extent loss amounts to -0.39 +/- 0.03 106 km2 per decade (-2.55% per decade). These values are in agreement with those assessed in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Meredith et al. 2019, with estimates up until year 2018). September 2022 had the 11th lowest mean September sea ice extent. Sea ice extent in September 2012 is to date the record minimum Northern Hemisphere sea ice extent value since the beginning of the satellite record, followed by September values in 2020.\n\nFigure caption\n\na) The seasonal cycle of Northern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Northern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line in panel b).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00191\n\nReferences:\n\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n", "doi": "10.48670/moi-00191", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent-obs,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_OMI_TEMPSAL_FWC": {"abstract": "DEFINITION\n\nEstimates of Arctic liquid Freshwater Content (FWC in meters) are obtained from integrated differences of the measured salinity and a reference salinity (set to 34.8) from the surface to the bottom per unit area in the Arctic region with a water depth greater than 500m as function of salinity (S), the vertical cell thickness of the dataset (dz) and the salinity reference (Sref). Waters saltier than the 34.8 reference are not included in the estimation. The regional FWC values from 1993 up to real time are then averaged aiming to:\n* obtain the mean FWC as expressed in cubic km (km3) \n* monitor the large-scale variability and change of liquid freshwater stored in the Arctic Ocean (i.e. the change of FWC in time).\n\nCONTEXT\n\nThe Arctic region is warming twice as fast as the global mean and its climate is undergoing unprecedented and drastic changes, affecting all the components of the Arctic system. Many of these changes affect the hydrological cycle. Monitoring the storage of freshwater in the Arctic region is essential for understanding the contemporary Earth system state and variability. Variations in Arctic freshwater can induce changes in ocean stratification. Exported southward downstream, these waters have potential future implications for global circulation and heat transport. \n\nCMEMS KEY FINDINGS\n\nSince 1993, the Arctic Ocean freshwater has experienced a significant increase of 423 \u00b1 39 km3/year. The year 2016 witnessed the highest freshwater content in the Artic since the last 24 years. Second half of 2016 and first half of 2017 show a substantial decrease of the FW storage. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00193\n\nReferences:\n\n* G. Garric, O. Hernandez, C. Bricaud, A. Storto, K. A. Peterson, H. Zuo, 2018: Arctic Ocean freshwater content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s70\u2013s72, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00193", "instrument": null, "keywords": "arctic-ocean,arctic-omi-tempsal-fwc,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Freshwater Content from Reanalysis"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"abstract": "This Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, dissolved iron, dissolved cdom, hydrogen sulfide, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann et al, 2021) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the Baltic Sea physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00009", "doi": "10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-bgc-003-007,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"abstract": "This Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00010", "doi": "10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-phy-003-006,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"abstract": "This Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00011", "doi": "10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-wav-003-010,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-12-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Analysis and Forecast"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"abstract": "This Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\nDOI (product):\n\nhttps://doi.org/10.48670/moi-00012", "doi": "10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-bgc-003-012,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Reanalysis"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"abstract": "This Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00013", "doi": "10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-phy-003-011,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Reanalysis"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"abstract": "This product has been archived \n\n\n\nThis Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00014", "doi": "10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-wav-003-015,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Hindcast"}, "BALTIC_OMI_HEALTH_codt_volume": {"abstract": "DEFINITION\n\nThe cod reproductive volume has been derived from regional reanalysis modelling results for the Baltic Sea BALTICSEA_MULTIYEAR_PHY_003_011 and BALTICSEA_MULTIYEAR_BGC_003_012. The volume has been calculated taking into account the three most important influencing abiotic factors of cod reproductive success: salinity > 11 g/kg, oxygen concentration\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C (MacKenzie et al., 1996; Heikinheimo, 2008; Plikshs et al., 2015). The daily volumes are calculated as the volumes of the water with salinity > 11 g/kg, oxygen content\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C in the Baltic Sea International Council for the Exploration of the Sea subdivisions of 25-28 (ICES, 2019).\n\nCONTEXT\n\nCod (Gadus morhua) is a characteristic fish species in the Baltic Sea with major economic importance. Spawning stock biomasses of the Baltic cod have gone through a steep decline in the late 1980s (Bryhn et al., 2022). Water salinity and oxygen concentration affect cod stock through the survival of eggs (Westin and Nissling, 1991; Wieland et al., 1994). Major Baltic Inflows provide a suitable environment for cod reproduction by bringing saline oxygenated water to the deep basins of the Baltic Sea (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Increased cod reproductive volume has a positive effect on cod reproduction success, which should reflect an increase of stock size indicator 4\u20135 years after the Major Baltic Inflow (Raudsepp et al., 2019). Eastern Baltic cod reaches maturity around age 2\u20133, depending on the population density and environmental conditions. Low oxygen and salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin, 1999; Karasiova et al., 2008). There are a number of environmental factors affecting cod populations (Bryhn et al., 2022).\n\nKEY FINDINGS\n\nTypically, the cod reproductive volume in the Baltic Sea oscillates between 200 and 400 km\u00b3. There have been two distinct periods of significant increase, with maximum values reaching over 1200 km\u00b3, corresponding to the aftermath of Major Baltic Inflows (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland) from 2003 to 2004 and from 2016 to 2017. Following a decline to the baseline of 200 km\u00b3 in 2018, there was a rise to 800 km\u00b3 in 2019. The cod reproductive volume hit a second peak of 800 km\u00b3 in 2022 and has since stabilized at 600 km\u00b3. However, Bryhn et al. (2022) report no observed increase in the spawning stock biomass of the eastern Baltic Sea cod.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00196\n\nReferences:\n\n* Cardinale, M., Modin, J., 1999. Changes in size-at-maturity of Baltic cod (Gadus morhua) during a period of large variations in stock size and environmental conditions. Vol. 41 (3), 285-295. https://doi.org/10.1016/S0165-7836(99)00021-1\n* Heikinheimo, O., 2008. Average salinity as an index for environmental forcing on cod recruitment in the Baltic Sea. Boreal Environ Res 13:457\n* ICES, 2019. Baltic Sea Ecoregion \u2013 Fisheries overview, ICES Advice, DOI:10.17895/ices.advice.5566 Karasiova, E.M., Voss, R., Eero, M., 2008. Long-term dynamics in eastern Baltic cod spawning time: from small scale reversible changes to a recent drastic shift. ICES CM 2008/J:03\n* MacKenzie, B., St. John, M., Wieland, K., 1996. Eastern Baltic cod: perspectives from existing data on processes affecting growth and survival of eggs and larvae. Mar Ecol Prog Ser Vol. 134: 265-281.\n* Plikshs, M., Hinrichsen, H. H., Elferts, D., Sics, I., Kornilovs, G., K\u00f6ster, F., 2015. Reproduction of Baltic cod, Gadus morhua (Actinopterygii: Gadiformes: Gadidae), in the Gotland Basin: Causes of annual variability. Acta Ichtyologica et Piscatoria, Vol. 45, No. 3, 2015, p. 247-258.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s106\u2013s110, DOI: 10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Westin, L., Nissling, A., 1991. Effects of salinity on spermatozoa motility, percentage of fertilized eggs and egg development of Baltic cod Gadus morhua, and implications for cod stock fluctuations in the Baltic. Mar. Biol. 108, 5 \u2013 9.\n* Wieland, K., Waller, U., Schnack, D., 1994. Development of Baltic cod eggs at different levels of temperature and oxygen content. Dana 10, 163 \u2013 177.\n* Bryhn, A.C.., Bergek, S., Bergstr\u00f6m,U., Casini, M., Dahlgren, E., Ek, C., Hjelm, J., K\u00f6nigson, S., Ljungberg, P., Lundstr\u00f6m, K., Lunneryd, S.G., Oveg\u00e5rd, M., Sk\u00f6ld, M., Valentinsson, D., Vitale, F., Wennhage, H., 2022. Which factors can affect the productivity and dynamics of cod stocks in the Baltic Sea, Kattegat and Skagerrak? Ocean & Coastal Management, 223, 106154. https://doi.org/10.1016/j.ocecoaman.2022.106154\n", "doi": "10.48670/moi-00196", "instrument": null, "keywords": "baltic-omi-health-codt-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-volume,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Cod Reproductive Volume from Reanalysis"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe method for calculating the ocean heat content anomaly is based on the daily mean sea water potential temperature fields (Tp) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total heat content is determined using the following formula:\n\nHC = \u03c1 * cp * ( Tp +273.15).\n\nHere, \u03c1 and cp represent spatially varying sea water density and specific heat, respectively, which are computed based on potential temperature, salinity and pressure using the UNESCO 1983 polynomial developed by Fofonoff and Millard (1983). The vertical integral is computed using the static cell vertical thicknesses sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial averaging is performed over the Baltic Sea spatial domain, defined as the region between 13\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N. To obtain the OHC annual anomaly time series in (J/m2), the mean heat content over the reference period of 1993-2014 was subtracted from the annual mean total heat content.\nWe evaluate the uncertainty from the mean annual error of the potential temperature compared to the observations from the Baltic Sea (Giorgetti et al., 2020). The shade corresponds to the RMSD of the annual mean heat content biases (\u00b1 35.3 MJ/m\u00b2) evaluated from the observed temperatures and corresponding model values. \nLinear trend (W/m2) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\nCONTEXT\n\nOcean heat content is a key ocean climate change indicator. It accounts for the energy absorbed and stored by oceans. Ocean Heat Content in the upper 2,000 m of the World Ocean has increased with the rate of 0.35 \u00b1 0.08 W/m2 in the period 1955\u20132019, while during the last decade of 2010\u20132019 the warming rate was 0.70 \u00b1 0.07 W/m2 (Garcia-Soto et al., 2021). The high variability in the local climate of the Baltic Sea region is attributed to the interplay between a temperate marine zone and a subarctic continental zone. Therefore, the Ocean Heat Content of the Baltic Sea could exhibit strong interannual variability and the trend could be less pronounced than in the ocean.\n\nKEY FINDINGS\n\nThe ocean heat content (OHC) of the Baltic Sea exhibits an increasing trend of 0.3\u00b10.1 W/m\u00b2, along with multi-year oscillations. This increase is less pronounced than the global OHC trend (Holland et al. 2019; von Schuckmann et al. 2019) and that of some other marginal seas (von Schuckmann et al. 2018; Lima et al. 2020). The relatively low trend values are attributed to the Baltic Sea's shallowness, which constrains heat accumulation in its waters. The most significant OHC anomaly was recorded in 2020, and following a decline from this peak, the anomaly has now stabilized at approximately 250 MJ/m\u00b2.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00322\n\nReferences:\n\n* Garcia-Soto C, Cheng L, Caesar L, Schmidtko S, Jewett EB, Cheripka A, Rigor I, Caballero A, Chiba S, B\u00e1ez JC, Zielinski T and Abraham JP (2021) An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation). Front. Mar. Sci. 8:642372. doi: 10.3389/fmars.2021.642372\n* Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp., p.39. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf\n* Giorgetti, A., Lipizer, M., Molina Jack, M.E., Holdsworth, N., Jensen, H.M., Buga, L., Sarbu, G., Iona, A., Gatti, J., Larsen, M. and Fyrberg, L., 2020. Aggregated and Validated Datasets for the European Seas: The Contribution of EMODnet Chemistry. Frontiers in Marine Science, 7, p.583657.\n* Holland E, von Schuckmann K, Monier M, Legeais J-F, Prado S, Sathyendranath S, Dupouy C. 2019. The use of Copernicus Marine Service products to describe the state of the tropical western Pacific Ocean around the islands: a case study. In: Copernicus Marine Service Ocean State Report, Issue 3. J Oper Oceanogr. 12(suppl. 1):s43\u2013s48. doi:10.1080/1755876X.2019.1633075\n* Lima L, Peneva E, Ciliberti S, Masina S, Lemieux B, Storto A, Chtirkova B. 2020. Ocean heat content in the Black Sea. In: Copernicus Marine Service Ocean State Report, Issue 4. J Oper Oceanogr. 13(suppl. 1):s41\u2013s48. doi:10.1080/1755876X.2020.1785097.\n* von Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G. 2019. Copernicus Marine Service Ocean State report. J Oper Oceanogr. 12(suppl. 1):s1\u2013s123. doi:10.1080/1755876X.2019.1633075.\n* von Schuckmann K, Storto A, Simoncelli S, Raj RP, Samuelsen A, Collar A, Sotillo MG, Szerkely T, Mayer M, Peterson KA, et al. 2018. Ocean heat content. In: Copernicus Marine Service Ocean State Report, issue 2. J Oper Oceanogr. 11 (Suppl. 1):s1\u2013s142. doi:10.1080/1755876X.2018.1489208.\n", "doi": "10.48670/mds-00322", "instrument": null, "keywords": "baltic-omi-ohc-area-averaged-anomalies,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ohc-balrean,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Heat Content Anomaly (0-300m) from Reanalysis"}, "BALTIC_OMI_SI_extent": {"abstract": "DEFINITION\n\nSea ice extent is defined as the area covered by sea ice, that is the area of the ocean having more than 15% sea ice concentration. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. Daily sea ice extent values are computed from the daily sea ice concentration maps. All sea ice covering the Baltic Sea is included, except for lake ice. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010). The annual course of the sea ice extent has been calculated as daily mean ice extent for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice extent have been calculated from daily values using a 7-day moving average filter.\n\nCONTEXT\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation. The presence of sea ice cover sets special requirements for navigation, both for the construction of the ships and their behavior in ice, as in many cases, merchant ships need icebreaker assistance. Temporal trends of the sea ice extent could be a valuable indicator of the climate change signal in the Baltic Sea region. It has been estimated that a 1 \u00b0C increase in the average air temperature results in the retreat of ice-covered area in the Baltic Sea about 45,000 km2 (Granskog et al., 2006). Decrease in maximum ice extent may influence vertical stratification of the Baltic Sea (Hordoir and Meier, 2012) and affect the onset of the spring bloom (Eilola et al., 2013). In addition, statistical sea ice coverage information is crucial for planning of coastal and offshore construction. Therefore, the knowledge about ice conditions and their variability is required and monitored in Copernicus Marine Service.\n\nKEY FINDINGS\n\nSea ice in the Baltic Sea exhibits a strong seasonal pattern. Typically, formation begins in October and can persist until June. The 2022/23 ice season saw a relatively low maximum ice extent in the Baltic Sea, peaking at around 65,000 km\u00b2. Formation started in November and accelerated in the second week of December. The ice extent then remained fairly stable and below the climatological average until the end of January. From February to the second week of March, the extent of sea ice steadily grew to its maximum of 65,000 km\u00b2, before gradually receding. The peak day for sea ice extent varies annually but generally oscillates between the end of February and the start of March (Singh et al., 2024). The past 30 years saw the highest sea ice extent at 260,000 km\u00b2 in 2010/11. Despite a downward trend in sea ice extent in the Baltic Sea from 1993 to 2022, the linear trend does not show statistical significance.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00200\n\nReferences:\n\n* Eilola K, M\u00e5rtensson S, Meier HEM, 2013. Modeling the impact of reduced sea ice cover in future climate on the Baltic Sea biogeochemistry. Geophysical Research Letters, 40, 149-154, doi:10.1029/2012GL054375\n* Granskog M, Kaartokallio H, Kuosa H, Thomas DN, Vainio J, 2006. Sea ice in the Baltic Sea \u2013 A review. Estuarine, Coastal and Shelf Science, 70, 145\u2013160. doi:10.1016/j.ecss.2006.06.001\n* Hordoir R., Meier HEM, 2012. Effect of climate change on the thermal stratification of the Baltic Sea: a sensitivity experiment. Climate Dynamics, 38, 1703-1713, doi:10.1007/s00382-011-1036-y\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010. Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, doi:10.1109/BALTIC.2010.5621649\n* Vihma T, Haapala J, 2009. Geophysics of sea ice in the Baltic Sea: A review. Progress in Oceanography, 80, 129-148, doi:10.1016/j.pocean.2009.02.002\n", "doi": "10.48670/moi-00200", "instrument": null, "keywords": "baltic-omi-si-extent,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Extent from Observations Reprocessing"}, "BALTIC_OMI_SI_volume": {"abstract": "DEFINITION\n\nThe sea ice volume is a product of sea ice concentration and sea ice thickness integrated over respective area. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. The Baltic Sea area having more than 15% of sea ice concentration is included into the sea ice volume analysis. Daily sea ice volume values are computed from the daily sea ice concentration and sea ice thickness maps. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010; https://www.smhi.se/data/oceanografi/havsis). The annual course of the sea ice volume has been calculated as daily mean ice volume for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice volume have been calculated from daily values using a 7-day moving average filter.\n\nCONTEXT\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Knowledge of the sea ice volume facilitates planning of icebreaking activity and operation of the icebreakers (Valdez Banda et al., 2015; Bostr\u00f6m and \u00d6sterman, 2017). A long-term monitoring of ice parameters is required for design and installation of offshore constructions in seasonally ice covered seas (Heinonen and Rissanen, 2017). A reduction of the sea ice volume in the Baltic Sea has a critical impact on the population of ringed seals (Harkonen et al., 2008). Ringed seals need stable ice conditions for about two months for breeding and moulting (Sundqvist et al., 2012). The sea ice is a habitat for diverse biological assemblages (Enberg et al., 2018).\n\nKEY FINDINGS\n\nIn the Baltic Sea, the ice season may begin in October and last until June. The maximum sea ice volume typically peaks in March, but in 2023, it was observed in April. The 2022/23 ice season saw a low maximum sea ice volume of approximately 14 km\u00b3. From 1993 to 2023, the annual maximum ice volume ranged from 4 km\u00b3 in 2020 to 60 km\u00b3 in 1996. There is a statistically significant downward trend of -0.73 km\u00b3/year (p=0.01) in the Baltic Sea's maximum sea ice volume. Recent trends indicate a decrease in sea ice fraction and thickness across the Baltic Sea, particularly in the Bothnian Bay, as reported by Singh et al. (2024).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00201\n\nReferences:\n\n* Bostr\u00f6m M, \u00d6sterman C, 2017, Improving operational safety during icebreaker operations, WMU Journal of Maritime Affairs, 16, 73-88, DOI: 10.1007/s13437-016-0105-9\n* Enberg S, Majaneva M, Autio R, Blomster J, Rintala J-M, 2018, Phases of microalgal succession in sea ice and the water column in the baltic sea from autumn to spring. Marine Ecology Progress Series, 559, 19-34. DOI: 10.3354/meps12645\n* Harkonen T, J\u00fcssi M, J\u00fcssi I, Verevkin M, Dmitrieva L, Helle E, Sagitov R, Harding KC, 2008, Seasonal Activity Budget of Adult Baltic Ringed Seals, PLoS ONE 3(4): e2006, DOI: 0.1371/journal.pone.0002006\n* Heinonen J, Rissanen S, 2017, Coupled-crushing analysis of a sea ice - wind turbine interaction \u2013 feasibility study of FAST simulation software, Ships and Offshore Structures, 12, 1056-1063. DOI: 10.1080/17445302.2017.1308782\n* Sundqvist L, Harkonen T, Svensson CJ, Harding KC, 2012, Linking Climate Trends to Population Dynamics in the Baltic Ringed Seal: Impacts of Historical and Future Winter Temperatures, AMBIO, 41: 865, DOI: 10.1007/s13280-012-0334-x\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010, Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, DOI: 10.1109/BALTIC.2010.5621649\n* Valdez Banda OA, Goerlandt F, Montewka J, Kujala P, 2015, A risk analysis of winter navigation in Finnish sea areas, Accident Analysis & Prevention, 79, 100\u2013116, DOI: 10.1016/j.aap.2015.03.024\n", "doi": "10.48670/moi-00201", "instrument": null, "keywords": "baltic-omi-si-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-volume,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Volume from Observations Reprocessing"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"abstract": "DEFINITION\n\nThe subsurface salinity trends have been derived from regional reanalysis and forecast modelling results of the Copernicus Marine Service BAL MFC group for the Baltic Sea (product reference BALTICSEA_MULTIYEAR_PHY_003_011). The salinity trend has been obtained through a linear fit for each time series of horizontally averaged (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait) annual salinity and at each depth level.\n\nCONTEXT\n\nThe Baltic Sea is a brackish semi-enclosed sea in North-Eastern Europe. The surface salinity varies horizontally from ~10 near the Danish Straits down to ~2 at the northernmost and easternmost sub-basins of the Baltic Sea. The halocline, a vertical layer with rapid changes of salinity with depth that separates the well-mixed surface layer from the weakly stratified layer below, is located at the depth range of 60-80 metres (Matth\u00e4us, 1984). The bottom layer salinity below the halocline depth varies from 15 in the south down to 3 in the northern Baltic Sea (V\u00e4li et al., 2013). The long-term salinity is determined by net precipitation and river discharge as well as saline water inflows from the North Sea (Lehmann et al., 2022). Long-term salinity decrease may reduce the occurrence and biomass of the Fucus vesiculosus - Idotea balthica association/symbiotic aggregations (Kotta et al., 2019). Changes in salinity and oxygen content affect the survival of the Baltic cod eggs (Raudsepp et al, 2019; von Dewitz et al., 2018).\n\nKEY FINDINGS\n\nThe subsurface salinity from 1993 to 2023 exhibits distinct variations at different depths. In the surface layer up to 25 meters, which aligns with the average upper mixed layer depth in the Baltic Sea, there is no discernible trend. The salinity trend increases steadily from zero at a 25-meter depth to 0.04 per year at 70 meters. The most pronounced trend, 0.045 per year, is found within the extended halocline layer ranging from 70 to 150 meters. It is noteworthy that there is a slight reduction in the salinity trend to 0.04 per year between depths of 150 and 220 meters. Although this decrease is minor, it suggests that salt transport into the extended halocline layer is more pronounced than into the deeper layers. The Major Baltic Inflows are responsible for the significant salinity trend of 0.05 per year observed in the deepest layer of the Baltic Sea. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00207\n\nReferences:\n\n* von Dewitz B, Tamm S, Ho\u00c8flich K, Voss R, Hinrichsen H-H., 2018. Use of existing hydrographic infrastructure to forecast the environmental spawning conditions for Eastern Baltic cod, PLoS ONE 13(5): e0196477, doi:10.1371/journal.pone.0196477\n* Kotta, J., Vanhatalo, J., J\u00e4nes, H., Orav-Kotta, H., Rugiu, L., Jormalainen, V., Bobsien, I., Viitasalo, M., Virtanen, E., Nystr\u00f6m Sandman, A., Isaeus, M., Leidenberger, S., Jonsson, P.R., Johannesson, K., 2019. Integrating experimental and distribution data to predict future species patterns. Scientific Reports, 9: 1821, doi:10.1038/s41598-018-38416-3\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Sandrine Mulet, Bruno Buongiorno Nardelli, Simon Good, Andrea Pisano, Eric Greiner, Maeva Monier, Emmanuelle Autret, Lars Axell, Fredrik Boberg, Stefania Ciliberti, Marie Dr\u00e9villon, Riccardo Droghei, Owen Embury, J\u00e9rome Gourrion, Jacob H\u00f8yer, M\u00e9lanie Juza, John Kennedy, Benedicte Lemieux-Dudon, Elisaveta Peneva, Rebecca Reid, Simona Simoncelli, Andrea Storto, Jonathan Tinker, Karina von Schuckmann and Sarah L. Wakelin. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI:10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. 2.7 Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* V\u00e4li G, Meier HEM, Elken J, 2013, Simulated halocline variability in the baltic sea and its impact on hypoxia during 1961-2007, Journal of Geophysical Research: Oceans, 118(12), 6982\u20137000, DOI:10.1002/2013JC009192\n", "doi": "10.48670/moi-00207", "instrument": null, "keywords": "baltic-omi-tempsal-stz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-salinity-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Salinity trend from Reanalysis"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"abstract": "DEFINITION\n\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic Sea (product references BALTICSEA_MULTIYEAR_PHY_003_011). Horizontal averaging has been done over the Baltic Sea domain (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait). The temperature trend has been obtained through a linear fit for each time series of horizontally averaged annual temperature and at each depth level. \n\nCONTEXT\n\nThe Baltic Sea is a semi-enclosed sea in North-Eastern Europe. The temperature of the upper mixed layer of the Baltic Sea is characterised by a strong seasonal cycle driven by the annual course of solar radiation (Lepp\u00e4ranta and Myrberg, 2008). The maximum water temperatures in the upper layer are reached in July and August and the minimum during February, when the Baltic Sea becomes partially frozen (CMEMS OMI Baltic Sea Sea Ice Extent, CMEMS OMI Baltic Sea Sea Ice Volume). Seasonal thermocline, developing in the depth range of 10-30 m in spring, reaches its maximum strength in summer and is eroded in autumn. During autumn and winter the Baltic Sea is thermally mixed down to the permanent halocline in the depth range of 60-80 metres (Matth\u00e4us, 1984). The 20\u201350\u202fm thick cold intermediate layer forms below the upper mixed layer in March and is observed until October within the 15-65 m depth range (Chubarenko and Stepanova, 2018; Liblik and Lips, 2011). The deep layers of the Baltic Sea are disconnected from the ventilated upper ocean layers, and temperature variations are predominantly driven by mixing processes and horizontal advection. A warming trend of the sea surface waters is positively correlated with the increasing trend of diffuse attenuation of light (Kd490) and satellite-detected chlorophyll concentration (Kahru et al., 2016). Temperature increase in the water column could accelerate oxygen consumption during organic matter oxidation (Savchuk, 2018).\n\nKEY FINDINGS\n\nAnalysis of subsurface temperatures from 1993 to 2023 indicates that the Baltic Sea is experiencing warming across all depth intervals. The temperature trend in the upper mixed layer (0-25 m) is approximately 0.055 \u00b0C/year, decreasing to 0.045 \u00b0C/year within the seasonal thermocline layer. A peak temperature trend of 0.065 \u00b0C/year is observed at a depth of 70 m, aligning with the base of the cold intermediate layer. Beyond this depth, the trend stabilizes, closely matching the 0.065 \u00b0C/year value. At a 95% confidence level, it can be stated that the Baltic Sea's warming is consistent with depth, averaging around 0.06 \u00b0C/year. Notably, recent trends show a significant increase; for instance, Savchuk's 2018 measurements indicate an average temperature trend of 0.04 \u00b0C/year in the Baltic Proper's deep layers (>60m) from 1979 to 2016.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00208\n\nReferences:\n\n* Chubarenko, I., Stepanova, N. 2018. Cold intermediate layer of the Baltic Sea: Hypothesis of the formation of its core. Progress in Oceanography, 167, 1-10, doi: 10.1016/j.pocean.2018.06.012\n* Kahru, M., Elmgren, R., and Savchuk, O. P. 2016. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009\u20131018. doi: 10.5194/bg-13-1009-2016\n* Lepp\u00e4ranta, M., Myrberg, K. 2008. Physical Oceanography of the Baltic Sea. Springer, Praxis Publishing, Chichester, UK, pp. 370\n* Liblik, T., Lips, U. 2011. Characteristics and variability of the vertical thermohaline structure in the Gulf of Finland in summer. Boreal Environment Research, 16, 73-83.\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Savchuk, .P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00208", "instrument": null, "keywords": "baltic-omi-tempsal-ttz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Temperature trend from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"abstract": "DEFINITION\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the Baltic Sea- Bornholm basin, Gdansk basin and Gotland basin. The Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona basin and in the Bornholm basin and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland basin (BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Bottom salinity increase in the Arkona basin is the first indication of the saline water inflow, but not necessarily Major Baltic Inflow. Abrupt increase of bottom salinity of 2-3 units in the more downstream Bornholm basin is a solid indicator that Major Baltic Inflow has occurred.\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic \n\nCONTEXT\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). The saline and oxygenated water inflows to the Baltic Sea through the Danish straits, especially the Major Baltic Inflows, occur only intermittently (e.g. Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and the overall weakening of vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s Lehmann et al., 2022). Bottom salinity variations in the Arkona Basin represent water exchange between the Baltic Sea and Skagerrak-Kattegat area. The increasing salinity signal in that area does not indicate that a Major Baltic Inflow has occurred. The mean sea level of the Baltic Sea derived from satellite altimetry data can be used as a proxy for the detection of saline water inflows to the Baltic Sea from the North Sea (Raudsepp et al., 2018). The medium and strong inflow events increase oxygen concentration in the near-bottom layer of the Bornholm Basin while some medium size inflows have no impact on deep water salinity (Mohrholz, 2018). \n\nKEY FINDINGS\n\nTime series data of bottom salinity variations in the Arkona Basin are instrumental for monitoring the sporadic nature of water inflow and outflow events. The bottom salinity in the Arkona Basin fluctuates between 11 and 25 g/kg. The highest recorded bottom salinity value is associated with the Major Baltic Inflow of 2014, while other significant salinity peaks align with the Major Baltic Inflows of 1993 and 2002. Low salinity episodes in the Arkona Basin mark the occasions of barotropic outflows of brackish water from the Baltic Sea. In the Bornholm Basin, the bottom salinity record indicates three Major Baltic Inflow events: the first in 1993, followed by 2002, and the most recent in 2014. Following the last Major Baltic Inflow, the bottom salinity in the Bornholm Basin rose to 20 g/kg. Over the subsequent nine years, it has declined to 16 g/kg. The winter of 2023/24 did not experience a Major Baltic Inflow.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00209\n\nReferences:\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz V, 2018, Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, doi: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, doi: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00209", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-bottom-salinity-arkona-bornholm,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: bottom salinity from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"abstract": "\"_DEFINITION_'\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the central Baltic Sea, i.e. the Gotland Basin. These Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona Basin and in the Bornholm Basin (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland Basin. Temperature, salinity and dissolved oxygen profiles in the Gotland Basin enable us to estimate the amount of the Major Baltic Inflow water that has reached central Baltic, the depth interval of which has been the most affected, and how much the oxygen conditions have been improved. \n\nCONTEXT\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). This implies that fresher water lies on top of water with higher salinity. The saline water inflows to the Baltic Sea through the Danish Straits, especially the Major Baltic Inflows, shape hydrophysical conditions in the Gotland Basin of the central Baltic Sea, which in turn have a substantial influence on marine ecology on different trophic levels (Bergen et al., 2018; Raudsepp et al.,2019). In the absence of the Major Baltic Inflows, oxygen in the deeper layers of the Gotland Basin is depleted and replaced by hydrogen sulphide (e.g., Savchuk, 2018). As the Baltic Sea is connected to the North Sea only through very narrow and shallow channels in the Danish Straits, inflows of high salinity and oxygenated water into the Baltic occur only intermittently (e.g., Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and overall weakening of the vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s (Lehmann et al., 2022).\n\nKEY FINDINGS\n\nThe Major Baltic Inflows of 1993, 2002, and 2014 (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) present a distinct signal in the Gotland Basin, influencing water salinity, temperature, and dissolved oxygen up to a depth of 100 meters. Following each event, deep layer salinity in the Gotland Basin increases, reaching peak bottom salinities approximately 1.5 years later, with elevated salinity levels persisting for about three years. Post-2017, salinity below 150 meters has declined, while the halocline has risen, suggesting saline water movement to the Gotland Basin's intermediate layers. Typically, temperatures fall immediately after a Major Baltic Inflow, indicating the descent of cold water from nearby upstream regions to the Gotland Deep's bottom. From 1993 to 1997, deep water temperatures remained relatively low (below 6 \u00b0C). Since 1998, these waters have warmed, with even moderate inflows in 1997/98, 2006/07, and 2018/19 introducing warmer water to the Gotland Basin's bottom layer. From 2019 onwards, water warmer than 7 \u00b0C has filled the layer beneath 100 meters depth. The water temperature below the halocline has risen by approximately 2 \u00b0C since 1993, and the cold intermediate layer's temperature has also increased from 1993 to 2023. Oxygen levels begin to drop sharply after the temporary reoxygenation of the bottom waters. The decline in 2014 was attributed to a shortage of smaller inflows that could bring oxygen-rich water to the Gotland Basin (Neumann et al., 2017) and an increase in biological oxygen demand (Savchuk, 2018; Meier et al., 2018). Additionally, warmer water has accelerated oxygen consumption in the deep layer, leading to increased anoxia. By 2023, oxygen was completely depleted below the depth of 75 metres.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00210\n\nReferences:\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Bergen, B., Naumann, M., Herlemann, D.P.R., Gr\u00e4we, U., Labrenz, M., J\u00fcrgens, K., 2018. Impact of a Major inflow event on the composition and distribution of bacterioplankton communities in the Baltic Sea. Frontiers in Marine Science, 5:383, doi: 10.3389/fmars.2018.00383\n* Meier, H.E.M., V\u00e4li, G., Naumann, M., Eilola, K., Frauen, C., 2018. Recently Accelerated Oxygen Consumption Rates Amplify Deoxygenation in the Baltic Sea. , J. Geophys. Res. Oceans, doi:10.1029/2017JC013686\|\n* Mohrholz, V., 2018. Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, DOI: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* Savchuk, P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00210", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-sto2tz-gotland,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: time/depth evolution S,T,O2 from Observations Reprocessing"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"abstract": "BLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.2-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, zooplankton and phytoplankton carbon biomass, oxygen-demand-units, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\nDOI (product): \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\n\nReferences:\n\n* Gr\u00e9goire, M., Vandenbulcke, L. and Capet, A. (2020) \u201cBlack Sea Biogeochemical Analysis and Forecast (CMEMS Near-Real Time BLACKSEA Biogeochemistry).\u201d Copernicus Monitoring Environment Marine Service (CMEMS). doi: 10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\"\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-bgc-007-010,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"abstract": "The BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 3 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\nDOI (Product): \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6\n\nReferences:\n\n* Jansen, E., Martins, D., Stefanizzi, L., Ciliberti, S. A., Gunduz, M., Ilicak, M., Lecci, R., Cret\u00ed, S., Causio, S., Aydo\u011fdu, A., Lima, L., Palermo, F., Peneva, E. L., Coppini, G., Masina, S., Pinardi, N., Palazov, A., and Valchev, N. (2022). Black Sea Physical Analysis and Forecast (Copernicus Marine Service BS-Currents, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS5\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-phy-007-001,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"abstract": "The wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\nCitation: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n\nDOI (Product): \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5\n\nReferences:\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n* Ricker, M., Behrens, A., & Staneva, J. (2024). The operational CMEMS wind wave forecasting system of the Black Sea. Journal of Operational Oceanography, 1\u201322. https://doi.org/10.1080/1755876X.2024.2364974\n", "doi": "10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-wav-007-003,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-04-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Analysis and Forecast"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"abstract": "The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\nDOI (product):\nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI\n\nReferences:\n\n* Gr\u00e9goire, M., Vandenbulcke, L., & Capet, A. (2020). Black Sea Biogeochemical Reanalysis (CMEMS BS-Biogeochemistry) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_REANALYSIS_BIO_007_005_BAMHBI\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,blksea-multiyear-bgc-007-005,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Reanalysis"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"abstract": "The BLKSEA_MULTIYEAR_PHY_007_004 product provides ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamic core is based on the NEMOv4.0 general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/40\u00ba and 121 vertical levels. NEMO is forced by atmospheric fluxes computed from a bulk formulation applied to ECMWF ERA5 atmospheric fields at the resolution of 1/4\u00ba in space and 1-h in time. A heat flux correction through sea surface temperature (SST) relaxation is employed using the ESA-CCI SST-L4 product. This version has an open lateral boundary, a new model characteristic that allows a better inflow/outflow representation across the Bosphorus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly (SLA) along-track observations from Copernicus and available in situ vertical profiles of temperature and salinity from both SeaDataNet and Copernicus datasets. Upgrades on data assimilation include an improved background error covariance matrix and an observation-based mean dynamic topography for the SLA assimilation.\n\nDOI (Product): \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n\nReferences:\n\n* Lima, L., Aydogdu, A., Escudier, R., Masina, S., Ciliberti, S. A., Azevedo, D., Peneva, E. L., Causio, S., Cipollone, A., Clementi, E., Cret\u00ed, S., Stefanizzi, L., Lecci, R., Palermo, F., Coppini, G., Pinardi, N., & Palazov, A. (2020). Black Sea Physical Reanalysis (CMEMS BS-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,blksea-multiyear-phy-007-004,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Reanalysis"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"abstract": "The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.\n\nCitation: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). \n\nDOI (Product): \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4\n\nReferences:\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_WAV_007_006_EAS4\n", "doi": "10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,blksea-multiyear-wav-007-006,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Reanalysis"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"abstract": "DEFINITION\n\nThe oxygenation status of the Black Sea open basin is described by three complementary indicators, derived from vertical profiles and spatially averaged over the Black Sea open basin (depth > 50m). (1) The oxygen penetration depth is the depth at which [O2] < 20\u00b5M, expressed in [m]. (2) The oxygen penetration density is the potential density anomaly at the oxygen penetration depth [kg/m\u00b3]. (3) The oxygen inventory is the vertically integrated oxygen content [mol O2/m\u00b2]. The 20\u00b5M threshold was chosen to minimize the indicator sensitivity to sensor\u2019s precision. Those three metrics are complementary: Oxygen penetration depth is more easily understood, but present more spatial variability. Oxygen penetration density helps in dissociating biogeochemical processes from shifts in the physical structure. Although less intuitive, the oxygen inventory is a more integrative diagnostic and its definition is more easily transposed to other areas.\n\nCONTEXT\n\nThe Black Sea is permanently stratified, due to the contrast in density between large riverine and Mediterranean inflows. This stratification restrains the ventilation of intermediate and deep waters and confines, within a restricted surface layer, the waters that are oxygenated by photosynthesis and exchanges with the atmosphere. The vertical extent of the oxic layer determines the volume of habitat available for pelagic populations (Ostrovskii and Zatsepin 2011, Sak\u0131nan and G\u00fcc\u00fc 2017) and present spatial and temporal variations (Murray et al. 1989; Tugrul et al. 1992; Konovalov and Murray 2001). At long and mid-term, these variations can be monitored with three metrics (Capet et al. 2016), derived from the vertical profiles that can obtained from traditional ship casts or autonomous Argo profilers (Stanev et al., 2013). A large source of uncertainty associated with the spatial and temporal average of those metrics stems from the small number of Argo floats, scarcely adequate to sample the known spatial variability of those metrics.\n\nCMEMS KEY FINDINGS\n\nDuring the past 60 years, the vertical extent of the Black Sea oxygenated layer has narrowed from 140m to 90m (Capet et al. 2016). The Argo profilers active for 2016 suggested an ongoing deoxygenation trend and indicated an average oxygen penetration depth of 72m at the end of 2016, the lowest value recorded during the past 60 years. The oxygenation of subsurface water is closely related to the intensity of cold water formation, an annual ventilation processes which has been recently limited by warmer-than-usual winter air temperature (Capet et al. 2020). In 2017, 2018 and 2020, cold waters formation resulted in a partial reoxygenation of the intermediate layer. Yet, such ventilation has been lacking in winter 2020-2021, and the updated 2021 indicators reveals the lowest oxygen inventory ever reported in this OMI time series. This results in significant detrimental trends now depicted also over the Argo period (2012-2021).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00213\n\nReferences:\n\n* Capet, A., Vandenbulcke, L., & Gr\u00e9goire, M. (2020). A new intermittent regime of convective ventilation threatens the Black Sea oxygenation status. Biogeosciences , 17(24), 6507\u20136525.\n* Capet A, Stanev E, Beckers JM, Murray J, Gr\u00e9goire M. (2016). Decline of the Black Sea oxygen inventory. Biogeosciences. 13:1287-1297.\n* Capet Arthur, Vandenbulcke Luc, Veselka Marinova, Gr\u00e9goire Marilaure. (2018). Decline of the Black Sea oxygen inventory. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Konovalov S, Murray JW. (2001). Variations in the chemistry of the Black Sea on a time scale of decades (1960\u20131995). J Marine Syst. 31: 217\u2013243.\n* Murray J, Jannasch H, Honjo S, Anderson R, Reeburgh W, Top Z, Friederich G, Codispoti L, Izdar E. (1989). Unexpected changes in the oxic/anoxic interface in the Black Sea. Nature. 338: 411\u2013413.\n* Ostrovskii A and Zatsepin A. (2011). Short-term hydrophysical and biological variability over the northeastern Black Sea continental slope as inferred from multiparametric tethered profiler surveys, Ocean Dynam., 61, 797\u2013806, 2011.\n* \u00d6zsoy E and \u00dcnl\u00fcata \u00dc. (1997). Oceanography of the Black Sea: a review of some recent results. Earth-Science Reviews. 42(4):231-72.\n* Sak\u0131nan S, G\u00fcc\u00fc AC. (2017). Spatial distribution of the Black Sea copepod, Calanus euxinus, estimated using multi-frequency acoustic backscatter. ICES J Mar Sci. 74(3):832-846. doi:10.1093/icesjms/fsw183\n* Stanev E, He Y, Grayek S, Boetius A. (2013). Oxygen dynamics in the Black Sea as seen by Argo profiling floats. Geophys Res Lett. 40(12), 3085-3090.\n* Tugrul S, Basturk O, Saydam C, Yilmaz A. (1992). Changes in the hydrochemistry of the Black Sea inferred from water density profiles. Nature. 359: 137-139.\n* von Schuckmann, K. et al. Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography 11, S1\u2013S142 (2018).\n", "doi": "10.48670/moi-00213", "instrument": null, "keywords": "black-sea,blksea-omi-health-oxygen-trend,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,ocean-mole-content-of-dissolved-molecular-oxygen,oceanographic-geographical-features,sea-water-sigma-theta-defined-by-mole-concentration-of-dissolved-molecular-oxygen-in-sea-water-above-threshold,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1955-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Oxygen Trend from Observations Reprocessing"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS BLKSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_WAV_007_006) and the Analysis product (BLKSEA_ANALYSISFORECAST_WAV_007_003).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1979-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\nCONTEXT\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, including biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015, IPCC, 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (IPCC, 2021).\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence demonstrating that the highest values of the average annual 99th percentiles are in the areas where high winds and long fetch are simultaneously present. The largest values of the mean 99th percentile in the Black Sea in the southewestern Black Sea are around 3.5 m, while in the eastern part of the basin are around 2.5 m (Staneva et al., 2019a and 2019b).\n\nCMEMS KEY FINDINGS\n\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence with largest values in the southwestern Black Sea, with values as high as 3.5 m, while the 99th percentile values in the eastern part of the basin are around 2.5 m. The Black Sea, the 99th mean percentile for 2002-2019 shows a similar pattern demonstrating that the highest values of the mean annual 99th percentile are in the western Black Sea. This pattern is consistent with the previous studies, e.g. of (Akp\u0131nar and K\u00f6m\u00fcrc\u00fc, 2012; and Akpinar et al., 2016).\nThe anomaly of the 99th percentile in 2020 is mostly negative with values down to ~-45 cm. The highest negative anomalies for 2020 are observed in the southeastern area where the multi-year mean 99th percentile is the lowest. The highest positive anomalies of the 99th percentile in 2020 are located in the southwestern Black Sea and along the eastern coast. The map of anomalies for 2020, presenting alternate bands of positive and negative values depending on latitude, is consistent with the yearly west-east displacement of the tracks of the largest storms. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00214\n\nReferences:\n\n* Akp\u0131nar, A.; K\u00f6m\u00fcrc\u00fc, M.\u02d9I. Wave energy potential along the south-east coasts of the Black Sea. Energy 2012, 42, 289\u2013302.\n* Akp\u0131nar, A., Bing\u00f6lbali, B., Van Vledder, G., 2016. Wind and wave characteristics in the Black Sea based on the SWAN wave model forced with the CFSR winds. Ocean Eng. 126, 276\u2014298, http://dx. doi.org/10.1016/j.oceaneng.2016.09.026.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Staneva, J. Behrens, A., Gayer G, Ricker M. (2019a) Black sea CMEMS MYP QUID Report\n* Staneva J, Behrens A., Gayer G, Aouf A., (2019b). Synergy between CMEMS products and newly available data from SENTINEL, Section 3.3, In: Schuckmann K,et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, doi: 10.1080/1755876X.2019.1633075.\n", "doi": "10.48670/moi-00214", "instrument": null, "keywords": "black-sea,blksea-omi-seastate-extreme-var-swh-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Significant Wave Height extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS BLKSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_PHY_007_004) and the Analysis product (BLKSEA_ANALYSIS_FORECAST_PHYS_007_001).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. Particularly in the Black Sea, ocean-atmospheric processes together with its general cyclonic circulation (Rim Current) play an important role on the sea surface temperature variability (Capet et al. 2012). As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. The 99th mean percentile of sea surface temperature provides a worth information about the variability of the sea surface temperature and warming trends but has not been investigated with details in the Black Sea.\nWhile the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013). Recent studies indicated a warming trend of the sea surface temperature in the Black Sea in the latest years (Mulet et al., 2018; Sakali and Ba\u015fusta, 2018). A specific analysis on the interannual variability of the basin-averaged sea surface temperature revealed a higher positive trend in its eastern region (Ginzburg et al., 2004). For the past three decades, Sakali and Ba\u015fusta (2018) presented an increase in sea surface temperature that varied along both east\u2013west and south\u2013north directions in the Black Sea. \n\nCMEMS KEY FINDINGS\n\nThe mean annual 99th percentile in the period 1993\u20132019 exhibits values ranging from 25.50 to 26.50 oC in the western and central regions of the Black Sea. The values increase towards the east, exceeding 27.5 oC. This contrasting west-east pattern may be linked to the basin wide cyclonic circulation. There are regions showing lower values, below 25.75 oC, such as a small area west of Crimean Peninsula in the vicinity of the Sevastopol anticyclone, the Northern Ukraine region, in particular close to the Odessa and the Karkinytska Gulf due to the freshwaters from the land and a narrow area along the Turkish coastline in the south. Results for 2020 show negative anomalies in the area of influence of the Bosporus and the Bulgarian offshore region up to the Crimean peninsula, while the North West shelf exhibits a positive anomaly as in the Eastern basin. The highest positive value is occurring in the Eastern Tukish coastline nearest the Batumi gyre area. This may be related to the variously increase of sea surface temperature in such a way the southern regions have experienced a higher warming.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00216\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Capet, A., Barth, A., Beckers, J. M., & Marilaure, G. (2012). Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns. Deep Sea Research Part II: Topical Studies in Oceanography, 77, 128-142. https://doi.org/10.1016/j.dsr2.2012.04.010\n* Ginzburg, A. I.; Kostianoy, A. G.; Sheremet, N. A. (2004). Seasonal and interannual variability of the Black Sea surface temperature as revealed from satellite data (1982\u20132000), Journal of Marine Systems, 52, 33-50. https://doi.org/10.1016/j.jmarsys.2004.05.002.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sakalli A, Ba\u015fusta N. 2018. Sea surface temperature change in the Black Sea under climate change: A simulation of the sea surface temperature up to 2100. International Journal of Climatology, 38(13), 4687-4698. https://doi.org/10.1002/joc.5688\n", "doi": "10.48670/moi-00216", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-extreme-var-temp-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "\"_DEFINITION_'\n\nThe blksea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Black Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure. This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\n\nKEY FINDINGS\n\nDuring 2023, the Black Sea basin average SST anomaly was ~1.1 \u00b0C above the 1991-2020 climatology, doubling that of previous year (~0.5 \u00b0C). The Black Sea SST monthly anomalies ranged between -1.0/+1.0 \u00b0C. The highest temperature anomaly (~1.8 \u00b0C) was reached in January 2023, while the lowest (~-0.28 \u00b0C) in May. This year, along with 2022, was characterized by milder temperature anomalies with respect to the previous three consecutive years (2018-2020) marked by peaks of ~3 \u00b0C occurred in May 2018, June 2019, and October 2020.\nOver the period 1982-2023, the Black Sea SST has warmed at a rate of 0.065 \u00b1 0.002 \u00b0C/year, which corresponds to an average increase of about 2.7 \u00b0C during these last 42 years.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00217\n\nReferences:\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n", "doi": "10.48670/moi-00217", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-sst-area-averaged-anomalies,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature time series and trend from Observations Reprocessing"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"abstract": "DEFINITION\n\nThe blksea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Black Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the timeseries (42). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\nKEY FINDINGS\n\nOver the past four decades (1982-2023), the Black Sea surface temperature (SST) warmed at a rate of 0.065 \u00b1 0.002 \u00b0C per year, corresponding to a mean surface temperature warming of about 2.7 \u00b0C. The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.053 \u00b0C/year to 0.080 \u00b0C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas.\nDOI (product): \nhttps://doi.org/10.48670/moi-00218\n\nReferences:\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00218", "instrument": null, "keywords": "baltic-sea,blksea-omi-tempsal-sst-trend,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"abstract": "The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related [QuID](https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-028.pdf)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00015", "doi": "10.48670/moi-00015", "instrument": null, "keywords": "cell-height,cell-thickness,cell-width,coastal-marine-environment,forecast,global-analysisforecast-bgc-001-028,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"abstract": "The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00016", "doi": "10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,forecast,global-analysisforecast-phy-001-024,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"abstract": "The operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00017\n\nReferences:\n\n* F. Ardhuin, R. Magne, J-F. Filipot, A. Van der Westhyusen, A. Roland, P. Quefeulou, J. M. Lef\u00e8vre, L. Aouf, A. Babanin and F. Collard : Semi empirical dissipation source functions for wind-wave models : Part I, definition and calibration and validation at global scales. Journal of Physical Oceanography, March 2010.\n* P. Janssen, L. Aouf, A. Behrens, G. Korres, L. Cavalieri, K. Christiensen, O. Breivik : Final report of work-package I in my wave project. December 2014.\n", "doi": "10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,forecast,global-analysisforecast-wav-001-027,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T03:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Analysis and Forecast"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"abstract": "The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4 ocean physics produced at Mercator-Ocean and ERA-Interim atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00019", "doi": "10.48670/moi-00019", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-bgc-001-029,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Hindcast"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"abstract": "The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [CLS](https://www.cls.fr) on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\nForcings sources:\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\nVertical coverage:\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00020\n\nReferences:\n\n* Lehodey P., Murtugudde R., Senina I. (2010). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanography, 84, p. 69-84.\n* Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R. (2015) Optimization of a micronekton model with acoustic data. ICES Journal of Marine Science, 72(5), p. 1399-1412.\n* Conchon A. (2016). Mod\u00e9lisation du zooplancton et du micronecton marins. Th\u00e8se de Doctorat, Universit\u00e9 de La Rochelle, 136 p.\n", "doi": "10.48670/moi-00020", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,euphotic-zone-depth,global-multiyear-bgc-001-033,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"abstract": "The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00021", "doi": "10.48670/moi-00021", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,global-multiyear-phy-001-030,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Reanalysis"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"abstract": "You can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution: monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00024", "doi": "10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-multiyear-phy-ens-001-031,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Ensemble Physics Reanalysis"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"abstract": "GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\nDOI (product):\nhttps://doi.org/10.48670/moi-00022", "doi": "10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-wav-001-032,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"abstract": "\"_DEFINITION_'\n\nNINO34 sub surface temperature anomaly (\u00b0C) is defined as the difference between the subsurface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031). Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. \n\nCONTEXT\n\nEl Nino Southern Oscillation (ENSO) is one of the most important sources of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 sub-surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific el Nino conditions and enable to monitor the evolution the ENSO phase.\n\nCMEMS KEY FINDINGS \n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO (el nino) phases in particular during the 1997/1998 winter and the 2015/2016 winter, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like during the 1998/1999 winter and during the 2010/2011 winter. \nThe NINO34 subsurface indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of atmospheric conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00220\n\nReferences:\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00220", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-tzt-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Temporal Evolution of Vertical Profile of Temperature from Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"abstract": "DEFINITION\n\nNINO34 sea surface temperature anomaly (\u00b0C) is defined as the difference between the sea surface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031) . Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. El Nino or La Nina events are defined when the NINO3.4 SST anomalies exceed +/- 0.5\u00b0C during a period of six month.\n\nCONTEXT\n\nEl Nino Southern Oscillation (ENSO) is one of the most important source of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 Sea surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific El Nino conditions and enable to monitor the evolution the ENSO phase.\n\nCMEMS KEY FINDINGS\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO phases in particular in 1998 and 2016, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like in 2000 and 2008. \nThe NINO34 indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of meteorological conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00219\n\nReferences:\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Sea Surface Temperature time series from Reanalysis"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"abstract": "DEFINITION\n\nThe global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble.\n\nCONTEXT\n\nSince the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277\u00b13 ppm (Joos and Spahni, 2008) to 412.44\u00b10.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 \u00b1 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). \n\nCMEMS KEY FINDINGS\n\nThe rate of change of the integrated yearly surface downward flux has increased by 0.04\u00b10.03e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06\u00b10.04e-1 PgC/yr2. In 2021 (resp. 2020), the global ocean CO2 sink was 2.41\u00b10.13 (resp. 2.50\u00b10.12) PgC/yr. The average over the full period is 1.61\u00b10.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr. In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of 0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45\u00b10.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78\u00b10.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00223\n\nReferences:\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Que\u0301re\u0301, C., Myneni, R., Piao, S., and Thorn- ton, P.: Chapter 6: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge University Press, Cambridge, 2013.\n* Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl. noaa.gov/gmd/ccgg/trends/global.html, last access: 11 March 2022.\n* Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425\u20131430, https://doi.org/10.1073/pnas.0707386105, 2008.\n* Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Qu\u00e9r\u00e9, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., G\u00fcrses, \u00d6., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., K\u00f6rtzinger, A., Landsch\u00fctzer, P., Lauvset, S. K., Lef\u00e8vre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., R\u00f6denbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., S\u00e9f\u00e9rian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917\u20132005, https://doi.org/10.5194/essd-14-1917-2022, 2022.\n* Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and Gloor, M. (2007), A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* Resplandy, L., Keeling, R. F., R\u00f6denbeck, C., Stephens, B. B., Khatiwala, S., Rodgers, K. B., Long, M. C., Bopp, L. and Tans, P. P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nature Geoscience, 11(7), p.504, 2018.\n", "doi": "10.48670/moi-00223", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-co2-flux-integrated,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Yearly CO2 Sink from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"abstract": "DEFINITION\n\nOcean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, \n* Obtain values for alkalinity based on the so called \u201clocally interpolated alkalinity regression (LIAR)\u201d method after Carter et al., 2016; 2018. \n* Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/)\n* Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity.\nThe global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1\u03c3 ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017\u00b10.0004e-1 pH units per year.\nThe indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time. Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements. \nA full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).\n\nCONTEXT\n\nThe decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as \u201ca reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean\u201d. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14). \n\nCMEMS KEY FINDINGS\n\nSince the year 1985, global ocean surface pH is decreasing at a rate of -0.0017\u00b10.0004e-1 per year. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00224\n\nReferences:\n\n* Bakker, D. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383-413, https://doi.org/10.5194/essd-8-383-2016, 2016.\n* Bopp, L. et al.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225\u20136245, doi: 10.5194/bg-10-6225-2013, 2013.\n* Carter, B.R., et al.: Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnol. Oceanogr.: Methods 16, 119\u2013131, 2018.\n* Carter, B. R., et al.: Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr.: Methods 14: 268\u2013277. doi:10.1002/lom3.10087, 2016.\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022. Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M.D. Mastrandrea, M. Tignor and K.L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, California, United States of America, pp.164.\n* Kroeker, K. J. et al.: Meta- analysis reveals negative yet variable effects of ocean acidifica- tion on marine organisms, Ecol. Lett., 13, 1419\u20131434, 2010.\n* Orr, J. C. et al.: Anthropogenic ocean acidification over the twenty-first century and its impact on cal- cifying organisms, Nature, 437, 681\u2013686, 2005.\n* van Heuven, S., et al.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., US DOE, Oak Ridge, Tenn., 2011.\n", "doi": "10.48670/moi-00224", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-area-averaged,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"abstract": "DEFINITION\n\nThis ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 0.25\u00b0\u00d70.25\u00b0 resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (\u03bc) and residual standard deviation (\u03c3) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e., \u03c3-to-\u00b5 ratio over a threshold of 1 0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean.\n\nCONTEXT\n\nA decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; P\u00e9rez et al, 2021). As projected in Gattuso et al (2015), \u201cunder our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050\u201d. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; P\u00f6rtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and P\u00e9rez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1\u03c3-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 0.25\u00b0\u00d70.25\u00b0 grid cells over the global ocean.\n\nCMEMS KEY FINDINGS\n\nSince 1985, pH has been decreasing at a rate between -0.0008 yr-1 and -0.0022 yr-1 over most of the global ocean basins. Tropical and subtropical regions, the eastern equatorial Pacific excepted, show pH trends falling in the interquartile range of all the trend estimates (between -0.0012 yr-1 and -0.0018 yr-1). pH over the eastern equatorial Pacific decreases much faster, reaching a growth rate larger than -0.0024 yr-1. Such a high rate of change in pH is also observed over a sector south of the Indian Ocean. Part of the polar and subpolar North Atlantic and the Southern Ocean has no significant trend. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00277\n\nReferences:\n\n* Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383\u2013413, DOI:10.5194/essd-8-383- 2016, 2016.\n* Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J., and Magdalena Santana-Casiano, J.: A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27, 126\u2013141, 2014.\n* Chau, T. T. T., Gehlen, M., Chevallier, F. : Global Ocean Surface Carbon: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information, DOI:10.48670/moi-00047, 2022a.\n* Chau, T. T. T., Gehlen, M., Chevallier, F.: Global mean seawater pH (GLOBAL_OMI_HEALTH_carbon_ph_area_averaged), E.U. Copernicus Marine Service Information, DOI: 10.48670/moi-00224, 2022b.\n* Doney, S. C., Balch, W. M., Fabry, V. J., and Feely, R. A.: Ocean Acidification: A critical emerging problem for the ocean sciences, Oceanography, 22, 16\u201325, 2009.\n* Gattuso, J-P., Alexandre Magnan, Rapha\u00ebl Bill\u00e9, William WL Cheung, Ella L. Howes, Fortunat Joos, Denis Allemand et al. \"\"Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios.\"\" Science 349, no. 6243 (2015).\n* Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Chau T T T., Conchon A., Denvil-Sommer A., Chevallier F., Vrac M., Mejia C. : Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI:10.1080/1755876X.2020.1785097, 2020.\n* Lauvset, S. K., Gruber, N., Landsch\u00fctzer, P., Olsen, A., and Tjiputra, J.: Trends and drivers in global surface ocean pH over the past 3 decades, Biogeosciences, 12, 1285\u20131298, DOI:10.5194/bg-12-1285-2015, 2015.\n* Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., \u00c1lvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterstr\u00f6m, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., P\u00e9rez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565\u20135589, DOI:10.5194/essd-13-5565-2021, 2021.\n* P\u00f6rtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Wiley IPCC Intergovernmental Panel on Climate Change, Geneva, 2019).\n* P\u00e9rez FF, Olafsson J, \u00d3lafsd\u00f3ttir SR, Fontela M, Takahashi T. Contrasting drivers and trends of ocean acidification in the subarctic Atlantic. Sci Rep 11, 13991, DOI:10.1038/s41598-021-93324-3, 2021.\n", "doi": "10.48670/moi-00277", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-trend,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,trend-of-surface-ocean-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"abstract": "DEFINITION\n\nThe Atlantic Meridional Overturning profile at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth. A climatological mean is then taken over time. This is done by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 over the whole time period (1993-2023) and over the period for which there are comparable observations (Apr 2004-Mar 2023). The observations come from the RAPID array (Smeed et al, 2017). \n\nCONTEXT\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). There are uncertainties in the deep profile associated with how much transport is returned in the upper (1-3km) or lower (3-5km) North Atlantic deep waters (Roberts et al 2013, Sinha et al 2018).\n\nCMEMS KEY FINDINGS \n\nThe AMOC strength at 1000m is found to be 17.0 \u00b1 3.2 Sv (1 Sverdrup=106m3/s; range is 2 x standard deviation of multi-product). See also Jackson et al (2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00231\n\nReferences:\n\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* Roberts, C. D., J. Waters, K. A. Peterson, M. D. Palmer, G. D. McCarthy, E. Frajka\u2010Williams, K. Haines, D. J. Lea, M. J. Martin, D. Storkey, E. W. Blockley and H. Zuo (2013), Atmosphere drives recent interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N, Geophys. Res. Lett., 40, 5164\u20135170 doi: 10.1002/grl.50930.\n* Sinha, B., Smeed, D.A., McCarthy, G., Moat, B.I., Josey, S.A., Hirschi, J.J.-M., Frajka-Williams, E., Blaker, A.T., Rayner, D. and Madec, G. (2018), The accuracy of estimates of the overturning circulation from basin-wide mooring arrays. Progress in Oceanography, 160. 101-123\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00231", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-26n-profile,marine-resources,marine-safety,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"abstract": "DEFINITION\n\nThe Atlantic Meridional Overturning strength at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 . The maximum value in depth is then taken as the strength in Sverdrups (Sv=1x106m3/s). The observations come from the RAPID array (Smeed et al, 2017).\n\nCONTEXT\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). The observations have revealed variability at monthly to decadal timescales including a temporary weakening in 2009/10 (McCarthy et al, 2012) and a decrease from 2005-2012 (Smeed et al, 2014; Smeed et al, 2018). Other studies have suggested that this weakening may be a result of variability (Smeed et al, 2014; Jackson et al 2017).\n\nCMEMS KEY FINDINGS \n\nThe AMOC strength exhibits significant variability on many timescales with a temporary weakening in 2009/10. There has been a weakening from 2005-2012 (-0.67 Sv/year, (p=0.03) in the observations and -0.53 Sv/year (p=0.04) in the multi-product mean). The multi-product suggests an earlier increase from 2001-2006 (0.48 Sv/yr, p=0.04), and a weakening in 1998-99, however before this period there is significant uncertainty. This indicates that the changes observed are likely to be variability rather than an ongoing trend (see also Jackson et al, 2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00232\n\nReferences:\n\n* Jackson, L. C., Peterson, K. A., Roberts, C. D. & Wood, R. A. (2016). Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nature Geosci, 9, 518\u2014522\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* McCarthy, G., Frajka-Williams, E., Johns, W. E., Baringer, M. O., Meinen, C. S., Bryden, H. L., Rayner, D., Duchez, A., Roberts, C. & Cunningham, S. A. (2012). Observed interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N. Geophys. Res. Lett., 39, L19609+\n* Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A. & Bryden, H. L. (2014). Observed decline of the Atlantic meridional overturning circulation 2004&2012. Ocean Science, 10, 29--38.\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Smeed, D. A., Josey, S. A., Beaulieu, C., Johns, W. E., Moat, B. I., Frajka-Williams, E., Rayner, D., Meinen, C. S., Baringer, M. O., Bryden, H. L. & McCarthy, G. D. (2018). The North Atlantic Ocean Is in a State of Reduced Overturning. Geophys. Res. Lett., 45, 2017GL076350+. doi: 10.1002/2017gl076350\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00232", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-max26n-timeseries,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005, the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 1.0 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00235\n\nReferences:\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00235", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-2000,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005, the near-surface (0-300m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.4 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00233\n\nReferences:\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00233", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-300,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005, the upper (0-700m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.6 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00234\n\nReferences:\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00234", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-700,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-700m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_trend": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nRegional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show warming at rates ranging from the global mean average up to more than 8 W/m2 in some specific regions (e.g. northern hemisphere western boundary current regimes). There are specific regions where a negative trend is observed above noise at rates up to about -5 W/m2 such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00236\n\nReferences:\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00236", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-trend,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"abstract": "DEFINITION\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\nCONTEXT\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005 the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 1.3\u00b10.2 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00240\n\nReferences:\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00240", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-2000,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"abstract": "DEFINITION\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\nCONTEXT\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005 the upper (0-700m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 0.9\u00b10.1 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00239\n\nReferences:\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00239", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-700,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_trend": {"abstract": "DEFINITION\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m).\n\nCONTEXT\n\nMost of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60\u00b0 latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. \n\nCMEMS KEY FINDINGS\n\nSignificant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -8 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00241\n\nReferences:\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Stammer D, Cazenave A, Ponte RM, Tamisiea ME (2013) Causes for contemporary regional sea level changes. Ann Rev Mar Sci 5:21\u201346. doi:10.1146/annurev-marine-121211-172406\n* Meyssignac, B., C. G. Piecuch, C. J. Merchant, M.-F. Racault, H. Palanisamy, C. MacIntosh, S. Sathyendranath, R. Brewin, 2016: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011\n", "doi": "10.48670/moi-00241", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"abstract": "DEFINITION\n\nThe linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in \u00b0C.\nThe linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details.\n\nCONTEXT\n\nLarge-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017).\n\nCMEMS KEY FINDINGS\n\nThe results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40\u00b0S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called \u2018cold event\u2019 prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00244\n\nReferences:\n\n* Dubois, C., K. von Schuckmann, S. Josey, A. Ceschin, 2018: Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Gattuso, J-P., et al. (2015): Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, no. 6243.\n* Gourrion, J., J. Deshayes, M. Juza, T. Szekely, J. Tontore, 2018: A persisting regional cold and fresh water anomaly in the Northern Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Hoegh-Guldberg, O., et al., 2018: Impacts of 1.5\u00baC Global Warming on Natural and Human Systems. In: Global Warming of 1.5\u00b0C. An IPCC Special Report on the impacts of global warming of 1.5\u00b0C above preindustrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. P\u00f6rtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma- Okia, C. P\u00e9an, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.\n* IPCC, 2019: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Molinos, J.G., et al. (2016): Climate velocity and the future global redistribution of marine biodiversity, NATURE Climate Change 6 doi:10.10383/NCLIMATE2769.\n* Mulet S, Buongiorno Nardelli B, Good S, A. Pisano A, E. Greiner, Monier M, 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Ram\u00edrez, F., I. Af\u00e1n, L.S. Davis, and A. Chiaradia (2017): Climate impacts on global hot spots of marine biodiversity. Science Advances 3, no. 2 : e1601198.\n* Sun, Y., Z. Zhong, T. Li, L. Yi, Y. Hu, H. Wan, H. Chen, Q. Liao, C. Ma and Q. Li, 2017: Impact of Ocean Warming on Tropical Cyclone Size and Its Destructiveness, Nature Scientific Reports, Volume 7 (8154), https://doi.org/10.1038/s41598-017-08533-6.\n* Trenberth, K. E., L. J. Cheng, P. Jacobs, Y. X. Zhang, and J. Fasullo (2018): Hurricane Harvey links to ocean heat content and climate change adaptation. Earth's Future, 6, 730--744, https://doi.org/10.1029/2018EF000825.\n", "doi": "10.48670/moi-00244", "instrument": null, "keywords": "change-over-time-in-sea-water-temperature,coastal-marine-environment,global-ocean,global-omi-tempsal-tyz-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Zonal Mean Subsurface Temperature cumulative trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "DEFINITION\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tAn area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. \n5.\tThe time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n6.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\nCONTEXT\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\nCMEMS KEY FINDINGS\n\nOver the period 1993 to 2021, the global average linear trend was 0.015 \u00b1 0.001\u00b0C / year (95% confidence interval). 2021 is nominally the sixth warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Ni\u00f1o and La Ni\u00f1a conditions. For example, peaks in the time series coincide with the strong El Ni\u00f1o events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00242\n\nReferences:\n\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Gasparin, F., von Schuckmann, K., Desportes, C., Sathyendranath, S. and Pardo, S. 2018. El Ni\u00f1o southern oscillation. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n", "doi": "10.48670/moi-00242", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"abstract": "DEFINITION\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tThe time series for each grid cell was passed through the X11 seasonal adjustment procedure, which decomposes a time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n5.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\nCONTEXT\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\nCMEMS KEY FINDINGS\n\nWarming trends occurred over most of the globe between 1993 and 2021. One of the exceptions is the North Atlantic, which has a region south of Greenland where a cooling trend is found. The cooling in this area has been previously noted as occurring on centennial time scales (IPCC, 2013; Caesar et al., 2018; Sevellee et al., 2017).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00243\n\nReferences:\n\n* Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. and Saba, V., 2018. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), p.191. DOI: 10.1038/s41586-018-0006-5.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n* S\u00e9vellec, F., Fedorov, A.V. and Liu, W., 2017. Arctic sea-ice decline weakens the Atlantic meridional overturning circulation. Nature Climate Change, 7(8), p.604, doi: 10.1038/nclimate3353.\n", "doi": "10.48670/moi-00243", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature trend map from Observations Reprocessing"}, "GLOBAL_OMI_WMHE_heattrp": {"abstract": "DEFINITION\n\nHeat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\nCONTEXT\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\nCMEMS KEY FINDINGS\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00245\n\nReferences:\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n* Bricaud C, Drillet Y, Garric G. 2016. Ocean mass and heat transport. In CMEMS Ocean State Report, Journal of Operational Oceanography, 9, http://dx.doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00245", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-heattrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Heat Transport across sections from Reanalysis"}, "GLOBAL_OMI_WMHE_northward_mht": {"abstract": "DEFINITION\n\nMeridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. \nThey are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\nCONTEXT\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\nCMEMS KEY FINDINGS\n\nAfter an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 \u00b0 N in the Pacific Ocean during the El Ni\u00f1o event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00246\n\nReferences:\n\n* Crosnier L, Barnier B, Treguier AM, 2001. Aliasing inertial oscillations in a 1/6\u00b0 Atlantic circulation model: impact on the mean meridional heat transport. Ocean Modelling, vol 3, issues 1-2, pp21-31. https://doi.org/10.1016/S1463-5003(00)00015-9\n* Ganachaud, A. , Wunsch C. 2003. Large-Scale Ocean Heat and Freshwater Transports during the World Ocean Circulation Experiment. J. Climate, 16, 696\u2013705, https://doi.org/10.1175/1520-0442(2003)016<0696:LSOHAF>2.0.CO;2\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00246", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-northward-mht,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis"}, "GLOBAL_OMI_WMHE_voltrp": {"abstract": "DEFINITION\n\nVolume transport across lines are obtained by integrating the volume fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in Sverdrup (Sv).\n\nCONTEXT\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\nCMEMS KEY FINDINGS\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdi\u00e8re and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00247\n\nReferences:\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00247", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-voltrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Volume Transport across sections from Reanalysis"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"abstract": "The IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00026\n\nReferences:\n\n* Gutknecht, E. and Reffray, G. and Mignot, A. and Dabrowski, T. and Sotillo, M. G. Modelling the marine ecosystem of Iberia-Biscay-Ireland (IBI) European waters for CMEMS operational applications. Ocean Sci., 15, 1489\u20131516, 2019. https://doi.org/10.5194/os-15-1489-2019\n", "doi": "10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-bgc-005-004,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"abstract": "The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00027\n\nReferences:\n\n* Sotillo, M.G.; Campuzano, F.; Guihou, K.; Lorente, P.; Olmedo, E.; Matulka, A.; Santos, F.; Amo-Baladr\u00f3n, M.A.; Novellino, A. River Freshwater Contribution in Operational Ocean Models along the European Atlantic Fa\u00e7ade: Impact of a New River Discharge Forcing Data on the CMEMS IBI Regional Model Solution. J. Mar. Sci. Eng. 2021, 9, 401. https://doi.org/10.3390/jmse9040401\n* Mason, E. and Ruiz, S. and Bourdalle-Badie, R. and Reffray, G. and Garc\u00eda-Sotillo, M. and Pascual, A. New insight into 3-D mesoscale eddy properties from CMEMS operational models in the western Mediterranean. Ocean Sci., 15, 1111\u20131131, 2019. https://doi.org/10.5194/os-15-1111-2019\n* Lorente, P. and Garc\u00eda-Sotillo, M. and Amo-Baladr\u00f3n, A. and Aznar, R. and Levier, B. and S\u00e1nchez-Garrido, J. C. and Sammartino, S. and de Pascual-Collar, \u00c1. and Reffray, G. and Toledano, C. and \u00c1lvarez-Fanjul, E. Skill assessment of global, regional, and coastal circulation forecast models: evaluating the benefits of dynamical downscaling in IBI (Iberia-Biscay-Ireland) surface waters. Ocean Sci., 15, 967\u2013996, 2019. https://doi.org/10.5194/os-15-967-2019\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n* Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Reffray, G., Amo-Baladr\u00f3n, A., Benkiran, M., and Alvarez Fanjul, E.: The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products and roadmap to the future Copernicus Service, J. Oper. Oceanogr., 8, 63\u201379, https://doi.org/10.1080/1755876X.2015.1014663, 2015.\n", "doi": "10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-phy-005-001,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"abstract": "The IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 1/36\u00ba of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00025\n\nReferences:\n\n* Toledano, C.; Ghantous, M.; Lorente, P.; Dalphinet, A.; Aouf, L.; Sotillo, M.G. Impacts of an Altimetric Wave Data Assimilation Scheme and Currents-Wave Coupling in an Operational Wave System: The New Copernicus Marine IBI Wave Forecast Service. J. Mar. Sci. Eng. 2022, 10, 457. https://doi.org/10.3390/jmse10040457\n", "doi": "10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-wav-005-005,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "IBI_MULTIYEAR_BGC_005_003": {"abstract": "The IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, zooplankton and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\nFor all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00028\n\nReferences:\n\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n", "doi": "10.48670/moi-00028", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,iberian-biscay-irish-seas,ibi-multiyear-bgc-005-003,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "IBI_MULTIYEAR_PHY_005_002": {"abstract": "The IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Besides, daily means of air-sea fluxes are provided. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. For all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00029", "doi": "10.48670/moi-00029", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,iberian-biscay-irish-seas,ibi-multiyear-phy-005-002,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "IBI_MULTIYEAR_WAV_005_006": {"abstract": "The IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1980 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product and with the same horizontal resolution (1/36\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Besides, air-sea fluxes are provided. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00030", "doi": "10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,iberian-biscay-irish-seas,ibi-multiyear-wav-005-006,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "IBI_OMI_CURRENTS_cui": {"abstract": "DEFINITION\n\nThe Coastal Upwelling Index (CUI) is computed along the African and the Iberian Peninsula coasts. For each latitudinal point from 27\u00b0N to 42\u00b0N the Coastal Upwelling Index is defined as the temperature difference between the maximum and minimum temperature in a range of distance from the coast up to 3.5\u00ba westwards.\n\u3016CUI\u3017_lat=max\u2061(T_lat )-min\u2061(T_lat)\nA high Coastal Upwelling Index indicates intense upwelling conditions.\nThe index is computed from the following Copernicus Marine products:\n\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (1993-2019)\n\tIBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001 (2020 onwards)\n\nCONTEXT\n\nCoastal upwelling process occurs along coastlines as the result of deflection of the oceanic water away from the shore. Such deflection is produced by Ekman transport induced by persistent winds parallel to the coastline (Sverdrup, 1938). When this transported water is forced, the mass balance is maintained by pumping of ascending intermediate water. This water is typically denser, cooler and richer in nutrients. The Iberia-Biscay-Ireland domain contains two well-documented Eastern Boundary Upwelling Ecosystems, they are hosted under the same system known as Canary Current Upwelling System (Fraga, 1981; Hempel, 1982). This system is one of the major coastal upwelling regions of the world and it is produced by the eastern closure of the Subtropical Gyre. The North West African (NWA) coast presents an intense upwelling region that extends from Morocco to south of Senegal, likewise the western coast of the Iberian Peninsula (IP) shows a seasonal upwelling behavior. These two upwelling domains are separated by the presence of the Gulf of Cadiz, where the coastline does not allow the formation of upwelling conditions from 34\u00baN up to 37\u00baN.\nThe Copernicus Marine Service Coastal Upwelling Index is defined following the steps of several previous upwelling indices described in literature. More details and full scientific evaluation can be found in the dedicated section in the first issue of the Copernicus Marine Service Ocean State report (Sotillo et al., 2016).\n\nCMEMS KEY FINDINGS\n\nThe NWA coast (latitudes below 34\u00baN) shows a quite constantlow variability of the periodicity and the intensity of the upwelling, few periods of upwelling intensifications are found in years 1993-1995, and 2003-2004.\nIn the IP coast (latitudes higher than 37\u00baN) the interannual variability is more remarkable showing years with high upwelling activity (1994, 2004, and 2015-2017) followed by periods of lower activity (1996-1998, 2003, 2011, and 2013).\nAccording to the results of the IBI-NRT system, 2020 was a year with weak upwelling in the IP latitudes. \nWhile in the NWA coast the upwelling activity was more intense than the average.\nThe analysis of trends in the period 1993-2019 shows significant positive trend of the upwelling index in the IP latitudes. This trend implies an increase of temperature differences between the coastal and offshore waters of approximately 0.02 \u00b0C/Year.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00248\n\nReferences:\n\n* Fraga F. 1981. Upwelling off the Galician Coast, Northwest Spain. In: Richardson FA, editor. Coastal Upwelling. Washington (DC): Am Geoph Union; p. 176\u2013182.\n* Hempel G. 1982. The Canary current: studies of an upwelling system. Introduction. Rapp. Proc. Reun. Cons. Int. Expl. Mer., 180, 7\u20138.\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016 Iberian-Biscay-Irish Sea. In von Scuckmann et al. (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Sverdrup HV. 1938. On the process of upwelling. J Mar Res. 1:155\u2013164.\n", "doi": "10.48670/moi-00248", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-currents-cui,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Coastal Upwelling Index from Reanalysis"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSIS_FORECAST_WAV_005_005).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2022.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\nCONTEXT\n\nThe sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world\u2019s greatest wave generating regions (M\u00f8rk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes.\nThe North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Mart\u00ednez-Asensio et al. 2016; Bacon and Carter, 1991; Bouws et al., 1996; Bauer, 2001; Wolf et al., 2002; Gleeson et al., 2017). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006). \nAdditionally a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mart\u00ednez-Asensio et al., 2016). \nIn this context, long\u2010term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences.\n\nCMEMS KEY FINDINGS\n\nThe climatic mean of 99th percentile (1993-2021) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described.\nThe 99th percentile anomalies in 2023 show that during this period, the central latitudes of the domain (between 37 \u00baN and 50 \u00baN) were affected by extreme wave events that exceeded up to twice the standard deviation of the anomalies. These events impacted not only the open waters of the Northeastern Atlantic but also European coastal areas such as the west coast of Portugal, the Spanish Atlantic coast, and the French coast, including the English Channel.\nAdditionally, the impact of significant wave extremes exceeding twice the standard deviation of anomalies was detected in the Mediterranean region of the Balearic Sea and the Algerian Basin. This pattern is commonly associated with the impact of intense Tramontana winds originating from storms that cross the Iberian Peninsula from the Gulf of Biscay.\n\nFigure caption\n\nIberia-Biscay-Ireland Significant Wave Height extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00249\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: .Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Marshall, J., Kushnir, Y., Battisti, D., Chang, P., Czaja, A., Dickson, R., ... & Visbeck, M. (2001). North Atlantic climate variability: phenomena, impacts and mechanisms. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(15), 1863-1898.\n", "doi": "10.48670/moi-00249", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-extreme-var-swh-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Significant Wave Height extreme from Reanalysis"}, "IBI_OMI_SEASTATE_swi": {"abstract": "DEFINITION\n\nThe Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art.\nTwo CMEMS products are used to compute the Strong Wave Incidence index:\n\u2022\tIBI-WAV-MYP: IBI_REANALYSIS_WAV_005_006\n\u2022\tIBI-WAV-NRT: IBI_ANALYSIS_FORECAST_WAV_005_005\nThe Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product:\nSWI = Fobs(SWH > ThP90) \u2013 Fclim(SWH > ThP90)\nSince the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively.\n\nCONTEXT\n\nOcean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. The Strong Wave Incidence index based on the CMEMS regional analysis and reanalysis product provides information on the frequency of severe wave events.\nThe IBI-MFC covers the Europe\u2019s Atlantic coast in a region bounded by the 26\u00baN and 56\u00baN parallels, and the 19\u00baW and 5\u00baE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (M\u00f8rk et al., 2010), one of the world\u2019s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kursnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995; Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mat\u00ednez-Asensio et al., 2016).\nThe Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions (Figure 1.A) are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands.\nMore details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020).\n\nCMEMS KEY FINDINGS\n\nThe analysis of the index in the last decades do not show significant trends of the strong wave conditions over the period 1992-2021 with 99% confidence. The maximum wave event reported in region WAV1 (B) occurred in February 2014, producing an increment of 25% of strong wave conditions in the region. Two maximum wave events are found in WAV2 (C) with an increment of 15% of high wave conditions in November 2009 and February 2014. As in regions WAV1 and WAV2, in the region WAV3 (D), a strong wave event took place in February 2014, this event is one of the maximum events reported in the region with an increment of strong wave conditions of 20%, two months before (December 2013) there was a storm of similar characteristics affecting this region, other events of similar magnitude are detected on October 2000 and November 2009. The region WAV4 (E) present its maximum wave event in January 1996, such event produced a 25% of increment of strong wave conditions in the region. Despite of each monitoring region is affected by independent wave events; the analysis shows several past higher-than-average wave events that were propagated though several monitoring regions: November-December 2010 (WAV3 and WAV2); February 2014 (WAV1, WAV2, and WAV3); and February-March 2018 (WAV1 and WAV4).\nThe analysis of the NRT period (January 2022 onwards) depicts a significant event that occurred in November 2022, which affected the WAV2 and WAV3 regions, resulting in a 15% and 25% increase in maximum wave conditions, respectively. In the case of the WAV3 region, this event was the strongest event recorded in this region.\nIn the WAV4 region, an event that occurred in February 2024 was the second most intense on record, showing an 18% increase in strong wave conditions in the region.\nIn the WAV1 region, the NRT period includes two high-intensity events that occurred in February 2024 (21% increase in strong wave conditions) and April 2022 (18% increase in maximum wave conditions).\n\nFigure caption\n\n(A) Mean 90th percentile of Sea Wave Height computed from IBI_REANALYSIS_WAV_005_006 product at an hourly basis. Gray dotted lines denote the four monitoring areas where the Strong Wave Incidence index is computed. (B, C, D, and E) Strong Wave Incidence index averaged in monitoring regions WAV1 (A), WAV2 (B), WAV3 (C), and WAV4 (D). Panels show merged results of two CMEMS products: IBI_REANALYSIS_WAV_005_006 (blue), IBI_ANALYSIS_FORECAST_WAV_005_005 (orange). The trend and 99% confidence interval of IBI-MYP product is included (bottom right).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00251\n\nReferences:\n\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Dodet G, Bertin X, Taborda R. 2010. Wave climate variability in the North-East Atlantic Ocean over the last six decades, Ocean Modelling, 31, 120\u2013131.\n* Dupuis H, Michel D, Sottolichio A. 2006. Wave climate evolution in the Bay of Biscay over two decades. Journal of Marine Systems, 63, 105\u2013114.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation, Science, 269:676\u2013679.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* Pascual A., Levier B., Aznar R., Toledano C., Garc\u00eda-Valdecasas JM., Garc\u00eda M., Sotillo M., Aouf L., \u00c1lvarez E. (2020) Monitoring of wave sea state in the Iberia-Biscay-Ireland regional seas. In von Scuckmann et al. (2020) Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097\n* Tsimplis M N, Woolf D K, Osborn T J, Wakelin S, Wolf J, Flather R, Shaw A G P, Woodworth P, Challenor P, Blackman D, Pert F, Yan Z, Jevrejeva S. 2005. Towards a vulnerability assessment of the UK and northern European coasts: the role of regional climate variability. Phil. Trans. R. Soc. A, Vol. 363, 1329\u20131358 doi:10.1098/rsta.2005.1571.\n* Wang X, Swail V. 2004. Historical and possible future changes of wave heights in northern hemisphere oceans. In: Perrie W (ed), Atmosphere ocean interactions, vol 2. Wessex Institute of Technology Press, Ashurst.\n* WASA-Group. 1998. Changing waves and storms in the Northeast Atlantic?, Bull. Am. Meteorol. Soc., 79:741\u2013760.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Young I R, Zieger S, Babanin A V. 2011. Global Trends in Wind Speed and Wave Height. Science, Vol. 332, Issue 6028, 451-455, doi: 10.1126/science.1197219.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n", "doi": "10.48670/moi-00251", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-swi,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Strong Wave Incidence index from Reanalysis"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThe Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018).\n\nThe IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003).\nAlso relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980).\n\nCMEMS KEY FINDINGS\n\nIn the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in \u00c1lvarez Fanjul (2019) for the period 1993-2016.\nThe analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43\u00baN parallel.\nExtreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer.\nIt is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis.\n\nFigure caption\n\nIberia-Biscay-Ireland Surface Temperature extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00254\n\nReferences:\n\n* Alvarez I, Gomez-Gesteira M, DeCastro M, Lorenzo MN, Crespo AJC, Dias JM. 2011. Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Continental Shelf Research, 31(5), 388-399.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Czaja A, Frankignoul C. 2002. Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. Journal of Climate, 15(6), 606-623.\n* Dickson RR, Gurbutt PA, Pillai VN. 1980. Satellite evidence of enhanced upwelling along the European continental slope. Journal of Physical Oceanography, 10(5), 813-819.\n* Dubois C, von Schuckmann K, Josey S. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 2.9, s66\u2013s70, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Edwards A, Jones K, Graham JM, Griffiths CR, MacDougall N, Patching J, Raine R. 1996. Transient coastal upwelling and water circulation in Bantry Bay, a ria on the south-west coast of Ireland. Estuarine, Coastal and Shelf Science, 42(2), 213-230.\n* Flatau MK, Talley L, Niiler PP. 2003. The North Atlantic Oscillation, surface current velocities, and SST changes in the subpolar North Atlantic. Journal of Climate, 16(14), 2355-2369.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016. Iberian-Biscay-Irish Sea. In von Schuckmann et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report No.1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00254", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-extreme-var-temp-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis"}, "IBI_OMI_WMHE_mow": {"abstract": "DEFINITION\n\nVariations of the Mediterranean Outflow Water at 1000 m depth are monitored through area-averaged salinity anomalies in specifically defined boxes. The salinity data are extracted from several CMEMS products and averaged in the corresponding monitoring domain: \n* IBI-MYP: IBI_MULTIYEAR_PHY_005_002\n* IBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001\n* GLO-MYP: GLOBAL_REANALYSIS_PHY_001_030\n* CORA: INSITU_GLO_TS_REP_OBSERVATIONS_013_002_b\n* ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012\n\nThe anomalies of salinity have been computed relative to the monthly climatology obtained from IBI-MYP. Outcomes from diverse products are combined to deliver a unique multi-product result. Multi-year products (IBI-MYP, GLO,MYP, CORA, and ARMOR) are used to show an ensemble mean and the standard deviation of members in the covered period. The IBI-NRT short-range product is not included in the ensemble, but used to provide the deterministic analysis of salinity anomalies in the most recent year.\n\nCONTEXT\n\nThe Mediterranean Outflow Water is a saline and warm water mass generated from the mixing processes of the North Atlantic Central Water and the Mediterranean waters overflowing the Gibraltar sill (Daniault et al., 1994). The resulting water mass is accumulated in an area west of the Iberian Peninsula (Daniault et al., 1994) and spreads into the North Atlantic following advective pathways (Holliday et al. 2003; Lozier and Stewart 2008, de Pascual-Collar et al., 2019).\nThe importance of the heat and salt transport promoted by the Mediterranean Outflow Water flow has implications beyond the boundaries of the Iberia-Biscay-Ireland domain (Reid 1979, Paillet et al. 1998, van Aken 2000). For example, (i) it contributes substantially to the salinity of the Norwegian Current (Reid 1979), (ii) the mixing processes with the Labrador Sea Water promotes a salt transport into the inner North Atlantic (Talley and MacCartney, 1982; van Aken, 2000), and (iii) the deep anti-cyclonic Meddies developed in the African slope is a cause of the large-scale westward penetration of Mediterranean salt (Iorga and Lozier, 1999).\nSeveral studies have demonstrated that the core of Mediterranean Outflow Water is affected by inter-annual variability. This variability is mainly caused by a shift of the MOW dominant northward-westward pathways (Bozec et al. 2011), it is correlated with the North Atlantic Oscillation (Bozec et al. 2011) and leads to the displacement of the boundaries of the water core (de Pascual-Collar et al., 2019). The variability of the advective pathways of MOW is an oceanographic process that conditions the destination of the Mediterranean salt transport in the North Atlantic. Therefore, monitoring the Mediterranean Outflow Water variability becomes decisive to have a proper understanding of the climate system and its evolution (e.g. Bozec et al. 2011, Pascual-Collar et al. 2019).\nThe CMEMS IBI-OMI_WMHE_mow product is aimed to monitor the inter-annual variability of the Mediterranean Outflow Water in the North Atlantic. The objective is the establishment of a long-term monitoring program to observe the variability and trends of the Mediterranean water mass in the IBI regional seas. To do that, the salinity anomaly is monitored in key areas selected to represent the main reservoir and the three main advective spreading pathways. More details and a full scientific evaluation can be found in the CMEMS Ocean State report Pascual et al., 2018 and de Pascual-Collar et al. 2019.\n\nCMEMS KEY FINDINGS\n\nThe absence of long-term trends in the monitoring domain Reservoir (b) suggests the steadiness of water mass properties involved on the formation of Mediterranean Outflow Water.\nResults obtained in monitoring box North (c) present an alternance of periods with positive and negative anomalies. The last negative period started in 2016 reaching up to the present. Such negative events are linked to the decrease of the northward pathway of Mediterranean Outflow Water (Bozec et al., 2011), which appears to return to steady conditions in 2020 and 2021. \nResults for box West (d) reveal a cycle of negative (2015-2017) and positive (2017 up to the present) anomalies. The positive anomalies of salinity in this region are correlated with an increase of the westward transport of salinity into the inner North Atlantic (de Pascual-Collar et al., 2019), which appear to be maintained for years 2020-2021.\nResults in monitoring boxes North and West are consistent with independent studies (Bozec et al., 2011; and de Pascual-Collar et al., 2019), suggesting a westward displacement of Mediterranean Outflow Water and the consequent contraction of the northern boundary.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00258\n\nReferences:\n\n* Bozec A, Lozier MS, Chassignet EP, Halliwell GR. 2011. On the variability of the Mediterranean outflow water in the North Atlantic from 1948 to 2006. J Geophys Res 116:C09033. doi:10.1029/2011JC007191.\n* Daniault N, Maze JP, Arhan M. 1994. Circulation and mixing of MediterraneanWater west of the Iberian Peninsula. Deep Sea Res. Part I. 41:1685\u20131714.\n* de Pascual-Collar A, Sotillo MG, Levier B, Aznar R, Lorente P, Amo-Baladr\u00f3n A, \u00c1lvarez-Fanjul E. 2019. Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019.\n* Holliday NP. 2003. Air-sea interaction and circulation changes in the northeast Atlantic. J Geophys Res. 108(C8):3259. doi:10.1029/2002JC001344.\n* Iorga MC, Lozier MS. 1999. Signatures of the Mediterranean outflow from a North Atlantic climatology: 1. Salinity and density fields. Journal of Geophysical Research: Oceans, 104(C11), 25985-26009.\n* Lozier MS, Stewart NM. 2008. On the temporally varying northward penetration of Mediterranean overflow water and eastward penetration of Labrador Sea water. J Phys Oceanogr. 38(9):2097\u20132103. doi:10.1175/2008JPO3908.1.\n* Paillet J, Arhan M, McCartney M. 1998. Spreading of labrador Sea water in the eastern North Atlantic. J Geophys Res. 103 (C5):10223\u201310239.\n* Pascual A, Levier B, Sotillo M, Verbrugge N, Aznar R, Le Cann B. 2018. Characterisation of Mediterranean outflow w\u00e1ter in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Braseur, P., Fennel, K., Djavidnia, S.: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11, sup1, s1-s142, doi:10.1080/1755876X.2018.1489208, 2018.\n* Reid JL. 1979. On the contribution of the Mediterranean Sea outflow to the Norwegian\u2010Greenland Sea, Deep Sea Res., Part A, 26, 1199\u20131223, doi:10.1016/0198-0149(79)90064-5.\n* Talley LD, McCartney MS. 1982. Distribution and circulation of Labrador Sea water. Journal of Physical Oceanography, 12(11), 1189-1205.\n* van Aken HM. 2000. The hydrography of the mid-latitude northeast Atlantic Ocean I: the deep water masses. Deep Sea Res. Part I. 47:757\u2013788.\n", "doi": "10.48670/moi-00258", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-wmhe-mow,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterrranean Outflow Water Index from Reanalysis & Multi-Observations Reprocessing"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"abstract": "Arctic Oceans - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00031", "doi": "10.48670/moi-00031", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-arc-phybgcwav-discrete-mynrt-013-031,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean- In Situ Near Real Time Observations"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"abstract": "Baltic Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00032", "doi": "10.48670/moi-00032", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-bal-phybgcwav-discrete-mynrt-013-032,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- In Situ Near Real Time Observations"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"abstract": "Black Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00033", "doi": "10.48670/moi-00033", "instrument": null, "keywords": "black-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-blk-phybgcwav-discrete-mynrt-013-034,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea- In-Situ Near Real Time Observations"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"abstract": "Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2024 and GLobal Ocean Data Analysis Project GLODAPv2.2023. \nThe SOCATv2024-OBS dataset contains >38 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2024. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2024-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean.\nGLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. \nSOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.\n\nDOI (product): \nhttps://doi.org/10.17882/99089\n\nReferences:\n\n* Bakker et al., 2016. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth Syst. Sci. Data, 8, 383\u2013413, https://doi.org/10.5194/essd-8-383-2016.\n* Lauvset et al. 2024. The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product. Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-468.\n* Lauvset et al., 2016. A new global interior ocean mapped climatology: t\u202f\u00d7\u2009\u202f1\u00b0 GLODAP version 2. Earth Syst. Sci. Data, 8, 325\u2013340, https://doi.org/10.5194/essd-8-325-2016.\n", "doi": "10.17882/99089", "instrument": null, "keywords": "coastal-marine-environment,fugacity-of-carbon-dioxide-in-sea-water,global-ocean,in-situ-observation,insitu-glo-bgc-carbon-discrete-my-013-050,level-3,marine-resources,marine-safety,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,multi-year,oceanographic-geographical-features,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1957-10-22T22:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - In Situ reprocessed carbon observations - SOCATv2024 / GLODAPv2.2023"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"abstract": "For the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data.\n\nDOI (product): \nhttps://doi.org/10.17882/86207", "doi": "10.17882/86207", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-bgc-discrete-my-013-046,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-silicate-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Biogeochemical product"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"abstract": "Global Ocean - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00036", "doi": "10.48670/moi-00036", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phybgcwav-discrete-mynrt-013-030,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- In-Situ Near-Real-Time Observations"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"abstract": "This product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices.\n\nDOI (product): \nhttps://doi.org/10.17882/93670", "doi": "10.17882/93670", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ssh-discrete-my-013-053,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1821-05-25T05:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Sea level product"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"abstract": "For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. This version is a merged product between the previous verion of CORA and EN4 distributed by the Met Office for the period 1950-1990.\n\nDOI (product): \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-discrete-my-013-001,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"abstract": "Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\nDOI (product): \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-my-013-052,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1960-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"abstract": "For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00037", "doi": "10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-nrt-013-002,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Real time in-situ observations objective analysis"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"abstract": "Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed:\n* The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided.\n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program.\n* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms\n* The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories\n\nDOI (product):\nhttps://doi.org/10.17882/86236", "doi": "10.17882/86236", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-my-013-044,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1979-12-11T04:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"abstract": "This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed:\n* The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP\u2019s Global Drifter Program. These data are delivered together with wind stress components, surface temperature and a wind-slippage correction for drogue-off and drogue-on drifters trajectories. \n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth and in surface, calculated along the trajectories of the floats which are part of the Argo Program.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00041", "doi": "10.48670/moi-00041", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-nrt-013-048,level-2,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,surface-eastward-sea-water-velocity,surface-northward-sea-water-velocity,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1986-06-02T09:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- in-situ Near real time observations of ocean currents"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"abstract": "These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.\n\nDOI (product): \nhttps://doi.org/10.17882/70345", "doi": "10.17882/70345", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-wav-discrete-my-013-045,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-04-27T18:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Wave product"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"abstract": "IBI Seas - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00043", "doi": "10.48670/moi-00043", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,iberian-biscay-irish-seas,in-situ-observation,insitu-ibi-phybgcwav-discrete-mynrt-013-033,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Irish Ocean- In-Situ Near Real Time Observations"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"abstract": "Mediterranean Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00044", "doi": "10.48670/moi-00044", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-med-phybgcwav-discrete-mynrt-013-035,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea- In-Situ Near Real Time Observations"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"abstract": "NorthWest Shelf area - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00045", "doi": "10.48670/moi-00045", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-nws-phybgcwav-discrete-mynrt-013-036,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean In-Situ Near Real Time observations"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"abstract": "The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\nSalon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997\u20131022. DOI: https://doi.org/10.5194/os-15-997-2019\n\n_DOI (Product)_: \nhttps://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4\n\nReferences:\n\n* Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Teruzzi, A., Di Biagio, V., Coidessa, G., Alvarez, E., Amadio, C., & Cossarini, G. (2022). Mediterranean Sea Biogeochemical Analysis and Forecast (CMEMS MED-Biogeochemistry, MedBFM4 system) (Version 1) [Data set]. Copernicus Marine Service. https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_BGC_006_014_MEDBFM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,medsea-analysisforecast-bgc-006-014,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"abstract": "The physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\"\n\n_DOI (Product)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n\nReferences:\n\n* Clementi, E., Aydogdu, A., Goglio, A. C., Pistoia, J., Escudier, R., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Coppini, G., Masina, S., & Pinardi, N. (2021). Mediterranean Sea Physical Analysis and Forecast (CMEMS MED-Currents, EAS6 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n", "doi": "10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-phy-006-013,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"abstract": "MEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\nDOI (product): https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4\n\nReferences:\n\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Analysis and Forecast (Copernicus Marine Service MED-Waves, MEDWA\u039c4 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-wav-006-017,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-04-19T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Analysis and Forecast"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"abstract": "The Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n_DOI (Interim dataset)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n\nReferences:\n\n* Teruzzi, A., Di Biagio, V., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3\n* Teruzzi, A., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Di Biagio, V., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis INTERIM (CMEMS MED-Biogeochemistry, MedBFM3i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS) https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n", "doi": "10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,medsea-multiyear-bgc-006-008,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"abstract": "The Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\nProduct Citation: \nPlease refer to our Technical FAQ for citing products\n\nDOI (Product): \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\nDOI (Interim dataset):\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n\nReferences:\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini G., Pinardi, N. (2021). A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, 9, 1060, https://www.frontiersin.org/article/10.3389/feart.2021.702285, DOI=10.3389/feart.2021.702285\n* Nigam, T., Escudier, R., Pistoia, J., Aydogdu, A., Omar, M., Clementi, E., Cipollone, A., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2021). Mediterranean Sea Physical Reanalysis INTERIM (CMEMS MED-Currents, E3R1i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n", "doi": "10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-phy-006-004,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Reanalysis"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"abstract": "MEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 \n\n_DOI (Interim dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I \n \n_DOI (climatological dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM \n\nDOI (Interim dataset):\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n\nReferences:\n\n* Korres, G., Ravdas, M., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis INTERIM (CMEMS Med-Waves, MedWAM3I system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Monthly Climatology (CMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM\n* Korres, G., Ravdas, M., Zacharioudaki, A., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis (CMEMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012\n", "doi": "10.25423/cmcc/medsea_multiyear_wav_006_012", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-wav-006-012,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Reanalysis"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_yr-T_clim )dz \t\t\t\t\t\t\t\t[1]\nwith a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009).\nTime series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and is evaluated for topography deeper than 300m.\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nThe quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). \nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4 degree horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031): \nGLORYS, C-GLORS, ORAS5, FOAM\n\tTwo observation based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\nCMEMS KEY FINDINGS\n\nThe ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2019 at rate of 1.4\u00b10.3 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00261\n\nReferences:\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Iona, A., A. Theodorou, S. Sofianos, S. Watelet, C. Troupin, J.-M. Beckers, 2018: Mediterranean Sea climatic indices: monitoring long term variability and climate changes, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-51, in review.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841. DOI: 10.1007/s00382-015-2728-5\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, s1-s142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00261", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,medsea-omi-ohc-area-averaged-anomalies,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS MEDSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_WAV_006_012) and the Analysis product (MEDSEA_ANALYSIS_FORECAST_WAV_006_017).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\nCONTEXT\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2014). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Mediterranean Sea is an almost enclosed basin where the complexity of its orographic characteristics deeply influences the atmospheric circulation at local scale, giving rise to strong regional wind regimes (Drobinski et al. 2018). Therefore, since waves are primarily driven by winds, high waves are present over most of the Mediterranean Sea and tend to reach the highest values where strong wind and long fetch (i.e. the horizontal distance over which wave-generating winds blow) are simultaneously present (Lionello et al. 2006). Specifically, as seen in figure and in agreement with other studies (e.g. Sartini et al. 2017), the highest values (5 \u2013 6 m in figure, top) extend from the Gulf of Lion to the southwestern Sardinia through the Balearic Sea and are sustained southwards approaching the Algerian coast. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that become stronger due to orographic effects (Menendez et al. 2014), and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves (4-5 m in figure, top). In the Aegean and Levantine Seas, high waves (4-5 m in figure, top) are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014). In general, northerly winds are responsible for most high waves in the Mediterranean (e.g. Chronis et al. 2011; Menendez et al. 2014). In agreement with figure (top), studies on the eastern Mediterranean and the Hellenic Seas have found that the typical wave height range in the Aegean Sea is similar to the one observed in the Ionian Sea despite the shorter fetches characterizing the former basin (Zacharioudaki et al. 2015). This is because of the numerous islands in the Aegean Sea which cause wind funneling and enhance the occurrence of extreme winds and thus of extreme waves (Kotroni et al. 2001). Special mention should be made of the high waves, sustained throughout the year, observed east and west of the island of Crete, i.e. around the exiting points of the northerly airflow in the Aegean Sea (Zacharioudaki et al. 2015). This airflow is characterized by consistently high magnitudes that are sustained during all seasons in contrast to other airflows in the Mediterranean Sea that exhibit a more pronounced seasonality (Chronis et al. 2011). \n\nCMEMS KEY FINDINGS\n\nIn 2020 (bottom panel), higher-than-average values of the 99th percentile of Significant Wave Height are seen over most of the northern Mediterranean Sea, in the eastern Alboran Sea, and along stretches of the African coast (Tunisia, Libya and Egypt). In many cases they exceed the climatic standard deviation. Regions where the climatic standard deviation is exceeded twice are the European and African coast of the eastern Alboran Sea, a considerable part of the eastern Spanish coast, the Ligurian Sea and part of the east coast of France as well as areas of the southern Adriatic. These anomalies correspond to the maximum positive anomalies computed in the Mediterranean Sea for year 2020 with values that reach up to 1.1 m. Spatially constrained maxima are also found at other coastal stretches (e.g. Algeri, southeast Sardinia). Part of the positive anomalies found along the French and Spanish coast, including the coast of the Balearic Islands, can be associated with the wind storm \u201cGloria\u201d (19/1 \u2013 24/1) during which exceptional eastern winds originated in the Ligurian Sea and propagated westwards. The storm, which was of a particularly high intensity and long duration, caused record breaking wave heights in the region, and, in return, great damage to the coast (Amores et al., 2020; de Alfonso et al., 2021). Other storms that could have contributed to the positive anomalies observed in the western Mediterranean Sea include: storm Karine (25/2 \u2013 5/4), which caused high waves from the eastern coast of Spain to the Balearic Islands (Copernicus, Climate Change Service, 2020); storm Bernardo (7/11 \u2013 18/11) which also affected the Balearic islands and the Algerian coast and; storm Herv\u00e9 (2/2 \u2013 8/2) during which the highest wind gust was recorded at north Corsica (Wikiwand, 2021). In the eastern Mediterranean Sea, the medicane Ianos (14/9 \u2013 21/9) may have contributed to the positive anomalies shown in the central Ionian Sea since this area coincides with the area of peak wave height values during the medicane (Copernicus, 2020a and Copernicus, 2020b). Otherwise, higher-than-average values in the figure are the result of severe, yet not unusual, wind events, which occurred during the year. Negative anomalies occur over most of the southern Mediterranean Sea, east of the Alboran Sea. The maximum negative anomalies reach about -1 m and are located in the southeastern Ionian Sea and west of the south part of mainland Greece as well as in coastal locations of the north and east Aegean They appear to be quite unusual since they are greater than two times the climatic standard deviation in the region. They could imply less severe southerly wind activity during 2020 (Drobinski et al., 2018). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00262\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Amores, A., Marcos, M., Carri\u00f3, Di.S., Gomez-Pujol, L., 2020. Coastal impacts of Storm Gloria (January 2020) over the north-western Mediterranean. Nat. Hazards Earth Syst. Sci. 20, 1955\u20131968. doi:10.5194/nhess-20-1955-2020\n* Chronis T, Papadopoulos V, Nikolopoulos EI. 2011. QuickSCAT observations of extreme wind events over the Mediterranean and Black Seas during 2000-2008. Int J Climatol. 31: 2068\u20132077.\n* Copernicus: Climate Change Service. 2020a (Last accessed July 2021): URL: https://surfobs.climate.copernicus.eu/stateoftheclimate/march2020.php\n* Copernicus, Copernicus Marine Service. 2020b (Last accessed July 2021): URL: https://marine.copernicus.eu/news/following-cyclone-ianos-across-mediterranean-sea\n* de Alfonso, M., Lin-Ye, J., Garc\u00eda-Valdecasas, J.M., P\u00e9rez-Rubio, S., Luna, M.Y., Santos-Mu\u00f1oz, D., Ruiz, M.I., P\u00e9rez-G\u00f3mez, B., \u00c1lvarez-Fanjul, E., 2021. Storm Gloria: Sea State Evolution Based on in situ Measurements and Modeled Data and Its Impact on Extreme Values. Front. Mar. Sci. 8, 1\u201317. doi:10.3389/fmars.2021.646873\n* Drobinski P, Alpert P, Cavicchia L, Flaoumas E, Hochman A, Kotroni V. 2018. Strong winds Observed trends, future projections, Sub-chapter 1.3.2, p. 115-122. In: Moatti JP, Thi\u00e9bault S (dir.). The Mediterranean region under climate change: A scientific update. Marseille: IRD \u00c9ditions.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2014. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2014 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Kotroni V, Lagouvardos K, Lalas D. 2001. The effect of the island of Crete on the Etesian winds over the Aegean Sea. Q J R Meteorol Soc. 127: 1917\u20131937. doi:10.1002/qj.49712757604\n* Lionello P, Rizzoli PM, Boscolo R. 2006. Mediterranean climate variability, developments in earth and environmental sciences. Elsevier.\n* Menendez M, Garc\u00eda-D\u00edez M, Fita L, Fern\u00e1ndez J, M\u00e9ndez FJ, Guti\u00e9rrez JM. 2014. High-resolution sea wind hindcasts over the Mediterranean area. Clim Dyn. 42:1857\u20131872.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sartini L, Besio G, Cassola F. 2017. Spatio-temporal modelling of extreme wave heights in the Mediterranean Sea. Ocean Modelling, 117, 52-69.\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Wikiwand: 2019 - 20 European windstorm season. URL: https://www.wikiwand.com/en/2019%E2%80%9320_European_windstorm_season\n* Zacharioudaki A, Korres G, Perivoliotis L, 2015. Wave climate of the Hellenic Seas obtained from a wave hindcast for the period 1960\u20132001. Ocean Dynamics. 65: 795\u2013816. https://doi.org/10.1007/s10236-015-0840-z\n", "doi": "10.48670/moi-00262", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-seastate-extreme-var-swh-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Significant Wave Height extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid \u201880s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend.\nThe Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14\u00b0C in the Northwestern part of the basin to ~23\u00b0C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80\u2019s as shown in Marb\u00e0 et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018).\n\nCMEMS KEY FINDINGS\n\nThe Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 \u00b0C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23\u201325 \u00b0C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016.\nThe 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3\u00b0C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00266\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Darmaraki S, Somot S, Sevault F, Nabat P, Cabos W, Cavicchia L, et al. 2019. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371\u20131392. doi: 10.1007/s00382-019-04661-z\n* Ducrocq V., Drobinski P., Gualdi S., Raimbault P. 2016. The water cycle in the Mediterranean. Chapter 1.2.1 in The Mediterranean region under climate change. IRD E\u0301ditions. DOI : 10.4000/books.irdeditions.22908.\n* Marb\u00e0 N, Jord\u00e0 G, Agust\u00ed S, Girard C, Duarte CM. 2015. Footprints of climate change on Mediterranean Sea biota. Front.Mar.Sci.2:56. doi: 10.3389/fmars.2015.00056\n* Mariotti A and Dell\u2019Aquila A. 2012. Decadal climate variability in the Mediterranean region: roles of large-scale forcings and regional processes. Clim Dyn. 38,1129\u20131145. doi:10.1007/s00382-011-1056-7\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-zP\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Buongiorno Nardelli B. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sens. 2020, 12, 132.\n", "doi": "10.48670/moi-00266", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-extreme-var-temp-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe medsea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Mediterranean Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST satellite product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\nKEY FINDINGS\n\nDuring 2023, the Mediterranean Sea continued experiencing the intense sea surface temperatures\u2019 warming (marine heatwave event) that started in May 2022 (Marullo et al., 2023). The basin average SST anomaly was 0.9 \u00b1 0.1 \u00b0C in 2023, the highest in this record. The Mediterranean SST warming started in May 2022, when the mean anomaly increased abruptly from 0.01 \u00b0C (April) to 0.76 \u00b0C (May), reaching the highest values during June (1.66 \u00b0C) and July (1.52 \u00b0C), and persisting until summer 2023 with anomalies around 1 \u00b0C above the 1991-2020 climatology. The peak of July 2023 (1.76 \u00b0C) set the record of highest SST anomaly ever recorded since 1982. The 2022/2023 Mediterranean marine heatwave is comparable to that occurred in 2003 (see e.g. Olita et al., 2007) in terms of anomaly magnitude but longer in duration.\nOver the period 1982-2023, the Mediterranean SST has warmed at a rate of 0.041 \u00b1 0.001 \u00b0C/year, which corresponds to an average increase of about 1.7 \u00b0C during these last 42 years. Within its error (namely, the 95% confidence interval), this warming trend is consistent with recent trend estimates in the Mediterranean Sea (Pisano et al., 2020; Pastor et al., 2020). However, though the linear trend being constantly increasing during the whole period, the picture of the Mediterranean SST trend in 2022 seems to reveal a restarting after the pause occurred in the last years (since 2015-2021).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00268\n\nReferences:\n\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Olita, A., Sorgente, R., Natale, S., Gaber\u0161ek, S., Ribotti, A., Bonanno, A., & Patti, B. (2007). Effects of the 2003 European heatwave on the Central Mediterranean Sea: surface fluxes and the dynamical response. Ocean Science, 3(2), 273-289.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00268", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-area-averaged-anomalies,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"abstract": "DEFINITION\n\nThe medsea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Mediterranean Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the time series (42 years). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the SST trend over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterize the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\nKEY FINDINGS\n\nOver the past four decades (1982-2023), the Mediterranean Sea surface temperature (SST) warmed at a rate of 0.041 \u00b1 0.001 \u00b0C per year, corresponding to a mean surface temperature warming of about 1.7 \u00b0C. The spatial pattern of the Mediterranean SST trend shows a general warming tendency, ranging from 0.002 \u00b0C/year to 0.063 \u00b0C/year. Overall, a higher SST trend intensity characterizes the Eastern and Central Mediterranean basin with respect to the Western basin. In particular, the Balearic Sea, Tyrrhenian and Adriatic Seas, as well as the northern Ionian and Aegean-Levantine Seas show the highest SST trends (from 0.04 \u00b0C/year to 0.05 \u00b0C/year on average). Trend patterns of warmer intensity characterize some of main sub-basin Mediterranean features, such as the Pelops Anticyclone, the Cretan gyre and the Rhodes Gyre. On the contrary, less intense values characterize the southern Mediterranean Sea (toward the African coast), where the trend attains around 0.025 \u00b0C/year. The SST warming rate spatial change, mostly showing an eastward increase pattern (see, e.g., Pisano et al., 2020, and references therein), i.e. the Levantine basin getting warm faster than the Western, appears now to have tilted more along a North-South direction.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00269\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734 Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Kirtman, B., Power, S. B, Adedoyin, J. A., Boer, G. J., Bojariu, R. et al., 2013. Near-term climate change: Projections and Predictability. In: Stocker, T.F., et al. (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00269", "instrument": null, "keywords": "change-over-time-in-sea-surface-temperature,coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-trend,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"abstract": "This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor.\nThe method called CANYON (Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) is based on a neural network trained using high-quality nutrient data collected over the last 30 years (GLODAPv2 database, https://www.glodap.info/). The method is applied to each Argo float equipped with an oxygen sensor using as input the properties measured by the float (pressure, temperature, salinity, oxygen), and its date and position.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00048\n\nReferences:\n\n* Sauzede R., H. C. Bittig, H. Claustre, O. Pasqueron de Fommervault, J.-P. Gattuso, L. Legendre and K. S. Johnson, 2017: Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A novel Approach Based on Neural Networks. Front. Mar. Sci. 4:128. doi: 10.3389/fmars.2017.00128.\n* Bittig H. C., T. Steinhoff, H. Claustre, B. Fiedler, N. L. Williams, R. Sauz\u00e8de, A. K\u00f6rtzinger and J.-P. Gattuso,2018: An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Front. Mar. Sci. 5:328. doi: 10.3389/fmars.2018.00328.\n", "doi": "10.48670/moi-00048", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,moles-of-nitrate-per-unit-mass-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,moles-of-phosphate-per-unit-mass-in-sea-water,moles-of-silicate-per-unit-mass-in-sea-water,multi-year,multiobs-glo-bgc-nutrients-carbon-profiles-mynrt-015-009,none,oceanographic-geographical-features,partial-pressure-of-carbon-dioxide-in-sea-water,sea-water-ph-reported-on-total-scale,sea-water-pressure,sea-water-salinity,sea-water-temperature,total-alkalinity-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nutrient and carbon profiles vertical distribution"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"abstract": "This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\nDOI (product):\nhttps://doi.org/10.48670/moi-00046\n\nReferences:\n\n* Sauzede R., H. Claustre, J. Uitz, C. Jamet, G. Dall\u2019Olmo, F. D\u2019Ortenzio, B. Gentili, A. Poteau, and C. Schmechtig, 2016: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC011408.\n", "doi": "10.48670/moi-00046", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,multi-year,multiobs-glo-bio-bgc-3d-rep-015-010,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"abstract": "This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00047\n\nReferences:\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO2 fugacity and air-sea fluxes with low latency. Geophysical Research Letters, 51, e2023GL106670. https://doi.org/10.1029/2023GL106670\n* Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25\u00b0, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121\u2013160, https://doi.org/10.5194/essd-16-121-2024, 2024.\n", "doi": "10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-bio-carbon-surface-mynrt-015-008,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Surface ocean carbon fields"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"abstract": "This product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00327\n\nReferences:\n\n* Rio, M.-H., S. Mulet, and N. Picot: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, doi:10.1002/2014GL061773, 2014.\n", "doi": "10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-mynrt-015-003,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\nDOI (product): \nhttps://doi.org/10.1016/j.rse.2016.02.061\n\nReferences:\n\n* Boutin, J., J. L. Vergely, S. Marchand, F. D'Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin, and J. Vialard (2018), New SMOS Sea Surface Salinity with reduced systematic errors and improved variability, Remote Sensing of Environment, 214, 115-134. doi:https://doi.org/10.1016/j.rse.2018.05.022\n* Kolodziejczyk, N., J. Boutin, J.-L. Vergely, S. Marchand, N. Martin, and G. Reverdin (2016), Mitigation of systematic errors in SMOS sea surface salinity, Remote Sensing of Environment, 180, 164-177. doi:https://doi.org/10.1016/j.rse.2016.02.061\n", "doi": "10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l3-mynrt-015-014,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-01-12T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version: https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version: https://archimer.ifremer.fr/doc/00665/77702/\n\nDOI (product): \nhttps://doi.org/10.1175/JTECH-D-20-0093.1\n\nReferences:\n\n* Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n", "doi": "10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l4-my-015-015,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-06-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"abstract": "This product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\nProduct citation: \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00051\n\nReferences:\n\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2016: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface. J. Atmos. Oceanic Technol. doi:10.1175/JTECH-D-15-0194.1.\n* Buongiorno Nardelli, B., R. Droghei, and R. Santoleri, 2016: Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data. Rem. Sens. Environ., doi:10.1016/j.rse.2015.12.052.\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2018: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993\u20132016), Front. Mar. Sci., 5(March), 1\u201313, doi:10.3389/fmars.2018.00084.\n* Sammartino, Michela, Salvatore Aronica, Rosalia Santoleri, and Bruno Buongiorno Nardelli. (2022). Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data, Remote Sensing 14, 2502: https://doi.org/10.3390/rs14102502.\n", "doi": "10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-s-surface-mynrt-015-013,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"abstract": "You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 5 datasets: \n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1D-m, which delivers near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1M-m, which delivers near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy_my_0.125deg_P1D-m, which delivers multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy_my_0.125deg_P1M-m, which delivers multi-year reprocessed (REP) monthly data\n* cmems_obs-mob_glo_phy_mynrt_0.125deg-climatology-uncertainty_P1M-m, which delivers monthly static uncertainty data\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00052\n\nReferences:\n\n* Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845\u2013857.\n* Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77\u201380(0):70\u201381.\n", "doi": "10.48670/moi-00052", "instrument": null, "keywords": "coastal-marine-environment,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-tsuv-3d-mynrt-015-012,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"abstract": "You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\nDOI (product): \nhttps://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007\n\nReferences:\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n", "doi": "10.25423/cmcc/multiobs_glo_phy_w_rep_015_007", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-w-3d-rep-015-007,northward-sea-water-velocity,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013).\nTwo parameters are included on this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThis domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself.\n\nCMEMS KEY FINDINGS\n\nThe 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product)\nhttps://doi.org/10.48670/moi-00273\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Elliott, A.J., Clarke, T., Li, ., 1990: Monthly distributions of surface and bottom temperatures in the northwest European shelf seas. Continental Shelf Research, Vol 11, no 5, pp 453-466, http://doi.org/10.1016/0278-4343(91)90053-9\n* Holt, J., Hughes, S., Hopkins, J., Wakelin, S., Holliday, P.N., Dye, S., Gonz\u00e1lez-Pola, C., Hj\u00f8llo, S., Mork, K., Nolan, G., Proctor, R., Read, J., Shammon, T., Sherwin, T., Smyth, T., Tattersall, G., Ward, B., Wiltshire, K., 2012: Multi-decadal variability and trends in the temperature of the northwest European continental shelf: A model-data synthesis. Progress in Oceanography, 96-117, 106, http://doi.org/10.1016/j.pocean.2012.08.001\n* Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., Sparks, T. and Garforth, J. (2021), State of the UK Climate 2020. Int J Climatol, 41 (Suppl 2): 1-76. https://doi.org/10.1002/joc.7285\n* Liu, M., Tanhua, T., 2021: Water masses in the Atlantic Ocean: characteristics and distributions. Ocean Sci, 17, 463-486, http://doi.org/10.5194/os-17-463-2021\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00273", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northwestshelf-omi-tempsal-extreme-var-temp-mean-and-anomaly,numerical-model,oceanographic-geographical-features,temp-percentile99-anom,temp-percentile99-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Sea Surface Temperature extreme from Reanalysis"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"abstract": "The NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00056", "doi": "10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-bgc-004-002,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"abstract": "The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 10-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00054", "doi": "10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,nwshelf-analysisforecast-phy-004-013,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"abstract": "The NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00055\n\nReferences:\n\n* The impact of ocean-wave coupling on the upper ocean circulation during storm events (Bruciaferri, D., Tonani, M., Lewis, H., Siddorn, J., Saulter, A., Castillo, J.M., Garcia Valiente, N., Conley, D., Sykes, P., Ascione, I., McConnell, N.) in Journal of Geophysical Research, Oceans, 2021, 126, 6. https://doi.org/10.1029/2021JC017343\n", "doi": "10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-wav-004-014,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-10-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"abstract": "Short Description:\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009). Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [CMEMS-NWS-QUID-004-011](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for details.\n\nAssociated products:\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009).\nAn analysis-forecast product is available from: [NWSHELF_MULTIYEAR_BGC_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00058\n\nReferences:\n\n* Ciavatta, S., Brewin, R. J. W., Sk\u00e1kala, J., Polimene, L., de Mora, L., Artioli, Y., & Allen, J. I. (2018). [https://doi.org/10.1002/2017JC013490 Assimilation of ocean\u2010color plankton functional types to improve marine ecosystem simulations]. Journal of Geophysical Research: Oceans, 123, 834\u2013854. https://doi.org/10.1002/2017JC013490\n", "doi": "10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,nwshelf-multiyear-bgc-004-011,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"abstract": "Short Description:\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [GLOBAL_REANALYSIS_PHY_001_026](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026) and at the Baltic boundary by the [BALTICSEA_REANALYSIS_PHY_003_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011). The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [CMEMS-NWS-QUID-004-009](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for further details.\n\nAssociated products:\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011). An analysis-forecast product is available from [NWSHELF_ANALYSISFORECAST_PHY_LR_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001).\nThe product is updated biannually provinding six-month extension of the time series.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00059", "doi": "10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,nwshelf-multiyear-phy-004-009,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "NWSHELF_REANALYSIS_WAV_004_015": {"abstract": "Short description:\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [ECMWF ERA-5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [CMEMS-NWS-PUM-004-015](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf) for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [CMEMS-NWS-QUID-004-015](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf).\nThe product is updated biannually provinding six-month extension of the time series.\n\nAssociated products:\n\n[NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00060", "doi": "10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,nwshelf-reanalysis-wav-004-015,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m\ncmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m\ncmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00061\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00061", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l3-nrt-009-201,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\nDataset names: \ncmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00062\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00062", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l4-nrt-009-207,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the \"multi\" products and S3A & S3B only for the \"OLCI\" products.\n* Variables: Chlorophyll-a (CHL), Diffuse Attenuation (KD490) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 4 km (multi) or 300 m (OLCI).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00292", "doi": "10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-arc-bgc-l3-my-009-123,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Suspended Matter (SPM), Diffuse Attenuation (KD490), Detrital and Dissolved Material Absorption Coef. (ADG443_), Phytoplankton Absorption Coef. (APH443_), Total Absorption Coef. (ATOT443_) and Reflectance (RRS_').\n\n* Temporal resolutions: daily, monthly.\n* Spatial resolutions: 300 meters (olci).\n* Recent products are organized in datasets called Near Real Time (NRT).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00290", "doi": "10.48670/moi-00290", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l3-nrt-009-121,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the \"multi\" products , and S3A & S3B only for the \"OLCI\" products.\n* Variables: Chlorophyll-a (CHL), Diffuse Attenuation (KD490)\n\n\n* Temporal resolutions: monthly.\n* Spatial resolutions: 4 km (multi) or 300 meters (OLCI).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00293", "doi": "10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-arc-bgc-l4-my-009-124,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL) and Diffuse Attenuation (KD490).\n\n* Temporal resolutions:monthly.\n* Spatial resolutions: 300 meters (olci).\n* Recent products are organized in datasets called Near Real Time (NRT).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00291", "doi": "10.48670/moi-00291", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l4-nrt-009-122,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton and Transparency L4 NRT monthly observations"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 1 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00286", "doi": "10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-atl-bgc-l3-my-009-113,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 1 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00284", "doi": "10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-atl-bgc-l3-nrt-009-111,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and S3A & S3B only for the \"olci\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: 1 km.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"GlobColour\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00289", "doi": "10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-atl-bgc-l4-my-009-118,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and S3A & S3B only for the \"olci\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: 1 km.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"GlobColour\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00288", "doi": "10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-atl-bgc-l4-nrt-009-116,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00079\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l3-nrt-009-202,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00080\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l4-nrt-009-208,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-08T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolution: daily\n\nSpatial resolution: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00296\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-bal-bgc-l3-my-009-133,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: OLCI-S3A & S3B \n\nTemporal resolution: daily\n\nSpatial resolution: 300 meters \n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00294\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n", "doi": "10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-bal-bgc-l3-nrt-009-131,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly\n\nSpatial resolution: 1 km for \"\"multi\"\" and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00308\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-bal-bgc-l4-my-009-134,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\nUpstreams: OLCI-S3A & S3B \n\nTemporal resolution: monthly \n\nSpatial resolution: 300 meters \n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00295\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-bal-bgc-l4-nrt-009-132,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00086\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l3-nrt-009-206,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\nDOI (product): \nhttps://doi.org/10.48670/moi-00087\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l4-nrt-009-212,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and OLCI-S3A & S3B for the \"olci\" products\n\nTemporal resolution: daily\n\nSpatial resolution: 1 km for \"multi\" and 300 meters for \"olci\"\n\nTo find this product in the catalogue, use the search keyword \"OCEANCOLOUR_BLK_BGC_L3_MY\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00303\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-blk-bgc-l3-my-009-153,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolution: daily\n\nSpatial resolutions: 1 km for \"\"multi\"\" and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00301\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-blk-bgc-l3-nrt-009-151,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated gap-free Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly and daily (for \"\"gap-free\"\", \"\"pp\"\" and climatology data)\n\nSpatial resolution: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BLK_BGC_L4_MY\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00304\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-blk-bgc-l4-my-009-154,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated gap-free Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"\"multi\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly and daily (for \"\"gap-free\"\" and \"\"pp\"\" data)\n\nSpatial resolutions: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00302\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-blk-bgc-l4-nrt-009-152,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\"\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00280", "doi": "10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-103,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"abstract": "For the Global Ocean Satellite Observations, Brockmann Consult (BC) is providing Bio-Geo_Chemical (BGC) products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the \"\"multi\"\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT) and Reflectance (RRS).\n\n* Temporal resolutions: daily, monthly.\n* Spatial resolutions: 4 km (multi).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find these products in the catalogue, use the search keyword \"\"ESA-CCI\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00282", "doi": "10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-107,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00278", "doi": "10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l3-nrt-009-101,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\"\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00281", "doi": "10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-104,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"abstract": "For the Global Ocean Satellite Observations, Brockmann Consult (BC) is providing Bio-Geo_Chemical (BGC) products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the \"\"multi\"\" products.\n* Variables: Chlorophyll-a (CHL).\n\n* Temporal resolutions: monthly.\n* Spatial resolutions: 4 km (multi).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find these products in the catalogue, use the search keyword \"\"ESA-CCI\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00283", "doi": "10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-108,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and S3A & S3B only for the \"olci\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"GlobColour\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00279", "doi": "10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l4-nrt-009-102,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "2023-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00107\n\nReferences:\n\n Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l3-nrt-009-204,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00108\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l4-nrt-009-210,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00109\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l3-nrt-009-205,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00110\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l4-nrt-009-211,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"multi\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* _pp_ with the Integrated Primary Production (PP)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and OLCI-S3A & S3B for the \"olci\" products\n\nTemporal resolution: daily\n\nSpatial resolution: 1 km for \"multi\" and 300 meters for \"olci\"\n\nTo find this product in the catalogue, use the search keyword \"OCEANCOLOUR_MED_BGC_L3_MY\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00299\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l3-my-009-143,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"\"multi\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolution: daily\n\nSpatial resolutions: 1 km for \"\"multi\"\" and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00297\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l3-nrt-009-141,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated gap-free Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and OLCI-S3A & S3B for the \"olci\" products\n\nTemporal resolutions: monthly and daily (for \"gap-free\" and climatology data)\n\nSpatial resolution: 1 km for \"multi\" and 300 meters for \"olci\"\n\nTo find this product in the catalogue, use the search keyword \"OCEANCOLOUR_MED_BGC_L4_MY\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00300\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l4-my-009-144,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated gap-free Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"\"multi\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly and daily (for \"\"gap-free\"\" and \"\"pp\"\" data)\n\nSpatial resolutions: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00298\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l4-nrt-009-142,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00118\n\nReferences:\n\n Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-nws-bgc-hr-l3-nrt-009-203,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00119\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,oceancolour-nws-bgc-hr-l4-nrt-009-209,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"abstract": "DEFINITION\n\nThe Black Sea Rim Current index (BSRCI) reflects the intensity of the Rim current, which is a main feature of the Black Sea circulation, a basin scale cyclonic current. The index was computed using sea surface current speed averaged over two areas of intense currents based on reanalysis data. The areas are confined between the 200 and 1800 m isobaths in the northern section 33-39E (from the Caucasus coast to the Crimea Peninsula), and in the southern section 31.5-35E (from Sakarya region to near Sinop Peninsula). Thus, three indices were defined: one for the northern section (BSRCIn), for the southern section (BSRCIs) and an average for the entire basin (BSRCI).\nBSRCI=(V \u0305_ann-V \u0305_cl)/V \u0305_cl \nwhere V \u0305 denotes the representative area average, the \u201cann\u201d denotes the annual mean for each individual year in the analysis, and \u201ccl\u201d indicates the long-term mean over the whole period 1993-2020. In general, BSRCI is defined as the relative annual anomaly from the long-term mean speed. An index close to zero means close to the average conditions a positive index indicates that the Rim current is more intense than average, or negative - if it is less intense than average. In other words, positive BSRCI would mean higher circumpolar speed, enhanced baroclinicity, enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, intensification of the heat redistribution, etc.\nThe BSRCI is introduced in the fifth issue of the Ocean State Report (von Schuckmann et al., 2021). The Black Sea Physics Reanalysis (BLKSEA_REANALYSIS_PHYS_007_004) has been used as a data base to build the index. Details on the products are delivered in the PUM and QUID of this OMI.\n\nCONTEXT\n\nThe Black Sea circulation is driven by the regional winds and large freshwater river inflow in the north-western part (including the main European rivers Danube, Dnepr and Dnestr). The major cyclonic gyre encompasses the sea, referred to as Rim current. It is quasi-geostrophic and the Sverdrup balance approximately applies to it. \nThe Rim current position and speed experiences significant interannual variability (Stanev and Peneva, 2002), intensifying in winter due to the dominating severe northeastern winds in the region (Stanev et al., 2000). Consequently, this impacts the vertical stratification, Cold Intermediate Water formation, the biological activity distribution and the coastal mesoscale eddies\u2019 propagation along the current and their evolution. The higher circumpolar speed leads to enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, enhanced baroclinicity, intensification of the heat redistribution which is important for the winter freezing in the northern zones (Simonov and Altman, 1991). Fach (2015) finds that the anchovy larval dispersal in the Black Sea is strongly controlled at the basin scale by the Rim Current and locally - by mesoscale eddies. \nSeveral recent studies of the Black Sea pollution claim that the understanding of the Rim Current behavior and how the mesoscale eddies evolve would help to predict the transport of various pollution such as oil spills (Korotenko, 2018) and floating marine litter (Stanev and Ricker, 2019) including microplastic debris (Miladinova et al., 2020) raising a serious environmental concern today. \nTo summarize, the intensity of the Black Sea Rim Current could give valuable integral measure for a great deal of physical and biogeochemical processes manifestation. Thus our objective is to develop a comprehensive index reflecting the annual mean state of the Black Sea general circulation to be used by policy makers and various end users. \n\nCMEMS KEY FINDINGS\n\nThe Black Sea Rim Current Index is defined as the relative annual anomaly of the long-term mean speed. The BSRCI value characterizes the annual circulation state: a value close to zero would mean close to average conditions, positive value indicates enhanced circulation, and negative value \u2013 weaker circulation than usual. The time-series of the BSRCI suggest that the Black Sea Rim current speed varies within ~30% in the period 1993-2020 with a positive trend of ~0.1 m/s/decade. In the years 2005 and 2014 there is evidently higher mean velocity, and on the opposite end are the years \u20132004, 2013 and 2016. The time series of the BSRCI gives possibility to check the relationship with the wind vorticity and validate the Sverdrup balance hypothesis. \n\nFigure caption\n\nTime series of the Black Sea Rim Current Index (BSRCI) at the north section (BSRCIn), south section (BSRCIs), the average (BSRCI) and its tendency for the period 1993-2020.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00326\n\nReferences:\n\n* Capet, A., A. Barth, J.-M. Beckers, and M. Gr\u00e9goire (2012), Interannual variability of Black Sea\u2019s hydrodynamics and connection to atmospheric patterns, Deep-Sea Res. Pt. II, 77 \u2013 80, 128\u2013142, doi:10.1016/j.dsr2.2012.04.010\n* Fach, B., (2015), Modeling the Influence of Hydrodynamic Processes on Anchovy Distribution and Connectivity in the Black Sea, Turkish Journal of Fisheries and Aquatic Sciences 14: 1-2, doi: 10.4194/1303-2712-v14_2_06\n* Ivanov V.A., Belokopytov V.N. (2013) Oceanography of the Black Sea. Editorial publishing board of Marine Hydrophysical Institute, 210 p, Printed by ECOSY-Gidrofizika, Sevastopol Korotaev, G., T. Oguz, A. Nikiforov, and C. Koblinsky. Seasonal, interannual, and mesoscale variability of the Black Sea upper layer circulation derived from altimeter data. Journal of Geophysical Research (Oceans). 108. C4. doi: 10. 1029/2002JC001508, 2003\n* Korotenko KA. Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: an assessment of the environmental impacts. PeerJ. 2018 Aug 29;6:e5448. doi: 10.7717/peerj.5448. PMID: 30186680; PMCID: PMC6119461.\n* Kubryakov, A. A., and S.V. Stanichny (2015), Seasonal and interannual variability of the Black Sea eddies and its dependence on characteristics of the large-scale circulation, Deep-Sea Res. Pt. I, 97, 80-91, https://doi.org/10.1016/j.dsr.2014.12.002\n* Miladinova S., A. Stips, D. Macias Moy, E. Garcia-Gorriz, (2020a) Pathways and mixing of the north western river waters in the Black Sea Estuarine, Coastal and Shelf Science, Volume 236, 5 May 2020, https://doi\n", "doi": "10.48670/mds-00326", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-boundary-blksea-rim-current-index,rim-current-intensity-index,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Rim Current Intensity Index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"abstract": "DEFINITION\n\nThe indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149\u00b0E and 32-37\u00b0N and computed from the DUACS (https://duacs.cls.fr) delayed-time (reprocessed version DT-2021, CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047, including \u201cmy\u201d (multi-year) & \u201cmyint\u201d (multi-year interim) datasets) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the EKE (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the Kuroshio extension (correlation coefficient of 0.988 for the total period, 0.994 for the delayed time period only). \n\nCONTEXT\n\nThe Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35\u00b0N, 140\u00b0E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014).\n\nCMEMS KEY FINDINGS\n\nThe different states of the Kuroshio extension phase have been presented and validated by (Bessi\u00e8res et al., 2013) and further reported by Dr\u00e9villon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an \u2018elongated state\u2019 (also called \u2018strong state\u2019) corresponding to a narrow strong steady jet, and a \u2018contracted state\u2019 (also called \u2018weak state\u2019) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then.\n\nFigure caption\n\nStandardized Eddy Kinetic Energy over the Kuroshio region (following Bessi\u00e8res et al., 2013) Blue shaded areas correspond to well established strong elongated states periods, while orange shaded areas fit weak contracted states periods. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red line shows the date of the transition between \u201cmyint\u201d and \u201cnrt\u201d products used.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00222\n\nReferences:\n\n* Bessi\u00e8res, L., Rio, M.H., Dufau, C., Boone, C., Pujol, M.I., 2013. Ocean state indicators from MyOcean altimeter products. Ocean Sci. 9, 545\u2013560. https://doi.org/10.5194/os-9-545-2013\n* Dr\u00e9villon, M., Legeais, J.-F., Peterson, A., Zuo, H., Rio, M.-H., Drillet, Y., Greiner, E., 2018. Western boundary currents. J. Oper. Oceanogr., Copernicus Marine Service Ocean State Report Issue 2, s60\u2013s65. https://doi.org/10.1080/1755876X.2018.1489208\n* Jayne, S.R., Hogg, N.G., Waterman, S.N., Rainville, L., Donohue, K.A., Randolph Watts, D., Tracey, K.L., McClean, J.L., Maltrud, M.E., Qiu, B., Chen, S., Hacker, P., 2009. The Kuroshio Extension and its recirculation gyres. Deep Sea Res. Part Oceanogr. Res. Pap. 56, 2088\u20132099. https://doi.org/10.1016/j.dsr.2009.08.006\n* Kelly, K.A., Small, R.J., Samelson, R.M., Qiu, B., Joyce, T.M., Kwon, Y.-O., Cronin, M.F., 2010. Western Boundary Currents and Frontal Air\u2013Sea Interaction: Gulf Stream and Kuroshio Extension. J. Clim. 23, 5644\u20135667. https://doi.org/10.1175/2010JCLI3346.1\n* Niiler, P.P., Maximenko, N.A., Panteleev, G.G., Yamagata, T., Olson, D.B., 2003. Near-surface dynamical structure of the Kuroshio Extension. J. Geophys. Res. Oceans 108. https://doi.org/10.1029/2002JC001461\n* Qiu, B., 2003. Kuroshio Extension Variability and Forcing of the Pacific Decadal Oscillations: Responses and Potential Feedback. J. Phys. Oceanogr. 33, 2465\u20132482. https://doi.org/10.1175/2459.1\n* Qiu, B., 2002. The Kuroshio Extension System: Its Large-Scale Variability and Role in the Midlatitude Ocean-Atmosphere Interaction. J. Oceanogr. 58, 57\u201375. https://doi.org/10.1023/A:1015824717293\n* Qiu, B., Chen, S., 2010. Eddy-mean flow interaction in the decadally modulating Kuroshio Extension system. Deep Sea Res. Part II Top. Stud. Oceanogr., North Pacific Oceanography after WOCE: A Commemoration to Nobuo Suginohara 57, 1098\u20131110. https://doi.org/10.1016/j.dsr2.2008.11.036\n* Qiu, B., Chen, S., 2005. Variability of the Kuroshio Extension Jet, Recirculation Gyre, and Mesoscale Eddies on Decadal Time Scales. J. Phys. Oceanogr. 35, 2090\u20132103. https://doi.org/10.1175/JPO2807.1\n", "doi": "10.48670/moi-00222", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-circulation-boundary-pacific-kuroshio-phase-area-averaged,satellite-observation,specific-turbulent-kinetic-energy-of-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Kuroshio Phase from Observations Reprocessing"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"abstract": "DEFINITION\n\nThis ocean monitoring indicator (OMI) provides a time series of Meridional Overturning Circulation (MOC) Strength in density coordinates, area-averaged and calculated for the period from 1993 to the most recent year with the availability of reanalysis data in the Black Sea (BS). It contains 1D (time dimension) maximum MOC data computed from the Black Sea Reanalysis (BLK-REA; BLKSEA_MULTIYEAR_PHY_007_004) (Ilicak et al., 2022). The MOC is calculated by summing the meridional transport provided by the Copernicus Marine BLK-REA within density bins. The Black Sea MOC indicator represents the maximum MOC value across the basin for a density range between 22.45 and 23.85 kg/m\u00b3, which corresponds approximately to a depth interval of 25 to 80 m. To understand the overturning circulation of the Black Sea, we compute the residual meridional overturning circulation in density space. Residual overturning as a function of latitude (y) and density (\u03c3 \u0305) bins can be computed as follows:\n\u03c8^* (y,\u03c3 \u0305 )=-1/T \u222b_(t_0)^(t_1)\u2592\u222b_(x_B1)^(x_B2)\u2592\u3016\u222b_(-H)^0\u2592H[\u03c3 \u0305-\u03c3(x,y,z,t)] \u00d7\u03bd(x,y,z,t)dzdxdt,\u3017\nwhere H is the Heaviside function and \u03bd is the meridional velocity. We used 100 \u03c3_2 (potential density anomaly with reference pressure of 2000 dbar) density bins to remap the mass flux fields.\n\nCONTEXT\n\nThe BS meridional overturning circulation (BS-MOC) is a clockwise circulation in the northern part up to 150 m connected to cold intermediate layer (CIL) and an anticlockwise circulation in the southern part that could be connected to the influence of the Mediterranean Water inflow into the BS. In contrast to counterparts observed in the deep Atlantic and Mediterranean overturning circulations, the BS-MOC is characterized by shallowness and relatively low strength. However, its significance lies in its capacity to monitor the dynamics and evolution of the CIL which is crucial for the ventilation of the subsurface BS waters. The monitoring of the BS-MOC evolution from the BLK-REA can support the understanding how the CIL formation is affected due to climate change. The study of Black Sea MOC is relatively new. For more details, see Ilicak et al., (2022).\n\nKEY FINDINGS\n\nThe MOC values show a significant decline from 1994 to 2009, corresponding to the reduction in the CIL during that period. However, after 2010, the MOC in the Black Sea increased from 0.07 Sv (1 Sv = 106 m3/s) to 0.10 Sv. The CIL has nearly disappeared in recent years, as discussed by Stanev et al. (2019) and Lima et al. (2021) based on observational data and reanalysis results. The opposite pattern observed since 2010 suggests that mechanisms other than the CIL may be influencing the Black Sea MOC.\nFor the OMI we have used an updated version of the reanalysis (version E4R1) which has a different spinup compared to the OSR6 (version E3R1).\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00349\n\nReferences:\n\n* Ilicak, M., Causio, S., Ciliberti, S., Coppini, G., Lima, L., Aydogdu, A., Azevedo, D., Lecci, R., Cetin, D. U., Masina, S., Peneva, E., Gunduz, M., Pinardi, N. (2022). The Black Sea overturning circulation and its indicator of change. In: Copernicus Ocean State Report, issue 6, Journal of Operational Oceanography, 15:sup1, s64:s71; DOI: doi.org/10.1080/1755876X.2022.2095169\n* Lima, L., Ciliberti, S.A., Aydo\u011fdu, A., Masina, S., Escudier, R., Cipollone, A., Azevedo, D., Causio, S., Peneva, E., Lecci, R., Clementi, E., Jansen, E., Ilicak, M., Cret\u00ec, S., Stefanizzi, L., Palermo, F., Coppini, G. (2021). Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis. Front. Mar. Sci. 8:710973. doi: 10.3389/fmars.2021.710973\n* Stanev, E. V., Peneva, E., Chtirkova, B. (2019). Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans 124, 4803\u20134819. doi: 10.1029/2019JC015076\n", "doi": "10.48670/mds-00349", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-blksea-area-averaged-mean,s,sla,t,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"abstract": "DEFINITION\n\nTime mean meridional Eulerian streamfunctions are computed using the velocity field estimate provided by the Copernicus Marine Mediterranean Sea reanalysis over the last 35 years (1987\u20132021). The Eulerian meridional streamfunction is evaluated by integrating meridional velocity daily data first in a vertical direction, then in a meridional direction, and finally averaging over the reanalysis period.\nThe Mediterranean overturning indices are derived for the eastern and western Mediterranean Sea by computing the annual streamfunction in the two areas separated by the Strait of Sicily around 36.5\u00b0N, and then considering the associated maxima. \nIn each case a geographical constraint focused the computation on the main region of interest. For the western index, we focused on deep-water formation regions, thus excluding both the effect of shallow physical processes and the Gibraltar net inflow. For the eastern index, we investigate the Levantine and Cretan areas corresponding to the strongest meridional overturning cell locations, thus only a zonal constraint is defined.\nTime series of annual mean values is provided for the Mediterranean Sea using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021).\nMore details can be found in the Copernicus Marine Ocean State Report issue 4 (OSR4, von Schuckmann et al., 2020) Section 2.4 (Lyubartsev et al., 2020).\n\nCONTEXT\n\nThe western and eastern Mediterranean clockwise meridional overturning circulation is connected to deep-water formation processes. The Mediterranean Sea 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021) is used to show the interannual variability of the Meridional Overturning Index. Details on the product are delivered in the PUM and QUID of this OMI. \nThe Mediterranean Meridional Overturning Index is defined here as the maxima of the clockwise cells in the eastern and western Mediterranean Sea and is associated with deep and intermediate water mass formation processes that occur in specific areas of the basin: Gulf of Lion, Southern Adriatic Sea, Cretan Sea and Rhodes Gyre (Pinardi et al., 2015).\nAs in the global ocean, the overturning circulation of the western and eastern Mediterranean are paramount to determine the stratification of the basins (Cessi, 2019). In turn, the stratification and deep water formation mediate the exchange of oxygen and other tracers between the surface and the deep ocean (e.g., Johnson et al., 2009; Yoon et al., 2018). In this sense, the overturning indices are potential gauges of the ecosystem health of the Mediterranean Sea, and in particular they could instruct early warning indices for the Mediterranean Sea to support the Sustainable Development Goal (SDG) 13 Target 13.3.\n\nCMEMS KEY FINDINGS\n\nThe western and eastern Mediterranean overturning indices (WMOI and EMOI) are synthetic indices of changes in the thermohaline properties of the Mediterranean basin related to changes in the main drivers of the basin scale circulation. The western sub-basin clockwise overturning circulation is associated with the deep-water formation area of the Gulf of Lion, while the eastern clockwise meridional overturning circulation is composed of multiple cells associated with different intermediate and deep-water sources in the Levantine, Aegean, and Adriatic Seas. \nOn average, the EMOI shows higher values than the WMOI indicating a more vigorous overturning circulation in eastern Mediterranean. The difference is mostly related to the occurrence of the eastern Mediterranean transient (EMT) climatic event, and linked to a peak of the EMOI in 1992. In 1999, the difference between the two indices started to decrease because EMT water masses reached the Sicily Strait flowing into the western Mediterranean Sea (Schroeder et al., 2016). The western peak in 2006 is discussed to be linked to anomalous deep-water formation during the Western Mediterranean Transition (Smith, 2008; Schroeder et al., 2016). Thus, the WMOI and EMOI indices are a useful tool for long-term climate monitoring of overturning changes in the Mediterranean Sea. \n\nFigure caption\n\nTime series of Mediterranean overturning indices [Sverdrup] calculated from the annual average of the meridional streamfunction over the period 1987 to 2021. Blue: Eastern Mediterranean Overturning Index (lat<36.5\u00b0N); Red: Western Mediterranean Overturning Index (lat\u226540\u00b0N, z>300m). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00317\n\nReferences:\n\n* Cessi, P. 2019. The global overturning circulation. Ann Rev Mar Sci. 11:249\u2013270. DOI:10.1146/annurev-marine- 010318-095241. Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini, G., Pinardi, N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Gertman, I., Pinardi, N., Popov, Y., Hecht, A. 2006. Aegean Sea water masses during the early stages of the eastern Mediterranean climatic Transient (1988\u20131990). J Phys Oceanogr. 36(9):1841\u20131859. DOI:10.1175/JPO2940.1.\n* Johnson, K.S., Berelson, W.M., Boss, E.S., Chase, Z., Claustre, H., Emerson, S.R., Gruber, N., Ko\u0308rtzinger, A., Perry, M.J., Riser, S.C. 2009. Observing biogeochemical cycles at global scales with profiling floats and gliders: prospects for a global array. Oceanography. 22:216\u2013225. DOI:10.5670/oceanog. 2009.81.\n* Lyubartsev, V., Borile, F., Clementi, E., Masina, S., Drudi, M/. Coppini, G., Cessi, P., Pinardi, N. 2020. Interannual variability in the Eastern and Western Mediterranean Overturning Index. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097.\n* Pinardi, N., Cessi, P., Borile, F., Wolfe, C.L.P. 2019. The Mediterranean Sea overturning circulation. J Phys Oceanogr. 49:1699\u20131721. DOI:10.1175/JPO-D-18-0254.1.\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., Tonani, M., Lyubartsev, V., Dobricic, S., Bonaduce, A. 2015. Mediterranean Sea large-scale, low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Prog Oceanogr. 132:318\u2013332. DOI:10.1016/j.pocean.2013.11.003.\n* Roether, W., Klein, B., Hainbucher, D. 2014. Chap 6. The eastern Mediterranean transient. In: GL Eusebi Borzelli, M Gacic, P Lionello, P Malanotte-Rizzoli, editors. The Mediterranean Sea. American Geophysical Union (AGU); p. 75\u201383. DOI:10.1002/9781118847572.ch6.\n* Roether, W., Manca, B.B., Klein, B., Bregant, D., Georgopoulos, D., Beitzel, V., Kovac\u030cevic\u0301, V., Luchetta, A. 1996. Recent changes in the eastern Mediterranean deep waters. Science. 271:333\u2013335. DOI:10.1126/science.271.5247.333.\n* Schroeder, K., Chiggiato, J., Bryden, H., Borghini, M., Ismail, S.B. 2016. Abrupt climate shift in the western Mediterranean Sea. Sci Rep. 6:23009. DOI:10.1038/srep23009.\n* Smith, R.O., Bryden, H.L., Stansfield, K. 2008. Observations of new western Mediterranean deep water formation using Argo floats 2004-2006. Ocean Science, 4 (2), 133-149.\n* Von Schuckmann, K. et al. 2020. Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097.\n* Yoon, S., Chang, K., Nam, S., Rho, T.K., Kang, D.J., Lee, T., Park, K.A., Lobanov, V., Kaplunenko, D., Tishchenko, P., Kim, K.R. 2018. Re-initiation of bottom water formation in the East Sea (Japan Sea) in a warming world. Sci Rep. 8:1576. DOI:10. 1038/s41598-018-19952-4.\n", "doi": "10.48670/mds-00317", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-medsea-area-averaged-mean,sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Meridional Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"abstract": "DEFINITION\n\nNet (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1): Faroe Shetland Channel (Water mass criteria, T > 5 \u00b0C); Barents Sea Opening (T > 3 \u00b0C) and the Fram Strait (T > 2 \u00b0C). Net volume transport of Overflow Waters (\u03c3\u03b8 >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).\n\nCONTEXT\n\nThe poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sand\u00f8 et al., 2010; \u00c5rthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).\n\nCMEMS KEY FINDINGS\n\nThe flow of Atlantic Water through the F\u00e6r\u00f8y-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.5 Sv for the period 1993-2021. \nIn the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 1.8 Sv (Smedsrud et al., 2013).\nIn the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 \u00b0C).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00189\n\nReferences:\n\n* Berx B. Hansen B, \u00d8sterhus S, Larsen KM, Sherwin T, Jochumsen K. 2013. Combining in situ measurements and altimetry to estimate volume, heat and salt transport variability through the F\u00e6r\u00f8y-Shetland Channel. Ocean Sci. 9, 639-654\n* Dickson RR, Brown J. 1994. The production of North-Atlantic deep-water \u2013 sources, rates, and pathways. J Geophys Res Oceans. 99(C6), 12319-12341\n* Eldevik T, Nilsen JE\u00d8, Iovino D, Olsson KA, Sand\u00f8 AB, Drange H. 2009. Observed sources and variability of Nordic seas overflow. Nature Geosci. 2(6), 405-409\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan M.M, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Climate Change. 5, 673-678.\n* Mauritzen C. 1996. Production of dense overflow waters feeding the North Atlantic across the Greenland-Scotland Ridge. 1. Evidence for a revised circulation scheme. Deep-Sea Res Part I. 43(6), 769-806\n* Mauritzen C, Hansen E, Andersson M, Berx B, Beszczynzka-M\u00f6ller A, Burud I, Christensen KH, Debernard J, de Steur L, Dodd P, et al. 2011. Closing the loop \u2013 Approaches to monitoring the state of the Arctic Mediterranean during the International Polar Year 2007-2008. Prog Oceanogr. 90, 62-89\n* Onarheim IH, Eldevik T, \u00c5rthun M, Ingvaldsen RB, Smedsrud LH. 2015. Skillful prediction of Barents Sea ice cover. Geophys Res Lett. 42(13), 5364-5371\n* Raj RP, Johannessen JA, Eldevik T, Nilsen JE\u00d8, Halo I. 2016. Quantifying mesoscale eddies in the Lofoten basin. J Geophys Res Oceans. 121. doi:10.1002/2016JC011637\n* Rudels B. 2012. Arctic Ocean circulation and variability \u2013 advection and external forcing encounter constraints and local processes. Ocean Sci. 8(2), 261-286\n* Sand\u00f8, A.B., J.E.\u00d8. Nilsen, Y. Gao and K. Lohmann, 2010: Importance of heat transport and local air-sea heat fluxes for Barents Sea climate variability. J Geophys Res. 115, C07013\n* von Schuckmann K, et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. J Oper Oceanogr. 9, 235-320\n* von Schuckmann K. 2018. Copernicus Marine Service Ocean State Report, J Oper Oceanogr. 11, sup1, S1-S142. Smedsrud LH, Esau I, Ingvaldsen RB, Eldevik T, Haugan PM, Li C, Lien VS, Olsen A, Omar AM, Otter\u00e5 OH, Risebrobakken B, Sand\u00f8 AB, Semenov VA, Sorokina SA. 2013. The role of the Barents Sea in the climate system. Rev Geophys. 51, 415-449\n* Sundby, S., 2000. Recruitment of Atlantic cod stocks in relation to temperature and advection of copepod populations. Sarsia. 85, 277-298.\n* Walczowski W, Piechura J, Goszczko I, Wieczorek P. 2012. Changes in Atlantic water properties: an important factor in the European Arctic marine climate. ICES J Mar Sys. 69(5), 864-869.\n* Piechura J, Walczowski W. 2009. Warming of the West Spitsbergen Current and sea ice north of Svalbard. Oceanol. 51(2), 147-164\n* \u00c5rthun, M., Eldevik, T., Smedsrud, L.H., Skagseth, \u00d8., Ingvaldsen, R.B., 2012. Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat. J. Climate. 25, 4736-4743.\n", "doi": "10.48670/moi-00189", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,omi-circulation-voltrans-arctic-averaged,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nordic Seas Volume Transports from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"abstract": "DEFINITION\n\nThe product OMI_IBI_CURRENTS_VOLTRANS_section_integrated_anomalies is defined as the time series of annual mean volume transport calculated across a set of vertical ocean sections. These sections have been chosen to be representative of the temporal variability of various ocean currents within the IBI domain.\nThe currents that are monitored include: transport towards the North Sea through Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), Canary Current (CC) (Knoll et al. 2002, Mason et al. 2011), Azores Current (AC) (Mason et al., 2011), Algerian Current (ALG) (Tintor\u00e9 et al, 1988; Benzohra and Millot, 1995; Font et al., 1998), and net transport along the 48\u00baN latitude parallel (N48) (see OMI Figure).\nTo provide ensemble-based results, four Copernicus products have been used. Among these products are three reanalysis products (GLO-REA, IBI-REA and MED-REA) and one product obtained from reprocessed observations (GLO-ARM).\n\u2022\tGLO-REA: GLOBAL_MULTIYEAR_PHY_001_030 (Reanalysis)\n\u2022\tIBI-REA: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tMED-REA: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\n\u2022\tMED-REA: MEDSEA_MULTIYEAR_PHY_006_004MEDSEA_MULTIYEAR_PHY_006_004 (Reanalysis)\nThe time series comprises the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with the sign reversed (red dashed line) to indicate the threshold of anomaly values that would entail a reversal of the current transport. Additionally, the analysis of trends in the time series at the 95% confidence interval is included in the bottom right corner of each diagram.\nDetails on the product are given in the corresponding Product User Manual (de Pascual-Collar et al., 2024a) and QUality Information Document (de Pascual-Collar et al., 2024b) as well as the CMEMS Ocean State Report: de Pascual-Collar et al., 2024c.\n\nCONTEXT\n\nThe IBI area is a very complex region characterized by a remarkable variety of ocean currents. Among them, Podemos destacar las que se originan como resultado del closure of the North Atlantic Drift (Mason et al., 2011; Holliday et al., 2008; Peliz et al., 2007; Bower et al., 2002; Knoll et al., 2002; P\u00e9rez et al., 2001; Jia, 2000), las corrientes subsuperficiales que fluyen hacia el norte a lo largo del talud continental (de Pascual-Collar et al., 2019; Pascual et al., 2018; Machin et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Maz\u00e9 et al., 1997; White & Bowyer, 1997). Y las corrientes de intercambio que se producen en el Estrecho de Gibraltar y el Mar de Alboran (Sotillo et al., 2016; Font et al., 1998; Benzohra and Millot, 1995; Tintor\u00e9 et al., 1988).\nThe variability of ocean currents in the IBI domain is relevant to the global thermohaline circulation and other climatic and environmental issues. For example, as discussed by Fasullo and Trenberth (2008), subtropical gyres play a crucial role in the meridional energy balance. The poleward salt transport of Mediterranean water, driven by subsurface slope currents, has significant implications for salinity anomalies in the Rockall Trough and the Nordic Seas, as studied by Holliday (2003), Holliday et al. (2008), and Bozec et al. (2011). The Algerian current serves as the sole pathway for Atlantic Water to reach the Western Mediterranean.\n\nCMEMS KEY FINDINGS\n\nThe volume transport time series show periods in which the different monitored currents exhibited significantly high or low variability. In this regard, we can mention the periods 1997-1998 and 2014-2015 for the RTE current, the period 2012-2014 in the N48 section, the years 2006 and 2017 for the ALG current, the year 2021 for the AC current, and the period 2009-2012 for the CC current.\nAdditionally, periods are detected where the anomalies are large enough (in absolute value) to indicate a reversal of the net transport of the current. This is the case for the years 1999, 2003, and 2012-2014 in the N48 section (with a net transport towards the north), the year 2017 in the ALC current (with net transport towards the west), and the year 2010 in the CC current (with net transport towards the north).\nThe trend analysis of the monitored currents does not detect any significant trends over the analyzed period (1993-2022). However, the confidence interval for the trend in the RTE section is on the verge of rejecting the hypothesis of no trend.\n\nFigure caption\n\nAnnual anomalies of cross-section volume transport in monitoring sections RTE, N48, AC, ALC, and CC. Time series computed and averaged from different Copernicus Marine products for each window (see section Definition) providing a multi-product result. The blue line represents the ensemble mean, and shaded grey areas represent the standard deviation of the ensemble. Red dashed lines depict the velocity value at which the direction of the current reverses. This aligns with the average transport value (with sign reversed) and the point where absolute transport becomes zero. The analysis of trends (at 95% confidence interval) computed in the period 1993\u20132021 is included (bottom right box). Trend lines (gray dashed line) are only included in the figures when a significant trend is obtained.\n\nDOI (product):\nhttps://doi.org/10.48670/mds-00351\n\nReferences:\n\n* Benzohra, M., Millot, C.: Characteristics and circulation of the surface and intermediate water masses off Algeria. Deep Sea Research Part I: Oceanographic Research Papers, 42(10), 1803-1830, https://doi.org/10.1016/0967-0637(95)00043-6, 1995.\n* Bower, A. S., Le Cann, B., Rossby, T., Zenk, T., Gould, J., Speer, K., Richardson, P. L., Prater, M. D., Zhang, H.-M.: Directly measured mid-depth circulation in the northeastern North Atlantic Ocean: Nature, 419, 6907, 603\u2013607, https://doi.org/10.1038/nature01078, 2002.\n* Bozec, A., Lozier, M. S., Chasignet, E. P., Halliwel, G. R.: On the variability of the Mediterranean Outflow Water in the North Atlantic from 1948 to 2006, J. Geophys. Res.-Oceans, 116, C09033, https://doi.org/10.1029/2011JC007191, 2011.\n* Fasullo, J. T., Trenberth, K. E.: The annual cycle of the energy budget. Part II: Meridional structures and poleward transports. Journal of Climate, 21(10), 2313-2325, https://doi.org/10.1175/2007JCLI1936.1, 2008.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Friocourt Y, Levier B, Speich S, Blanke B, Drijfhout SS. A regional numerical ocean model of the circulation in the Bay of Biscay, J. Geophys. Res.,112:C09008, https://doi.org/10.1029/2006JC003935, 2007.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Holliday, N. P., Hughes, S. L., Bacon, S., Beszczynska-M\u00f6ller, A., Hansen, B., Lav\u00edn, A., Loeng, H., Mork, K. A., \u00d8sterhus, S., Sherwin, T., Walczowski, W.: Reversal of the 1960s to 1990s freshening trend in the northeast North Atlantic and Nordic Seas, Geophys. Res. Lett., 35, L03614, https://doi.org/10.1029/2007GL032675, 2008.\n* Holliday, N. P.: Air\u2010sea interactionand circulation changes in the north- east Atlantic, J. Geophys. Res., 108(C8), 3259, https://doi.org/10.1029/2002JC001344, 2003.\n* Jia, Y.: Formation of an Azores Current Due to Mediterranean Overflow in a Modeling Study of the North Atlantic. J. Phys. Oceanogr., 30, 9, 2342\u20132358, https://doi.org/10.1175/1520-0485(2000)030<2342:FOAACD>2.0.CO;2, 2000.\n* Knoll, M., Hern\u00e1ndez-Guerra, A., Lenz, B., L\u00f3pez Laatzen, F., Mach\u0131\u0301n, F., M\u00fcller, T. J., Siedler, G.: The Eastern Boundary Current system between the Canary Islands and the African Coast, Deep-Sea Research. 49-17, 3427-3440, https://doi.org/10.1016/S0967-0645(02)00105-4, 2002.\n* Lozier, M. S., Stewart, N. M.: On the temporally varying penetration ofMediterranean overflowwaters and eastward penetration ofLabrador Sea Water, J. Phys. Oceanogr., 38,2097\u20132103, https://doi.org/10.1175/2008JPO3908.1, 2008.\n* Mach\u00edn, F., Pelegr\u00ed, J. L., Fraile-Nuez, E., V\u00e9lez-Belch\u00ed, P., L\u00f3pez-Laatzen, F., Hern\u00e1ndez-Guerra, A., Seasonal Flow Reversals of Intermediate Waters in the Canary Current System East of the Canary Islands. J. Phys. Oceanogr, 40, 1902\u20131909, https://doi.org/10.1175/2010JPO4320.1, 2010.\n* Mason, E., Colas, F., Molemaker, J., Shchepetkin, A. F., Troupin, C., McWilliams, J. C., Sangra, P.: Seasonal variability of the Canary Current: A numerical study. Journal of Geophysical Research: Oceans, 116(C6), https://doi.org/10.1029/2010JC006665, 2011.\n* Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997. Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997.\n* Pascual, A., Levier ,B., Sotillo, M., Verbrugge, N., Aznar, R., Le Cann, B.: Characterization of Mediterranean Outflow Water in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann et al. (2018) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product Quality Information Document for Global Reanalysis Products, OMI_CURRENTS_VOLTRANS_section_integrated_anomalies, Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-IBI-OMI-QUID-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024a.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product User Manual for OMI_CURRENTS_VOLTRANS_section_integrated_anomalies. Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-IBI-OMI-PUM-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024b.\n* de Pascual-Collar, A., Aznar, R., Levier, B., Garc\u00eda-Sotillo, M.: Monitoring Main Ocean Currents of the IBI Region, in: 8th edition of the Copernicus Ocean State Report (OSR8), accepted pending of publication, 2004c.\n* de Pascual-Collar, A., Sotillo, M. G., Levier, B., Aznar, R., Lorente, P., Amo-Baladr\u00f3n, A., \u00c1lvarez-Fanjul E.: Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019, 2019.\n* Peliz, A., Dubert, J., Marchesiello, P., Teles\u2010Machado, A.: Surface circulation in the Gulf of Cadiz: Model and mean flow structure. Journal of Geophysical Research: Oceans, 112, C11, https://doi.org/10.1029/2007JC004159, 2007.\n* Perez, F. F., Castro, C. G., \u00c1lvarez-Salgado, X. A., R\u00edos, A. F.: Coupling between the Iberian basin-scale circulation and the Portugal boundary current system: a chemical study, Deep-Sea Research. I 48,1519 -1533, https://doi.org/10.1016/S0967-0637(00)00101-1, 2001.\n* Sotillo, M. G., Amo-Baladr\u00f3n, A., Padorno, E., Garcia-Ladona, E., Orfila, A., Rodr\u00edguez-Rubio, P., Conti, D., Jim\u00e9nez Madrid, J. A., de los Santos, F. J., Alvarez Fanjul E.: How is the surface Atlantic water inflow through the Gibraltar Strait forecasted? A lagrangian validation of operational oceanographic services in the Alboran Sea and the Western Mediterranean, Deep-Sea Research. 133, 100-117, https://doi.org/10.1016/j.dsr2.2016.05.020, 2016.\n* Tintore, J., La Violette, P. E., Blade, I., Cruzado, A.: A study of an intense density front in the eastern Alboran Sea: the Almeria\u2013Oran front. Journal of Physical Oceanography, 18, 10, 1384-1397, https://doi.org/10.1175/1520-0485(1988)018%3C1384:ASOAID%3E2.0.CO;2, 1988.\n* White, M., Bowyer, P.: The shelf-edge current north-west of Ireland. Annales Geophysicae 15, 1076\u20131083. https://doi.org/10.1007/s00585-997-1076-0, 1997.\n", "doi": "10.48670/mds-00351", "instrument": null, "keywords": "coastal-marine-environment,cur-armor,cur-glo-myp,cur-ibi-myp,cur-mean,cur-med-myp,cur-nws-myp,cur-std,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-voltrans-ibi-section-integrated-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Volume Transport Anomaly in Selected Vertical Sections"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe Ocean Freshwater Content (OFC) is calculated according to Boyer et al. (2007)\nOFC = \u03c1(Sref, Tref, p) / \u03c1(0, Tref, p ) \u00b7 ( Sref - S) / Sref\nwhere S(x, y, z, t) and Sref (x, y, z) are actual salinity and reference salinity, respectively, and x,y,z,t are zonal, meridional, vertical and temporal coordinates, respectively. The density, \u03c1, is calculated according to the TEOS10 (IOC et al., 2010). The key issue of OFC calculations lies in how the reference salinity is defined. The climatological range of salinity in the Baltic Sea varies from the freshwater conditions in the northern and eastern parts to the oceanic water conditions in the Kattegat. We follow the Boyer et al. (2007) formulation and calculate the climatological OFC from the three-dimensional temperature (Tref) and salinity (Sref) fields averaged over the period of 1993\u20132014.\nThe method for calculating the ocean freshwater content anomaly is based on the daily mean sea water salinity fields (S) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total freshwater content anomaly is determined using the following formula:\nOFC(t) = \u222dV OFC(x, y, z, t) dx dy dz\nThe vertical integral is computed using the static cell vertical thicknesses (dz) sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial integration is performed over the Baltic Sea spatial domain, defined as the region between 9\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N using product grid definition in cmems_mod_bal_phy_my_static. \nWe evaluate the uncertainty from the mean standard deviation of monthly mean OFC. The shaded area in the figure corresponds to the annual standard deviation of monthly mean OFC. \nLinear trend (km3y-1) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\nCONTEXT\nClimate warming has resulted in the intensification of the global hydrological cycle but not necessarily on the regional scale (Pratap and Markonis, 2022). The increase of net precipitation over land and sea areas, decrease of ice cover, and increase of river runoff are the main components of the global hydrological cycle that increase freshwater content in the ocean (Boyer et al., 2007) and decrease ocean salinity.\nThe Baltic Sea is one of the marginal seas where water salinity and OFC are strongly influenced by the water exchange with the North Sea. The Major Baltic Inflows (MBIs) are the most voluminous event-type sources of saline water to the Baltic Sea (Mohrholz, 2018). The frequency and intensity of the MBIs and other large volume inflows have no long-term trends but do have a multidecadal variability of about 30 years (Mohrholz, 2018; Lehmann and Post, 2015; Lehmann et al., 2017; Radtke et al., 2020). Smaller barotropic and baroclinically driven inflows transport saline water into the halocline or below it, depending on the density of the inflow water (Reissmann et al., 2009). \n\nKEY FINDINGS\n\nThe Baltic Sea's ocean freshwater content is exhibiting a declining trend of -37\u00b18.8 km\u00b3/year, along with decadal fluctuations as also noted by Lehmann et al. (2022). Elevated freshwater levels were recorded prior to the Major Baltic Inflows of 1993, 2002, and 2013, which subsequently led to a swift decrease in freshwater content. The lowest ocean freshwater content was recorded in 2019. Over the past four years, the freshwater content anomaly has remained comparatively stable. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00347\n\nReferences:\n\n* Boyer, T., Levitus, S., Antonov, J., Locarnini, R., Mishonov, A., Garcia, H., Josey, S.A., 2007. Changes in freshwater content in the North Atlantic Ocean 1955\u20132006. Geophysical Research Letters, 34(16), L16603. Doi: 10.1029/2007GL030126\n* IOC, SCOR and IAPSO, 2010: The international thermodynamic equation of seawater - 2010: Calculation and use of thermodynamic properties. Intergovernmental Oceanographic Commission, Manuals and Guides No. 56, UNESCO (English), 196 pp. Available from http://www.TEOS-10.org (11.10.2021).\n* Lehmann, A., Post, P., 2015. Variability of atmospheric circulation patterns associated with large volume changes of the Baltic Sea. Advances in Science and Research, 12, 219\u2013225, doi:10.5194/asr-12-219-2015\n* Lehmann, A., H\u00f6flich, K., Post, P., Myrberg, K., 2017. Pathways of deep cyclones associated with large volume changes (LVCs) and major Baltic inflows (MBIs). Journal of Marine Systems, 167, pp.11-18. doi:10.1016/j.jmarsys.2016.10.014\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz, V., 2018. Major Baltic inflow statistics\u2013revised. Frontiers in Marine Science, 5, p.384. doi:10.3389/fmars.2018.00384\n* Pratap, S., Markonis, Y., 2022. The response of the hydrological cycle to temperature changes in recent and distant climatic history, Progress in Earth and Planetary Science 9(1),30. doi:10.1186/s40645-022-00489-0\n* Radtke, H., Brunnabend, S.-E., Gr\u00e4we, U., Meier, H. E. M., 2020. Investigating interdecadal salinity changes in the Baltic Sea in a 1850\u20132008 hindcast simulation, Climate of the Past, 16, 1617\u20131642, doi:10.5194/cp-16-1617-2020\n* Reissmann, J. H., Burchard, H., Feistel,R., Hagen, E., Lass, H. U., Mohrholz, V., Nausch, G., Umlauf, L., Wiecczorek, G., 2009. Vertical mixing in the Baltic Sea and consequences for eutrophication a review, Progress in Oceanography, 82, 47\u201380. doi:10.1016/j.pocean.2007.10.004\n", "doi": "10.48670/mds-00347", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ofc-balrean,ofc-balrean-lower-rmsd,ofc-balrean-upper-rmsd,omi-climate-ofc-baltic-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Freshwater Content Anomaly (0-300m) from Reanalysis"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"abstract": "DEFINITION\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 300 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_m-T_clim )dz [1]\nwith a reference density = 1020 kg m-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009; Lima et al., 2020); T_m corresponds to the monthly average temperature and T_clim is the climatological temperature of the corresponding month that varies according to each individual product.\nTime series of monthly mean values area averaged ocean heat content is provided for the Black Sea (40.86\u00b0N, 46.8\u00b0N; 27.32\u00b0E, 41.96\u00b0E) and is evaluated in areas where the topography is deeper than 300m. The Azov and Marmara Seas are not considered.\nThe quality evaluation of OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). Three global products and one regional (Black Sea) product have been used to build an ensemble mean, and its associated ensemble spread. Details on the products are delivered in the PUM and QUID of this OMI.\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nSeveral studies discuss a warming in the Black Sea using either observations or model results (Akpinar et al., 2017; Stanev et al. 2019; Lima et al. 2020). Using satellite sea surface temperature observations (SST), Degtyarev (2000) detected a positive temperature trend of 0.016 \u00baC years-1 in the 50-100 m layer from 1985 to 1997. From Argo floats Stanev et al. (2019) found a warming trend in the cold intermediate layer (CIL; at approximately 25 \u2013 70 m) of about 0.05 oC year-1 in recent years. The warming signal was also present in ocean heat content analyses conducted by Lima et al. (2020). Their results from the Black Sea regional reanalysis showed an increase rate of 0.880\u00b10.181 W m-2 in the upper layers (0 \u2013 200 m), which has been reflected in the disappearance of Black Sea cold intermediate layer in recent years. The newest version of reanalysis also presents a warming of 0.814\u00b10.045 W m-2 in 0 \u2013 200 m (Lima et al. (2021). This warming has been reflected in a more incidence of marine heat waves in the Black Sea over the past few years (Mohammed et al. 2022).\n\nCMEMS KEY FINDINGS\n\nTime series of ocean heat content anomalies present a significant interannual variability, altering between cool and warm events. This important characteristic becomes evident over the years 2012 to 2015: a minimum of ocean heat content anomaly is registered close to \u2013 2.00 x 108 J m-2 in 2012, followed by positive values around 2.00 x 108 J m-2 in 2013 and above 2.0 x 108 J m-2 most of time in 2014 and 2015. Since 2005 the Black Sea experienced an increase in ocean heat content (0-300 m), and record OHC values are noticed in 2020. The Black Sea is warming at a rate of 0.995\u00b10.084 W m-2, which is higher than the global average warming rate.\nThe increase in ocean heat content weakens the CIL, whereas its decreasing favours the CIL restoration (Akpinar et al., 2017). The years 2012 and 2017 exhibited a more evident warming interruption that induced a replenishment of the CIL (Lima et al. 2021).\n\nFigure caption\n\nTime series of the ensemble mean and ensemble spread (shaded area) of the monthly Black Sea averaged ocean heat content anomalies integrated over the 0-300m depth layer (J m\u20132) during Jan 2005 \u2013 December 2020. The monthly ocean heat content anomalies are defined as the deviation from the climatological ocean heat content mean (1993\u20132014) of each corresponding month. Mean trend values are also reported at the bottom right corner. The ensemble is based on different data products, i.e. Black Sea Reanalysis, global ocean reanalysis GLORYS12V1; global observational based products CORA5.2, ARMOR3D. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\nDOI (product): \n\u00a0https://doi.org/10.48670/moi-00306\n\nReferences:\n\n* Akpinar, A., Fach, B. A., Oguz, T., 2017: Observing the subsurface thermal signature of the Black Sea cold intermediate layer with Argo profiling floats. Deep Sea Res. I Oceanogr. Res. Papers 124, 140\u2013152. doi: 10.1016/j.dsr.2017.04.002.\n* Lima, L., Peneva, E., Ciliberti, S., Masina, S., Lemieux, B., Storto, A., Chtirkova, B., 2020: Ocean heat content in the Black Sea. In: Copernicus marine service Ocean State Report, issue 4, Journal of Operational Oceanography, 13:Sup1, s41\u2013s47, doi: 10.1080/1755876X.2020.1785097.\n* Lima L., Ciliberti S. A., Aydo\u011fdu A., Masina S., Escudier R., Cipollone A., Azevedo D., Causio S., Peneva E., Lecci R., Clementi E., Jansen E., Ilicak M., Cret\u00ec S., Stefanizzi L., Palermo F., Coppini G., 2021: Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis, Frontier in Marine Science, 8:710973, doi: 10.3389/fmars.2021.710973.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841, DOI: 10.1007/s00382-015-2728-5.\n* Stanev, E. V., Peneva, E., and Chtirkova, B. 2019: Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans, 124, 4803\u20134819, doi: 10.1029/2019JC015076.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:Sup1, s1-s142, doi: 10.1080/1755876X.2018.1489208.\n* Degtyarev, A. K., 2000: Estimation of temperature increase of the Black Sea active layer during the period 1985\u2013 1997, Meteorilogiya i Gidrologiya, 6, 72\u2013 76 (in Russian).\n", "doi": "10.48670/moi-00306", "instrument": null, "keywords": "black-sea,coastal-marine-environment,in-situ-observation,integral-wrt-depth-of-sea-water-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-blksea-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Ocean Heat Content Anomaly (0-300m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"abstract": "DEFINITION\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-20210) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 2000 m depth:\n \n With a reference density of \u03c10 = 1030 kgm-3 and a specific heat capacity of cp = 3980 J/kg\u00b0C (e.g. von Schuckmann et al., 2009)\nAveraged time series for ocean heat content and their error bars are calculated for the Iberia-Biscay-Ireland region (26\u00b0N, 56\u00b0N; 19\u00b0W, 5\u00b0E).\nThis OMI is computed using IBI-MYP, GLO-MYP reanalysis and CORA, ARMOR data from observations which provide temperatures. Where the CMEMS product for each acronym is:\n\u2022\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tGLO-MYP: GLOBAL_REANALYSIS_PHY_001_031 (Reanalysis)\n\u2022\tCORA: INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b (Observations)\n\u2022\tARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\nThe figure comprises ensemble mean (blue line) and the ensemble spread (grey shaded). Details on the product are given in the corresponding PUM for this OMI as well as the CMEMS Ocean State Report: von Schuckmann et al., 2016; von Schuckmann et al., 2018.\n\nCONTEXT\n\nChange in OHC is a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and can impact marine ecosystems and human livelihoods (IPCC, 2019). Additionally, OHC is one of the six Global Climate Indicators recommended by the World Meterological Organisation (WMO, 2017). \nIn the last decades, the upper North Atlantic Ocean experienced a reversal of climatic trends for temperature and salinity. While the period 1990-2004 is characterized by decadal-scale ocean warming, the period 2005-2014 shows a substantial cooling and freshening. Such variations are discussed to be linked to ocean internal dynamics, and air-sea interactions (Fox-Kemper et al., 2021; Collins et al., 2019; Robson et al 2016). Together with changes linked to the connectivity between the North Atlantic Ocean and the Mediterranean Sea (Masina et al., 2022), these variations affect the temporal evolution of regional ocean heat content in the IBI region.\nRecent studies (de Pascual-Collar et al., 2023) highlight the key role that subsurface water masses play in the OHC trends in the IBI region. These studies conclude that the vertically integrated trend is the result of different trends (both positive and negative) contributing at different layers. Therefore, the lack of representativeness of the OHC trends in the surface-intermediate waters (from 0 to 1000 m) causes the trends in intermediate and deep waters (from 1000 m to 2000 m) to be masked when they are calculated by integrating the upper layers of the ocean (from surface down to 2000 m).\n\nCMEMS KEY FINDINGS\n\nThe ensemble mean OHC anomaly time series over the Iberia-Biscay-Ireland region are dominated by strong year-to-year variations, and an ocean warming trend of 0.41\u00b10.4 W/m2 is barely significant.\n\nFigure caption\n\nTime series of annual mean area averaged ocean heat content in the Iberia-Biscay-Ireland region (basin wide) and integrated over the 0-2000m depth layer during 1993-2022: ensemble mean (blue line) and ensemble spread (shaded area). The ensemble mean is based on different data products i.e., the IBI Reanalysis, global ocean reanalysis, and the global observational based products CORA, and ARMOR3D. Trend of ensemble mean (dashed line and bottom-right box) with 95% confidence interval computed in the period 1993-2022. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00316\n\nReferences:\n\n* Collins M., M. Sutherland, L. Bouwer, S.-M. Cheong, T. Fr\u00f6licher, H. Jacot Des Combes, M. Koll Roxy, I. Losada, K. McInnes, B. Ratter, E. Rivera-Arriaga, R.D. Susanto, D. Swingedouw, and L. Tibig, 2019: Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 589\u2013655. https://doi.org/10.1017/9781009157964.008.\n* Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. A\u00f0algeirsd\u00f3ttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sall\u00e9e, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211\u20131362, doi: 10.1017/9781009157896.011.\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Masina, S., Pinardi, N., Cipollone, A., Banerjee, D. S., Lyubartsev, V., von Schuckmann, K., Jackson, L., Escudier, R., Clementi, E., Aydogdu, A. and Iovino D., (2022). The Atlantic Meridional Overturning Circulation forcing the mean se level in the Mediterranean Sea through the Gibraltar transport. In: Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography,15:sup1, s119\u2013s126; DOI: 10.1080/1755876X.2022.2095169\n* Potter, R. A., and Lozier, M. S. 2004: On the warming and salinification of the Mediterranean outflow waters in the North Atlantic, Geophys. Res. Lett., 31, 1\u20134, doi:10.1029/2003GL018161.\n* Robson, J., Ortega, P., Sutton, R., 2016: A reversal of climatic trends in the North Atlantic since 2005. Nature Geosci 9, 513\u2013517. https://doi.org/10.1038/ngeo2727.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/mds-00316", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,integral-wrt-depth-of-sea-water-potential-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-ibi-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Ocean Heat Content Anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"abstract": "DEFINITION\n\nOcean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:\n\u00afS=1/V \u222bV S dV\nTime series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.\n\nCONTEXT\n\nThe freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link \u2028to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.\nThe OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the \u201cmulti-product\u201d approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.\nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24\u00b0horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4\u00b0horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, \nGLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)\n\tTwo observation-based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI: https://doi.org/10.17882/46219, Szekely et al., 2022) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\nCMEMS KEY FINDINGS\n\nThe Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2019 at rate of 5.610-3 \u00b13.510-4 psu yr-1. \nThe overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993\u20132019 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.\n\nFigure caption\n\nTime series of annual mean volume ocean salt content in the Mediterranean Sea (basin wide), integrated over the 0-300m depth layer during 1993-2019 (or longer according to data availability) including ensemble mean and ensemble spread (shaded area). The ensemble mean and associated ensemble spread are based on different data products, i.e., Mediterranean Sea Reanalysis (MED-REA), global ocean reanalysis (GLORYS, C-GLORS, ORAS5, and FOAM) and global observational based products (CORA and ARMOR3D). Details on the products are given in the corresponding PUM and QUID for this OMI.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00325\n\nReferences:\n\n* Aydogdu, A., Miraglio, P., Escudier, R., Clementi, E., Masina, S.: The dynamical role of upper layer salinity in the Mediterranean Sea, State of the Planet, accepted, 2023.\n* Desportes, C., Garric, G., R\u00e9gnier, C., Dr\u00e9villon, M., Parent, L., Drillet, Y., Masina, S., Storto, A., Mirouze, I., Cipollone, A., Zuo, H., Balmaseda, M., Peterson, D., Wood, R., Jackson, L., Mulet, S., Grenier, E., and Gounou, A.: EU Copernicus Marine Service Quality Information Document for the Global Ocean Ensemble Physics Reanalysis, GLOBAL_REANALYSIS_PHY_001_031, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-031.pdf (last access: 3 May 2023), 2022.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020).\n* Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Grenier, E., Verbrugge, N., Mulet, S., and Guinehut, S.: EU Copernicus Marine Service Quality Information Document for the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-MOB-QUID-015-012.pdf (last access: 3 May 2023), 2021.\n* Szekely, T.: EU Copernicus Marine Service Quality Information Document for the Global Ocean-Delayed Mode gridded CORA \u2013 In-situ Observations objective analysis in Delayed Mode, INSITU_GLO_PHY_TS_OA_MY_013_052, issue 1.2, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-INS-QUID-013-052.pdf (last access: 4 April 2023), 2022.\n", "doi": "10.48670/mds-00325", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,integral-wrt-depth-of-sea-water-salinity-expressed-as-salt-content,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-osc-medsea-volume-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Salt Content (0-300m)"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Baltic Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nThe Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gr\u00e4we et al., 2019).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Baltic Sea rises at a rate of 4.1 \uf0b1 0.8 mm/year with an acceleration of 0.10 \uf0b1\uf0200.07 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00202\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Gr\u00e4we, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Clim., 32, 3089\u20133108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00202", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-baltic-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Black Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \nIn the Black Sea, major drivers of change have been attributed to anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Black Sea rises at a rate of 1.00 \u00b1 0.80 mm/year with an acceleration of -0.47 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00215\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Volkov, D. L. and Landerer, F. W.: Internal and external forcing of sea level variability in the Black Sea, Clim. Dyn., 45, 2633\u20132646, https://doi.org/10.1007/s00382-015-2498-0, 2015.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00215", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-blksea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Northeast Atlantic Ocean and adjacent seas Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nUncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, sea level variations are influenced by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016) and the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. This region encompasses the Mediterranean, IBI, North-West shelf and Baltic regions with different sea level dynamics detailed in the regional indicators.\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Northeast Atlantic Ocean and adjacent seas area rises at a rate of 3.2 \u00b1 0.80 mm/year with an acceleration of 0.10 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00335\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/mds-00335", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,oceanographic-geographical-features,omi-climate-sl-europe-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European Seas Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Global Ocean weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and global GIA correction of -0.3mm/yr (common global GIA correction, see Spada, 2017). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty of 0.3 mm/yr is provided at 90% confidence interval using altimeter error budget (Gu\u00e9rou et al., 2022). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, global mean sea level rises at a rate of 3.4 \u00b1 0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada, 2017) to consider the ongoing movement of land. The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Ni\u00f1o Southern Oscillation (Cazenave and Cozannet, 2014).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00237\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Oppenheimer, M., Glavovic, B. C., Hinkel, J., Van de Wal, R., Magnan, A. K., Abd-Elgaward, A., Cai, R., Cifuentes Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities \u2014 Special Report on the Ocean and Cryosphere in a Changing Climate: Chapter 4, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Gu\u00e9rou, A., Meyssignac, B., Prandi, P., Ablain, M., Ribes, A., and Bignalet-Cazalet, F.: Current observed global mean sea level rise and acceleration estimated from satellite altimetry and the associated uncertainty, EGUsphere, 1\u201343, https://doi.org/10.5194/egusphere-2022-330, 2022.\n* Cazenave, A. and Cozannet, G. L.: Sea level rise and its coastal impacts, Earths Future, 2, 15\u201334, https://doi.org/10.1002/2013EF000188, 2014.\n", "doi": "10.48670/moi-00237", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"abstract": "DEFINITION\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. The product is distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). At each grid point, the trends/accelerations are estimated on the time series corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional GIA correction (GIA map of a 27 ensemble model following Spada et Melini, 2019) and adjusted from annual and semi-annual signals. Regional uncertainties on the trends estimates can be found in Prandi et al., 2021.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and regional sea level change is also influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2019, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \n\nKEY FINDINGS\n\nThe altimeter sea level trends over the [1993/01/01, 2023/07/06] period exhibit large-scale variations with trends up to +10 mm/yr in regions such as the western tropical Pacific Ocean. In this area, trends are mainly of thermosteric origin (Legeais et al., 2018; Meyssignac et al., 2017) in response to increased easterly winds during the last two decades associated with the decreasing Interdecadal Pacific Oscillation (IPO)/Pacific Decadal Oscillation (e.g., McGregor et al., 2012; Merrifield et al., 2012; Palanisamy et al., 2015; Rietbroek et al., 2016).\nPrandi et al. (2021) have estimated a regional altimeter sea level error budget from which they determine a regional error variance-covariance matrix and they provide uncertainties of the regional sea level trends. Over 1993-2019, the averaged local sea level trend uncertainty is around 0.83 mm/yr with local values ranging from 0.78 to 1.22 mm/yr. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00238\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nature Clim Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press., 2019.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., von Schuckmann, K., Melet, A., Storto, A., and Meyssignac, B.: Sea Level, Journal of Operational Oceanography, 11, s13\u2013s16, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Journal of Operational Oceanography, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* McGregor, S., Gupta, A. S., and England, M. H.: Constraining Wind Stress Products with Sea Surface Height Observations and Implications for Pacific Ocean Sea Level Trend Attribution, 25, 8164\u20138176, https://doi.org/10.1175/JCLI-D-12-00105.1, 2012.\n* Merrifield, M. A., Thompson, P. R., and Lander, M.: Multidecadal sea level anomalies and trends in the western tropical Pacific, 39, https://doi.org/10.1029/2012GL052032, 2012.\n* Meyssignac, B., Piecuch, C. G., Merchant, C. J., Racault, M.-F., Palanisamy, H., MacIntosh, C., Sathyendranath, S., and Brewin, R.: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011, in: Integrative Study of the Mean Sea Level and Its Components, edited by: Cazenave, A., Champollion, N., Paul, F., and Benveniste, J., Springer International Publishing, Cham, 191\u2013219, https://doi.org/10.1007/978-3-319-56490-6_9, 2017.\n* Palanisamy, H., Cazenave, A., Delcroix, T., and Meyssignac, B.: Spatial trend patterns in the Pacific Ocean sea level during the altimetry era: the contribution of thermocline depth change and internal climate variability, Ocean Dynamics, 65, 341\u2013356, https://doi.org/10.1007/s10236-014-0805-7, 2015.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Rietbroek, R., Brunnabend, S.-E., Kusche, J., Schr\u00f6ter, J., and Dahle, C.: Revisiting the contemporary sea-level budget on global and regional scales, 113, 1504\u20131509, https://doi.org/10.1073/pnas.1519132113, 2016.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat Commun, 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00238", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-regional-trends,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level trend map from Observations Reprocessing"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Irish-Biscay-Iberian (IBI) Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT \n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the IBI area rises at a rate of 4.00 \uf0b1 0.80 mm/year with an acceleration of 0.14 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00252\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00252", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-ibi-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator of regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Mediterranean Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nBeside a clear long-term trend, the regional mean sea level variation in the Mediterranean Sea shows an important interannual variability, with a high trend observed between 1993 and 1999 (nearly 8.4 mm/y) and relatively lower values afterward (nearly 2.4 mm/y between 2000 and 2022). This variability is associated with a variation of the different forcing. Steric effect has been the most important forcing before 1999 (Fenoglio-Marc, 2002; Vigo et al., 2005). Important change of the deep-water formation site also occurred in the 90\u2019s. Their influence contributed to change the temperature and salinity property of the intermediate and deep water masses. These changes in the water masses and distribution is also associated with sea surface circulation changes, as the one observed in the Ionian Sea in 1997-1998 (e.g. Ga\u010di\u0107 et al., 2011), under the influence of the North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases (Incarbona et al., 2016). These circulation changes may also impact the sea level trend in the basin (Vigo et al., 2005). In 2010-2011, high regional mean sea level has been related to enhanced water mass exchange at Gibraltar, under the influence of wind forcing during the negative phase of NAO (Landerer and Volkov, 2013).The relatively high contribution of both sterodynamic (due to steric and circulation changes) and gravitational, rotational, and deformation (due to mass and water storage changes) after 2000 compared to the [1960, 1989] period is also underlined by (Calafat et al., 2022).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Mediterranean Sea rises at a rate of 2.5 \u00b1 0.8 mm/year with an acceleration of 0.01 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00264\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Ga\u010di\u0107, M., Civitarese, G., Eusebi Borzelli, G. L., Kova\u010devi\u0107, V., Poulain, P.-M., Theocharis, A., Menna, M., Catucci, A., and Zarokanellos, N.: On the relationship between the decadal oscillations of the northern Ionian Sea and the salinity distributions in the eastern Mediterranean, J. Geophys. Res. Oceans, 116, https://doi.org/10.1029/2011JC007280, 2011.\n* Incarbona, A., Martrat, B., Mortyn, P. G., Sprovieri, M., Ziveri, P., Gogou, A., Jord\u00e0, G., Xoplaki, E., Luterbacher, J., Langone, L., Marino, G., Rodr\u00edguez-Sanz, L., Triantaphyllou, M., Di Stefano, E., Grimalt, J. O., Tranchida, G., Sprovieri, R., and Mazzola, S.: Mediterranean circulation perturbations over the last five centuries: Relevance to past Eastern Mediterranean Transient-type events, Sci. Rep., 6, 29623, https://doi.org/10.1038/srep29623, 2016.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Landerer, F. W. and Volkov, D. L.: The anatomy of recent large sea level fluctuations in the Mediterranean Sea, Geophys. Res. Lett., 40, 553\u2013557, https://doi.org/10.1002/grl.50140, 2013.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J. Geophys. Res. Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n", "doi": "10.48670/moi-00264", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-medsea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the North-West Shelf Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, the time series shows decadal variations. As observed over the global ocean, the main actors of the long-term sea level trend are associated with anthropogenic global/regional warming (IPCC WGII, 2021). Decadal variability is mainly linked to the Strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. They also underscored the influence of the inverse barometer forcing in some coastal regions.\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the NWS area rises at a rate of 3.2 \uf0b1 0.8 mm/year with an acceleration of 0.09 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nFigure caption\n\nRegional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the North-West Shelf region, derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) altimeter sea level gridded products distributed by the Copernicus Climate Change Service (C3S), and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off), and the curve is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00271\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00271", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Atlantic Shelf Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2023, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product. \n\nCONTEXT\n\nSea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 oC when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\nCMEMS KEY FINDINGS\n\nThe basin-average trend of SST anomalies for Baltic Sea region amounts to 0.038\u00b10.004\u00b0C/year over the period 1982-2023 which corresponds to an average warming of 1.60\u00b0C. Adding the North Sea area, the average trend amounts to 0.029\u00b10.002\u00b0C/year over the same period, which corresponds to an average warming of 1.22\u00b0C for the entire region since 1982. \n\nFigure caption\n\nTime series of monthly mean (turquoise line) and annual mean (blue line) of sea surface temperature anomalies for January 1982 to December 2023, relative to the 1991-2020 mean, combined for the Baltic Sea and North Sea SST (OMI_CLIMATE_SST_BAL_area_averaged_anomalies). The data are based on the multi-year Baltic Sea L4 satellite SST reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00205\n\nReferences:\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Mar-ta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda La-fuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Mari-nova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n", "doi": "10.48670/moi-00205", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_trend": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_SST_BAL_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\nCMEMS KEY FINDINGS\n\nSST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1982 to December 2023. The average trend for the Baltic Sea domain (east of 9\u00b0E longitude) is 0.038\u00b0C/year, which represents an average warming of 1.60\u00b0C for the 1982-2023 period considered here. When the North Sea domain is included, the trend decreases to 0.029\u00b0C/year corresponding to an average warming of 1.22\u00b0C for the 1982-2023 period. \nFigure caption\n\nFigure caption\n\nCumulative trends in sea surface temperature anomalies calculated from 1982 to 2023 for the Baltic Sea (OMI_CLIMATE_SST_BAL_trend). Trend calculations are based on the multi-year Baltic Sea L4 SST satellite product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00206\n\nReferences:\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Marta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda Lafuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Marinova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00206", "instrument": null, "keywords": "baltic-sea,change-over-time-in-sea-surface-foundation-temperature,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe omi_climate_sst_ibi_area_averaged_anomalies product for 2022 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Iberia-Biscay-Irish Seas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v2.1.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberia-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019), Copernicus Climate Change Service (C3S) initiatives and Eumetsat data. Anomalies are computed against the 1993-2014 reference period.\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018).\n\nCMEMS KEY FINDINGS\n\nThe overall trend in the SST anomalies in this region is 0.013 \u00b10.001 \u00b0C/year over the period 1993-2022. \n\nFigure caption\n\nTime series of monthly mean and 12-month filtered sea surface temperature anomalies in the Iberia-Biscay-Irish Seas during the period 1993-2022. Anomalies are relative to the climatological period 1993-2014 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-IBI-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00256\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00256", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_trend": {"abstract": "DEFINITION\n\nThe omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\nCMEMS KEY FINDINGS\n\nThe overall trend in the SST anomalies in this region is 0.022 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\nFigure caption\nSea surface temperature trend over the period 1982-2023 in the Iberia-Biscay-Irish areas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00257\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389\n", "doi": "10.48670/moi-00257", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_anomaly product includes the 2D annual mean surface temperature anomaly for the Arctic Ocean for 2023. The annual mean surface temperature anomaly is calculated from the climatological mean estimated from 1991 to 2020, defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). The SST/IST Level 4 analysis that provides the input to the climatology and mean anomaly calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\nKEY FINDINGS\n\nThe area average anomaly of 2023 is 1.70\u00b11.08\u00b0C (\u00b1 means 1 standard deviation in this case). The majority of anomalies are positive and exceed 2\u00b0C for most areas of the Arctic Ocean, while the largest regional anomalies exceeded 6\u00b0C. Near zero and slightly negative anomalies are observed in some areas of the Barents, Norwegian and Greenland Sea and around the Bering Strait. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00353\n\nReferences:\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42\n", "doi": "10.48670/mds-00353", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-anomaly,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"abstract": "DEFINITION \n\nThe OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_area_averaged_anomalies product includes time series of monthly mean SST/IST anomalies over the period 1982-2023, relative to the 1991-2020 climatology (30 years), averaged for the Arctic Ocean. The SST/IST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. The climatological period used is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\nKEY FINDINGS\n\nThe basin-average trend of SST/IST anomalies for the Arctic Ocean region amounts to 0.104\u00b10.005 \u00b0C/year over the period 1982-2023 (42 years) which corresponds to an average warming of 4.37\u00b0C. The 2-d map of warming trends indicates these are highest for the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. The 2d map of Arctic anomalies for 2023 reveals regional peak warming exceeding 6\u00b0C.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00323\n\nReferences:\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00323", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-area-averaged-anomalies,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly time series based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_sst_ist_ARCTIC_sst_ist_trend product includes the cumulative/net trend in combined sea and ice surface temperature anomalies for the Arctic Ocean from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST/IST Level 4 analysis that provides the input to the trend calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude.\n\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the reference climatology defined over the period 1991-2020, according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate), using daily level 4 SST/IST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al., 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\nKEY FINDINGS\n\nSST/IST trends were calculated for the Arctic Ocean over the period January 1982 to December 2023. The cumulative trends are upwards of 2\u00b0C for the greatest part of the Arctic Ocean, with the largest trends occur in the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. Zero to slightly negative trends are found at the North Atlantic part of the Arctic Ocean. The combined sea and sea ice surface temperature trend is 0.104+/-0.005\u00b0C/yr, i.e. an increase by around 4.37\u00b0C between 1982 and 2023. The 2d map of Arctic anomalies reveals regional peak warming exceeding 6\u00b0C.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00324\n\nReferences:\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00324", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-trend,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature 2D trend from climatology based on reprocessed observations"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe omi_climate_sst_northwestshelf_area_averaged_anomalies product for 2023 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the European North West Shelf Seas. The NORTHWESTSHELF SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish areas(SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST- NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1991-2020 reference period. \n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\nCMEMS KEY FINDINGS \n\nThe overall trend in the SST anomalies in this region is 0.024 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\nFigure caption\n\nTime series of monthly mean and 24-month filtered sea surface temperature anomalies in the European North West Shelf Seas during the period 1982-2023. Anomalies are relative to the climatological period 1991-2020 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-NORTHWESTSHELF-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00275\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00275", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"abstract": "DEFINITION\n\nThe omi_climate_sst_northwestshelf_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the SST trend over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\nCONTEXT \n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\nCMEMS KEY FINDINGS \n\nOver the period 1982-2023, the European North West Shelf Seas mean Sea Surface Temperature (SST) increased at a rate of 0.024 \u00b1 0.002 \u00b0C/Year.\n\nFigure caption\n\nSea surface temperature trend over the period 1982-2023 in the European North West Shelf Seas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005;) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00276\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00276", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"abstract": "DEFINITION\n\nThe North Pacific Gyre Oscillation (NPGO) is a climate pattern introduced by Di Lorenzo et al. (2008) and further reported by Tranchant et al. (2019) in the CMEMS Ocean State Report #3. The NPGO is defined as the second dominant mode of variability of Sea Surface Height (SSH) anomaly and SST anomaly in the Northeast Pacific (25\u00b0\u2013 62\u00b0N, 180\u00b0\u2013 250\u00b0E). The spatial and temporal pattern of the NPGO has been deduced over the [1950-2004] period using an empirical orthogonal function (EOF) decomposition on sea level and sea surface temperature fields produced by the Regional Ocean Modeling System (ROMS) (Di Lorenzo et al., 2008; Shchepetkin and McWilliams, 2005). Afterward, the sea level spatial pattern of the NPGO is used/projected with satellite altimeter delayed-time sea level anomalies to calculate and update the NPGO index.\nThe NPGO index disseminated on CMEMS was specifically updated from 2004 onward using up-to-date altimeter products (DT2021 version; SEALEVEL_GLO_PHY_L4_MY _008_047 CMEMS product, including \u201cmy\u201d & \u201cmyint\u201d datasets, and the near-real time SEALEVEL_GLO_PHY_L4_NRT _008_046 CMEMS product). Users that previously used the index disseminated on www.o3d.org/npgo/ web page will find slight differences induced by this update. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the SSH anomaly (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the NPGO. \n\nCONTEXT\n\nNPGO mode emerges as the leading mode of decadal variability for surface salinity and upper ocean nutrients (Di Lorenzo et al., 2009). The North Pacific Gyre Oscillation (NPGO) term is used because its fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyres circulations (Chhak et al., 2009). This index measures change in the North Pacific gyres circulation and explains key physical-biological ocean variables including temperature, salinity, sea level, nutrients, chlorophyll-a. A positive North Pacific Gyre Oscillation phase is a dipole pattern with negative SSH anomaly north of 40\u00b0N and the opposite south of 40\u00b0N. (Di Lorenzo et al., 2008) suggested that the North Pacific Gyre Oscillation is the oceanic expression of the atmospheric variability of the North Pacific Oscillation (Walker and Bliss, 1932), which has an expression in both the 2nd EOFs of SSH and Sea Surface Temperature (SST) anomalies (Ceballos et al., 2009). This finding is further supported by the recent work of (Yi et al., 2018) showing consistent pattern features between the atmospheric North Pacific Oscillation and the oceanic North Pacific Gyre Oscillation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) database.\n\nCMEMS KEY FINDINGS\n\nThe NPGO index is presently in a negative phase, associated with a positive SSH anomaly north of 40\u00b0N and negative south of 40\u00b0N. This reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients. \n\nFigure caption\n\nNorth Pacific Gyre Oscillation (NPGO) index monthly averages. The NPGO index has been projected on normalized satellite altimeter sea level anomalies. The NPGO index is derived from (Di Lorenzo et al., 2008) before 2004, the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red lines show the date of the transition between the historical Di Lorenzo\u2019s series and the DUACS product, then between the DUACS \u201cmyint\u201d and \u201cnrt\u201d products used.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00221\n\nReferences:\n\n* Ceballos, L. I., E. Di Lorenzo, C. D. Hoyos, N. Schneider and B. Taguchi, 2009: North Pacific Gyre Oscillation Synchronizes Climate Fluctuations in the Eastern and Western Boundary Systems. Journal of Climate, 22(19) 5163-5174, doi:10.1175/2009jcli2848.1\n* Chhak, K. C., E. Di Lorenzo, N. Schneider and P. F. Cummins, 2009: Forcing of Low-Frequency Ocean Variability in the Northeast Pacific. Journal of Climate, 22(5) 1255-1276, doi:10.1175/2008jcli2639.1.\n* Di Lorenzo, E., N. Schneider, K.M. Cobb, K. Chhak, P.J.S. Franks, A.J. Miller, J.C. McWilliams, S.J. Bograd, H. Arango, E. Curchister, and others. 2008. North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophysical Research Letters 35, L08607, https://doi.org/10.1029/2007GL032838.\n* Di Lorenzo, E., J. Fiechter, N. Schneider, A. Bracco, A. J. Miller, P. J. S. Franks, S. J. Bograd, A. M. Moore, A. C. Thomas, W. Crawford, A. Pena and A. J. Hermann, 2009: Nutrient and salinity decadal variations in the central and eastern North Pacific. Geophysical Research Letters, 36, doi:10.1029/2009gl038261.\n* Di Lorenzo, E., K. M. Cobb, J. C. Furtado, N. Schneider, B. T. Anderson, A. Bracco, M. A. Alexander and D. J. Vimont, 2010: Central Pacific El Nino and decadal climate change in the North Pacific Ocean. Nature Geoscience, 3(11) 762-765, doi:10.1038/ngeo984\n* Tranchant, B. I. Pujol, E. Di Lorenzo and JF Legeais (2019). The North Pacific Gyre Oscillation. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Yi, D. L., Gan, B. Wu., L., A.J. Miller, 2018. The North Pacific Gyre Oscillation and Mechanisms of Its Decadal Variability in CMIP5 Models: Journal of Climate: Vol 31, No 6, 2487-2509.\n", "doi": "10.48670/moi-00221", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-climvar-pacific-npgo-sla-eof-mode-projection,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Oscillation from Observations Reprocessing"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nTemperature deviation from the 30-year (1991-2020) daily climatological mean temperature for the Barents Sea region (68\u00b0N - 80\u00b0N, 18\u00b0E - 55\u00b0E), relative to the difference between the daily climatological average and the 90th percentile above the climatological mean. Thus, when the index is above 1 the area is in a state of a marine heatwave, and when the index is below -1 the area is in a state of a marine cold spell, following the definition by Hobday et al. (2016). For further details, see Lien et al. (2024).\"\"\n\nCONTEXT\nAnomalously warm oceanic events, often termed marine heatwaves, can potentially impact the ecosystem in the affected region. The marine heatwave concept and terminology was systemized by Hobday et al. (2016), and a generally adopted definition of a marine heatwave is a period of more than five days where the temperature within a region exceeds the 90th percentile of the seasonally varying climatological average temperature for that region. The Barents Sea region has warmed considerably during the most recent climatological average period (1991-2020) due to a combination of climate warming and positive phase of regional climate variability (e.g., Lind et al., 2018 ; Skagseth et al., 2020 ; Smedsrud et al., 2022), with profound consequences for marine life where boreal species are moving northward at the expense of arctic species (e.g., Fossheim et al., 2015; Oziel et al., 2020; Husson et al., 2022).\n\nKEY FINDINGS\n\nThere is a clear tendency of reduced frequency and intensity of marine cold spells, and a tendency towards increased frequency and intensity of marine heat waves in the Barents Sea. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00346\n\nReferences:\n\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Hobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Gupta AS, Wernberg T. 2016. A hierarchical approach to defining marine heatwaves. Progr. Oceanogr., 141, 227-238\n* Husson B, Lind S, Fossheim M, Kato-Solvang H, Skern-Mauritzen M, P\u00e9cuchet L, Ingvaldsen RB, Dolgov AV, Primicerio R. 2022. Successive extreme climatic events lead to immediate, large-scale, and diverse responses from fish in the Arctic. Global Change Biol, 28, 3728-3744\n* Lien VS, Raj RP, Chatterjee S. 2024. Surface and bottom marine heatwave characteristics in the Barents Sea: a model study. State of the Planet (in press)\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import. Nat Clim Change, 8, 634-639\n* Oziel L, Baudena A, Ardyna M, Massicotte P, Randelhoff A, Sallee J-B, Ingvaldsen RB, Devred E, Babin M. 2020. Faster Atlantic currents drive poleward expansion of temperate phytoplankton in the Arctic Ocean. Nat Commun., 11(1), 1705, doi:10.1038/s41467-020-15485-5\n* Skagseth \u00d8, Eldevik T, \u00c5rthun M, Asbj\u00f8rnsen H, Lien VS, Smedsrud LH. 2020. Reduced efficiency of the Barents Sea cooling machine. Nat Clim Change, doi.org/10.1038/s41558-020-0772-6\n* Smedsrud LH, Muilwijk M, Brakstad A, Madonna E, Lauvset SK, Spensberger C, Born A, Eldevik T, Drange H, Jeansson E, Li C, Olsen A, Skagseth \u00d8, Slater DA, Straneo F, V\u00e5ge K, \u00c5rthun M. 2022.\n* Nordic Seas heat loss, Atlantic inflow, and Arctic sea ice cover over the last century. Rev Geophys., 60, e2020RG000725\n", "doi": "10.48670/mds-00346", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mhw-index-bottom,mhw-index-surface,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-mhw-arctic-area-averaged-anomalies,temperature-bottom,temperature-surface,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Marine Heatwave Index in the Barents Sea from Reanalysis"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"abstract": "DEFINITION\n\nSignificant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr.\n\nCONTEXT\n\nGrasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends.\n\nKEY FINDINGS\n\nOver the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 \u00b1 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 \u00b1 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 \u00b1 1.5 cm/year (a), with the mean SWH increasing by 0.7 \u00b1 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 \u00b1 2.15 cm/year (a) and the mean SWH trend is 1.4 \u00b1 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024).\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00352\n\nReferences:\n\n* Barnard, P. L., Hoover, D., Hubbard, D. M., Snyder, A., Ludka, B. C., Allan, J., Kaminsky, G. M., Ruggiero, P., Gallien, T. W., Gabel, L., McCandless, D., Weiner, H. M., Cohn, N., Anderson, D. L., and Serafin, K. A.: Extreme oceanographic forcing and coastal response due to the 2015\u20132016 El Ni\u00f1o, Nature Communications, 8, https://doi.org/10.1038/ncomms14365, 2017.\n* Donelan, M. A., Drennan, W. M., and Katsaros, K. B.: The air\u2013sea momentum flux in conditions of wind sea and swell, Journal of Physical Oceanography, 27, 2087\u20132099, https://doi.org/10.1175/1520-0485(1997)0272.0.co;2, 1997.\n* Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Dosio, A., and Feyen, L.: Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns, Geophysical Research Letters, 44, 2416\u20132426, https://doi.org/10.1002/2016gl072488, 2017\n* Thomas, S., Babanin, A. V., Walsh, K. J. E., Stoney, L., and Heil, P.: Effect of wave-induced mixing on Antarctic sea ice in a high-resolution ocean model, Ocean Dynamics, 69, 737\u2013746, https://doi.org/10.1007/s10236-019-01268-0, 2019.\n* Gommenginger, C. P., Srokosz, M. A., Challenor, P. G., and Cotton, P. D.: Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset, IEEE Transactions on Geoscience and Remote Sensing, 40, 251\u2013260, https://doi.org/10.1109/36.992782, 2002.\n* Storlazzi, C. D., Gingerich, S. B., van Dongeren, A., Cheriton, O. M., Swarzenski, P. W., Quataert, E., Voss, C. I., Field, D. W., Annamalai, H., Piniak, G. A., and McCall, R.: Most atolls will be uninhabitable by the mid-21st century because of sea level rise exacerbating wave-driven flooding, Science Advances, 4, https://doi.org/10.1126/sciadv.aap9741, 2018.\n* Husson, R., Charles, E.: EU Copernicus Marine Service Product User Manual for the Global Ocean L 4 Significant Wave Height From Reprocessed Satellite Measurements Product, WAVE_GLO_PHY_SWH_L4_MY_014_007, Issue 2.0, Mercator Ocean International, https://documentation.marine.copernicus.eu/PUM/CMEMS-WAV-PUM-014-005-006-007.pdf, last access: 21 July 2023, 2021 Laloue, A., Ghantous, M., Faug\u00e8re, Y., Dalphinet. A., Aouf, L.: Statistical analysis of global ocean significant wave heights from satellite altimetry over the past two decades. OSR-8 (under review)\n", "doi": "10.48670/mds-00352", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-seastate-global-swh-mean-and-p95-obs,satellite-observation,sea-surface-significant-height,sea-surface-significant-height-seasonal-number-of-observations,sea-surface-significant-height-trend-uncertainty-95percentile,sea-surface-significant-height-trend-uncertainty-mean,sea-surface-wave-significant-height-95percentile-trend,sea-surface-wave-significant-height-mean-trend,sea-surface-wave-significant-height-seasonal-95percentile,sea-surface-wave-significant-height-seasonal-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean, extreme and mean significant wave height trends from satellite observations - seasonal trends"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_baltic_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Baltic Sea is affected by vertical land motion due to the Glacial Isostatic Adjustment (Ludwigsen et al., 2020) and consequently relative sea level trends (as measured by tide gauges) have been shown to be strongly negative, especially in the northern part of the basin. On the other hand, Baltic Sea absolute sea level trends (from altimetry-based observations) show statistically significant positive trends (Passaro et al., 2021).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\nUp to 45 stations fulfill the completeness index criteria in this region, a few less than in 2020 (51). The spatial variation of the mean 99th percentiles follow the tidal range pattern, reaching its highest values in the northern end of the Gulf of Bothnia (e.g.: 0.81 and 0.78 m above mean sea level at the Finnish stations Kemi and Oulu, respectively) and the inner part of the Gulf of Finland (e.g.: 0.83 m above mean sea level in St. Petersburg, Russia). Smaller tides and therefore 99th percentiles are found along the southeastern coast of Sweden, between Stockholm and Gotland Island (e.g.: 0.42 m above mean sea level in Visby, Gotland Island-Sweden). Annual 99th percentiles standard deviation ranges between 3-5 cm in the South (e.g.: 3 cm in Korsor, Denmark) to 10-13 cm in the Gulf of Finland (e.g.: 12 cm in Hamina). Negative anomalies of 2022 99th percentile are observed in the northern part of the basin, in the Gulf of Bothnia, in the inner part of the Gulf of Finland and in Lolland Island stations (Denmark) reaching maximum values of -12 cm in Kemi, -9 cm in St. Petersburg and -8 cm in Rodby, respectively.. Positive anomalies of 2022 99th percentile are however found in the central and southeastern parts of the basin, with maximum values reaching 7 cm in Paldisky (Estonia) and Slipshavn (Denmark). \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00203\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Passaro M, M\u00fcller F L, Oelsmann J, Rautiainen L, Dettmering D, Hart-Davis MG, Abulaitijiang A, Andersen, OB, H\u00f8yer JL, Madsen, KS, Ringgaard IM, S\u00e4rkk\u00e4 J, Scarrott R, Schwatke C, Seitz F, Tuomi L, Restano M, and Benveniste J. 2021. Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A Revisit, Front Mar Sci, 8, 647607, https://doi.org/10.3389/FMARS.2021.647607/BIBTEX.\n", "doi": "10.48670/moi-00203", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-baltic-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe completeness index criteria is fulfilled by 57 stations in 2021, two more than those available in 2021 (55), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol and English Channels (e.g.: 6.26, 5.58 and 5.17 m above mean sea level in Newport, St. Malo and St. Helier, respectively). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-2 cm in the Canary Islands to 12 cm in Newport (Bristol Channel). Although less pronounced and general than in 2021, negative or close to zero anomalies of 2022 99th percentile still prevail throughout the region this year reaching up to -14 cm in St.Helier (Jersey Island, Channel Islands), or -12 cm in St. Malo. Positive anomalies of 2022 99th percentile are found in the northern part of the region (Irish eastern coast and west Scotland coast) and at a couple of stations in Southern England, with values reaching 9 cm in Bangor (Northern Ireland) and 6 cm in Portsmouth (South England). \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00253\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Chafik L, Nilsen JE\u00d8, Dangendorf S et al. 2019. North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era. Sci Rep 9, 1041. https://doi.org/10.1038/s41598-018-37603-6\n", "doi": "10.48670/moi-00253", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-ibi-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_medsea_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Mediterranean Sea shows statistically significant positive sea level trends over the whole basin. However, at sub-basin scale sea level trends show spatial variability arising from local circulation (Calafat et al., 2022; Meli et al., 2023).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe completeness index criteria is fulfilled in this region by 38 stations, 26 more than in 2021, significantly increasing spatial coverage with new in situ data in the central Mediterranean Sea, primarily from Italian stations. The mean 99th percentiles reflect the spatial variability of the tide, a microtidal regime, along the Spanish, French and Italian coasts, ranging from around 0.20 m above mean sea level in Sicily and the Balearic Islands (e.g.: 0.22 m in Porto Empedocle, 0.23 m in Ibiza)) to around 0.60 m above mean sea level in the Northern Adriatic Sea (e.g.: 0.63 m in Trieste, 0.61 m in Venice). . The annual 99th percentiles standard deviation ranges between 2 cm in M\u00e1laga and Motril (South of Spain) to 8 cm in Marseille. . The 2022 99th percentile anomalies present negative values mainly along the Spanish coast (as in 2021) and in the islands of Corsica and Sardinia (Western part of the region), while positive values are observed along the Eastern French Mediterranean coast and at most of the Italian stations (closer to the central part of the region), with values ranging from -4 cm in M\u00e1laga and Motril (Spain) to +5 cm in Ancona (Italy). \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00265\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J Geophys Res Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n* Legeais J-F, Llovel W, Melet A, and Meyssignac B. 2020. Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, s77\u2013s82, https://doi.org/10.1080/1755876X.2020.1785097.\n* Meli M, Camargo CML, Olivieri M, Slangen ABA, and Romagnoli C. 2023. Sea-level trend variability in the Mediterranean during the 1993\u20132019 period, Front Mar Sci, 10, 1150488, https://doi.org/10.3389/FMARS.2023.1150488/BIBTEX.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n", "doi": "10.48670/moi-00265", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sl-medsea-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe North West Shelf area presents positive sea level trends with higher trend estimates in the German Bight and around Denmark, and lower trends around the southern part of Great Britain (Dettmering et al., 2021).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe completeness index criteria is fulfilled in this region by 34 stations, eight more than in 2021 (26), most of them from Norway. The mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in East England and at the entrance of the English channel, and lowest values along the Danish and Swedish coasts, ranging from the 3.08 m above mean sea level in Immingan (East England) to 0.57 m above mean sea level in Ringhals (Sweden) and Helgeroa (Norway). The standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in Harwich, 3 cm in Dunkerke) and 7-8 cm in the eastern part and the Kattegat (e.g. 8 cm in Stenungsund, Sweden).. The 99th percentile anomalies for 2022 show positive values in Southeast England, with a maximum value of +8 cm in Lowestoft, and negative values in the eastern part of the Kattegat, reaching -8 cm in Oslo. The remaining stations exhibit minor positive or negative values. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00272\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Dettmering D, M\u00fcller FL, Oelsmann J, Passaro M, Schwatke C, Restano M, Benveniste J, and Seitz F. 2021. North SEAL: A new dataset of sea level changes in the North Sea from satellite altimetry, Earth Syst Sci Data, 13, 3733\u20133753, https://doi.org/10.5194/ESSD-13-3733-2021.\n", "doi": "10.48670/moi-00272", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sl-northwestshelf-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Baltic Sea has showed in the last two decades a warming trend across the whole basin with more frequent and severe heat waves (IPCC, 2022). This trend is significantly higher when considering only the summer season, which would affect the high extremes (e.g. H\u00f8yer and Karagali, 2016).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area go from 19.6\u00baC in Tallinn station to 21.4\u00baC in Rohukula station, and the standard deviation ranges between 1\u00baC and 5.4\u00baC reached in the Estonian Coast.\nResults for this year show either positive or negative low anomalies in the Coast of Sweeden (-0.7/+0.5\u00baC) within the standard deviation margin and a general positive anomaly in the rest of the region. This anomaly is noticeable in Rohukula and Virtsu tide gauges (Estonia) with +3.9\u00baC, but inside the standard deviation in both locations. In the South Baltic two stations, GreifswalderOie and Neustadt, reach an anomaly of +2\u00baC, but around the standard deviation.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00204\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00204", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-baltic-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Iberia Biscay Ireland area is characterized by a great complexity in terms of processes that take place in it. The sea surface temperature varies depending on the latitude with higher values to the South. In this area, the clear warming trend observed in other European Seas is not so evident. The northwest part is influenced by the refreshing trend in the North Atlantic, and a mild warming trend has been observed in the last decade (Pisano et al. 2020).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present a range from 16-20\u00baC in the Southwest of the British Isles and the English Channel, 19-21\u00baC in the West of Galician Coast, 21-23\u00baC in the south of Bay of Biscay, 23.5\u00baC in the Gulf of Cadiz to 24.5\u00baC in the Canary Island. The standard deviations are between 0.5\u00baC and 1.3\u00baC in the region except in the English Channel where the standard deviation is higher, reaching 3\u00baC.\nResults for this year show either positive or negative low anomalies below the 45\u00ba parallel, with a slight positive anomaly in the Gulf of Cadiz and the Southeast of the Bay of Biscay over 1\u00baC. In the Southwest of the British Isles and the English Channel, the anomaly is clearly positive, with some stations with an anomaly over 2\u00baC, but inside the standard deviation in the area. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00255\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00255", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-ibi-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann et al., 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Mediterranean Sea has showed a constant increase of the SST in the last three decades across the whole basin with more frequent and severe heat waves (Juza et al., 2022). Deep analyses of the variations have displayed a non-uniform rate in space, being the warming trend more evident in the eastern Mediterranean Sea with respect to the western side. This variation rate is also changing in time over the three decades with differences between the seasons (e.g. Pastor et al. 2018; Pisano et al. 2020), being higher in Spring and Summer, which would affect the extreme values.\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present values from 25\u00baC in Ionian Sea and 26\u00ba in the Alboran sea and Gulf of Lion to 27\u00baC in the East of Iberian Peninsula. The standard deviation ranges from 0.6\u00baC to 1.2\u00baC in the Western Mediterranean and is around 2.2\u00baC in the Ionian Sea.\nResults for this year show a slight negative anomaly in the Ionian Sea (-1\u00baC) inside the standard deviation and a clear positive anomaly in the Western Mediterranean Sea reaching +2.2\u00baC, almost two times the standard deviation in the area.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00267\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-z.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00267", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sst-medsea-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. H\u00f8yer and Karagali, 2016).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\nThe mean 99th percentiles showed in the area present a range from 14-16\u00baC in the North of the British Isles, 16-19\u00baC in the Southwest of the North Sea to 19-21\u00baC around Denmark (Helgoland Bight, Skagerrak and Kattegat Seas). The standard deviation ranges from 0.5-1\u00baC in the North of the British Isles, 0.5-2\u00baC in the Southwest of the North Sea to 1-3\u00baC in the buoys around Denmark.\nResults for this year show either positive or negative low anomalies around their corresponding standard deviation in in the North of the British Isles (-0.5/+0.6\u00baC) and a clear positive anomaly in the other two areas reaching +2\u00baC even when they are around the standard deviation margin.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00274\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer JL, Karagali I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00274", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sst-northwestshelf-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the Baltic Sea, the particular bathymetry and geography of the basin intensify the seasonal and spatial fluctuations in wave conditions. No clear statistically significant trend in the sea state has been appreciated except a rising trend in significant wave height in winter season, linked with the reduction of sea ice coverage (Soomere, 2023; Tuomi et al., 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles shown in the area are from 3 to 4 meters and the standard deviation ranges from 0.2 m to 0.4 m. \nResults for this year show a slight positive or negative anomaly in all the stations, from -0.24 m to +0.36 m, inside the margin of the standard deviation.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00199\n\nReferences:\n\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n", "doi": "10.48670/moi-00199", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-wave-baltic-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"abstract": "DEFINITION\n\nExtreme wave characteristics are computed by analysing single storm events and their long-term means and trends based on the product BLKSEA_MULTIYEAR_WAV_007_006. These storm events were detected using the method proposed by Weisse and G\u00fcnther (2007). The basis of the method is the definition of a severe event threshold (SET), which we define as the 99th percentile of the significant wave height (SWH). Then, the exceedance and shortfall of the SWH at every grid point was determined and counted as a storm event. The analysis of extreme wave events also comprises the following three parameters but are not part of this OMI. The time period between each exceedance and shortfall of the SET is the lifetime of an event. The difference in the maximum SWH of each event and the SET is defined as the event intensity. The geographic area of storm events and exceedance of the SET are defined as the maximum event area. The number, lifetime, and intensity of events were averaged over each year. Finally, the yearly values were used to compute the long-term means. In addition to these parameters, we estimated the difference (anomaly) of the last available year in the multiyear dataset compared against the long-term average as well as the linear trend. To show multiyear variability, each event, fulfilling the above-described definition, is considered in the statistics. This was done independent of the events\u2019 locations within the domain. To obtain long-term trends, a linear regression was applied to the yearly time series. The statistics are based on the period 1950 to -1Y. This approach has been presented in Staneva et al. (2022) for the area of the Black Sea and was later adapted to the South Atlantic in Gramcianinov et al. (2023a, 2023b).\n\nCONTEXT\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect and in the frame of climate change, which also affects regional wind patterns and therewith the wave climate, it is important for coastal regions to gain insights into wave extreme characteristics and the related trends. These insights are crucial to initiate necessary abatement strategies especially in combination with extreme wave power statistics (see OMI OMI_EXTREME_WAVE_BLKSEA_wave_power).\n\nKEY FINDINGS\n\nThe yearly mean number of storm events is rather low in regions where the average annual lifetime and intensity of storms are high. In contrast, the number of events is high where their lifetime and intensity are low. While the southwest Black Sea is exposed to yearly mean storm event numbers of below the long-term spatial averages (7.3 events), it is observed that the yearly mean lifetime of the events in the same region is higher than the long-term averages. The extreme wave statistics based on the 99th percentile threshold of the significant wave height (SWH) are very similar to the wind sea wave parameter, and the swell contribution is much lower. On overall, the yearly trend of the storm events is slightly negative (-0.01 events/year) with two areas showing positive trends located in the very east and west. In terms of the mean number of storm events in 2022, a pronounced area with positive values is located along the eastern coast and another in the western basin. The rest of the Black Sea area mostly experienced less events in 2022.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00348\n\nReferences:\n\n* Gramcianinov, C.B., Staneva, J., de Camargo, R., & da Silva Dias, P.L. (2023a): Changes in extreme wave events in the southwestern South Atlantic Ocean. Ocean Dynamics, doi:10.1007/s10236-023-01575-7\n* Gramcianinov, C.B., Staneva, J., Souza, C.R.G., Linhares, P., de Camargo, R., & da Silva Dias, P.L. (2023b): Recent changes in extreme wave events in the south-western South Atlantic. In: von Schuckmann, K., Moreira, L., Le Traon, P.-Y., Gr\u00e9goire, M., Marcos, M., Staneva, J., Brasseur, P., Garric, G., Lionello, P., Karstensen, J., & Neukermans, G. (eds.): 7th edition of the Copernicus Ocean State Report (OSR7). Copernicus Publications, State Planet, 1-osr7, 12, doi:10.5194/sp-1-osr7-12-2023\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n* Weisse, R., & G\u00fcnther, H. (2007): Wave climate and long-term changes for the Southern North Sea obtained from a high-resolution hindcast 1958\u20132002. Ocean Dynamics, 57(3), 161\u2013172, doi:10.1007/s10236-006-0094-x\n", "doi": "10.48670/mds-00348", "instrument": null, "keywords": "2022-anomaly-of-yearly-mean-number-of-wave-storm-events,black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-wave-blksea-recent-changes,swh,weather-climate-and-seasonal-forecasting,wind-speed,yearly-mean-number-of-wave-storm-events,yearly-trend-of-mean-number-of-wave-storm-events", "license": "proprietary", "missionStartDate": "1986-01-30T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea extreme wave events"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"abstract": "DEFINITION\n\nThe Wave Power P is defined by:\nP=(\u03c1g^2)/64\u03c0 H_s^2 T_e\nWhere \u03c1 is the surface water density, g the acceleration due to gravity, Hs the significant wave height (VHM0), and Te the wave energy period (VTM10) also abbreviated with Tm-10. The extreme statistics and related recent changes are defined by (1) the 99th percentile of the Wave Power, (2) the linear trend of 99th percentile of the Wave Power, and (3) the difference (anomaly) of the 99th percentile of the last available year in the multiyear dataset BLKSEA_MULTIYEAR_WAV_007_006 compared against the long-term average. The statistics are based on the period 1950 to -1Y and are obtained from yearly averages. This approach has been presented in Staneva et al. (2022).\n\nCONTEXT\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect, the Wave Power is a crucial quantity to plan and operate wave energy converters (WEC) and for coastal and offshore structures. For both reliable estimates of long-term Wave Power extremes are important to secure a high efficiency and to guarantee a robust and secure design, respectively.\n\nKEY FINDINGS\n\nThe 99th percentile of wave power mean patterns are overall consistent with the respective significant wave height pattern. The maximum 99th percentile of wave power is observed in the southwestern Black Sea. Typical values of in the eastern basin are ~20 kW/m and in the western basin ~45 kW/m. The trend of the 99th percentile of the wave power is decreasing with typical values of 50 W/m/year and a maximum of 120 W/m/year, which is equivalent to a ~25% decrease over whole period with respect to the mean. The pattern of the anomaly of the 99th percentile of wave power in 2022 correlates well with that of the wind speed anomaly in 2022, revealing a negative wave-power anomaly in the western Black Sea (P_2020<P_average) and a mix of positive (P_2020<P_average) and negative anomalies in the eastern basin, where the positive anomalies are mainly present in coastal regions.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00350\n\nReferences:\n\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n", "doi": "10.48670/mds-00350", "instrument": null, "keywords": "2022-anomaly-of-yearly-average-of-99th-percentile-of-wave-power,black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-wave-blksea-wave-power,swh,weather-climate-and-seasonal-forecasting,wind-speed,yearly-average-of-99th-percentile-of-wave-power,yearly-trend-of-99th-percentile-of-wave-power", "license": "proprietary", "missionStartDate": "1986-01-30T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea wave power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al. (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young, 2011; Young & Ribal, 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present a wide range from 2-3.5m in the Canary Island with 0.1-0.3 m of standard deviation (std), 3.5m in the Gulf of Cadiz with 0.5m of std, 3-6m in the English Channel and the Irish Sea with 0.5-0.6m of std, 4-7m in the Bay of Biscay with 0.4-0.9m of std to 8-10m in the West of the British Isles with 0.7-1.4m of std. \nResults for this year show close to zero anomalies in the Canary Island (-0.2/+0.1m), the Gulf of Cadiz (-0.2m) and the English Channel and the Irish Sea (-0.1/+0.1), a general slight negative anomaly in the Bay of Biscay reaching -0.7m but inside the range of the standard deviation, and a positive anomaly (+1.0/+1.55m) in the West of the British Isles, barely out of the standard deviation range in the area. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00250\n\nReferences:\n\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Young IR, Zieger S, and Babanin AV. 2011. Global Trends in Wind Speed and Wave Height, Science, 332, 451\u2013455, https://doi.org/10.1126/science.1197219\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "doi": "10.48670/moi-00250", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-wave-ibi-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nFor the Mediterranean Sea an interesting publication (De Leo et al., 2024) analyses recent studies in this basin showing the variability in the different results and the difficulties to reach a consensus, especially in the mean wave conditions. The only significant conclusion is the positive trend in extreme values for the western Mediterranean Sea and in particular in the Gulf of Lion and in the Tyrrhenian Sea.\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present a range from 1.5-3.5 in the Gibraltar Strait and Alboran Sea with 0.25-0.55 of standard deviation (std), 2-5m in the East coast of the Iberian Peninsula and Balearic Islands with 0.2-0.4m of std, 3-4m in the Aegean Sea with 0.4-0.6m of std to 3-5m in the Gulf of Lyon with 0.3-0.5m of std. \nResults for this year show a positive anomaly in the Gibraltar Strait (+0.8m), and a negative anomaly in the Aegean Sea (-0.8m), the East Coast of the Iberian Peninsula (-0.7m) and in the Gulf of Lyon (-0.6), all of them slightly over the standard deviation in the respective areas.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00263\n\nReferences:\n\n* De Leo F, Briganti R & Besio G. 2024. Trends in ocean waves climate within the Mediterranean Sea: a review. Clim Dyn 62, 1555\u20131566. https://doi.org/10.1007/s00382-023-06984-4\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "doi": "10.48670/moi-00263", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-wave-medsea-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al. (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young et al., 2011; Young & Ribal, 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS \n\nThe mean 99th percentiles showed in the area present a wide range from 2.5 meters in the English Channel with 0.3m of standard deviation (std), 3-5m in the southern and central North Sea with 0.3-0.6m of std, 4 meters in the Skagerrak Strait with 0.6m of std, 7.5m in the northern North Sea with 0.6m of std to 8 meters in the North of the British Isles with 0.6m of std. \nResults for this year show either low positive or negative anomalies between -0.6m and +0.4m, inside the margin of the standard deviation for all the stations. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00270\n\nReferences:\n\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Young IR, Zieger S, and Babanin AV. 2011. Global Trends in Wind Speed and Wave Height, Science, 332, 451\u2013455, https://doi.org/10.1126/science.1197219\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "doi": "10.48670/moi-00270", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-wave-northwestshelf-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf significant wave height extreme variability mean and anomaly (observations)"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms to remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deasonalised time series is derived by subtracting the seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. \n\nCONTEXT\n\nPhytoplankton \u2013 and chlorophyll concentration , which is a measure of phytoplankton concentration \u2013 respond rapidly to changes in environmental conditions. Chlorophyll concentration is highly seasonal in the Arctic Ocean region due to a strong dependency on light and nutrient availability, which in turn are driven by seasonal sunlight and sea-ice cover dynamics, as well as changes in mixed layer. In the past two decades, an increase in annual net primary production by Arctic Ocean phytoplankton has been observed and linked to sea-ice decline (Arrigo and van Dijken, 2015); in the same line Kahru et al. (2011) have showed that chlorophyll concentration peaks are appearing increasingly earlier in the year in parts of the Arctic. It is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales in the area, in order to be able to separate potential long-term climate signals from natural variability in the short term.\n\nCMEMS KEY FINDINGS\n\nWhile the overall trend average for the 1997-2021 period in the Arctic Sea is positive (0.86 \u00b1 0.17 % per year), a continued plateau in the linear trend, initiated in 2013 is observed in the time series extension, with both the amplitude and the baseline of the cycle continuing to decrease during 2021 as reported for previous years (Sathyendranath et al., 2018). In particular, the annual average for the region in 2021 is 1.05 mg m-3 - a 30% reduction on 2020 values. There appears to be no appreciable changes in the timings or amplitude of the 2021 spring and autumn blooms. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00188\n\nReferences:\n\n* Arrigo, K. R., & van Dijken, G. L., 2015. Continued increases in Arctic Ocean primary production. Progress in Oceanography, 136, 60\u201370. doi: 10.1016/j.pocean.2015.05.002.\n* Kahru, M., Brotas, V., Manzano\u2010Sarabia, M., Mitchell, B. G., 2011. Are phytoplankton blooms occurring earlier in the Arctic? Global Change Biology, 17(4), 1733\u20131739. doi:10.1111/j.1365\u20102486.2010.02312.x.\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018. 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. 10.1093/icesjms/fsp107.\n", "doi": "10.48670/moi-00188", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,multi-year,oceanographic-geographical-features,omi-health-chl-arctic-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deseasonalised time series is derived by subtracting the mean seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. \n\nCONTEXT\n\nPhytoplankton \u2013 and chlorophyll concentration as a proxy for phytoplankton \u2013 respond rapidly to changes in environmental conditions, such as temperature, light and nutrients availability, and mixing. The response in the North Atlantic ranges from cyclical to decadal oscillations (Henson et al., 2009); it is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the North Atlantic are known to respond to climate variability associated with the North Atlantic Oscillation (NAO), with the initiation of the spring bloom showing a nominal correlation with sea surface temperature and the NAO index (Zhai et al., 2013).\n\nCMEMS KEY FINDINGS\n\nWhile the overall trend average for the 1997-2021 period in the North Atlantic Ocean is slightly positive (0.16 \u00b1 0.12 % per year), an underlying low frequency harmonic signal can be seen in the deseasonalised data. The annual average for the region in 2021 is 0.25 mg m-3. Though no appreciable changes in the timing of the spring and autumn blooms have been observed during 2021, a lower peak chlorophyll concentration is observed in the timeseries extension. This decrease in peak concentration with respect to the previous year is contributing to the reduction trend.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00194\n\nReferences:\n\n* Henson, S. A., Dunne, J. P. , and Sarmiento, J. L., 2009, Decadal variability in North Atlantic phytoplankton blooms, J. Geophys. Res., 114, C04013, doi:10.1029/2008JC005139.\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n* Zhai, L., Platt, T., Tang, C., Sathyendranath, S., Walne, A., 2013. The response of phytoplankton to climate variability associated with the North Atlantic Oscillation, Deep Sea Research Part II: Topical Studies in Oceanography, 93, 159-168, doi: 10.1016/j.dsr2.2013.04.009.\n", "doi": "10.48670/moi-00194", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-atlantic-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication": {"abstract": "DEFINITION\n\nWe have derived an annual eutrophication and eutrophication indicator map for the North Atlantic Ocean using satellite-derived chlorophyll concentration. Using the satellite-derived chlorophyll products distributed in the regional North Atlantic CMEMS MY Ocean Colour dataset (OC- CCI), we derived P90 and P10 daily climatologies. The time period selected for the climatology was 1998-2017. For a given pixel, P90 and P10 were defined as dynamic thresholds such as 90% of the 1998-2017 chlorophyll values for that pixel were below the P90 value, and 10% of the chlorophyll values were below the P10 value. To minimise the effect of gaps in the data in the computation of these P90 and P10 climatological values, we imposed a threshold of 25% valid data for the daily climatology. For the 20-year 1998-2017 climatology this means that, for a given pixel and day of the year, at least 5 years must contain valid data for the resulting climatological value to be considered significant. Pixels where the minimum data requirements were met were not considered in further calculations.\n We compared every valid daily observation over 2021 with the corresponding daily climatology on a pixel-by-pixel basis, to determine if values were above the P90 threshold, below the P10 threshold or within the [P10, P90] range. Values above the P90 threshold or below the P10 were flagged as anomalous. The number of anomalous and total valid observations were stored during this process. We then calculated the percentage of valid anomalous observations (above/below the P90/P10 thresholds) for each pixel, to create percentile anomaly maps in terms of % days per year. Finally, we derived an annual indicator map for eutrophication levels: if 25% of the valid observations for a given pixel and year were above the P90 threshold, the pixel was flagged as eutrophic. Similarly, if 25% of the observations for a given pixel were below the P10 threshold, the pixel was flagged as oligotrophic.\n\nCONTEXT\n\nEutrophication is the process by which an excess of nutrients \u2013 mainly phosphorus and nitrogen \u2013 in a water body leads to increased growth of plant material in an aquatic body. Anthropogenic activities, such as farming, agriculture, aquaculture and industry, are the main source of nutrient input in problem areas (Jickells, 1998; Schindler, 2006; Galloway et al., 2008). Eutrophication is an issue particularly in coastal regions and areas with restricted water flow, such as lakes and rivers (Howarth and Marino, 2006; Smith, 2003). The impact of eutrophication on aquatic ecosystems is well known: nutrient availability boosts plant growth \u2013 particularly algal blooms \u2013 resulting in a decrease in water quality (Anderson et al., 2002; Howarth et al.; 2000). This can, in turn, cause death by hypoxia of aquatic organisms (Breitburg et al., 2018), ultimately driving changes in community composition (Van Meerssche et al., 2019). Eutrophication has also been linked to changes in the pH (Cai et al., 2011, Wallace et al. 2014) and depletion of inorganic carbon in the aquatic environment (Balmer and Downing, 2011). Oligotrophication is the opposite of eutrophication, where reduction in some limiting resource leads to a decrease in photosynthesis by aquatic plants, reducing the capacity of the ecosystem to sustain the higher organisms in it. \nEutrophication is one of the more long-lasting water quality problems in Europe (OSPAR ICG-EUT, 2017), and is on the forefront of most European Directives on water-protection. Efforts to reduce anthropogenically-induced pollution resulted in the implementation of the Water Framework Directive (WFD) in 2000. \n\nCMEMS KEY FINDINGS\n\nThe coastal and shelf waters, especially between 30 and 400N that showed active oligotrophication flags for 2020 have reduced in 2021 and a reversal to eutrophic flags can be seen in places. Again, the eutrophication index is positive only for a small number of coastal locations just north of 40oN in 2021, however south of 40oN there has been a significant increase in eutrophic flags, particularly around the Azores. In general, the 2021 indicator map showed an increase in oligotrophic areas in the Northern Atlantic and an increase in eutrophic areas in the Southern Atlantic. The Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area (OSPAR ICG-EUT, 2017) reported an improvement from 2008 to 2017 in eutrophication status across offshore and outer coastal waters of the Greater North Sea, with a decrease in the size of coastal problem areas in Denmark, France, Germany, Ireland, Norway and the United Kingdom.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00195\n\nReferences:\n\n* Anderson, D.M., Glibert, P.M. & Burkholder, J.M. (2002). Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences. Estuaries 25, 704\u2013726 /10.1007/BF02804901.\n* Balmer, M.B., Downing, J.A. (2011), Carbon dioxide concentrations in eutrophic lakes: undersaturation implies atmospheric uptake, Inland Waters, 1:2, 125-132, 10.5268/IW-1.2.366.\n* Breitburg, D., Levin, L.A., Oschlies, A., Gr\u00e9goire, M., Chavez, F.P., Conley, D.J., Gar\u00e7on, V., Gilbert, D., Guti\u00e9rrez, D., Isensee, K. and Jacinto, G.S. (2018). Declining oxygen in the global ocean and coastal waters. Science, 359 (6371), p.eaam7240.\n* Cai, W., Hu, X., Huang, W. (2011) Acidification of subsurface coastal waters enhanced by eutrophication. Nature Geosci 4, 766\u2013770, 10.1038/ngeo1297.\n* Galloway, J.N., Townsend, A.R., Erisman, J.W., Bekunda, M., Cai, Z., Freney, J. R., Martinelli, L. A., Seitzinger, S. P., Sutton, M. A. (2008). Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions, Science 320, 5878, 889-892, 10.1126/science.1136674.\n* Howarth, R.W., Anderson, D., Cloern, J., Elfring, C., Hopkinson, C., Lapointe, B., Malone, T., & Marcus, N., McGlathery, K., Sharpley, A., Walker, D. (2000). Nutrient pollution of coastal rivers, bays and seas. Issues in Ecology, 7.\n* Howarth, R.W., Marino, R. (2006). Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades, Limnology and Oceanography, 51(1, part 2), 10.4319/lo.2006.51.1_part_2.0364.\n* Jickells, T. D. (1998). Nutrient biogeochemistry of the coastal zone. Science 281, 217\u2013222. doi: 10.1126/science.281.5374.217\n* OSPAR ICG-EUT. Axe, P., Clausen, U., Leujak, W., Malcolm, S., Ruiter, H., Prins, T., Harvey, E.T. (2017). Eutrophication Status of the OSPAR Maritime Area. Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area.\n* Schindler, D. W. (2006) Recent advances in the understanding and management of eutrophication. Limnology and Oceanography, 51, 356-363.\n* Smith, V.H. (2003). Eutrophication of freshwater and coastal marine ecosystems a global problem. Environmental Science and Pollution Research, 10, 126\u2013139, 10.1065/espr2002.12.142.\n* Van Meerssche, E., Pinckney, J.L. (2019) Nutrient Loading Impacts on Estuarine Phytoplankton Size and Community Composition: Community-Based Indicators of Eutrophication. Estuaries and Coasts 42, 504\u2013512, 10.1007/s12237-018-0470-z.\n* Wallace, R.B., Baumann, H., Grear, J.S., Aller, R.B., Gobler, C.J. (2014). Coastal ocean acidification: The other eutrophication problem, Estuarine, Coastal and Shelf Science, 148, 1-13, 10.1016/j.ecss.2014.05.027.\n", "doi": "10.48670/moi-00195", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-atlantic-oceancolour-eutrophication,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Eutrophication from Observations Reprocessing"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi-Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b). \nMonthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\nCONTEXT\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, in the Baltic Sea phytoplankton is known to respond to the variations of SST in the basin associated with climate variability (Kabel et al. 2012).\n\nKEY FINDINGS\n\nThe Baltic Sea shows a slight positive trend over the 1997-2023 period, with a slope of 0.30\u00b10.49% per year, indicating a decrease compared to the previous release. The maxima and minima values are relatively consistent year-to-year, with the absolute maximum occurring in 2008 and the minima observed in 2004 and 2014. A decrease in the chlorophyll signal has been evident over the past two years.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00197\n\nReferences:\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D\u2019Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D\u2019Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b (accepted). Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals. Remote Sens. 2021, 13, x.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kabel K, Moros M, Porsche C, Neumann T, Adolphi F, Andersen TJ, Siegel H, Gerth M, Leipe T, Jansen E, Sinninghe Damste\u0301 JS. 2012. Impact of climate change on the health of the Baltic Sea ecosystem over the last 1000 years. Nat Clim Change. doi:10.1038/nclimate1595.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., et al., 2018. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1. Technical Report Centre for Environmental Data Analysis. doi:10.5285/9c334fbe6d424a708cf3c4cf0c6a53f5.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., Me\u0301lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n", "doi": "10.48670/moi-00197", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,multi-year,oceanographic-geographical-features,omi-health-chl-baltic-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThis product includes the Baltic Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory (PML) using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b).\nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014) and anthropogenic climate change. Eutrophication is one of the most important issues for the Baltic Sea (HELCOM, 2018), therefore the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nKEY FINDINGS\n\nOn average, the trend for the Baltic Sea over the 1997-2023 period is relatively flat (0.08%). The pattern of positive and negative trends is quite similar to the previous release, indicating a general decrease in absolute values. This result aligns with the findings of Sathyendranath et al. (2018), which show an increasing trend in chlorophyll concentration in most of the European Seas.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00198\n\nReferences:\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D\u2019Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D\u2019Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b. Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* HELCOM (2018): HELCOM Thematic assessment of eutrophication 2011-2016. Baltic Sea Environment Proceedings No. 156.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Zibordi, G., Berthon, J.-F., M\u00e9lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n", "doi": "10.48670/moi-00198", "instrument": null, "keywords": "baltic-sea,change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-baltic-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\nCONTEXT\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014, Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003).\n\nKEY FINDINGS\n\nIn the Black Sea, the trend average for the 1997-2023 period is negative (-1.13\u00b11.07% per year). Nevertheless, this negative trend is lower than the one estimated in the previous release (both 1997-2021 and 1997-2022). The negative trend is mainly due to the marked change on chlorophyll concentrations between 2002 and 2004. From 2004 onwards, minima and maxima are strongly variable year by year. However, on average, the minima/maxima variability can be considered quite constant with a continuous decrease of maxima from 2015 up to mid 2020 where signal seems to change again with relative high chlorophyll values in 2021, 2022 and especially in the last year (2023). The general negative trend in the Black Sea is also confirmed by the analysis of Sathyendranath et al. (2018), that reveals an increasing trend in chlorophyll concentration in all the European Seas, except for the Black Sea.\nDOI (product): \nhttps://doi.org/10.48670/moi-00211\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf)\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1109/\u00acLGRS.2018.2883539\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Oguz, T., Cokacar, T., Malanotte\u2010Rizzoli, P., & Ducklow, H. W. (2003). Climatic warming and accompanying changes in the ecological regime of the Black Sea during 1990s. Global Biogeochemical Cycles, 17(3).\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., M\u00e9lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n* Zibordi, G., F. M\u00e9lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286, 2015.\n", "doi": "10.48670/moi-00211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,multi-year,oceanographic-geographical-features,omi-health-chl-blksea-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThis product includes the Black Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003). Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nKEY FINDINGS\n\nThe average Black Sea trend for the 1997-2023 period is absolutely similar to the previous ones (1997-2021 or 1997-2022) showing, on average, a trend of -1.24% per year. The trend is negative overall the basin, with weaker values in the central area, up to no significant trend percentages. The western side of the basin highlights markable negative trend. Negative values are shown in the Azov Sea with a strong inversion offshore the Don River. The overall negative trend in the map is in accordance with the results of Bengil and Mavruk (2018), that revealed a decreasing trend of chlorophyll during the post-eutrophication phase in the years 1997-2017.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00212\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Bengil, F., & Mavruk, S. (2018). Bio-optical trends of seas around Turkey: An assessment of the spatial and temporal variability. Oceanologia, 60(4), 488-499.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf)\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1109/\u00acLGRS.2018.2883539\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Oguz, T., Cokacar, T., Malanotte\u2010Rizzoli, P., & Ducklow, H. W. (2003). Climatic warming and accompanying changes in the ecological regime of the Black Sea during 1990s. Global Biogeochemical Cycles, 17(3).\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Zibordi, G., Berthon, J.-F., Me\u0301lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286, 2015.\n", "doi": "10.48670/moi-00212", "instrument": null, "keywords": "black-sea,change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-blksea-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al. 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the North Atlantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.14% year-1 increase in area relative to 2000-01-01 values. This trend has decreased compared with the 1997-2019 trend of 0.39%, and is no longer statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.21% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This is a slightly lower rate of change compared with the -0.24% trend for the 1997-2020 period but is still statistically significant (p<0.05).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00226\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00226", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-nag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the North Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 1.75% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 2.17% reported for the 1997-2020 period. The trend is statistically significant (p<0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.26% year-1) in the North Pacific gyre relative to 2000-01-01 values. This trend is slightly less negative than the trend of -0.31% year-1 for the 1997-2020 period, though the sign of the trend remains unchanged and is statistically significant (p<0.05). It must be noted that the difference is small and within the uncertainty of the calculations, indicating that the trend is significant, however there may be no change associated with the timeseries extension.\nFor 2016, The Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00227\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00227", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-npg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the South Altantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.01% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.09% rate for the 1997-2020 trend (though within the uncertainties associated with the two estimates) and is not statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.73% year-1) relative to 2000-01-01 values. This is a significant increase from the trend of 0.35% year-1 for the 1997-2020 period and is statistically significant (p<0.05). The last two years of the timeseries show an increased deviation from the mean.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00228\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00228", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-sag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the South Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.04% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.16% change for the 1997-2020 period, with the sign of the trend remaining unchanged and is not statistically significant (p<0.05). An underlying low frequency signal is observed with a period of approximately a decade.\nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.66% year-1) in the South Pacific gyre relative to 2000-01-01 values. This rate has increased compared to the rate of 0.45% year-1 for the 1997-2020 period and remains statistically significant (p<0.05). In the last two years of the timeseries, an increase in the variation from the mean is observed.\nFor 2016, the Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00229\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00229", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-spg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThe trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. \n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nCMEMS KEY FINDINGS\n\nThe average global trend for the 1997-2021 period was 0.51% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension. The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00230\n\nReferences:\n\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018b, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n", "doi": "10.48670/moi-00230", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\nCONTEXT\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Nin\u0303o Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016).\n\nKEY FINDINGS\n\nIn the Mediterranean Sea, the trend average for the 1997-2023 period is slightly negative (-0.73\u00b10.65% per year) emphasising the results obtained from previous release (1997-2022). This result is in contrast with the analysis of Sathyendranath et al. (2018) that reveals an increasing trend in chlorophyll concentration in all the European Seas. Starting from 2010-2011, except for 2018-2019, the decrease of chlorophyll concentrations is quite evident in the deseasonalized timeseries (in green), and in the maxima of the observations (grey line), starting from 2015. This attenuation of chlorophyll values of the last decade, results in an overall negative trend for the Mediterranean Sea.\nDOI (product): \nhttps://doi.org/10.48670/moi-00259\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G. (2004). Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., Santoleri, R., 2016. Mediterranean ocean colour chlorophyll trends. PLoS One 11, 1 16. https://doi.org/10.1371/journal.pone.0155756.\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00259", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThis product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) (at 1 km resolution) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). The Mediterranean Sea is an oligotrophic basin, where chlorophyll concentration decreases following a specific gradient from West to East (Colella et al. 2016). The highest concentrations are observed in coastal areas and at the river mouths, where the anthropogenic pressure and nutrient loads impact on the eutrophication regimes (Colella et al. 2016). The the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nKEY FINDINGS\n\nChlorophyll trend in the Mediterranean Sea, for the period 1997-2023, generally confirm trend results of the previous release with negative values over most of the basin. In Ligurian Sea, negative trend is slightly emphasized. As for the previous release, the southern part of the western Mediterranean basin, Rhode Gyre and in the northern coast of the Aegean Sea show weak positive trend areas but they seems weaker than previous ones. On average the trend in the Mediterranean Sea is about -0.83% per year, emphasizing the mean negative trend achieved in the previous release. Contrary to what shown by Salgado-Hernanz et al. (2019) in their analysis (related to 1998-2014 satellite observations), western and eastern part of the Mediterranean Sea do not show differences. In the Ligurian Sea, the trend switch to negative values, differing from the positive regime observed in the trend maps of both Colella et al. (2016) and Salgado-Hernanz et al. (2019), referred, respectively, to 1998-2009 and 1998-2014 period, respectively. The waters offshore the Po River mouth show weak negative trend values, partially differing from the markable negative regime observed in the 1998-2009 period (Colella et al., 2016), and definitely moving from the positive trend observed by Salgado-Hernanz et al. (2019).\nDOI (product): \nhttps://doi.org/10.48670/moi-00260\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 200.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Salgado-Hernanz, P. M., Racault, M. F., Font-Mu\u00f1oz, J. S., & Basterretxea, G. (2019). Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing. Remote Sensing of Environment, 221, 50-64.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00260", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"abstract": "DEFINITION\n\nThe Mediterranean water mass formation rates are evaluated in 4 areas as defined in the Ocean State Report issue 2 section 3.4 (Simoncelli and Pinardi, 2018) as shown in Figure 2: (1) the Gulf of Lions for the Western Mediterranean Deep Waters (WMDW); (2) the Southern Adriatic Sea Pit for the Eastern Mediterranean Deep Waters (EMDW); (3) the Cretan Sea for Cretan Intermediate Waters (CIW) and Cretan Deep Waters (CDW); (4) the Rhodes Gyre, the area of formation of the so-called Levantine Intermediate Waters (LIW) and Levantine Deep Waters (LDW).\nAnnual water mass formation rates have been computed using daily mixed layer depth estimates (density criteria \u0394\u03c3 = 0.01 kg/m3, 10 m reference level) considering the annual maximum volume of water above mixed layer depth with potential density within or higher the specific thresholds specified in Table 1 then divided by seconds per year.\nAnnual mean values are provided using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al. 2020, 2021).\n\nCONTEXT\n\nThe formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation. This circulation varies at interannual and multidecadal time scales and it is composed of an upper zonal cell (Zonal Overturning Circulation) and two main meridional cells in the western and eastern Mediterranean (Pinardi and Masetti 2000).\nThe objective is to monitor the main water mass formation events using the eddy resolving Mediterranean Sea Reanalysis (Escudier et al. 2020, 2021) and considering Pinardi et al. (2015) and Simoncelli and Pinardi (2018) as references for the methodology. The Mediterranean Sea Reanalysis can reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in the literature. This will permit constant monitoring of the open ocean deep convection process in the Mediterranean Sea and a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales. \nDeep and intermediate water formation events reveal themselves by a deep mixed layer depth distribution in four Mediterranean areas (Table 1 and Figure 2): Gulf of Lions, Southern Adriatic Sea Pit, Cretan Sea and Rhodes Gyre. \n\nCMEMS KEY FINDINGS\n\nThe Western Mediterranean Deep Water (WMDW) formation events in the Gulf of Lion appear to be larger after 1999 consistently with Schroeder et al. (2006, 2008) related to the Eastern Mediterranean Transient event. This modification of WMDW after 2005 has been called Western Mediterranean Transition. WMDW formation events are consistent with Somot et al. (2016) and the event in 2009 is also reported in Houpert et al. (2016). \nThe Eastern Mediterranean Deep Water (EMDW) formation in the Southern Adriatic Pit region displays a period of water mass formation between 1988 and 1993, in agreement with Pinardi et al. (2015), in 1996, 1999 and 2000 as documented by Manca et al. (2002). Weak deep water formation in winter 2006 is confirmed by observations in Vilibi\u0107 and \u0160anti\u0107 (2008). An intense deep water formation event is detected in 2012-2013 (Ga\u010di\u0107 et al., 2014). Last years are characterized by large events starting from 2017 (Mihanovic et al., 2021).\nCretan Intermediate Water formation rates present larger peaks between 1989 and 1993 with the ones in 1992 and 1993 composing the Eastern Mediterranean Transient phenomena. The Cretan Deep Water formed in 1992 and 1993 is characterized by the highest densities of the entire period in accordance with Velaoras et al. (2014).\nThe Levantine Deep Water formation rate in the Rhode Gyre region presents the largest values between 1992 and 1993 in agreement with Kontoyiannis et al. (1999). \n\nFigure caption\n\nWater mass formation rates [Sverdrup] computed in 4 regions: in the Gulf of Lion for the Western Mediterranean Deep Waters (WMDW); in the Southern Adriatic region for the Eastern Mediterranean Deep Waters (EMDW); in the Cretan Sea for the Cretan Intermediate Waters (CIW) and the Cretan Deep Waters (CDW); in the Rhode Gyre area for the Levantine Intermediate Waters (LIW) and the Levantine Deep Waters (LDW). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00318\n\nReferences:\n\n* Escudier R., Clementi E., Cipollone A., Pistoia J., Drudi M., Grandi A., Lyubartsev V., Lecci R., Aydogdu A., Delrosso D., Omar M., Masina S., Coppini G., Pinardi N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Ga\u010di\u0107, M., Civitarese, G., Kova\u010devi\u0107, V., Ursella, L., Bensi, M., Menna, M., et al. 2014. Extreme winter 2012 in the Adriatic: an example of climatic effect on the BiOS rhythm. Ocean Sci. 10, 513\u2013522. doi: 10.5194/os-10-513-2014\n* Houpert, L., de Madron, X.D., Testor, P., Bosse, A., D\u2019Ortenzio, F., Bouin, M.N., Dausse, D., Le Goff, H., Kunesch, S., Labaste, M., et al. 2016. Observations of open-ocean deep convection in the northwestern Mediterranean Sea: seasonal and inter- annual variability of mixing and deep water masses for the 2007-2013 period. J Geophys Res Oceans. 121:8139\u20138171. doi:10.1002/ 2016JC011857.\n* Kontoyiannis, H., Theocharis, A., Nittis, K. 1999. Structures and characteristics of newly formed water masses in the NW levantine during 1986, 1992, 1995. In: Malanotte-Rizzoli P., Eremeev V.N., editor. The eastern Mediterranean as a laboratory basin for the assessment of contrasting ecosys- tems. NATO science series (series 2: environmental secur- ity), Vol. 51. Springer: Dordrecht.\n* Manca, B., Kovacevic, V., Gac\u030cic\u0301, M., Viezzoli, D. 2002. Dense water formation in the Southern Adriatic Sea and spreading into the Ionian Sea in the period 1997\u20131999. J Mar Sys. 33/ 34:33\u2013154.\n* Mihanovi\u0107, H., Vilibi\u0107, I., \u0160epi\u0107, J., Mati\u0107, F., Ljube\u0161i\u0107, Z., Mauri, E., Gerin, R., Notarstefano, G., Poulain, P.-M.. 2021. Observation, preconditioning and recurrence of exceptionally high salinities in the Adriatic Sea. Frontiers in Marine Science, Vol. 8, https://www.frontiersin.org/article/10.3389/fmars.2021.672210\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., ... & Bonaduce, A. 2015. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Progress in Oceanography, 132, 318-332\n* Schroeder, K., Gasparini, G.P., Tangherlini, M., Astraldi, M. 2006. Deep and intermediate water in the western Mediterranean under the influence of the eastern Mediterranean transient. Geophys Res Lett. 33. doi:10. 1028/2006GL02712.\n* Schroeder, K., Ribotti, A., Borghini, M., Sorgente, R., Perilli, A., Gasparini, G.P. 2008. An extensive western Mediterranean deep water renewal between 2004 and 2006. Geophys Res Lett. 35(18):L18605. doi:10.1029/2008GL035146.\n* Simoncelli, S. and Pinardi, N. 2018. Water mass formation processes in the Mediterranean sea over the past 30 years. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208.\n* Somot, S., Houpert, L., Sevault, F., Testor, P., Bosse, A., Taupier-Letage, I., Bouin, M.N., Waldman, R., Cassou, C., Sanchez-Gomez, E., et al. 2016. Characterizing, modelling and under- standing the climate variability of the deep water formation in the North-Western Mediterranean Sea. Clim Dyn. 1\u201332. doi:10.1007/s00382-016-3295-0.\n* Velaoras, D., Krokos, G., Nittis, K., Theocharis, A. 2014. Dense intermediate water outflow from the Cretan Sea: a salinity driven, recurrent phenomenon, connected to thermohaline circulation changes. J Geophys Res Oceans. 119:4797\u20134820. doi:10.1002/2014JC009937.\n* Vilibic\u0301, I., S\u030cantic\u0301, D. 2008. Deep water ventilation traced by Synechococcus cyanobacteria. Ocean Dyn 58:119\u2013125. doi:10.1007/s10236-008-0135-8.\n* Von Schuckmann K. et al. (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/mds-00318", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-var-extreme-wmf-medsea-area-averaged-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Water Mass Formation Rates from Reanalysis"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"abstract": "For the Antarctic Sea - A sea ice concentration product based on satellite SAR imagery and microwave radiometer data: The algorithm uses SENTINEL-1 SAR EW and IW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00320", "doi": "10.48670/mds-00320", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,sea-ice-edge,seaice-ant-phy-auto-l3-nrt-011-012,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean - High Resolution Sea Ice Information"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"abstract": "Antarctic sea ice displacement during winter from medium resolution sensors since 2002\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00120", "doi": "10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,seaice-ant-phy-l3-my-011-018,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"abstract": "Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00343", "doi": "10.48670/mds-00343", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,floe-size;,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l3-mynrt-011-023,stage-of-development,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L3"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"abstract": "Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00344", "doi": "10.48670/mds-00344", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l4-mynrt-011-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L4"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"abstract": "For the European Arctic Sea - A sea ice concentration product based on SAR data and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. A sea ice type product covering the same area is produced from SENTINEL-1 SAR EW mode dual-polarized HH/HV data.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00122", "doi": "10.48670/moi-00122", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,seaice-arc-phy-auto-l4-nrt-011-015,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High resolution Sea Ice Concentration and Sea Ice Type"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"abstract": "Arctic Sea and Ice surface temperature\nDetailed description: Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00315", "doi": "10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l3-my-011-021,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"abstract": "Arctic Sea and Ice surface temperature\n\nDetailed description:\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00123", "doi": "10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l4-my-011-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"abstract": "For the Arctic Ocean - multiple Sentinel-1 scenes, Sigma0 calibrated and noise-corrected, with individual geographical map projections over Svalbard and Greenland Sea regions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00124", "doi": "10.48670/moi-00124", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,seaice-arc-phy-l3m-nrt-011-017,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ARCTIC Ocean and Sea-Ice Sigma-Nought"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"abstract": "Arctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer:\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00126", "doi": "10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-arc-seaice-l3-rep-observations-011-010,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-12-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"abstract": "For the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview chart is produced twice weekly.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00128", "doi": "10.48670/moi-00128", "instrument": null, "keywords": "arctic-ocean,ca,cb,cc,cd,cf,cn,coastal-marine-environment,concentration-range,ct,data-quality,fa,fb,fc,ice-poly-id-grid,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,polygon-id,polygon-type,sa,satellite-observation,sb,sc,sea-ice-area-fraction,seaice-arc-seaice-l4-nrt-observations-011-002,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"abstract": "The iceberg product contains 4 datasets (IW and EW modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.\n\nThe iceberg product also contains two additional datasets of individual iceberg positions in Greenland-Newfoundland-Labrador Waters. These datasets are in shapefile format to allow the best representation of the icebergs (the 1st dataset contains the iceberg point observations, the 2nd dataset contains the polygonized satellite coverage). These are also derived by applying a Constant False Alarm Rate (CFAR) algorithm on Sentinel-1 SAR imagery.\nDespite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00129", "doi": "10.48670/moi-00129", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,seaice-arc-seaice-l4-nrt-observations-011-007,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T04:11:59Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"abstract": "Arctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00130", "doi": "10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-seaice-l4-nrt-observations-011-008,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00131", "doi": "10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,seaice-bal-phy-l4-my-011-019,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-11-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea ice concentration, extent, and classification time series"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"abstract": "For the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00132\n\nReferences:\n\n* J. Karvonen, M. Simila, SAR-Based Estimation of the Baltic Sea Ice Motion, Proc. of the International Geoscience and Remote Sensing Symposium 2007 (IGARSS 07), pp. 2605-2608, 2007. (Unfortunately there is no publication of the new algorithm version yet).\n", "doi": "10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,seaice-bal-seaice-l4-nrt-observations-011-004,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"abstract": "For the Baltic Sea - The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The products are based on SAR images and are produced on pass-by-pass basis during the Baltic Sea ice season, and show the ice thickness and drift in a 500 m and 800m grid, respectively. The Baltic sea ice concentration product is based on data from SAR and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00133\n\nReferences:\n\n* J. Karvonen, Operational SAR-based sea ice drift monitoring over the Baltic Sea, Ocean Science, v. 8, pp. 473-483, (http://www.ocean-sci.net/8/473/2012/os-8-473-2012.html) 2012.\n", "doi": "10.48670/moi-00133", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-thickness,sea-ice-x-displacement,sea-ice-y-displacement,seaice-bal-seaice-l4-nrt-observations-011-011,target-application#seaiceclimate,target-application#seaiceforecastingapplication,target-application#seaiceinformation,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"abstract": "Arctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00127", "doi": "10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-climate-l3-my-011-013,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1995-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"abstract": "The product contains a reprocessed multi year version of the daily composite dataset from SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 covering the Sentinel1 years from autumn 2014 until 1 year before present\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00328", "doi": "10.48670/mds-00328", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-phy-l4-my-011-020,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift Time Series"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"abstract": "Arctic sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and Aprilt to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00125", "doi": "10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-l4-nrt-011-014,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Thickness derived from merging of L-Band radiometry and radar altimeter derived sea ice thickness"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"abstract": "For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00134", "doi": "10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"abstract": "DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00135", "doi": "10.48670/moi-00135", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-006,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"abstract": "The CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00136\n\nReferences:\n\n* [http://osisaf.met.no/docs/osisaf_cdop2_ss2_pum_sea-ice-conc-reproc_v2p2.pdf]\n", "doi": "10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,seaice-glo-seaice-l4-rep-observations-011-009,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-10-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_BLK is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Black Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00138", "doi": "10.48670/moi-00138", "instrument": null, "keywords": "black-sea,coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-blk-phy-mdt-l4-static-008-067,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "BLACK SEA MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n\u201c\u2019Associated products\u201d\u2019\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00139", "doi": "10.48670/moi-00139", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-my-008-061,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T07:53:03Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Seas. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\nAssociated products\n\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00140", "doi": "10.48670/moi-00140", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-nrt-008-059,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T03:04:52Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA LEVEL ANOMALIES NRT"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00141", "doi": "10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-my-008-068,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00142", "doi": "10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-nrt-008-060,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2024_EUR is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the European Seas. This is consistent with the reference time period also used in the SSALTO DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00337", "doi": "10.48670/mds-00337", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-eur-phy-mdt-l4-static-008-070,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"abstract": "DUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00145", "doi": "10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-climate-l4-my-008-057,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\nAssociated products\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00146", "doi": "10.48670/moi-00146", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-my-008-062,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T01:42:25Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\nAssociated products\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00147", "doi": "10.48670/moi-00147", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-nrt-008-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00148", "doi": "10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-my-008-047,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00149", "doi": "10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-nrt-008-046,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"abstract": "Mean Dynamic Topography that combines the global CNES-CLS-2022 MDT, the Black Sea CMEMS2020 MDT and the Med Sea CMEMS2020 MDT. It is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid. This is consistent with the reference time period also used in the DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00150", "doi": "10.48670/moi-00150", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,invariant,level-4,marine-resources,marine-safety,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-glo-phy-mdt-008-063,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"abstract": "In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00144", "doi": "10.48670/moi-00144", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-noise-l4-static-008-033,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00151", "doi": "10.48670/moi-00151", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-med-phy-mdt-l4-static-008-066,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY"}, "SST_ATL_PHY_L3S_MY_010_038": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00311", "doi": "10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sst-atl-phy-l3s-my-010-038,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "SST_ATL_PHY_L3S_NRT_010_037": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00310", "doi": "10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-atl-phy-l3s-nrt-010-037,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"abstract": "For the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00152", "doi": "10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-nrt-observations-010-025,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"abstract": "For the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00153", "doi": "10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-rep-observations-010-026,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_L3S_MY_010_040": {"abstract": "For the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00312\n\nReferences:\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-l3s-my-010-040,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"abstract": "For the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00309\n\nReferences:\n\n* Karagali I. and H\u00f8yer, J. L. (2014). Characterisation and quantification of regional diurnal cycles from SEVIRI. Ocean Science, 10 (5), 745-758.\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-subskin-l4-nrt-010-034,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00154\n\nReferences:\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l3s-nrt-observations-010-032,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-03-11T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00155", "doi": "10.48670/moi-00155", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l4-nrt-observations-010-007-b,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-12-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Analysis L4"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00156\n\nReferences:\n\n* H\u00f8yer, J. L., & Karagali, I. (2016). Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-bal-sst-l4-rep-observations-010-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_L3S_MY_010_041": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00313\n\nReferences:\n\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18. Pisano, A., Buongiorno Nardelli, B., Tronconi, C. & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sens. Environ. 176, 107\u2013116.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n", "doi": "10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sst-bs-phy-l3s-my-010-041,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"abstract": "For the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00157\n\nReferences:\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-bs-phy-subskin-l4-nrt-010-035,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00158\n\nReferences:\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-bs-sst-l3s-nrt-observations-010-013,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00159\n\nReferences:\n\n* Buongiorno Nardelli B., S. Colella, R. Santoleri, M. Guarracino, A. Kholod, 2009: A re-analysis of Black Sea Surface Temperature, J. Mar. Sys.., doi:10.1016/j.jmarsys.2009.07.001\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-nrt-observations-010-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00160\n\nReferences:\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-rep-observations-010-022,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_GLO_PHY_L3S_MY_010_039": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00329", "doi": "10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-phy-l3s-my-010-039,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_PHY_L4_MY_010_044": {"abstract": "For the global ocean. The IFREMER/ODYSSEA Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.10deg. x 0.10deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00345", "doi": "10.48670/mds-00345", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-my-010-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean ODYSSEA L4 Sea Surface Temperature"}, "SST_GLO_PHY_L4_NRT_010_043": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00321", "doi": "10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-nrt-010-043,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00164", "doi": "10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-sst-l3s-nrt-observations-010-010,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"abstract": "For the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of: Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00165\n\nReferences:\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n* Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n* John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. McCulloch, 2007, OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system., Oceans 07 IEEE Aberdeen, conference proceedings. Marine challenges: coastline to deep sea. Aberdeen, Scotland.IEEE.\n", "doi": "10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-nrt-observations-010-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"abstract": "The OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00168\n\nReferences:\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720\n", "doi": "10.48670/moi-00168", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-rep-observations-010-011,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"abstract": "The ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00169\n\nReferences:\n\n* Good, S., Fiedler, E., Mao, C., Martin, M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720.\n* Lavergne, T., S\u00f8rensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkj\u00e6r, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\n", "doi": "10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,sst-glo-sst-l4-rep-observations-010-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "SST_MED_PHY_L3S_MY_010_042": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00314\n\nReferences:\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sst-med-phy-l3s-my-010-042,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"abstract": "For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[How to cite](https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00170\n\nReferences:\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-med-phy-subskin-l4-nrt-010-036,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00171\n\nReferences:\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-med-sst-l3s-nrt-observations-010-012,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. Since November 2024, the L4 MED UHR processing chain makes use of an improved background field as initial guess for the Optimal Interpolation of this product. The improvement is obtained in terms of the effective spatial resolution via the application of a convolutional neural network (CNN). These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00172\n\nReferences:\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n* Fanelli, C., Ciani, D., Pisano, A., & Buongiorno Nardelli, B. (2024). Deep Learning for Super-Resolution of Mediterranean Sea Surface Temperature Fields. EGUsphere, 2024, 1-18 (pre-print)\n", "doi": "10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-nrt-observations-010-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00173\n\nReferences:\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-rep-observations-010-021,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"abstract": "Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra.\nUsing linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered.\nThe dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land).\nThe integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.\nThis product is processed by the WAVE-TAC multi-mission SAR data processing system.\nIt processes near-real-time data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM.\nOne file is produced for each mission and is available in two formats depending on the user needs: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00178", "doi": "10.48670/moi-00178", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-fwk-l3-nrt-014-002,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"abstract": "Multi-Year mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00174", "doi": "10.48670/moi-00174", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-my-014-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"abstract": "Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. \n\nDOI (product): \nN/A", "doi": null, "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,global-ocean,iberian-biscay-irish-seas,level-3,mediterranean-sea,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-nrt-014-009,wave-spectrum", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"abstract": "Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00175", "doi": "10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l4-nrt-014-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"abstract": "Multi-Year mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission and in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nThis product is based on the ESA Sea State Climate Change Initiative data Level 3 product (version 2) and is formatted by the WAVE-TAC to be homogeneous with the CMEMS Level 3 Near-real-time product. It is based on the reprocessing of GDR data from the following altimeter missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa and Jason-3. CFOSAT Multi-Year dataset is based on the reprocessing of CFOSAT Level-2P products (CNES/CLS), inter-calibrated on Jason-3 reference mission issued from the CCI Sea State dataset.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH) and the unfiltered SWH (VAVH_UNFILTERED).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00176", "doi": "10.48670/moi-00176", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-my-014-005,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T06:29:22Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"abstract": "Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00179", "doi": "10.48670/moi-00179", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-nrt-014-001,weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"abstract": "Multi-Year gridded multi-mission merged satellite significant wave height based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2\u00b0 horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5\u00b0 horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00177", "doi": "10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,wave-glo-phy-swh-l4-my-014-007,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"abstract": "Near-Real-Time gridded multi-mission merged satellite significant wave height, based on CMEMS level-3 SWH datasets. Onyl valid data are included. It merges multiple along-track SWH data (Sentinel-6A,\u00a0 Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C) and produces daily gridded data at a 2\u00b0 horizontal resolution. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00180", "doi": "10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l4-nrt-014-003,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00338", "doi": "10.48670/mds-00338", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-my-012-105,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.'\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00330", "doi": "10.48670/mds-00330", "instrument": null, "keywords": "arctic-ocean,eastward-wind,level-3,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-nrt-012-100,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Arctic Sea"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00339", "doi": "10.48670/mds-00339", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-my-012-106,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00331", "doi": "10.48670/mds-00331", "instrument": null, "keywords": "eastward-wind,iberian-biscay-irish-seas,level-3,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-nrt-012-101,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Atlantic Sea"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00340", "doi": "10.48670/mds-00340", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-my-012-107,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00332", "doi": "10.48670/mds-00332", "instrument": null, "keywords": "baltic-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-nrt-012-102,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Baltic Sea"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00341", "doi": "10.48670/mds-00341", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-my-012-108,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00333", "doi": "10.48670/mds-00333", "instrument": null, "keywords": "black-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-nrt-012-103,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Black Sea"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"abstract": "For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments. The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00181", "doi": "10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-4,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-climate-l4-my-012-003,wind-speed", "license": "proprietary", "missionStartDate": "1994-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L3_MY_012_005": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00183", "doi": "10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-my-012-005,wind-speed,wind-to-direction,wvc-index", "license": "proprietary", "missionStartDate": "1991-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L3_NRT_012_002": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\nThe ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\nThe OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \nThe HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00182", "doi": "10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-nrt-012-002,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": "proprietary", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L4_MY_012_006": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00185", "doi": "10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-my-012-006", "license": "proprietary", "missionStartDate": "1994-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L4_NRT_012_004": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00305", "doi": "10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-nrt-012-004", "license": "proprietary", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00342", "doi": "10.48670/mds-00342", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-my-012-109,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00334", "doi": "10.48670/mds-00334", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-nrt-012-104,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Mediterranean Sea"}}, "providers_config": {"ANTARCTIC_OMI_SI_extent": {"collection": "ANTARCTIC_OMI_SI_extent"}, "ANTARCTIC_OMI_SI_extent_obs": {"collection": "ANTARCTIC_OMI_SI_extent_obs"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"collection": "ARCTIC_ANALYSISFORECAST_BGC_002_004"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_002_001"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"collection": "ARCTIC_ANALYSIS_FORECAST_WAV_002_014"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"collection": "ARCTIC_MULTIYEAR_BGC_002_005"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"collection": "ARCTIC_MULTIYEAR_PHY_002_003"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"collection": "ARCTIC_MULTIYEAR_PHY_ICE_002_016"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"collection": "ARCTIC_MULTIYEAR_WAV_002_013"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"collection": "ARCTIC_OMI_SI_Transport_NordicSeas"}, "ARCTIC_OMI_SI_extent": {"collection": "ARCTIC_OMI_SI_extent"}, "ARCTIC_OMI_SI_extent_obs": {"collection": "ARCTIC_OMI_SI_extent_obs"}, "ARCTIC_OMI_TEMPSAL_FWC": {"collection": "ARCTIC_OMI_TEMPSAL_FWC"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"collection": "BALTICSEA_ANALYSISFORECAST_BGC_003_007"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"collection": "BALTICSEA_ANALYSISFORECAST_PHY_003_006"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"collection": "BALTICSEA_ANALYSISFORECAST_WAV_003_010"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"collection": "BALTICSEA_MULTIYEAR_BGC_003_012"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"collection": "BALTICSEA_MULTIYEAR_PHY_003_011"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"collection": "BALTICSEA_MULTIYEAR_WAV_003_015"}, "BALTIC_OMI_HEALTH_codt_volume": {"collection": "BALTIC_OMI_HEALTH_codt_volume"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"collection": "BALTIC_OMI_OHC_area_averaged_anomalies"}, "BALTIC_OMI_SI_extent": {"collection": "BALTIC_OMI_SI_extent"}, "BALTIC_OMI_SI_volume": {"collection": "BALTIC_OMI_SI_volume"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Stz_trend"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Ttz_trend"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"collection": "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"collection": "BALTIC_OMI_WMHE_mbi_sto2tz_gotland"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"collection": "BLKSEA_ANALYSISFORECAST_BGC_007_010"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"collection": "BLKSEA_ANALYSISFORECAST_PHY_007_001"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"collection": "BLKSEA_ANALYSISFORECAST_WAV_007_003"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"collection": "BLKSEA_MULTIYEAR_BGC_007_005"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"collection": "BLKSEA_MULTIYEAR_PHY_007_004"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"collection": "BLKSEA_MULTIYEAR_WAV_007_006"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"collection": "BLKSEA_OMI_HEALTH_oxygen_trend"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"collection": "BLKSEA_OMI_TEMPSAL_sst_trend"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"collection": "GLOBAL_ANALYSISFORECAST_BGC_001_028"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"collection": "GLOBAL_ANALYSISFORECAST_PHY_001_024"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"collection": "GLOBAL_ANALYSISFORECAST_WAV_001_027"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"collection": "GLOBAL_MULTIYEAR_BGC_001_029"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"collection": "GLOBAL_MULTIYEAR_BGC_001_033"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"collection": "GLOBAL_MULTIYEAR_PHY_001_030"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"collection": "GLOBAL_MULTIYEAR_PHY_ENS_001_031"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"collection": "GLOBAL_MULTIYEAR_WAV_001_032"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"collection": "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"collection": "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_trend"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_26N_profile"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_OHC_trend": {"collection": "GLOBAL_OMI_OHC_trend"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_SL_thsl_trend": {"collection": "GLOBAL_OMI_SL_thsl_trend"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"collection": "GLOBAL_OMI_TEMPSAL_Tyz_trend"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"collection": "GLOBAL_OMI_TEMPSAL_sst_trend"}, "GLOBAL_OMI_WMHE_heattrp": {"collection": "GLOBAL_OMI_WMHE_heattrp"}, "GLOBAL_OMI_WMHE_northward_mht": {"collection": "GLOBAL_OMI_WMHE_northward_mht"}, "GLOBAL_OMI_WMHE_voltrp": {"collection": "GLOBAL_OMI_WMHE_voltrp"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"collection": "IBI_ANALYSISFORECAST_BGC_005_004"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"collection": "IBI_ANALYSISFORECAST_PHY_005_001"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"collection": "IBI_ANALYSISFORECAST_WAV_005_005"}, "IBI_MULTIYEAR_BGC_005_003": {"collection": "IBI_MULTIYEAR_BGC_005_003"}, "IBI_MULTIYEAR_PHY_005_002": {"collection": "IBI_MULTIYEAR_PHY_005_002"}, "IBI_MULTIYEAR_WAV_005_006": {"collection": "IBI_MULTIYEAR_WAV_005_006"}, "IBI_OMI_CURRENTS_cui": {"collection": "IBI_OMI_CURRENTS_cui"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "IBI_OMI_SEASTATE_swi": {"collection": "IBI_OMI_SEASTATE_swi"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "IBI_OMI_WMHE_mow": {"collection": "IBI_OMI_WMHE_mow"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"collection": "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"collection": "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"collection": "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"collection": "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"collection": "INSITU_GLO_BGC_DISCRETE_MY_013_046"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"collection": "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"collection": "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"collection": "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"collection": "INSITU_GLO_PHY_TS_OA_MY_013_052"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"collection": "INSITU_GLO_PHY_TS_OA_NRT_013_002"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"collection": "INSITU_GLO_WAV_DISCRETE_MY_013_045"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"collection": "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"collection": "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"collection": "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"collection": "MEDSEA_ANALYSISFORECAST_BGC_006_014"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"collection": "MEDSEA_ANALYSISFORECAST_PHY_006_013"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"collection": "MEDSEA_ANALYSISFORECAST_WAV_006_017"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"collection": "MEDSEA_MULTIYEAR_BGC_006_008"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"collection": "MEDSEA_MULTIYEAR_PHY_006_004"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"collection": "MEDSEA_MULTIYEAR_WAV_006_012"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"collection": "MEDSEA_OMI_OHC_area_averaged_anomalies"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"collection": "MEDSEA_OMI_TEMPSAL_sst_trend"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"collection": "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"collection": "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"collection": "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"collection": "MULTIOBS_GLO_PHY_MYNRT_015_003"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"collection": "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"collection": "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"collection": "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"collection": "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"collection": "MULTIOBS_GLO_PHY_W_3D_REP_015_007"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"collection": "NWSHELF_ANALYSISFORECAST_BGC_004_002"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"collection": "NWSHELF_ANALYSISFORECAST_PHY_004_013"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"collection": "NWSHELF_ANALYSISFORECAST_WAV_004_014"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"collection": "NWSHELF_MULTIYEAR_BGC_004_011"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"collection": "NWSHELF_MULTIYEAR_PHY_004_009"}, "NWSHELF_REANALYSIS_WAV_004_015": {"collection": "NWSHELF_REANALYSIS_WAV_004_015"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"collection": "OCEANCOLOUR_ARC_BGC_L3_MY_009_123"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"collection": "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"collection": "OCEANCOLOUR_ARC_BGC_L4_MY_009_124"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"collection": "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"collection": "OCEANCOLOUR_ATL_BGC_L3_MY_009_113"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"collection": "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"collection": "OCEANCOLOUR_ATL_BGC_L4_MY_009_118"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"collection": "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"collection": "OCEANCOLOUR_BAL_BGC_L3_MY_009_133"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"collection": "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"collection": "OCEANCOLOUR_BAL_BGC_L4_MY_009_134"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"collection": "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"collection": "OCEANCOLOUR_BLK_BGC_L3_MY_009_153"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"collection": "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"collection": "OCEANCOLOUR_BLK_BGC_L4_MY_009_154"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"collection": "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_103"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_107"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"collection": "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_104"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_108"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"collection": "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"collection": "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"collection": "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"collection": "OCEANCOLOUR_MED_BGC_L3_MY_009_143"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"collection": "OCEANCOLOUR_MED_BGC_L3_NRT_009_141"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"collection": "OCEANCOLOUR_MED_BGC_L4_MY_009_144"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"collection": "OCEANCOLOUR_MED_BGC_L4_NRT_009_142"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"collection": "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"collection": "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"collection": "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"collection": "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"collection": "OMI_CLIMATE_OSC_MEDSEA_volume_mean"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"collection": "OMI_CLIMATE_SL_GLOBAL_regional_trends"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_BAL_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_trend": {"collection": "OMI_CLIMATE_SST_BAL_trend"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IBI_trend": {"collection": "OMI_CLIMATE_SST_IBI_trend"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_anomaly"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_trend"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_trend"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"collection": "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"collection": "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"collection": "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"collection": "OMI_EXTREME_WAVE_BLKSEA_recent_changes"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"collection": "OMI_EXTREME_WAVE_BLKSEA_wave_power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"collection": "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"collection": "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"collection": "SEAICE_ANT_PHY_L3_MY_011_018"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"collection": "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"collection": "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"collection": "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"collection": "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"collection": "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"collection": "SEAICE_ARC_PHY_L3M_NRT_011_017"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"collection": "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"collection": "SEAICE_BAL_PHY_L4_MY_011_019"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"collection": "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"collection": "SEAICE_GLO_PHY_L4_MY_011_020"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"collection": "SEAICE_GLO_PHY_L4_NRT_011_014"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"collection": "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"collection": "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"collection": "SEALEVEL_EUR_PHY_L3_MY_008_061"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"collection": "SEALEVEL_EUR_PHY_L3_NRT_008_059"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"collection": "SEALEVEL_EUR_PHY_L4_MY_008_068"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"collection": "SEALEVEL_EUR_PHY_L4_NRT_008_060"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"collection": "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"collection": "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"collection": "SEALEVEL_GLO_PHY_L3_MY_008_062"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"collection": "SEALEVEL_GLO_PHY_L3_NRT_008_044"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"collection": "SEALEVEL_GLO_PHY_L4_MY_008_047"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"collection": "SEALEVEL_GLO_PHY_L4_NRT_008_046"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"collection": "SEALEVEL_GLO_PHY_MDT_008_063"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"collection": "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"collection": "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066"}, "SST_ATL_PHY_L3S_MY_010_038": {"collection": "SST_ATL_PHY_L3S_MY_010_038"}, "SST_ATL_PHY_L3S_NRT_010_037": {"collection": "SST_ATL_PHY_L3S_NRT_010_037"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"collection": "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"collection": "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026"}, "SST_BAL_PHY_L3S_MY_010_040": {"collection": "SST_BAL_PHY_L3S_MY_010_040"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"collection": "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"collection": "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"collection": "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"collection": "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016"}, "SST_BS_PHY_L3S_MY_010_041": {"collection": "SST_BS_PHY_L3S_MY_010_041"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"collection": "SST_BS_PHY_SUBSKIN_L4_NRT_010_035"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"collection": "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"collection": "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"collection": "SST_BS_SST_L4_REP_OBSERVATIONS_010_022"}, "SST_GLO_PHY_L3S_MY_010_039": {"collection": "SST_GLO_PHY_L3S_MY_010_039"}, "SST_GLO_PHY_L4_MY_010_044": {"collection": "SST_GLO_PHY_L4_MY_010_044"}, "SST_GLO_PHY_L4_NRT_010_043": {"collection": "SST_GLO_PHY_L4_NRT_010_043"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"collection": "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"collection": "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024"}, "SST_MED_PHY_L3S_MY_010_042": {"collection": "SST_MED_PHY_L3S_MY_010_042"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"collection": "SST_MED_PHY_SUBSKIN_L4_NRT_010_036"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"collection": "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"collection": "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"collection": "SST_MED_SST_L4_REP_OBSERVATIONS_010_021"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"collection": "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"collection": "WAVE_GLO_PHY_SPC_L3_MY_014_006"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"collection": "WAVE_GLO_PHY_SPC_L3_NRT_014_009"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"collection": "WAVE_GLO_PHY_SPC_L4_NRT_014_004"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"collection": "WAVE_GLO_PHY_SWH_L3_MY_014_005"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"collection": "WAVE_GLO_PHY_SWH_L3_NRT_014_001"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"collection": "WAVE_GLO_PHY_SWH_L4_MY_014_007"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"collection": "WAVE_GLO_PHY_SWH_L4_NRT_014_003"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"collection": "WIND_ARC_PHY_HR_L3_MY_012_105"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"collection": "WIND_ARC_PHY_HR_L3_NRT_012_100"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"collection": "WIND_ATL_PHY_HR_L3_MY_012_106"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"collection": "WIND_ATL_PHY_HR_L3_NRT_012_101"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"collection": "WIND_BAL_PHY_HR_L3_MY_012_107"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"collection": "WIND_BAL_PHY_HR_L3_NRT_012_102"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"collection": "WIND_BLK_PHY_HR_L3_MY_012_108"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"collection": "WIND_BLK_PHY_HR_L3_NRT_012_103"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"collection": "WIND_GLO_PHY_CLIMATE_L4_MY_012_003"}, "WIND_GLO_PHY_L3_MY_012_005": {"collection": "WIND_GLO_PHY_L3_MY_012_005"}, "WIND_GLO_PHY_L3_NRT_012_002": {"collection": "WIND_GLO_PHY_L3_NRT_012_002"}, "WIND_GLO_PHY_L4_MY_012_006": {"collection": "WIND_GLO_PHY_L4_MY_012_006"}, "WIND_GLO_PHY_L4_NRT_012_004": {"collection": "WIND_GLO_PHY_L4_NRT_012_004"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"collection": "WIND_MED_PHY_HR_L3_MY_012_109"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"collection": "WIND_MED_PHY_HR_L3_NRT_012_104"}}}, "earth_search": {"product_types_config": {"cop-dem-glo-30": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme.", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters.", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "landsat-c2-l2": {"abstract": "Atmospherically corrected global Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "sentinel-1-grd": {"abstract": "Sentinel-1 is a pair of Synthetic Aperture Radar (SAR) imaging satellites launched in 2014 and 2016 by the European Space Agency (ESA). Their 6 day revisit cycle and ability to observe through clouds makes this dataset perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "sentinel-1", "platformSerialIdentifier": "sentinel-1a,sentinel-1b", "processingLevel": null, "title": "Sentinel-1 Level-1C Ground Range Detected (GRD)"}, "sentinel-2-c1-l2a": {"abstract": "Sentinel-2 Collection 1 Level-2A, data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Collection 1 Level-2A"}, "sentinel-2-l1c": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l1c,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-1C"}, "sentinel-2-l2a": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-2-pre-c1-l2a": {"abstract": "Sentinel-2 Pre-Collection 1 Level-2A (baseline < 05.00), with data and metadata matching collection sentinel-2-c1-l2a", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-pre-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Pre-Collection 1 Level-2A "}}, "providers_config": {"cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "naip": {"productType": "naip"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-2-c1-l2a": {"productType": "sentinel-2-c1-l2a"}, "sentinel-2-l1c": {"productType": "sentinel-2-l1c"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-2-pre-c1-l2a": {"productType": "sentinel-2-pre-c1-l2a"}}}, "eumetsat_ds": {"product_types_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszfr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszor02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszrr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0088", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0142", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0143", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0236", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0237", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0238", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0239", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0240", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0241", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0274", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0300", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0301", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0302", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0303", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0305", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0343", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0344", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0345", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0348", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0349", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0374", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0394", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0398", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0405", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0406", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0407", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0408", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0409", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0410", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0411", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0412", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0413", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0414", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0415", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0416", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0417", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0533", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0556", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0557", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0558", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0576", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0577", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0578", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0579", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0581", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0582", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0583", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0584", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0585", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0586", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0601", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0615", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0617", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0645", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0647", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0662", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0665", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0676": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0676", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0676"}, "EO:EUM:DAT:0677": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0677", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0677"}, "EO:EUM:DAT:0678": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0678", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0678"}, "EO:EUM:DAT:0683": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0683", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0683"}, "EO:EUM:DAT:0684": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0684", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0684"}, "EO:EUM:DAT:0685": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0685", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0685"}, "EO:EUM:DAT:0686": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0686", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0687", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0688", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0690", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0691", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0758", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0782", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0799": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0799", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0799"}, "EO:EUM:DAT:0833": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0833", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0834", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0835", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0836", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0837", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0838", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0839", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0840", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0841", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0842", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0850", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0851", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0852", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0853", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0854", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0855", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0856", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0857", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0858", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0859", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0862", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0863", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0880": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0880", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0881", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0882", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0894", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0895", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0959", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0960", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0961", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0962", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0963", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0964", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0986", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0987", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:0998": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0998", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0998"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:dmsp:osi-401-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:amsul1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszf1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszo1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszr1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:avhrrl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:glb-sst-nc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:gomel1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil1c-all", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil2cox", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iassnd02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:lsa-002", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:mhsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:nto", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:oas025", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-104", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-a", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo12", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo25", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:CTH": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:cth", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CTH"}, "EO:EUM:DAT:MSG:CTH-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:cth-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CTH-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:msg15-rss", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:rss-clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:mult:hirsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MULT:HIRSL1"}}, "providers_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"parentIdentifier": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"parentIdentifier": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"parentIdentifier": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"parentIdentifier": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"parentIdentifier": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"parentIdentifier": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"parentIdentifier": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"parentIdentifier": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"parentIdentifier": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"parentIdentifier": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"parentIdentifier": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"parentIdentifier": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"parentIdentifier": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"parentIdentifier": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"parentIdentifier": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"parentIdentifier": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"parentIdentifier": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"parentIdentifier": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"parentIdentifier": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"parentIdentifier": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"parentIdentifier": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"parentIdentifier": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"parentIdentifier": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"parentIdentifier": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"parentIdentifier": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"parentIdentifier": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"parentIdentifier": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"parentIdentifier": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"parentIdentifier": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"parentIdentifier": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"parentIdentifier": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"parentIdentifier": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"parentIdentifier": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"parentIdentifier": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"parentIdentifier": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"parentIdentifier": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"parentIdentifier": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"parentIdentifier": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"parentIdentifier": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"parentIdentifier": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"parentIdentifier": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"parentIdentifier": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"parentIdentifier": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"parentIdentifier": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"parentIdentifier": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"parentIdentifier": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"parentIdentifier": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"parentIdentifier": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"parentIdentifier": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"parentIdentifier": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"parentIdentifier": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"parentIdentifier": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"parentIdentifier": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"parentIdentifier": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"parentIdentifier": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"parentIdentifier": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"parentIdentifier": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0676": {"parentIdentifier": "EO:EUM:DAT:0676"}, "EO:EUM:DAT:0677": {"parentIdentifier": "EO:EUM:DAT:0677"}, "EO:EUM:DAT:0678": {"parentIdentifier": "EO:EUM:DAT:0678"}, "EO:EUM:DAT:0683": {"parentIdentifier": "EO:EUM:DAT:0683"}, "EO:EUM:DAT:0684": {"parentIdentifier": "EO:EUM:DAT:0684"}, "EO:EUM:DAT:0685": {"parentIdentifier": "EO:EUM:DAT:0685"}, "EO:EUM:DAT:0686": {"parentIdentifier": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"parentIdentifier": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"parentIdentifier": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"parentIdentifier": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"parentIdentifier": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"parentIdentifier": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"parentIdentifier": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0799": {"parentIdentifier": "EO:EUM:DAT:0799"}, "EO:EUM:DAT:0833": {"parentIdentifier": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"parentIdentifier": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"parentIdentifier": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"parentIdentifier": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"parentIdentifier": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"parentIdentifier": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"parentIdentifier": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"parentIdentifier": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"parentIdentifier": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"parentIdentifier": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"parentIdentifier": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"parentIdentifier": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"parentIdentifier": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"parentIdentifier": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"parentIdentifier": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"parentIdentifier": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"parentIdentifier": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"parentIdentifier": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"parentIdentifier": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"parentIdentifier": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"parentIdentifier": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"parentIdentifier": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0880": {"parentIdentifier": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"parentIdentifier": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"parentIdentifier": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"parentIdentifier": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"parentIdentifier": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"parentIdentifier": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"parentIdentifier": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"parentIdentifier": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"parentIdentifier": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"parentIdentifier": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"parentIdentifier": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"parentIdentifier": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"parentIdentifier": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:0998": {"parentIdentifier": "EO:EUM:DAT:0998"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"parentIdentifier": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"parentIdentifier": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"parentIdentifier": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"parentIdentifier": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"parentIdentifier": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"parentIdentifier": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"parentIdentifier": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"parentIdentifier": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:CTH": {"parentIdentifier": "EO:EUM:DAT:MSG:CTH"}, "EO:EUM:DAT:MSG:CTH-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:CTH-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"parentIdentifier": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"parentIdentifier": "EO:EUM:DAT:MULT:HIRSL1"}}}, "geodes": {"product_types_config": {"FLATSIM_AFAR_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, etc.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-afar-auxiliarydata-public,ground-to-radar,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar interferograms"}, "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-afar-interferogram-public,ground-geometry,insar,interferogram,landslides,radar-geometry,tectonics,unwrapped,volcanology,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar interferograms"}, "FLATSIM_AFAR_TIMESERIE_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-afar-timeserie-public,ground-geometry,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar time series"}, "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, etc.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-andes-auxiliarydata-public,ground-to-radar,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes auxiliary data"}, "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-andes-interferogram-public,ground-geometry,insar,interferogram,landslides,radar-geometry,tectonics,unwrapped,volcanology,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes interferograms"}, "FLATSIM_ANDES_TIMESERIE_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-andes-timeserie-public,ground-geometry,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes time series"}, "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,anthropogenic-and-climatic-hazards,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-balkans-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans auxiliary data"}, "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "anthropogenic-and-climatic-hazards,atmospheric-phase-screen,coherence,deformation,flatsim-balkans-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans interferograms"}, "FLATSIM_BALKANS_TIMESERIE_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "anthropogenic-and-climatic-hazards,data-cube,deformation,flatsim-balkans-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans time series"}, "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-caucase-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus auxiliary data"}, "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-caucase-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus interferograms"}, "FLATSIM_CAUCASE_TIMESERIE_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-caucase-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus time series"}, "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range, (2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes, (3) understand the link between the seismic cycle and relief building, (4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-chili-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile auxiliary data"}, "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range,(2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes,(3) understand the link between the seismic cycle and relief building,(4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-chili-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile interferograms"}, "FLATSIM_CHILI_TIMESERIE_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range, (2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes, (3) understand the link between the seismic cycle and relief building, (4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-chili-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile time series"}, "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.)This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-corse-auxiliarydata-public,ground-to-radar,hydrology,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,subsidences,tectonic,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse auxiliary data"}, "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.) Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-corse-interferogram-public,ground-geometry,hydrology,insar,interferogram,landslides,radar-geometry,subsidences,tectonic,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse interferograms"}, "FLATSIM_CORSE_TIMESERIE_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog. The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.) This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-corse-timeserie-public,ground-geometry,hydrology,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,subsidences,tectonic,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse time series"}, "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-france-auxiliarydata-public,ground-to-radar,hydrology,insar,landslides,lookup-tables,radar-to-ground,rock-glaciers,spectral-diversity,subsidence,tectonic,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France auxiliary data"}, "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform. After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog. The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-france-interferogram-public,ground-geometry,hydrology,insar,interferogram,landslides,radar-geometry,rock-glaciers,subsidence,tectonic,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France interferograms"}, "FLATSIM_FRANCE_TIMESERIE_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-france-timeserie-public,ground-geometry,hydrology,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,rock-glaciers,stack-list,subsidence,tectonic,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France time series"}, "FLATSIM_INDE_AUXILIARYDATA_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-inde-auxiliarydata-public,ground-to-radar,insar,landslide-instability,lookup-tables,radar-to-ground,spectral-diversity,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India auxiliary data"}, "FLATSIM_INDE_INTERFEROGRAM_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-inde-interferogram-public,ground-geometry,insar,interferogram,landslide-instability,radar-geometry,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India interferograms"}, "FLATSIM_INDE_TIMESERIE_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-inde-timeserie-public,ground-geometry,insar,landslide-instability,mean-los-velocity,quality-maps,radar-geometry,stack-list,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India time series"}, "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity).This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-levant-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant auxiliary data"}, "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-levant-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant interferograms"}, "FLATSIM_LEVANT_TIMESERIE_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-levant-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant time series"}, "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-maghreb-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb auxiliary data"}, "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-maghreb-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb interferograms"}, "FLATSIM_MAGHREB_TIMESERIE_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-maghreb-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb time series"}, "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-makran-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran auxiliary data"}, "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-makran-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran interferograms"}, "FLATSIM_MAKRAN_TIMESERIE_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-makran-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran time series"}, "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-mexique-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico auxiliary data"}, "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-mexique-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico interferograms"}, "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-mexique-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico time series"}, "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-mozambique-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique auxiliary data"}, "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-mozambique-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique interferograms"}, "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-mozambique-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique time series"}, "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-okavango-auxiliarydata-public,ground-to-radar,hydrology,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango auxiliary data"}, "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-okavango-interferogram-public,ground-geometry,hydrology,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango interferograms"}, "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-okavango-timeserie-public,ground-geometry,hydrology,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango time series"}, "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-ozark-auxiliarydata-public,ground-to-radar,hydrology,insar,lookup-tables,radar-to-ground,spectral-diversity,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark auxiliary data"}, "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-ozark-interferogram-public,ground-geometry,hydrology,insar,interferogram,radar-geometry,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark interferograms"}, "FLATSIM_OZARK_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-ozark-timeserie-public,ground-geometry,hydrology,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark time series"}, "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-showcase-auxiliarydata-public,ground-to-radar,insar,junit-vector,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase auxiliary data"}, "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-showcase-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase interferograms"}, "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-showcase-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase time series"}, "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tarim-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim auxiliary data"}, "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tarim-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim interferograms"}, "FLATSIM_TARIM_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-tarim-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim time series"}, "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults,(2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range,and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tianshan-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan auxiliary data"}, "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults, (2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range, and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tianshan-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan interferograms"}, "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults, (2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range, and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-tianshan-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan time series"}, "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tibethim-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet auxiliary data"}, "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tibethim-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet interferograms"}, "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-tibethim-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet time series"}, "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-turquie-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey auxiliary data"}, "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-turquie-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey interferograms"}, "FLATSIM_TURQUIE_TIMESERIE_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-turquie-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey time series"}, "MUSCATE_LANDSAT_LANDSAT8_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects along with a mask of clouds and their shadows, as well as a mask of water and snow. Landsat products are provided by Theia in surface reflectance (level 2A) with cloud masks, the processing being performed with the MAJA algorithm. They are orthorectified and cut on the same tiles as Sentinel-2 products.", "instrument": null, "keywords": "boa-reflectance,l2a,l8,landsat,landsat8,muscate-landsat-landsat8-l2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2013-04-11T10:13:56Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT8 L2A"}, "MUSCATE_Landsat57_LANDSAT5_N2A": {"abstract": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "instrument": null, "keywords": "boa-reflectance,l2a,l5,landsat,landsat5,muscate-landsat57-landsat5-n2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2009-01-09T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT5 L2A"}, "MUSCATE_Landsat57_LANDSAT7_N2A": {"abstract": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "instrument": null, "keywords": "boa-reflectance,l2a,l7,landsat,landsat7,muscate-landsat57-landsat7-n2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2009-01-03T10:42:49Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT7 L2A"}, "MUSCATE_OSO_RASTER_L3B-OSO": {"abstract": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The classification raster is a single raster covering the whole French metropolitan territory. It has a spatial resolution of 10 m. It results from the processing of the complete Sentinel-2 time series of the reference year using the iota\u00b2 processing chain. A Random Forest classification model is calibrated using a training dataset derived from a combination of several national and international vector data sources (BD TOPO IGN, Corine Land Cover, Urban Atlas, R\u00e9f\u00e9rentiel Parcellaire Graphique, etc.).", "instrument": null, "keywords": "l3b,l3b-oso,muscate-oso-raster-l3b-oso,oso,raster", "license": "Apache-2.0", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE OSO RASTER"}, "MUSCATE_OSO_VECTOR_L3B-OSO": {"abstract": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The Vector format is a product with a minimum collection unit of 0.1 ha derived from the 20 m raster with a procedure of regularization and a simplification of the polygons obtained. In order to preserve as much information as possible from the raster product, each polygon is characterized by a set of attributes: - The majority class, with the same nomenclature of the raster product. - The average number of cloud-free images used for classification and the standard deviation. These attributes are named validmean and validstd. - The confidence of the majority class obtained from the Random Forest classifier (value between 0 and 100). - The percentage of the area covered by each class of the classification. This percentage is calculated on the 10m raster, even if the simplified polygons are derived from the 20m raster. - The area of the polygon. - The product is clipped according to the administrative boundaries of the departments and stored in a zip archive containing the 4 files that make up the \u201cESRI Shapefile\u201d format.", "instrument": null, "keywords": "l3b,l3b-oso,muscate-oso-vector-l3b-oso,oso,vector", "license": "Apache-2.0", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE OSO VECTOR"}, "MUSCATE_PLEIADES_PLEIADES_ORTHO": {"abstract": "After the successful launch of Pleiades 1A (17 December 2011) and Pleiades 1B (1 December 2012), a Thematic Acceptance Phase (RTU) was set up by CNES, in cooperation with Airbus Defence and Space. The RTU took place over two years, from March 2012 to March 2014, with the objective of: - test THR imagery and the capabilities of Pleiades satellites (agility, stereo/tri-stereo,...) - benefit from the dedicated access policy for French institutions within the framework of the Delegation of Public Service (DSP) - thematically \u00abevaluate/validate\u00bb the value-added products and services defined through 130 thematic studies proposed by the various Working Groups. These studies covered 171 geographical sites, covering several fields (coastline, sea, cartography, geology, risks, hydrology, forestry, agriculture). - evaluate the algorithms and tools developed through the Methodological Component of the ORFEO programme. More than 650 Pleiades images representing a volume of nearly 170,000 km2 that were acquired by CNES and made available free of charge to some sixty French scientific and institutional laboratories. All images acquired within the specific framework of the RTU are considered as demonstration products for non-commercial use only.", "instrument": null, "keywords": "l1c,muscate-pleiades-pleiades-ortho,pleiades,reflectance,satellite-image,toa-reflectance", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Pleiades RTU L1C"}, "MUSCATE_PLEIADES_PLEIADES_PRIMARY": {"abstract": "After the successful launch of Pleiades 1A (17 December 2011) and Pleiades 1B (1 December 2012), a Thematic Acceptance Phase (RTU) was set up by CNES, in cooperation with Airbus Defence and Space. The RTU took place over two years, from March 2012 to March 2014, with the objective of: - test THR imagery and the capabilities of Pleiades satellites (agility, stereo/tri-stereo,...) - benefit from the dedicated access policy for French institutions within the framework of the Delegation of Public Service (DSP) - thematically \u00abevaluate/validate\u00bb the value-added products and services defined through 130 thematic studies proposed by the various Working Groups. These studies covered 171 geographical sites, covering several fields (coastline, sea, cartography, geology, risks, hydrology, forestry, agriculture). - evaluate the algorithms and tools developed through the Methodological Component of the ORFEO programme. More than 650 Pleiades images representing a volume of nearly 170,000 km2 that were acquired by CNES and made available free of charge to some sixty French scientific and institutional laboratories. All images acquired within the specific framework of the RTU are considered as demonstration products for non-commercial use only.", "instrument": null, "keywords": "l1a,muscate-pleiades-pleiades-primary,pleiades,pleiades-1a,pleiades-1b,primary,rtu,satellite-image", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Pleiades RTU L1A"}, "MUSCATE_SENTINEL2_SENTINEL2_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA (before called MACCS) for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-sentinel2-sentinel2-l2a,reflectance,s2,satellite-image,sentinel,sentinel2,surface", "license": "Apache-2.0", "missionStartDate": "2015-07-04T10:10:35.881Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SENTINEL2 L2A"}, "MUSCATE_SENTINEL2_SENTINEL2_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations.", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-sentinel2-sentinel2-l3a,reflectance,s2,satellite-image,sentinel,sentinel2,surface", "license": "Apache-2.0", "missionStartDate": "2017-07-15T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SENTINEL2 L3A"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot1-l1c,reflectance,satellite-image,spot,spot-1,spot1,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1986-03-18T20:21:50Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT1 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot2-l1c,reflectance,satellite-image,spot,spot-2,spot2,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1990-02-23T08:22:06Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT2 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot3-l1c,reflectance,satellite-image,spot,spot-3,spot3,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1993-10-02T13:56:34Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT3 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot4-l1c,reflectance,satellite-image,spot,spot-4,spot4,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1998-03-27T11:19:29Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT4 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Nowadays, two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot5-l1c,reflectance,satellite-image,spot,spot-5,spot5,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2002-06-21T09:52:57Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT5 L1C"}, "MUSCATE_Snow_LANDSAT8_L2B-SNOW": {"abstract": "The Theia snow product indicates the snow presence or absence on the land surface every fifth day if there is no cloud. The product is distributed by Theia as a raster file (8 bits GeoTIFF) of 20 m resolution and a vector file (Shapefile polygons). Level 2 offers monotemporal data, i.e. from ortho-rectified Sentinel-2 mono-date images, expressed in surface reflectance and accompanied by a cloud mask.", "instrument": null, "keywords": "cover,cryosphere,l2b,l2b-snow,l8,landsat,landsat8,muscate-snow-landsat8-l2b-snow,presence,snow,snow-mask", "license": "Apache-2.0", "missionStartDate": "2013-04-11T10:13:56Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Snow LANDSAT8 L2B"}, "MUSCATE_Snow_MULTISAT_L3B-SNOW": {"abstract": "Level 3 snow products offers annual syntheses of snow cover duration per pixel between September 1 and August 31. The L3B SNOW maps generated on September 1 are made from Sentinel-2 and Landsat-8 data. Those generated on September 2 will only be made from Sentinel-2. 4 raster files are provided in the product : 1- the snow cover duration map (SCD), pixel values within [0-number of days] corresponding the number of snow days, 2- the date of snow disappearance (Snow Melt-Out Date), defined as the last date of the longest snow period, 3- the date of snow appearance (Snow Onset Date), defined as the first date of the longest snow period, 4- the number of clear observations (NOBS) to compute the 3 other files.", "instrument": null, "keywords": "l3b,landsat,landsat8,muscate-snow-multisat-l3b-snow,presence,sentinel,sentinel2,snow,snow-cover", "license": "Apache-2.0", "missionStartDate": "2017-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE L3B Snow"}, "MUSCATE_Snow_SENTINEL2_L2B-SNOW": {"abstract": "Theia Snow product is generated from Sentinel-2 (20m resolution, every 5 days or less) and Landsat-8 images over selected areas of the globe. The processing chain used is Let-it-snow (LIS).", "instrument": null, "keywords": "cover,cryosphere,l2b,l2b-snow,muscate-snow-sentinel2-l2b-snow,presence,s2,sentinel2,snow,snow-mask", "license": "Apache-2.0", "missionStartDate": "2015-08-18T17:42:49Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Snow SENTINEL2 L2B"}, "MUSCATE_Spirit_SPOT5_L1A": {"abstract": "SPOT 5 stereoscopic survey of polar ice. The objectives of the SPIRIT project were to build a large archive of Spot 5 HRS images of polar ice and, for certain regions, to produce digital terrain models (DEMs) and high-resolution images for free distribution to the community. . scientist. The target areas were the coastal regions of Greenland and Antarctica as well as all other glacial masses (Alaska, Iceland, Patagonia, etc.) surrounding the Arctic Ocean and Antarctica. The SPIRIT project made it possible to generate an archive of DEMs at 40m planimetric resolution from the HRS instrument.", "instrument": null, "keywords": "1a,dem,glacier,ice,muscate-spirit-spot5-l1a,spirit,spot,spot5", "license": "Apache-2.0", "missionStartDate": "2003-08-06T12:54:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Spirit SPOT5 L1A"}, "MUSCATE_VENUSVM05_VM5_L1C": {"abstract": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13, bad pixel mask - channel 14, and cloudy pixels - channel 15).", "instrument": null, "keywords": "l1c,muscate-venusvm05-vm5-l1c,reflectance,satellite-image,toa-reflectance,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2022-03-09T11:42:13Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L1C"}, "MUSCATE_VENUSVM05_VM5_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-venusvm05-vm5-l2a,reflectance,satellite-image,surface,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2022-03-09T11:42:13Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L2A"}, "MUSCATE_VENUSVM05_VM5_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-venusvm05-vm5-l3a,reflectance,synthesis,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2023-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L3A"}, "MUSCATE_VENUS_VM1_L1C": {"abstract": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13, bad pixel mask - channel 14, and cloudy pixels - channel 15).", "instrument": null, "keywords": "l1c,muscate-venus-vm1-l1c,reflectance,satellite-image,toa-reflectance,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-11-01T10:06:54Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L1C"}, "MUSCATE_VENUS_VM1_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-venus-vm1-l2a,reflectance,satellite-image,surface,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-11-01T10:06:54Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L2A"}, "MUSCATE_VENUS_VM1_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-venus-vm1-l3a,reflectance,synthesis,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L3A"}, "MUSCATE_WaterQual_SENTINEL2_L2B-WATER": {"abstract": "The processing chain outputs rasters of the concentration of SPM estimated in the Bands B4 and B8. The concentration is given in mg/L. So a pixel value of 21.34 corresponds to 21.34 mg/L estimated at this point. The value -10000 signifies that there is no- or invalid data available. The concentration is always calculated only over the pixels classified as water. An RGB raster for the given ROI is also included. The values correspond to reflectance TOC (Top-Of-Canopy), which is unitless. Several masks generated by the Temporal-Synthesis are also included.", "instrument": null, "keywords": "l2b,l2b-water,muscate-waterqual-sentinel2-l2b-water,s2,sentinel,sentinel2,sentinel2a,sentinel2b", "license": "Apache-2.0", "missionStartDate": "2016-01-10T10:30:07Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE WaterQual SENTINEL2 L2B"}, "PEPS_S1_L1": {"abstract": "Sentinel-1 Level-1 products are the baseline products for the majority of users from which higher levels are derived. From data in each acquisition mode, the Instrument Processing Facility (IPF) generates focused Level-1 Single Look Complex (SLC) products and Level-1 Ground Range Detected (GRD) products. SAR parameters that vary with the satellite position in orbit, such as azimuth FM rate, Doppler centroid frequency and terrain height, are periodically updated to ensure the homogeneity of the scene when processing a complete data take. Similarly, products generated from WV data can contain any number of vignettes, potentially up to an entire orbit's worth. All Level-1 products are geo-referenced and time tagged with zero Doppler time at the centre of the swath. Geo-referencing is corrected for the azimuth bi-static bias by taking into account the pulse travel time delta between the centre of the swath and the range of each geo-referenced point. A Level-1 product can be one of the following two types: Single Look Complex (SLC) products or Ground Range Detected (GRD) products Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Level-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.", "instrument": null, "keywords": "backscatter,csar,grd,imagingradars,level1,peps-s1-l1,s1,sar,sentinel-1,sentinel1,slc", "license": "Apache-2.0", "missionStartDate": "2014-06-15T03:44:44.792Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-1 Level1"}, "PEPS_S1_L2": {"abstract": "Sentinel-1 Level-2 consists of geolocated geophysical products derived from Level-1. There is only one standard Level-2 product for wind, wave and currents applications - the Level-2 Ocean (OCN) product. The OCN product may contain the following geophysical components derived from the SAR data: - Ocean Wind field (OWI) - Ocean Swell spectra (OSW) - Surface Radial Velocity (RVL). OCN products are generated from all four Sentinel-1 imaging modes. From SM mode, the OCN product will contain all three components. From IW and EW modes, the OCN product will only contain OWI and RVL. From WV modes, the OCN product will only contain OSW and RVL.", "instrument": null, "keywords": "csar,oceans,oceanswellspectra,oceanwindfield,ocn,peps-s1-l2,s1,sar,sentinel1,surfaceradialvelocity,wavedirection,waveheight,wavelength,waveperiod,windstress", "license": "Apache-2.0", "missionStartDate": "2014-12-30T13:31:51.933Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-1 Level2"}, "PEPS_S2_L1C": {"abstract": "Sentinel-2 L1C tiles acquisition and storage from PEPS. Data are provided per S2 tile.", "instrument": null, "keywords": "l1c,peps-s2-l1c,reflectance,s2,sentinel2,toareflectance", "license": "Apache-2.0", "missionStartDate": "2015-07-04T10:10:06.027Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-2 L1C tiles"}, "PEPS_S2_L2A": {"abstract": "Sentinel-2 L2A tiles acquisition and storage from PEPS. Data are provdided per S2 tile.", "instrument": null, "keywords": "cloudcover,l2a,peps-s2-l2a,reflectance,s2,sentinel2,surfacereflectance", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-2 L2A tiles"}, "PEPS_S3_L1": {"abstract": "Sea surface topography measurements to at least the level of quality of the ENVISAT altimetry system, including an along track SAR capability of CRYOSAT heritage for improved measurement quality in coastal zones and over sea-ice", "instrument": null, "keywords": "altimetry,l1,level1,peps-s3-l1,s3,sentinel3,sral,ssh,stm,swh,windspeed", "license": "Apache-2.0", "missionStartDate": "2022-04-20T18:46:06.819Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GDH Sentinel-3 L1 STM Level-1 products"}, "POSTEL_LANDCOVER_GLOBCOVER": {"abstract": "A land cover map associates to each pixel of the surface a labelling characterizing the surface (ex : deciduous forest, agriculture area, etc) following a predefined nomenclature. A commonly used nomenclature is the LCCS (Land Cover Classification System) used by FAO and UNEP, and comprising 22 classes. POSTEL produces and makes available the global land cover map at 300 m resolution of the GLOBCOVER / ESA project, which can be viewed with a zooming capacity. Regional maps are also available with classes adapted to each bioclimatic area.", "instrument": null, "keywords": "classification,land-cover,land-surface,postel,postel-landcover-globcover", "license": "Apache-2.0", "missionStartDate": "2004-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL global land cover"}, "POSTEL_RADIATION_BRDF": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Bidirectional Reflectance Distribution Function (FDRB) describes how terrestrial surfaces reflect the sun radiation. Its potential has been demonstrated for several applications in land surface studies (see Bicheron and Leroy, 2000). The space-borne POLDER-1/ADEOS-1 instrument (November 1996 \u2013 June 1997) has provided the first opportunity to sample the BRDF of every point on Earth for viewing angles up to 60\u00b0-70\u00b0, and for the full azimuth range, at a spatial resolution of about 6km, when the atmospheric conditions are favorable (Hautecoeur et Leroy, 1998). From April to October 2003, the land surface BRDF was sampled by the POLDER-2/ADEOS-2 sensor. From March 2005, the POLDER-3 sensor onboard the PARASOL microsatellite measures the bi-driectional reflectance of the continental ecosystems. These successive observations allowed building : 1- a BRDF database from the 8 months of POLDER-1 mesurements : The POLDER-1 BRDF data base compiles 24,857 BRDFs acquired by ADEOS-1/POLDER-1 during 8 months, from November, 1996 to June, 1997, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-1 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms developed for the processing line of the ADEOS-2/POLDER-2 data. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000. 2- a BRDF database from the 7 months of POLDER-2 measurements : The POLDER-2 BRDF data base compiles 24,090 BRDFs acquired by ADEOS-2/POLDER-2 from April ro October 2003, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-2 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms described on the CNES scientific Web site. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000 land cover map. 3- 4 BRDF databases from one year of POLDER-3 measurements :The LSCE, one of the POSTEL Expertise Centre, defined a new method to select the BRDFs from POLDER-3/PARASOL data acquired from November 2005 to October 2006 in order to build 4 BRDF databases. 2 MONTHLY databases gathering the best quality BRDFs for each month, independently : one based upon the IGBP land cover map, the second based upon the GLC2000 land cover map. 2 YEARLY databases designed to monitor the annual cycle of surface reflectance and its directional signature. The selection of high quality pixels is based on the full year. The first database is based upon the IGBP land cover map, the second one is based upon the GLC2000 land cover map.", "instrument": null, "keywords": "bidirectional-reflectance-distribution-function,brdf,land,land-surface,polder,postel,postel-radiation-brdf,radiation,reflectance", "license": "Apache-2.0", "missionStartDate": "1996-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation BRDF"}, "POSTEL_RADIATION_DLR": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Downwelling Longwave Radiation (W.m-2) (DLR) is defined as the thermal irradiance reaching the surface in the thermal infrared spectrum (4 \u2013 100 \u00b5m). It is determined by the radiation that originates from a shallow layer close to the surface, about one third being emitted by the lowest 10 meters and 80% by the 500-meter layer. The DLR is derived from several sensors (Meteosat, MSG) using various approaches, in the framework of the Geoland project.", "instrument": null, "keywords": "geoland,irradiance,land,land-surface,long-wave-radiation-descending-flux,longwave,postel,postel-radiation-dlr,radiation,thermal", "license": "Apache-2.0", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Downwelling Longwave Radiation"}, "POSTEL_RADIATION_SURFACEALBEDO": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The albedo is the fraction of the incoming solar radiation reflected by the land surface, integrated over the whole viewing directions. The albedo can be directional (calculated for a given sun zenith angle, also called \u201cblack-sky albedo\u201d) or hemispheric (integrated over all illumination directions, also called \u201cwhite-sky albedo\u201d), spectral (for each narrow band of the sensor) or broadband (integrated over the solar spectrum). The surface albedos are derived from many sensors (Vegetation, Polder, Meteosat) in the frame of different projects, namely Geoland and Amma.", "instrument": null, "keywords": "albedo,amma,bio,geoland,land-surface-albedo,polder,postel,postel-radiation-surfacealbedo,radiation,surface", "license": "Apache-2.0", "missionStartDate": "1996-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation Surface Albedo"}, "POSTEL_RADIATION_SURFACEREFLECTANCE": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "instrument": null, "keywords": "boa-reflectance,land,parasol,polder,postel,postel-radiation-surfacereflectance,radiation,reflectance,surface,surface-reflectance", "license": "Apache-2.0", "missionStartDate": "1996-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation Surface Reflectance"}, "POSTEL_VEGETATION_FAPAR": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FAPAR is defined as the fraction of photosynthetically active radiation absorbed by vegetation for photosynthesis activity. The FAPAR can be instantaneous or daily. FAPAR is assessed using various approaches and algorithms applied to many sensors (Vegetation, Polder, Modis, AVHRR) in the frame of Polder and Amma projects.", "instrument": null, "keywords": "amma,bio,biosphere,fapar,fraction-of-absorbed-photosynthetically-active-radiation,geoland,polder,postel,postel-vegetation-fapar,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation FAPAR"}, "POSTEL_VEGETATION_FCOVER": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FCover is the fraction of ground surface covered by vegetation. Fcover is assessed using various approaches and algorithms applied to Vegetation, and Polder, data in the frame of the Cyclopes project.", "instrument": null, "keywords": "bio,biosphere,cyclopes,fcover,geoland,postel,postel-vegetation-fcover,vegetation,vegetation-cover-fraction", "license": "Apache-2.0", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation FCover"}, "POSTEL_VEGETATION_LAI": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation LAI is defined as half the total foliage area per unit of ground surface (Chen and Black, 1992). It is assessed using various approaches and algorithms applied to many sensors data (Vegetation, Polder, Modis, AVHRR) in the frame of the Amma project.", "instrument": null, "keywords": "amma,bio,biosphere,geoland,lai,leaf-area-index,postel,postel-vegetation-lai,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation LAI"}, "POSTEL_VEGETATION_NDVI": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. Postel Vegetation NDVI (Normalized Difference Vegetation Index) is calculated as the normalized ratio of the difference between the reflectances measured in the red and near-infrared sensor bands. The NDVI is the most frequently used vegetation index to assess the quantity of vegetation on the surface, and to monitor the temporal ecosystems variations. Postel provides NDVI, derived from observations of various sensors (Polder, AVHRR, Seviri) in the frame of different projects : Polder \u2013 Parasol and Amma.", "instrument": null, "keywords": "amma,bio,biosphere,ndvi,normalized-difference-vegetation-index,parasol,postel,postel-vegetation-ndvi,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation NDVI"}, "POSTEL_VEGETATION_SURFACEREFLECTANCE": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "instrument": null, "keywords": "boa-reflectance,cyclopes,geoland,globcover,land,postel,postel-vegetation-surfacereflectance,reflectance,surface,surface-reflectance,vegetation", "license": "Apache-2.0", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation Surface Reflectance"}, "POSTEL_WATER_PRECIP": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, IMP (University of Vienna) and EARS assess the precipitation amount from geo-stationnary sensors images using various approaches for applications of the Observatory Natural Carbon (ONC) and of the Observatory Food Security and Crop Monitoring (OFM). Postel Water PRECIP is global scale daily precipitation product based on existing multi-satellite products and bias-corrected precipitation gauge analyses.", "instrument": null, "keywords": "athmosphere,geoland,postel,postel-water-precip,precipitation,water", "license": "Apache-2.0", "missionStartDate": "1997-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Precipitation"}, "POSTEL_WATER_SOILMOISTURE": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, University of Bonn assess soil moisture parameters from passive micro-wave sensors measurements. Postel Water Soil Moisture is water column in mm, in the upper meter of soil.", "instrument": null, "keywords": "geoland,humidity,moisture,postel,postel-water-soilmoisture,soil,water,water-surface", "license": "Apache-2.0", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Soil Moisture"}, "POSTEL_WATER_SURFWET": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel SurfWet (Surface Wetness) is Soil moisture content in the 1-5 centimetre layer of the soil in relative units ranging between 0 wetness and total water capacity.", "instrument": null, "keywords": "geoland,postel,postel-water-surfwet,soil,soil-moisture,surface,surface-wetness,surfwet,water,water-surface", "license": "Apache-2.0", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Surface wet"}, "POSTEL_WATER_SWI": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel Water SWI (Soil Water Index) is soil moisture content in the 1st meter of the soil in relative units ranging between wilting level and field capacity.", "instrument": null, "keywords": "geoland,humidity,moisture,postel,postel-water-swi,soil,soil-water-index,swi,water,water-surface", "license": "Apache-2.0", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Soil Water Index"}, "SWH_SPOT123_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot123-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": "1986-02-23T08:53:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT1-2-3 Level1A"}, "SWH_SPOT4_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot4-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT4 Level1A"}, "SWH_SPOT5_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot5-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT5 Level1A"}, "TAKE5_SPOT4_L1C": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L1C products are data orthorectified reflectance at the top of the atmosphere.", "instrument": null, "keywords": "image,l1c,reflectance,satellite,spot,spot-4,spot4,take5,take5-spot4-l1c,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2013-01-31T01:57:43Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT4 LEVEL1C"}, "TAKE5_SPOT4_L2A": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "instrument": null, "keywords": "boa-reflectance,image,l2a,satellite,spot,spot-4,spot4,surface-reflectance,take5,take5-spot4-l2a", "license": "Apache-2.0", "missionStartDate": "2013-01-31T07:07:32Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT4 LEVEL2A"}, "TAKE5_SPOT5_L1C": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L1C products are data orthorectified reflectance at the top of the atmosphere.", "instrument": null, "keywords": "image,l1c,reflectance,satellite,spot,spot5,take5,take5-spot5-l1c,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2015-04-08T00:31:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT5 LEVEL1C"}, "TAKE5_SPOT5_L2A": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "instrument": null, "keywords": "boa-reflectance,image,l2a,reflectance,satellite,spot,spot5,take5,take5-spot5-l2a", "license": "Apache-2.0", "missionStartDate": "2015-04-08T00:31:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT5 LEVEL2A"}}, "providers_config": {"FLATSIM_AFAR_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_AFAR_AUXILIARYDATA_PUBLIC"}, "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC"}, "FLATSIM_AFAR_TIMESERIE_PUBLIC": {"productType": "FLATSIM_AFAR_TIMESERIE_PUBLIC"}, "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC"}, "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC"}, "FLATSIM_ANDES_TIMESERIE_PUBLIC": {"productType": "FLATSIM_ANDES_TIMESERIE_PUBLIC"}, "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC"}, "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC"}, "FLATSIM_BALKANS_TIMESERIE_PUBLIC": {"productType": "FLATSIM_BALKANS_TIMESERIE_PUBLIC"}, "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CAUCASE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CAUCASE_TIMESERIE_PUBLIC"}, "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CHILI_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CHILI_TIMESERIE_PUBLIC"}, "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CORSE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CORSE_TIMESERIE_PUBLIC"}, "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_FRANCE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_FRANCE_TIMESERIE_PUBLIC"}, "FLATSIM_INDE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_INDE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_INDE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_INDE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_INDE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_INDE_TIMESERIE_PUBLIC"}, "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC"}, "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC"}, "FLATSIM_LEVANT_TIMESERIE_PUBLIC": {"productType": "FLATSIM_LEVANT_TIMESERIE_PUBLIC"}, "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MAGHREB_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MAGHREB_TIMESERIE_PUBLIC"}, "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MAKRAN_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MAKRAN_TIMESERIE_PUBLIC"}, "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC"}, "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC"}, "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC"}, "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC"}, "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC": {"productType": "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC"}, "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC"}, "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC"}, "FLATSIM_OZARK_TIMESERIE_PUBLIC": {"productType": "FLATSIM_OZARK_TIMESERIE_PUBLIC"}, "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC"}, "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TARIM_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TARIM_TIMESERIE_PUBLIC"}, "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC"}, "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC"}, "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TURQUIE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TURQUIE_TIMESERIE_PUBLIC"}, "MUSCATE_LANDSAT_LANDSAT8_L2A": {"productType": "MUSCATE_LANDSAT_LANDSAT8_L2A"}, "MUSCATE_Landsat57_LANDSAT5_N2A": {"productType": "MUSCATE_Landsat57_LANDSAT5_N2A"}, "MUSCATE_Landsat57_LANDSAT7_N2A": {"productType": "MUSCATE_Landsat57_LANDSAT7_N2A"}, "MUSCATE_OSO_RASTER_L3B-OSO": {"productType": "MUSCATE_OSO_RASTER_L3B-OSO"}, "MUSCATE_OSO_VECTOR_L3B-OSO": {"productType": "MUSCATE_OSO_VECTOR_L3B-OSO"}, "MUSCATE_PLEIADES_PLEIADES_ORTHO": {"productType": "MUSCATE_PLEIADES_PLEIADES_ORTHO"}, "MUSCATE_PLEIADES_PLEIADES_PRIMARY": {"productType": "MUSCATE_PLEIADES_PLEIADES_PRIMARY"}, "MUSCATE_SENTINEL2_SENTINEL2_L2A": {"productType": "MUSCATE_SENTINEL2_SENTINEL2_L2A"}, "MUSCATE_SENTINEL2_SENTINEL2_L3A": {"productType": "MUSCATE_SENTINEL2_SENTINEL2_L3A"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C"}, "MUSCATE_Snow_LANDSAT8_L2B-SNOW": {"productType": "MUSCATE_Snow_LANDSAT8_L2B-SNOW"}, "MUSCATE_Snow_MULTISAT_L3B-SNOW": {"productType": "MUSCATE_Snow_MULTISAT_L3B-SNOW"}, "MUSCATE_Snow_SENTINEL2_L2B-SNOW": {"productType": "MUSCATE_Snow_SENTINEL2_L2B-SNOW"}, "MUSCATE_Spirit_SPOT5_L1A": {"productType": "MUSCATE_Spirit_SPOT5_L1A"}, "MUSCATE_VENUSVM05_VM5_L1C": {"productType": "MUSCATE_VENUSVM05_VM5_L1C"}, "MUSCATE_VENUSVM05_VM5_L2A": {"productType": "MUSCATE_VENUSVM05_VM5_L2A"}, "MUSCATE_VENUSVM05_VM5_L3A": {"productType": "MUSCATE_VENUSVM05_VM5_L3A"}, "MUSCATE_VENUS_VM1_L1C": {"productType": "MUSCATE_VENUS_VM1_L1C"}, "MUSCATE_VENUS_VM1_L2A": {"productType": "MUSCATE_VENUS_VM1_L2A"}, "MUSCATE_VENUS_VM1_L3A": {"productType": "MUSCATE_VENUS_VM1_L3A"}, "MUSCATE_WaterQual_SENTINEL2_L2B-WATER": {"productType": "MUSCATE_WaterQual_SENTINEL2_L2B-WATER"}, "PEPS_S1_L1": {"productType": "PEPS_S1_L1"}, "PEPS_S1_L2": {"productType": "PEPS_S1_L2"}, "PEPS_S2_L1C": {"productType": "PEPS_S2_L1C"}, "PEPS_S2_L2A": {"productType": "PEPS_S2_L2A"}, "PEPS_S3_L1": {"productType": "PEPS_S3_L1"}, "POSTEL_LANDCOVER_GLOBCOVER": {"productType": "POSTEL_LANDCOVER_GLOBCOVER"}, "POSTEL_RADIATION_BRDF": {"productType": "POSTEL_RADIATION_BRDF"}, "POSTEL_RADIATION_DLR": {"productType": "POSTEL_RADIATION_DLR"}, "POSTEL_RADIATION_SURFACEALBEDO": {"productType": "POSTEL_RADIATION_SURFACEALBEDO"}, "POSTEL_RADIATION_SURFACEREFLECTANCE": {"productType": "POSTEL_RADIATION_SURFACEREFLECTANCE"}, "POSTEL_VEGETATION_FAPAR": {"productType": "POSTEL_VEGETATION_FAPAR"}, "POSTEL_VEGETATION_FCOVER": {"productType": "POSTEL_VEGETATION_FCOVER"}, "POSTEL_VEGETATION_LAI": {"productType": "POSTEL_VEGETATION_LAI"}, "POSTEL_VEGETATION_NDVI": {"productType": "POSTEL_VEGETATION_NDVI"}, "POSTEL_VEGETATION_SURFACEREFLECTANCE": {"productType": "POSTEL_VEGETATION_SURFACEREFLECTANCE"}, "POSTEL_WATER_PRECIP": {"productType": "POSTEL_WATER_PRECIP"}, "POSTEL_WATER_SOILMOISTURE": {"productType": "POSTEL_WATER_SOILMOISTURE"}, "POSTEL_WATER_SURFWET": {"productType": "POSTEL_WATER_SURFWET"}, "POSTEL_WATER_SWI": {"productType": "POSTEL_WATER_SWI"}, "SWH_SPOT123_L1": {"productType": "SWH_SPOT123_L1"}, "SWH_SPOT4_L1": {"productType": "SWH_SPOT4_L1"}, "SWH_SPOT5_L1": {"productType": "SWH_SPOT5_L1"}, "TAKE5_SPOT4_L1C": {"productType": "TAKE5_SPOT4_L1C"}, "TAKE5_SPOT4_L2A": {"productType": "TAKE5_SPOT4_L2A"}, "TAKE5_SPOT5_L1C": {"productType": "TAKE5_SPOT5_L1C"}, "TAKE5_SPOT5_L2A": {"productType": "TAKE5_SPOT5_L2A"}}}, "planetary_computer": {"product_types_config": {"3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Classification"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Cloud"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Surface Model"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model (Native)"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Height above Ground"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Intensity"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Source"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Returns"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "missionStartDate": "1925-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Seamless DEMs"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "missionStartDate": "2016-12-07T00:00:00Z", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "title": "ALOS World 3D-30m"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS Forest/Non-Forest Annual Mosaic"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS PALSAR Annual Mosaic"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "missionStartDate": "2000-03-04T12:00:00Z", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "title": "ASTER L1T"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (13-class)"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (7-class)"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Use"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "missionStartDate": "2003-07-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chloris Biomass"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40] \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40] \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-SA-40] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)"}, "conus404": {"abstract": "[CONUS404](https://www.usgs.gov/data/conus404-four-kilometer-long-term-regional-hydroclimate-reanalysis-over-conterminous-united) is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the conterminous United States. CONUS404, so named because it covers the CONterminous United States for over 40 years at 4 km resolution, was produced by the Weather Research and Forecasting (WRF) model simulations run by NCAR as part of a collaboration with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water years 1980-2021) and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for CONUS surface waters.\n\nThe CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in [ds612.0](https://rda.ucar.edu/datasets/ds612.0/) (Liu, et al., 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the NOAH-Multiparameterization land surface model. It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5) as input and covers a longer period than CONUS1.", "instrument": null, "keywords": "climate,conus404,hydroclimate,hydrology,inland-waters,precipitation,weather", "license": "CC-BY-4.0", "missionStartDate": "1979-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CONUS404"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Hawaii"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual North America"}, "daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Puerto Rico"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Hawaii"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily North America"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Puerto Rico"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Hawaii"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly North America"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Puerto Rico"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Flood Maps"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n\| Name \| Scale \| Period \| Number of basins \|\n\|--------\|--------------------------\|-----------\|------------------\|\n\| ERA5 \| Global \| 1967-2020 \| 3236 \|\n\| CHIRPS \| Global (+/- 50 latitude) \| 1981-2020 \| 2951 \|\n\| EOBS \| Europe/North Africa \| 1979-2020 \| 682 \|\n\| NLDAS \| USA \| 1979-2020 \| 1090 \|\n\| BOM \| Australia \| 1979-2020 \| 116 \|\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "missionStartDate": "1970-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Water Availability"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Denver Regional Council of Governments Land Use Land Cover"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Urban Innovation Eclipse Sensor Data"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows forecast times* in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* stream is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* type is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* reference time is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the reference time changes and `step` as the forecast time changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast was made, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ECMWF Open Data (real-time)"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An analysis, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A forecast starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ERA5 - PDS"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "title": "ESA WorldCover"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "missionStartDate": "2020-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Forest Inventory and Analysis"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\nNOTE: Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "missionStartDate": "2022-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FWS National Wetlands Inventory"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS Gap Land Cover"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "missionStartDate": "2021-04-13T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Biodiversity Information Facility (GBIF)"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Rasters"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Tables"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "missionStartDate": "2017-02-28T00:16:52Z", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "title": "GOES-R Cloud & Moisture Imagery"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "missionStartDate": "2018-02-13T16:10:00Z", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "title": "GOES-R Lightning Detection"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "missionStartDate": "2000-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GPM IMERG"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gridMET"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "missionStartDate": "2010-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HGB: Harmonized Global Biomass for 2010"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. Access: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. Reliability: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. Usage: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. Nighttime Lights: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "missionStartDate": "2012-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HREA: High Resolution Electricity Access"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Biodiversity Intactness"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Esri 10-Meter Land Cover (10-class)"}, "io-lulc-9-class": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V1"}, "io-lulc-annual-v02": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201cBare\u201d and any of the vegetative classes \u201cTrees,\u201d \u201cCrops,\u201d \u201cFlooded Vegetation,\u201d and \u201cRangeland\u201d. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-annual-v02,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V2"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "missionStartDate": "1984-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "JRC Global Surface Water"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HydroForecast - Kwando & Upper Zambezi Rivers"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "missionStartDate": "1972-07-25T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "title": "Landsat Collection 2 Level-1"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "missionStartDate": "2020-04-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MoBI: Map of Biodiversity Importance"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (500m)"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (250m)"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover Daily"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover 8-day"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity Daily"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity 8-Day"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (500m)"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (250m)"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire Daily"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire 8-Day"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 8-Day"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 4-Day"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2001-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Evapotranspiration Yearly Gap-Filled"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Primary Production Yearly Gap-Filled"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Burned Area Monthly"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\nStage 1: Semantic Segmentation\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\nStage 2: Polygonization\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are not spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "missionStartDate": "2014-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Microsoft Building Footprints"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "missionStartDate": "1984-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MTBS: Monitoring Trends in Burn Severity"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- model is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- scenario is one of \"historical\", \"ssp245\" or \"ssp585\".\n- variable is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- year depends on the value of scenario. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some experimental reference files as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "missionStartDate": "2000-02-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NASADEM HGT v001"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "missionStartDate": "1975-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "C-CAP Regional Land Cover and Change"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR NetCDFs"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - Optimum Interpolation CDR"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR NetCDFs"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n\| Abbreviation \| Description \|\n\| ------------ \| ---------------------------------------- \|\n\| prcp \| Precipitation over the time period \|\n\| tavg \| Mean temperature over the time period \|\n\| tmax \| Maximum temperature over the time period \|\n\| tmin \| Minimum temperature over the time period \|\n\nAnd an \"aggregation\":\n\n\| Abbreviation \| Description \|\n\| ------------ \| ------------------------------------------------------------------------------ \|\n\| max \| Maximum of the variable over the time period \|\n\| min \| Minimum of the variable over the time period \|\n\| std \| Standard deviation of the value over the time period \|\n\| flag \| An count of the number of inputs (months, years, etc.) to calculate the normal \|\n\| norm \| The normal for the variable over the time period \|\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (NetCDF)"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Tabular Climate Normals"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 1 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 1"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 2 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 2"}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 24-Hour Pass 2 sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 24-Hour Pass 2"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains Monthly data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nNote: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.\n", "instrument": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "missionStartDate": "1895-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Land Cover of Canada"}, "planet-nicfi-analytic": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Analytic)"}, "planet-nicfi-visual": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Visual)"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31Z", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "missionStartDate": "2016-04-25T11:33:47.368562Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land (Full Resolution)"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "missionStartDate": "2017-11-01T00:07:01.738487Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Water (Full Resolution)"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2020-08-08T23:11:15.617203Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Fire Radiative Power"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2016-04-19T01:35:17.188500Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Temperature"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2017-10-31T23:59:57.451604Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Sea Surface Temperature"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2016-03-01T14:07:51.632846Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Radar Altimetry"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2017-01-28T00:59:14.149496Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Ocean Radar Altimetry"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2020-04-16T19:36:28.012367Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Global Aerosol"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-22T16:51:00.001276Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Reflectance and Aerosol"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-27T11:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-04T23:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-08T08:09:40.491227Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "missionStartDate": "2018-04-30T00:18:50Z", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "title": "Sentinel-5P Level-2"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1958-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TerraClimate"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "missionStartDate": "2021-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "US Census"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USDA Cropland Data Layers (CDLs)"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP CONUS Collection 1.3"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "missionStartDate": "2000-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP Hawaii Collection 1.0"}}, "providers_config": {"3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "3dep-seamless": {"productType": "3dep-seamless"}, "alos-dem": {"productType": "alos-dem"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "aster-l1t": {"productType": "aster-l1t"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "chloris-biomass": {"productType": "chloris-biomass"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "conus404": {"productType": "conus404"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "deltares-floods": {"productType": "deltares-floods"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "drcog-lulc": {"productType": "drcog-lulc"}, "eclipse": {"productType": "eclipse"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "era5-pds": {"productType": "era5-pds"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}, "fia": {"productType": "fia"}, "fws-nwi": {"productType": "fws-nwi"}, "gap": {"productType": "gap"}, "gbif": {"productType": "gbif"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "goes-cmi": {"productType": "goes-cmi"}, "goes-glm": {"productType": "goes-glm"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gridmet": {"productType": "gridmet"}, "hgb": {"productType": "hgb"}, "hrea": {"productType": "hrea"}, "io-biodiversity": {"productType": "io-biodiversity"}, "io-lulc": {"productType": "io-lulc"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-lulc-annual-v02": {"productType": "io-lulc-annual-v02"}, "jrc-gsw": {"productType": "jrc-gsw"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "mobi": {"productType": "mobi"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "ms-buildings": {"productType": "ms-buildings"}, "mtbs": {"productType": "mtbs"}, "naip": {"productType": "naip"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "nasadem": {"productType": "nasadem"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "terraclimate": {"productType": "terraclimate"}, "us-census": {"productType": "us-census"}, "usda-cdl": {"productType": "usda-cdl"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}}}, "usgs_satapi_aws": {"product_types_config": {"landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1972-07-25T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-1 Product"}, "landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product"}}, "providers_config": {"landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}}}, "wekeo_cmems": {"product_types_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\n\nThe operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00003", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1d-m-202105,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"abstract": "'''Short description:'''\n\nThe operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1d-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1m-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-pt1h-i-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-pt6h-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"abstract": "'''Short Description:'''\n\nThe Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\n'''DOI (product) :''' \n\nhttps://doi.org/10.48670/moi-00004", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-ice-002-011:cmems-mod-arc-phy-anfc-nextsim-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\n\nThe TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00006", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1d-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1y-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1d-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"abstract": "'''Short description:'''\n\nThe current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00007", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1y-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe Arctic Sea Ice Reanalysis system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, from Bering Strait to the North Atlantic. neXtSIM is forced by reanalyzed surface atmosphere forcings (ERA5) from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4b, the ARC MFC MYP system (002_003). neXtSIM assimilates satellite sea ice concentrations from Passive Microwave satellite sensors, and sea ice thickness from CS2SMOS in winter from October 2010 onwards. The output variables are sea ice concentrations (total, young ice, and multi-year ice), sea ice thickness, sea ice velocity, snow depth on sea ice, sea ice type, sea ice age, sea ice ridge volume fraction and sea ice albedo, provided at daily and monthly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00336", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1977-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1977-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Reanalysis"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"abstract": "'''Short description:'''\n\nThis Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, dissolved iron, dissolved cdom, hydrogen sulfide, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann et al, 2021) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the Baltic Sea physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-7-10days-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-ssh-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-ssh-anfc-detided-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1d-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1m-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :'''\n\nhttps://doi.org/10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1y-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1d-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1m-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1y-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"abstract": "'''This product has been archived''' \n\n'''Short description :'''\n\nThis Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-2km-climatology-p1m-m-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Hindcast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Hindcast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-aflux-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Hindcast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"abstract": "\"Short description:''' \n\nBLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.2-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, zooplankton and phytoplankton carbon biomass, oxygen-demand-units, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\n'''DOI (product) :''' \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-pt1h-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"abstract": "'''Short description''': \n\nThe BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 3 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"abstract": "'''Short description''': \n\nThe wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\n'''Citation''': \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-analysisforecast-wav-007-003:cmems-mod-blk-wav-anfc-2.5km-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"abstract": "'''Short description:''' \n\nThe biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\n'''DOI (product)''':\nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"abstract": "'''Short description''': \n\nThe BLKSEA_MULTIYEAR_PHY_007_004 product provides ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamic core is based on the NEMOv4.0 general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/40\u00ba and 121 vertical levels. NEMO is forced by atmospheric fluxes computed from a bulk formulation applied to ECMWF ERA5 atmospheric fields at the resolution of 1/4\u00ba in space and 1-h in time. A heat flux correction through sea surface temperature (SST) relaxation is employed using the ESA-CCI SST-L4 product. This version has an open lateral boundary, a new model characteristic that allows a better inflow/outflow representation across the Bosphorus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly (SLA) along-track observations from Copernicus and available in situ vertical profiles of temperature and salinity from both SeaDataNet and Copernicus datasets. Upgrades on data assimilation include an improved background error covariance matrix and an observation-based mean dynamic topography for the SLA assimilation.\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-aflux-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"abstract": "'''Short description''': \n\nThe wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.\n\n'''Citation''': \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). \n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-climatology-pt1m-m-202311,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-myint-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThe Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID[https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-028.pdf]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00015", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1d-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1m-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"abstract": "'''Short description'''\n\nThe Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-pt6h-i-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-pt6h-i-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-pt6h-i-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-climatology-uncertainty-p1m-m-202311,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1d-m-202411,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1m-m-202411,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-pt1h-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-sl-pt1h-i-202411,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-uv-pt1h-i-202211,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"abstract": "'''Short description:'''\n \nThe operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-analysisforecast-wav-001-027:cmems-mod-glo-wav-anfc-0.083deg-pt3h-i-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"abstract": "'''Short description'''\n\nThe biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4 ocean physics produced at Mercator-Ocean and ERA-Interim atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00019", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"abstract": "'''Short description:'''\nThe Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [https://www.cls.fr CLS] on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n'''Forcings sources:'''\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n'''Vertical coverage:'''\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00020", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-fphy-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-climatology-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"abstract": "'''Short description:'''\n \nThe GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00021", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\nYou can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution : monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-climatology-p1m-m-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-pt3h-i-202411,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"abstract": "'''Short description:'''\n\nGLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-myint-0.2deg-pt3h-i-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "\"''Short description:'''\n\nThe IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 1/36\u00ba of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-analysisforecast-wav-005-005:cmems-mod-ibi-wav-anfc-0.027deg-pt1h-i-202411,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1y-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-myint-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-myint-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-climatology-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1d-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1m-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, zooplankton and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\nFor all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00028", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1y-m-202211,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-myint-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-myint-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1y-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-2d-pt1h-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-climatology-p1m-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1d-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1m-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Besides, daily means of air-sea fluxes are provided. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. For all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00029", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1y-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-hflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-hflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-mflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-mflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wcur-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wcur-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-0.083deg-pt1h-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-aflux-0.027deg-p1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1980 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product and with the same horizontal resolution (1/36\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Besides, air-sea fluxes are provided. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-climatology-p1m-m-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-pt1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"abstract": "'''Short description:''''\nGlobal Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-my-013-052:cmems-obs-ins-glo-phy-temp-sal-my-cora-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1960-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"abstract": "'''Short description:'''\nFor the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-nrt-013-002:cmems-obs-ins-glo-phy-temp-sal-nrt-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Real time in-situ observations objective analysis"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202411": {"abstract": "'''Short Description'''\n\nThe biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\nSalon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997\u20131022. DOI: https://doi.org/10.5194/os-15-997-2019\n\n''DOI (Product)'': \nhttps://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "'''Short Description'''\n\nThe physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations. \n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\"\n\n''DOI (Product)'':\nhttps://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"abstract": "'''Short description:'''\n \nMEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product)''': https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-wav-006-017:cmems-mod-med-wav-anfc-4.2km-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"abstract": "'''Short Description'''\nThe Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n''DOI (Interim dataset)'':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-pft-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-plankton-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-cur-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mld-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-sal-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-ssh-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-tem-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-my-4.2km-climatology-p1m-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"abstract": "'''Short description:'''\n\nThe Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\n\nMEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. \n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 \n\n''DOI (Interim dataset)'': \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I \n \n''DOI (climatological dataset)'': \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM \n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-my-4.2km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-myint-4.2km-pt1h-i-202112,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:med-hcmr-wav-rean-h-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"abstract": "'''Short description:'''\n\nThis product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00046", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-climatology-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-p7d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"abstract": "'''Short description:'''\n\nThis product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-my-irr-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Surface ocean carbon fields"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-nrt-irr-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Surface ocean carbon fields"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThis product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\n'''DOI (product) :''' \nhttps://doi.org/10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-asc-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-des-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n\n'''DOI (product) :''' \nhttps://doi.org/10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l4-my-015-015:cmems-obs-mob-glo-phy-sss-my-multi-oi-p1w-202406,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-05-31", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"abstract": "'''Short description:'''\n\nThis product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\n'''Product citation''': \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"abstract": "'''Short description:'''\n\nYou can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 5 datasets: \n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1D-m, which delivers near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1M-m, which delivers near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy_my_0.125deg_P1D-m, which delivers multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy_my_0.125deg_P1M-m, which delivers multi-year reprocessed (REP) monthly data\n* cmems_obs-mob_glo_phy_mynrt_0.125deg-climatology-uncertainty_P1M-m, which delivers monthly static uncertainty data\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00052", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"abstract": "'''Short description''':\n\nYou can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\n'''DOI (product) :''' \nhttps://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:multiobs-glo-phy-w-3d-rep-015-007:cmems-obs-mob-glo-phy-cur-my-0.25deg-p7d-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 10-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-analysisforecast-wav-004-014:cmems-mod-nws-wav-anfc-0.027deg-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf CMEMS-NWS-QUID-004-011]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf CMEMS-NWS-PUM-004-009_011] for details.\n\n'''Associated products:'''\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009].\nAn analysis-forecast product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011 NWSHELF_MULTIYEAR_BGC_004_011].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-diato-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-dino-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-nano-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-pico-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-myint-7km-2d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026 GLOBAL_REANALYSIS_PHY_001_026] and at the Baltic boundary by the [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011 BALTICSEA_REANALYSIS_PHY_003_011]. The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf CMEMS-NWS-QUID-004-009]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf CMEMS-NWS-PUM-004-009_011] for further details.\n\n'''Associated products:'''\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011]. An analysis-forecast product is available from [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 NWSHELF_ANALYSISFORECAST_PHY_LR_004_011].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sss-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sst-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"abstract": "'''Short description:'''\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013 NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ECMWF ERA-5] Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf CMEMS-NWS-PUM-004-015] for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf CMEMS-NWS-QUID-004-015].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''Associated products:'''\n\n[https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014 NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-reanalysis-wav-004-015:meto-nws-wav-ran-202007,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-plankton-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 m''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-reflectance-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-transp-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products , and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''')\n\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 meters''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l4-my-009-124:cmems-obs-oc-arc-bgc-plankton-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-optics-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-multi-1km-p1d-202411,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-1km-p1d-202411,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-transp-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-optics-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-multi-1km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-1km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-transp-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-pp-my-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-pp-nrt-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l3-nrt-009-202:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l4-nrt-009-208:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-optics-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-optics-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-plankton-nrt-l3-olci-300m-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-multi-1km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': monthly \n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-nrt-009-132:cmems-obs-oc-bal-bgc-plankton-nrt-l4-olci-300m-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l3-nrt-009-206:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l4-nrt-009-212:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_BLK_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''', '''\"\"pp\"\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L4_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' and '''\"\"pp\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-optics-my-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-300m-p1d-202211,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\"\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily''', '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-optics-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-multi-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-300m-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-300m-p1d-202211,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-optics-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\"\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-climatology-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-pp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL''').\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-108:c3s-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-optics-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-multi-4km-p1m-202411,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-pp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l3-nrt-009-204:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l4-nrt-009-210:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l3-nrt-009-205:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l4-nrt-009-211:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"multi'''\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* '''''pp''''' with the Integrated Primary Production (PP)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"gap-free\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L4_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' and '''\"\"pp\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l3-nrt-009-203:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l4-nrt-009-209:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p2d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"abstract": "'''Short description:''' \n\nAntarctic sea ice displacement during winter from medium resolution sensors since 2002\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p2d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"abstract": "'''Short description:''' \n\nArctic Sea and Ice surface temperature\n'''Detailed description:'' 'Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l3-my-011-021:cmems-obs-si-arc-phy-my-l3s-dmioi-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"abstract": "'''Short description:''' \nArctic Sea and Ice surface temperature\n\n'''Detailed description:'''\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l4-my-011-016:cmems-obs-si-arc-phy-my-l4-dmioi-p1d-m-202105,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "'''Short description:''' \n\nArctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer :\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p30d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p6d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-007:dmi-arc-seaice-berg-mosaic-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS_201907": {"abstract": "'''Short description:''' \n\nThe iceberg product contains 4 datasets (IW and EW modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.\n\nThe iceberg product also contains two additional datasets of individual iceberg positions in Greenland-Newfoundland-Labrador Waters. These datasets are in shapefile format to allow the best representation of the icebergs (the 1st dataset contains the iceberg point observations, the 2nd dataset contains the polygonized satellite coverage). These are also derived by applying a Constant False Alarm Rate (CFAR) algorithm on Sentinel-1 SAR imagery.\nDespite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00129", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-007:dmi-arc-seaice-berg-mosaic-iw-l4-nrt-obs-201907,level-4,marine-resources,marine-safety,near-real-time,not-applicable,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"abstract": "'''Short description:'''\n\nArctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-008:dmi-arc-seaice-temp-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-phy-sit-my-l4-1km-p1d-m-202211,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1980-11-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"abstract": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-seaice-conc-my-1km-202112,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1980-11-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-conc-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"abstract": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-thick-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"abstract": "'''Short description:'''\n\nArctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-phy-climate-l3-my-011-013:c3s-obs-si-glo-phy-my-nh-l3-p1m-202411,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-04-30", "missionStartDate": "1995-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"abstract": "'''Short description:'''\n\nArctic sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and Aprilt to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-phy-l4-nrt-011-014:esa-obs-si-arc-phy-sit-nrt-l4-multi-p1d-m-202411,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-04-11", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Ice Thickness derived from merging of L-Band radiometry and radar altimeter derived sea ice thickness"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"abstract": "'''Short description:''' \n\nFor the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-nh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-sh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_north_d_202007": {"abstract": "'''Short description:'''\n\nDTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00135", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-006:cmems-sat-si-glo-drift-nrt-north-d-202007,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-006:cmems-sat-si-glo-drift-nrt-south-d-202007,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"abstract": "'''Short description:''' \nThe CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-nh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-sh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-nrt-008-060:cmems-obs-sl-eur-phy-ssh-nrt-allsat-l4-duacs-0.0625deg-p1d-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-nrt-008-060:cmems-obs-sl-eur-phy-ssh-nrt-allsat-l4-duacs-0.125deg-p1d-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"abstract": "'''Short description:''' \n\nDUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1d-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1m-m-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-nrt-008-046:cmems-obs-sl-glo-phy-ssh-nrt-allsat-l4-duacs-0.125deg-p1d-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-nrt-008-046:cmems-obs-sl-glo-phy-ssh-nrt-allsat-l4-duacs-0.25deg-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-my-010-038:cmems-obs-sst-atl-phy-my-l3s-p1d-m-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-gir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pmw-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-nrt-l3s-p1d-m-202211,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:'''\n\nFor the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-nrt-observations-010-025:ifremer-atl-sst-l4-nrt-obs-full-time-serie-201904,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2018-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"abstract": "'''Short description:''' \n\nFor the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-rep-observations-010-026:cmems-ifremer-atl-sst-l4-rep-obs-full-time-serie-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"abstract": "'''Short description:''' \nFor the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-l3s-my-010-040:cmems-obs-sst-bal-phy-my-l3s-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"abstract": "'''Short description:'''\nFor the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-subskin-l4-nrt-010-034:cmems-obs-sst-bal-phy-subskin-nrt-l4-pt1h-m-202211,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-05-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l3s-nrt-observations-010-032:dmi-baltic-sst-l3s-nrt-obs-full-time-serie-201904,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "EO:MO:DAT:SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_B:DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE": {"abstract": "'''Short description:'''\n\nFor the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00155", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l4-nrt-observations-010-007-b:dmi-baltic-sst-l4-nrt-obs-full-time-serie,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2018-12-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea- Sea Surface Temperature Analysis L4"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"abstract": "'''Short description:''' \nFor the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l4-rep-observations-010-016:dmi-bal-sst-l4-rep-observations-010-016-202012,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-l3s-my-010-041:cmems-obs-sst-bs-phy-my-l3s-p1d-m-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "'''Short description:'''\n\nFor the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-subskin-l4-nrt-010-035:cmems-obs-sst-blk-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-a-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-b-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-b,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-d,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-a-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-c-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-rep-observations-010-022:cmems-sst-bs-sst-l4-rep-observations-010-022-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l3s-my-010-039:cmems-obs-sst-glo-phy-my-l3s-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l4-nrt-010-043:cmems-obs-sst-glo-phy-nrt-l4-p1d-m-202303,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"abstract": "'''Short description:'''\n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:ifremer-glob-sst-l3-nrt-obs-full-time-serie-202211,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-gir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pmw-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of : Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-nrt-observations-010-001:metoffice-glo-sst-l4-nrt-obs-sst-v2,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"abstract": "'''Short description :'''\n\nThe OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00168", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,canary-current-system,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-011:metoffice-glo-sst-l4-rep-obs-sst-202003,global-ocean,level-4,marine-resources,marine-safety,modelling-data,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,south-brazilian-shelf,south-mid-atlantic-ridge,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-05-31", "missionStartDate": "1981-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "'''Short description:''' \nThe ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:c3s-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:esacci-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,eo:mo:dat:sst-med-phy-l3s-my-010-042:cmems-obs-sst-med-phy-my-l3s-p1d-m-202411,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "''' Short description: ''' \n\nFor the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services How to cite]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-phy-subskin-l4-nrt-010-036:cmems-obs-sst-med-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-a-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-b-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-b,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-d,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-a-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. Since November 2024, the L4 MED UHR processing chain makes use of an improved background field as initial guess for the Optimal Interpolation of this product. The improvement is obtained in terms of the effective spatial resolution via the application of a convolutional neural network (CNN). These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-c-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"abstract": "'''Short description:''' \n \nThe Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-rep-observations-010-021:cmems-sst-med-sst-l4-rep-observations-010-021-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"abstract": "'''Short description:'''\n\nNear-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-spc-l4-nrt-014-004:cmems-obs-wave-glo-phy-spc-nrt-multi-l4-1deg-pt3h-202112,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nMulti-Year gridded multi-mission merged satellite significant wave height based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2\u00b0 horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5\u00b0 horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-0.5deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nNear-Real-Time gridded multi-mission merged satellite significant wave height, based on CMEMS level-3 SWH datasets. Onyl valid data are included. It merges multiple along-track SWH data (Sentinel-6A,\u00a0 Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C) and produces daily gridded data at a 2\u00b0 horizontal resolution. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00338", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-arc-phy-hr-l3-my-012-105:cmems-obs-wind-arc-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-arc-phy-hr-l3-my-012-105:cmems-obs-wind-arc-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00339", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-atl-phy-hr-l3-my-012-106:cmems-obs-wind-atl-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-atl-phy-hr-l3-my-012-106:cmems-obs-wind-atl-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00340", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-bal-phy-hr-l3-my-012-107:cmems-obs-wind-bal-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-bal-phy-hr-l3-my-012-107:cmems-obs-wind-bal-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00341", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-blk-phy-hr-l3-my-012-108:cmems-obs-wind-blk-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-blk-phy-hr-l3-my-012-108:cmems-obs-wind-blk-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments. The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-climate-l4-my-012-003:cmems-obs-wind-glo-phy-my-l4-p1m-202411,global-ocean,iberian-biscay-irish-seas,level-4,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1994-07-16", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\nThe ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\nThe OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \nThe HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.125deg-pt1h-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.25deg-pt1h-202406,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-nrt-012-004:cmems-obs-wind-glo-phy-nrt-l4-0.125deg-pt1h-202207,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00342", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-med-phy-hr-l3-my-012-109:cmems-obs-wind-med-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-med-phy-hr-l3-my-012-109:cmems-obs-wind-med-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}}, "providers_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"collection": "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS_201907": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS_201907"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_north_d_202007": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_north_d_202007"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_B:DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE": {"collection": "EO:MO:DAT:SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_B:DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"collection": "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"collection": "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"collection": "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"collection": "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"collection": "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}}}} +{"cop_marine": {"product_types_config": {"ANTARCTIC_OMI_SI_extent": {"abstract": "DEFINITION\n\nEstimates of Antarctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Southern Hemisphere (excluding ice lakes) and from 1993 up to real time aiming to:\ni) obtain the Antarctic sea ice extent as expressed in millions of km squared (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss/gain since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013)). For the Southern Hemisphere, these trends are calculated from the annual mean values.\nThe Antarctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\nCONTEXT\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n \nCMEMS KEY FINDINGS\n\nWith quasi regular highs and lows, the annual Antarctic sea ice extent shows large variability until several monthly record high in 2014 and record lows in 2017, 2018 and 2023. Since the year 1993, the Southern Hemisphere annual sea ice extent regularly alternates positive and negative trend. The period 1993-2023 have seen a slight decrease at a rate of -0.28106km2 per decade. This represents a loss amount of -2.4% per decade of Southern Hemisphere sea ice extent during this period; with however large uncertainties. The last quarter of the year 2016 and years 2017 and 2018 experienced unusual losses of ice. The year 2023 is an exceptional year and its average has a strong impact on the whole time series.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00186\n\nReferences:\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00186", "instrument": null, "keywords": "antarctic-omi-si-extent,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Sea Ice Extent from Reanalysis"}, "ANTARCTIC_OMI_SI_extent_obs": {"abstract": "DEFINITION\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the September values (winter ice loss); (iii) from February values (summer ice loss). The annual mean trend is reported on the key figure, the September (maximum extent) and February (minimum extent) values are reported in the text below.\nSIE includes all sea ice, except for lake and river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\nCONTEXT\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019). \n\nCMEMS KEY FINDINGS\n\nSince 1979, there has been an overall slight increase of sea ice extent in the Southern Hemisphere but a sharp decrease was observed after 2016. Over the period 1979-2022, the annual rate amounts to +0.02 +/- 0.05 106 km2 per decade (+0.18% per decade). Winter (September) sea ice extent trend amounts to +0.06 +/- 0.05106 km2 per decade (+0.32% per decade). Summer (February) sea ice extent trend amounts to -0.01+/- 0.05 106 km2 per decade (-0.38% per decade). These trend estimates are hardly significant, which is in agreement with the IPCC SROCC, which has assessed that \u2018Antarctic sea ice extent overall has had no statistically significant trend (1979\u20132018) due to contrasting regional signals and large interannual variability (high confidence).\u2019 (IPCC, 2019). Both June and July 2022 had the lowest average sea ice extent values for these months since 1979. \n\nFigure caption\n\na) The seasonal cycle of Southern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Southern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line on panel b).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00187\n\nReferences:\n\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00187", "instrument": null, "keywords": "antarctic-omi-si-extent-obs,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Monthly Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"abstract": "The operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00003\n\nReferences:\n\n* Ardyna, M., Babin, M., Gosselin, M., Devred, E., B\u00e9langer, S., Matsuoka, A., and Tremblay, J.-\u00c9.: Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences, 10, 4383\u20134404, https://doi.org/10.5194/bg-10-4383-2013, 2013.\n* Yumruktepe, V. \u00c7., Samuelsen, A., and Daewel, U.: ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic, Geosci. Model Dev., 15, 3901\u20133921, https://doi.org/10.5194/gmd-15-3901-2022, 2022.\n", "doi": "10.48670/moi-00003", "instrument": null, "keywords": "arctic-analysisforecast-bgc-002-004,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"abstract": "The operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\nDOI (product):\nhttps://doi.org/10.48670/moi-00001\n\nReferences:\n\n* Sakov, P., Counillon, F., Bertino, L., Lis\u00e6ter, K. A., Oke, P. R. and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8(4), 633\u2013656, doi:10.5194/os-8-633-2012, 2012.\n* Melsom, A., Counillon, F., LaCasce, J. H. and Bertino, L.: Forecasting search areas using ensemble ocean circulation modeling, Ocean Dyn., 62(8), 1245\u20131257, doi:10.1007/s10236-012-0561-5, 2012.\n", "doi": "10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-analysisforecast-phy-002-001,arctic-ocean,coastal-marine-environment,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "2021-07-05T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"abstract": "The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\nDOI (product): \n\nhttps://doi.org/10.48670/moi-00004\n\nReferences:\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/moi-00004", "instrument": null, "keywords": "arctic-analysisforecast-phy-ice-002-011,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"abstract": "The Arctic Ocean Surface Currents Analysis and Forecast system uses the HYCOM model at 3 km resolution forced with tides at its lateral boundaries, surface winds sea level pressure from the ECMWF (European Centre for Medium-Range Weather Forecasts) and wave terms (Stokes-Coriolis drift, stress and parameterisation of mixing by Langmuir cells) from the Arctic wave forecast. HYCOM runs daily providing 10 days forecast. The output variables are the surface currents and sea surface heights, provided at 15 minutes frequency, which therefore include mesoscale signals (though without data assimilation so far), tides and storm surge signals. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00005", "doi": "10.48670/moi-00005", "instrument": null, "keywords": "arctic-analysisforecast-phy-tide-002-015,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-surface-elevation,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Tidal Analysis and Forecast"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"abstract": "The Arctic Ocean Wave Analysis and Forecast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) together with tidal currents and ice from the ARC MFC forecasts (Sea Ice concentration and thickness). WAM runs twice daily providing one hourly 10 days forecast and one hourly 5 days forecast. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00002", "doi": "10.48670/moi-00002", "instrument": null, "keywords": "arctic-analysis-forecast-wav-002-014,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Analysis and Forecast"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"abstract": "The TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00006\n\nReferences:\n\n* Simon, E., Samuelsen, A., Bertino, L. and Mouysset, S.: Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter, J. Mar. Syst., 152, 1\u201317, doi:10.1016/j.jmarsys.2015.07.004, 2015.\n", "doi": "10.48670/moi-00006", "instrument": null, "keywords": "arctic-multiyear-bgc-002-005,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"abstract": "The current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00007", "doi": "10.48670/moi-00007", "instrument": null, "keywords": "arctic-multiyear-phy-002-003,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"abstract": "The Arctic Sea Ice Reanalysis system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, from Bering Strait to the North Atlantic. neXtSIM is forced by reanalyzed surface atmosphere forcings (ERA5) from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4b, the ARC MFC MYP system (002_003). neXtSIM assimilates satellite sea ice concentrations from Passive Microwave satellite sensors, and sea ice thickness from CS2SMOS in winter from October 2010 onwards. The output variables are sea ice concentrations (total, young ice, and multi-year ice), sea ice thickness, sea ice velocity, snow depth on sea ice, sea ice type, sea ice age, sea ice ridge volume fraction and sea ice albedo, provided at daily and monthly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00336\n\nReferences:\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/mds-00336", "instrument": null, "keywords": "arctic-multiyear-phy-ice-002-016,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Reanalysis"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"abstract": "The Arctic Ocean Wave Hindcast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis together with ice from the ARC MFC reanalysis (Sea Ice concentration and thickness). Additionally, in the North Atlantic area, surface winds are used from a 2.5km atmospheric hindcast system. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00008", "doi": "10.48670/moi-00008", "instrument": null, "keywords": "arctic-multiyear-wav-002-013,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Hindcast"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"abstract": "DEFINITION\n\nNet sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79\u00b0N, 20\u00b0W - 10\u00b0E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80\u00b0N, 27\u00b0E - 60\u00b0E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60\u00b0E, 76\u00b0N - 80\u00b0N (positive westward). For further details, see Lien et al. (2021).\n\nCONTEXT\n\nThe Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015).\n\nCMEMS KEY FINDINGS\n\nThe sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00192\n\nReferences:\n\n* Aaboe S, Lind S, Hendricks S, Down E, Lavergne T, Ricker R. 2021. Sea-ice and ocean conditions surprisingly normal in the Svalbard-Barents Sea region after large sea-ice inflows in 2019. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 140-148\n* Aagaard K, Swift JH, Carmack EC. 1985. Thermohaline circulation in the Arctic Mediterranean seas. J Geophys Res. 90(C7), 4833-4846\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Lien VS, Raj RP, Chatterjee S. 2021. Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents seas. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 10-17\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea ice import. Nature Clim Change. doi:10.1038/s41558-018-0205-y\n* Rampal P, Weiss J, Dubois C, Campin J-M. 2011. IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. J Geophys Res. 116, C00D07. https://doi.org/10.1029/2011JC007110\n* Ricker R, Girard-Ardhuin F, Krumpen T, Lique C. 2018. Satellite-derived sea ice export and its impact on Arctic ice mass balance. Cryosphere. 12, 3017-3032\n* Vinje T, Nordlund N, Kvambekk \u00c5. 1998. Monitoring ice thickness in Fram Strait. J Geophys Res. 103(C5), 10437-10449\n", "doi": "10.48670/moi-00192", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-transport-nordicseas,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis"}, "ARCTIC_OMI_SI_extent": {"abstract": "DEFINITION\n\nEstimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to:\ni) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss).\nThe Arctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\nCONTEXT\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n\nCMEMS KEY FINDINGS\n\nSince the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00190\n\nReferences:\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00190", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea Ice Extent from Reanalysis"}, "ARCTIC_OMI_SI_extent_obs": {"abstract": "DEFINITION\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean that is covered with sea ice. It is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The annual mean trend is reported on the key figure, the March and September values are reported in the text below.\nSIE includes all sea ice, but not lake or river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\nCONTEXT\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea ice cover is changing is essential for monitoring the health of the Earth. Sea ice has a significant impact on ecosystems and Arctic communities, as well as economic activities such as fisheries, tourism, and transport (Meredith et al. 2019).\n\nCMEMS KEY FINDINGS\n\nSince 1979, the Northern Hemisphere sea ice extent has decreased at an annual rate of -0.51 +/- 0.03106 km2 per decade (-4.41% per decade). Loss of sea ice extent during summer exceeds the loss observed during winter periods: Summer (September) sea ice extent loss amounts to -0.81 +/- 0.06 106 km2 per decade (-12.73% per decade). Winter (March) sea ice extent loss amounts to -0.39 +/- 0.03 106 km2 per decade (-2.55% per decade). These values are in agreement with those assessed in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Meredith et al. 2019, with estimates up until year 2018). September 2022 had the 11th lowest mean September sea ice extent. Sea ice extent in September 2012 is to date the record minimum Northern Hemisphere sea ice extent value since the beginning of the satellite record, followed by September values in 2020.\n\nFigure caption\n\na) The seasonal cycle of Northern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Northern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line in panel b).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00191\n\nReferences:\n\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n", "doi": "10.48670/moi-00191", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent-obs,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_OMI_TEMPSAL_FWC": {"abstract": "DEFINITION\n\nEstimates of Arctic liquid Freshwater Content (FWC in meters) are obtained from integrated differences of the measured salinity and a reference salinity (set to 34.8) from the surface to the bottom per unit area in the Arctic region with a water depth greater than 500m as function of salinity (S), the vertical cell thickness of the dataset (dz) and the salinity reference (Sref). Waters saltier than the 34.8 reference are not included in the estimation. The regional FWC values from 1993 up to real time are then averaged aiming to:\n* obtain the mean FWC as expressed in cubic km (km3) \n* monitor the large-scale variability and change of liquid freshwater stored in the Arctic Ocean (i.e. the change of FWC in time).\n\nCONTEXT\n\nThe Arctic region is warming twice as fast as the global mean and its climate is undergoing unprecedented and drastic changes, affecting all the components of the Arctic system. Many of these changes affect the hydrological cycle. Monitoring the storage of freshwater in the Arctic region is essential for understanding the contemporary Earth system state and variability. Variations in Arctic freshwater can induce changes in ocean stratification. Exported southward downstream, these waters have potential future implications for global circulation and heat transport. \n\nCMEMS KEY FINDINGS\n\nSince 1993, the Arctic Ocean freshwater has experienced a significant increase of 423 \u00b1 39 km3/year. The year 2016 witnessed the highest freshwater content in the Artic since the last 24 years. Second half of 2016 and first half of 2017 show a substantial decrease of the FW storage. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00193\n\nReferences:\n\n* G. Garric, O. Hernandez, C. Bricaud, A. Storto, K. A. Peterson, H. Zuo, 2018: Arctic Ocean freshwater content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s70\u2013s72, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00193", "instrument": null, "keywords": "arctic-ocean,arctic-omi-tempsal-fwc,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Freshwater Content from Reanalysis"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"abstract": "This Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, dissolved iron, dissolved cdom, hydrogen sulfide, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann et al, 2021) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the Baltic Sea physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00009", "doi": "10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-bgc-003-007,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"abstract": "This Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00010", "doi": "10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-phy-003-006,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"abstract": "This Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00011", "doi": "10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-wav-003-010,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-12-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Analysis and Forecast"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"abstract": "This Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\nDOI (product):\n\nhttps://doi.org/10.48670/moi-00012", "doi": "10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-bgc-003-012,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Reanalysis"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"abstract": "This Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00013", "doi": "10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-phy-003-011,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Reanalysis"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"abstract": "This product has been archived \n\n\n\nThis Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00014", "doi": "10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-wav-003-015,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Hindcast"}, "BALTIC_OMI_HEALTH_codt_volume": {"abstract": "DEFINITION\n\nThe cod reproductive volume has been derived from regional reanalysis modelling results for the Baltic Sea BALTICSEA_MULTIYEAR_PHY_003_011 and BALTICSEA_MULTIYEAR_BGC_003_012. The volume has been calculated taking into account the three most important influencing abiotic factors of cod reproductive success: salinity > 11 g/kg, oxygen concentration\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C (MacKenzie et al., 1996; Heikinheimo, 2008; Plikshs et al., 2015). The daily volumes are calculated as the volumes of the water with salinity > 11 g/kg, oxygen content\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C in the Baltic Sea International Council for the Exploration of the Sea subdivisions of 25-28 (ICES, 2019).\n\nCONTEXT\n\nCod (Gadus morhua) is a characteristic fish species in the Baltic Sea with major economic importance. Spawning stock biomasses of the Baltic cod have gone through a steep decline in the late 1980s (Bryhn et al., 2022). Water salinity and oxygen concentration affect cod stock through the survival of eggs (Westin and Nissling, 1991; Wieland et al., 1994). Major Baltic Inflows provide a suitable environment for cod reproduction by bringing saline oxygenated water to the deep basins of the Baltic Sea (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Increased cod reproductive volume has a positive effect on cod reproduction success, which should reflect an increase of stock size indicator 4\u20135 years after the Major Baltic Inflow (Raudsepp et al., 2019). Eastern Baltic cod reaches maturity around age 2\u20133, depending on the population density and environmental conditions. Low oxygen and salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin, 1999; Karasiova et al., 2008). There are a number of environmental factors affecting cod populations (Bryhn et al., 2022).\n\nKEY FINDINGS\n\nTypically, the cod reproductive volume in the Baltic Sea oscillates between 200 and 400 km\u00b3. There have been two distinct periods of significant increase, with maximum values reaching over 1200 km\u00b3, corresponding to the aftermath of Major Baltic Inflows (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland) from 2003 to 2004 and from 2016 to 2017. Following a decline to the baseline of 200 km\u00b3 in 2018, there was a rise to 800 km\u00b3 in 2019. The cod reproductive volume hit a second peak of 800 km\u00b3 in 2022 and has since stabilized at 600 km\u00b3. However, Bryhn et al. (2022) report no observed increase in the spawning stock biomass of the eastern Baltic Sea cod.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00196\n\nReferences:\n\n* Cardinale, M., Modin, J., 1999. Changes in size-at-maturity of Baltic cod (Gadus morhua) during a period of large variations in stock size and environmental conditions. Vol. 41 (3), 285-295. https://doi.org/10.1016/S0165-7836(99)00021-1\n* Heikinheimo, O., 2008. Average salinity as an index for environmental forcing on cod recruitment in the Baltic Sea. Boreal Environ Res 13:457\n* ICES, 2019. Baltic Sea Ecoregion \u2013 Fisheries overview, ICES Advice, DOI:10.17895/ices.advice.5566 Karasiova, E.M., Voss, R., Eero, M., 2008. Long-term dynamics in eastern Baltic cod spawning time: from small scale reversible changes to a recent drastic shift. ICES CM 2008/J:03\n* MacKenzie, B., St. John, M., Wieland, K., 1996. Eastern Baltic cod: perspectives from existing data on processes affecting growth and survival of eggs and larvae. Mar Ecol Prog Ser Vol. 134: 265-281.\n* Plikshs, M., Hinrichsen, H. H., Elferts, D., Sics, I., Kornilovs, G., K\u00f6ster, F., 2015. Reproduction of Baltic cod, Gadus morhua (Actinopterygii: Gadiformes: Gadidae), in the Gotland Basin: Causes of annual variability. Acta Ichtyologica et Piscatoria, Vol. 45, No. 3, 2015, p. 247-258.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s106\u2013s110, DOI: 10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Westin, L., Nissling, A., 1991. Effects of salinity on spermatozoa motility, percentage of fertilized eggs and egg development of Baltic cod Gadus morhua, and implications for cod stock fluctuations in the Baltic. Mar. Biol. 108, 5 \u2013 9.\n* Wieland, K., Waller, U., Schnack, D., 1994. Development of Baltic cod eggs at different levels of temperature and oxygen content. Dana 10, 163 \u2013 177.\n* Bryhn, A.C.., Bergek, S., Bergstr\u00f6m,U., Casini, M., Dahlgren, E., Ek, C., Hjelm, J., K\u00f6nigson, S., Ljungberg, P., Lundstr\u00f6m, K., Lunneryd, S.G., Oveg\u00e5rd, M., Sk\u00f6ld, M., Valentinsson, D., Vitale, F., Wennhage, H., 2022. Which factors can affect the productivity and dynamics of cod stocks in the Baltic Sea, Kattegat and Skagerrak? Ocean & Coastal Management, 223, 106154. https://doi.org/10.1016/j.ocecoaman.2022.106154\n", "doi": "10.48670/moi-00196", "instrument": null, "keywords": "baltic-omi-health-codt-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-volume,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Cod Reproductive Volume from Reanalysis"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe method for calculating the ocean heat content anomaly is based on the daily mean sea water potential temperature fields (Tp) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total heat content is determined using the following formula:\n\nHC = \u03c1 * cp * ( Tp +273.15).\n\nHere, \u03c1 and cp represent spatially varying sea water density and specific heat, respectively, which are computed based on potential temperature, salinity and pressure using the UNESCO 1983 polynomial developed by Fofonoff and Millard (1983). The vertical integral is computed using the static cell vertical thicknesses sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial averaging is performed over the Baltic Sea spatial domain, defined as the region between 13\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N. To obtain the OHC annual anomaly time series in (J/m2), the mean heat content over the reference period of 1993-2014 was subtracted from the annual mean total heat content.\nWe evaluate the uncertainty from the mean annual error of the potential temperature compared to the observations from the Baltic Sea (Giorgetti et al., 2020). The shade corresponds to the RMSD of the annual mean heat content biases (\u00b1 35.3 MJ/m\u00b2) evaluated from the observed temperatures and corresponding model values. \nLinear trend (W/m2) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\nCONTEXT\n\nOcean heat content is a key ocean climate change indicator. It accounts for the energy absorbed and stored by oceans. Ocean Heat Content in the upper 2,000 m of the World Ocean has increased with the rate of 0.35 \u00b1 0.08 W/m2 in the period 1955\u20132019, while during the last decade of 2010\u20132019 the warming rate was 0.70 \u00b1 0.07 W/m2 (Garcia-Soto et al., 2021). The high variability in the local climate of the Baltic Sea region is attributed to the interplay between a temperate marine zone and a subarctic continental zone. Therefore, the Ocean Heat Content of the Baltic Sea could exhibit strong interannual variability and the trend could be less pronounced than in the ocean.\n\nKEY FINDINGS\n\nThe ocean heat content (OHC) of the Baltic Sea exhibits an increasing trend of 0.3\u00b10.1 W/m\u00b2, along with multi-year oscillations. This increase is less pronounced than the global OHC trend (Holland et al. 2019; von Schuckmann et al. 2019) and that of some other marginal seas (von Schuckmann et al. 2018; Lima et al. 2020). The relatively low trend values are attributed to the Baltic Sea's shallowness, which constrains heat accumulation in its waters. The most significant OHC anomaly was recorded in 2020, and following a decline from this peak, the anomaly has now stabilized at approximately 250 MJ/m\u00b2.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00322\n\nReferences:\n\n* Garcia-Soto C, Cheng L, Caesar L, Schmidtko S, Jewett EB, Cheripka A, Rigor I, Caballero A, Chiba S, B\u00e1ez JC, Zielinski T and Abraham JP (2021) An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation). Front. Mar. Sci. 8:642372. doi: 10.3389/fmars.2021.642372\n* Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp., p.39. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf\n* Giorgetti, A., Lipizer, M., Molina Jack, M.E., Holdsworth, N., Jensen, H.M., Buga, L., Sarbu, G., Iona, A., Gatti, J., Larsen, M. and Fyrberg, L., 2020. Aggregated and Validated Datasets for the European Seas: The Contribution of EMODnet Chemistry. Frontiers in Marine Science, 7, p.583657.\n* Holland E, von Schuckmann K, Monier M, Legeais J-F, Prado S, Sathyendranath S, Dupouy C. 2019. The use of Copernicus Marine Service products to describe the state of the tropical western Pacific Ocean around the islands: a case study. In: Copernicus Marine Service Ocean State Report, Issue 3. J Oper Oceanogr. 12(suppl. 1):s43\u2013s48. doi:10.1080/1755876X.2019.1633075\n* Lima L, Peneva E, Ciliberti S, Masina S, Lemieux B, Storto A, Chtirkova B. 2020. Ocean heat content in the Black Sea. In: Copernicus Marine Service Ocean State Report, Issue 4. J Oper Oceanogr. 13(suppl. 1):s41\u2013s48. doi:10.1080/1755876X.2020.1785097.\n* von Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G. 2019. Copernicus Marine Service Ocean State report. J Oper Oceanogr. 12(suppl. 1):s1\u2013s123. doi:10.1080/1755876X.2019.1633075.\n* von Schuckmann K, Storto A, Simoncelli S, Raj RP, Samuelsen A, Collar A, Sotillo MG, Szerkely T, Mayer M, Peterson KA, et al. 2018. Ocean heat content. In: Copernicus Marine Service Ocean State Report, issue 2. J Oper Oceanogr. 11 (Suppl. 1):s1\u2013s142. doi:10.1080/1755876X.2018.1489208.\n", "doi": "10.48670/mds-00322", "instrument": null, "keywords": "baltic-omi-ohc-area-averaged-anomalies,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ohc-balrean,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Heat Content Anomaly (0-300m) from Reanalysis"}, "BALTIC_OMI_SI_extent": {"abstract": "DEFINITION\n\nSea ice extent is defined as the area covered by sea ice, that is the area of the ocean having more than 15% sea ice concentration. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. Daily sea ice extent values are computed from the daily sea ice concentration maps. All sea ice covering the Baltic Sea is included, except for lake ice. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010). The annual course of the sea ice extent has been calculated as daily mean ice extent for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice extent have been calculated from daily values using a 7-day moving average filter.\n\nCONTEXT\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation. The presence of sea ice cover sets special requirements for navigation, both for the construction of the ships and their behavior in ice, as in many cases, merchant ships need icebreaker assistance. Temporal trends of the sea ice extent could be a valuable indicator of the climate change signal in the Baltic Sea region. It has been estimated that a 1 \u00b0C increase in the average air temperature results in the retreat of ice-covered area in the Baltic Sea about 45,000 km2 (Granskog et al., 2006). Decrease in maximum ice extent may influence vertical stratification of the Baltic Sea (Hordoir and Meier, 2012) and affect the onset of the spring bloom (Eilola et al., 2013). In addition, statistical sea ice coverage information is crucial for planning of coastal and offshore construction. Therefore, the knowledge about ice conditions and their variability is required and monitored in Copernicus Marine Service.\n\nKEY FINDINGS\n\nSea ice in the Baltic Sea exhibits a strong seasonal pattern. Typically, formation begins in October and can persist until June. The 2022/23 ice season saw a relatively low maximum ice extent in the Baltic Sea, peaking at around 65,000 km\u00b2. Formation started in November and accelerated in the second week of December. The ice extent then remained fairly stable and below the climatological average until the end of January. From February to the second week of March, the extent of sea ice steadily grew to its maximum of 65,000 km\u00b2, before gradually receding. The peak day for sea ice extent varies annually but generally oscillates between the end of February and the start of March (Singh et al., 2024). The past 30 years saw the highest sea ice extent at 260,000 km\u00b2 in 2010/11. Despite a downward trend in sea ice extent in the Baltic Sea from 1993 to 2022, the linear trend does not show statistical significance.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00200\n\nReferences:\n\n* Eilola K, M\u00e5rtensson S, Meier HEM, 2013. Modeling the impact of reduced sea ice cover in future climate on the Baltic Sea biogeochemistry. Geophysical Research Letters, 40, 149-154, doi:10.1029/2012GL054375\n* Granskog M, Kaartokallio H, Kuosa H, Thomas DN, Vainio J, 2006. Sea ice in the Baltic Sea \u2013 A review. Estuarine, Coastal and Shelf Science, 70, 145\u2013160. doi:10.1016/j.ecss.2006.06.001\n* Hordoir R., Meier HEM, 2012. Effect of climate change on the thermal stratification of the Baltic Sea: a sensitivity experiment. Climate Dynamics, 38, 1703-1713, doi:10.1007/s00382-011-1036-y\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010. Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, doi:10.1109/BALTIC.2010.5621649\n* Vihma T, Haapala J, 2009. Geophysics of sea ice in the Baltic Sea: A review. Progress in Oceanography, 80, 129-148, doi:10.1016/j.pocean.2009.02.002\n", "doi": "10.48670/moi-00200", "instrument": null, "keywords": "baltic-omi-si-extent,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Extent from Observations Reprocessing"}, "BALTIC_OMI_SI_volume": {"abstract": "DEFINITION\n\nThe sea ice volume is a product of sea ice concentration and sea ice thickness integrated over respective area. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. The Baltic Sea area having more than 15% of sea ice concentration is included into the sea ice volume analysis. Daily sea ice volume values are computed from the daily sea ice concentration and sea ice thickness maps. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010; https://www.smhi.se/data/oceanografi/havsis). The annual course of the sea ice volume has been calculated as daily mean ice volume for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice volume have been calculated from daily values using a 7-day moving average filter.\n\nCONTEXT\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Knowledge of the sea ice volume facilitates planning of icebreaking activity and operation of the icebreakers (Valdez Banda et al., 2015; Bostr\u00f6m and \u00d6sterman, 2017). A long-term monitoring of ice parameters is required for design and installation of offshore constructions in seasonally ice covered seas (Heinonen and Rissanen, 2017). A reduction of the sea ice volume in the Baltic Sea has a critical impact on the population of ringed seals (Harkonen et al., 2008). Ringed seals need stable ice conditions for about two months for breeding and moulting (Sundqvist et al., 2012). The sea ice is a habitat for diverse biological assemblages (Enberg et al., 2018).\n\nKEY FINDINGS\n\nIn the Baltic Sea, the ice season may begin in October and last until June. The maximum sea ice volume typically peaks in March, but in 2023, it was observed in April. The 2022/23 ice season saw a low maximum sea ice volume of approximately 14 km\u00b3. From 1993 to 2023, the annual maximum ice volume ranged from 4 km\u00b3 in 2020 to 60 km\u00b3 in 1996. There is a statistically significant downward trend of -0.73 km\u00b3/year (p=0.01) in the Baltic Sea's maximum sea ice volume. Recent trends indicate a decrease in sea ice fraction and thickness across the Baltic Sea, particularly in the Bothnian Bay, as reported by Singh et al. (2024).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00201\n\nReferences:\n\n* Bostr\u00f6m M, \u00d6sterman C, 2017, Improving operational safety during icebreaker operations, WMU Journal of Maritime Affairs, 16, 73-88, DOI: 10.1007/s13437-016-0105-9\n* Enberg S, Majaneva M, Autio R, Blomster J, Rintala J-M, 2018, Phases of microalgal succession in sea ice and the water column in the baltic sea from autumn to spring. Marine Ecology Progress Series, 559, 19-34. DOI: 10.3354/meps12645\n* Harkonen T, J\u00fcssi M, J\u00fcssi I, Verevkin M, Dmitrieva L, Helle E, Sagitov R, Harding KC, 2008, Seasonal Activity Budget of Adult Baltic Ringed Seals, PLoS ONE 3(4): e2006, DOI: 0.1371/journal.pone.0002006\n* Heinonen J, Rissanen S, 2017, Coupled-crushing analysis of a sea ice - wind turbine interaction \u2013 feasibility study of FAST simulation software, Ships and Offshore Structures, 12, 1056-1063. DOI: 10.1080/17445302.2017.1308782\n* Sundqvist L, Harkonen T, Svensson CJ, Harding KC, 2012, Linking Climate Trends to Population Dynamics in the Baltic Ringed Seal: Impacts of Historical and Future Winter Temperatures, AMBIO, 41: 865, DOI: 10.1007/s13280-012-0334-x\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010, Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, DOI: 10.1109/BALTIC.2010.5621649\n* Valdez Banda OA, Goerlandt F, Montewka J, Kujala P, 2015, A risk analysis of winter navigation in Finnish sea areas, Accident Analysis & Prevention, 79, 100\u2013116, DOI: 10.1016/j.aap.2015.03.024\n", "doi": "10.48670/moi-00201", "instrument": null, "keywords": "baltic-omi-si-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-volume,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Volume from Observations Reprocessing"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"abstract": "DEFINITION\n\nThe subsurface salinity trends have been derived from regional reanalysis and forecast modelling results of the Copernicus Marine Service BAL MFC group for the Baltic Sea (product reference BALTICSEA_MULTIYEAR_PHY_003_011). The salinity trend has been obtained through a linear fit for each time series of horizontally averaged (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait) annual salinity and at each depth level.\n\nCONTEXT\n\nThe Baltic Sea is a brackish semi-enclosed sea in North-Eastern Europe. The surface salinity varies horizontally from ~10 near the Danish Straits down to ~2 at the northernmost and easternmost sub-basins of the Baltic Sea. The halocline, a vertical layer with rapid changes of salinity with depth that separates the well-mixed surface layer from the weakly stratified layer below, is located at the depth range of 60-80 metres (Matth\u00e4us, 1984). The bottom layer salinity below the halocline depth varies from 15 in the south down to 3 in the northern Baltic Sea (V\u00e4li et al., 2013). The long-term salinity is determined by net precipitation and river discharge as well as saline water inflows from the North Sea (Lehmann et al., 2022). Long-term salinity decrease may reduce the occurrence and biomass of the Fucus vesiculosus - Idotea balthica association/symbiotic aggregations (Kotta et al., 2019). Changes in salinity and oxygen content affect the survival of the Baltic cod eggs (Raudsepp et al, 2019; von Dewitz et al., 2018).\n\nKEY FINDINGS\n\nThe subsurface salinity from 1993 to 2023 exhibits distinct variations at different depths. In the surface layer up to 25 meters, which aligns with the average upper mixed layer depth in the Baltic Sea, there is no discernible trend. The salinity trend increases steadily from zero at a 25-meter depth to 0.04 per year at 70 meters. The most pronounced trend, 0.045 per year, is found within the extended halocline layer ranging from 70 to 150 meters. It is noteworthy that there is a slight reduction in the salinity trend to 0.04 per year between depths of 150 and 220 meters. Although this decrease is minor, it suggests that salt transport into the extended halocline layer is more pronounced than into the deeper layers. The Major Baltic Inflows are responsible for the significant salinity trend of 0.05 per year observed in the deepest layer of the Baltic Sea. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00207\n\nReferences:\n\n* von Dewitz B, Tamm S, Ho\u00c8flich K, Voss R, Hinrichsen H-H., 2018. Use of existing hydrographic infrastructure to forecast the environmental spawning conditions for Eastern Baltic cod, PLoS ONE 13(5): e0196477, doi:10.1371/journal.pone.0196477\n* Kotta, J., Vanhatalo, J., J\u00e4nes, H., Orav-Kotta, H., Rugiu, L., Jormalainen, V., Bobsien, I., Viitasalo, M., Virtanen, E., Nystr\u00f6m Sandman, A., Isaeus, M., Leidenberger, S., Jonsson, P.R., Johannesson, K., 2019. Integrating experimental and distribution data to predict future species patterns. Scientific Reports, 9: 1821, doi:10.1038/s41598-018-38416-3\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Sandrine Mulet, Bruno Buongiorno Nardelli, Simon Good, Andrea Pisano, Eric Greiner, Maeva Monier, Emmanuelle Autret, Lars Axell, Fredrik Boberg, Stefania Ciliberti, Marie Dr\u00e9villon, Riccardo Droghei, Owen Embury, J\u00e9rome Gourrion, Jacob H\u00f8yer, M\u00e9lanie Juza, John Kennedy, Benedicte Lemieux-Dudon, Elisaveta Peneva, Rebecca Reid, Simona Simoncelli, Andrea Storto, Jonathan Tinker, Karina von Schuckmann and Sarah L. Wakelin. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI:10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. 2.7 Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* V\u00e4li G, Meier HEM, Elken J, 2013, Simulated halocline variability in the baltic sea and its impact on hypoxia during 1961-2007, Journal of Geophysical Research: Oceans, 118(12), 6982\u20137000, DOI:10.1002/2013JC009192\n", "doi": "10.48670/moi-00207", "instrument": null, "keywords": "baltic-omi-tempsal-stz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-salinity-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Salinity trend from Reanalysis"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"abstract": "DEFINITION\n\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic Sea (product references BALTICSEA_MULTIYEAR_PHY_003_011). Horizontal averaging has been done over the Baltic Sea domain (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait). The temperature trend has been obtained through a linear fit for each time series of horizontally averaged annual temperature and at each depth level. \n\nCONTEXT\n\nThe Baltic Sea is a semi-enclosed sea in North-Eastern Europe. The temperature of the upper mixed layer of the Baltic Sea is characterised by a strong seasonal cycle driven by the annual course of solar radiation (Lepp\u00e4ranta and Myrberg, 2008). The maximum water temperatures in the upper layer are reached in July and August and the minimum during February, when the Baltic Sea becomes partially frozen (CMEMS OMI Baltic Sea Sea Ice Extent, CMEMS OMI Baltic Sea Sea Ice Volume). Seasonal thermocline, developing in the depth range of 10-30 m in spring, reaches its maximum strength in summer and is eroded in autumn. During autumn and winter the Baltic Sea is thermally mixed down to the permanent halocline in the depth range of 60-80 metres (Matth\u00e4us, 1984). The 20\u201350\u202fm thick cold intermediate layer forms below the upper mixed layer in March and is observed until October within the 15-65 m depth range (Chubarenko and Stepanova, 2018; Liblik and Lips, 2011). The deep layers of the Baltic Sea are disconnected from the ventilated upper ocean layers, and temperature variations are predominantly driven by mixing processes and horizontal advection. A warming trend of the sea surface waters is positively correlated with the increasing trend of diffuse attenuation of light (Kd490) and satellite-detected chlorophyll concentration (Kahru et al., 2016). Temperature increase in the water column could accelerate oxygen consumption during organic matter oxidation (Savchuk, 2018).\n\nKEY FINDINGS\n\nAnalysis of subsurface temperatures from 1993 to 2023 indicates that the Baltic Sea is experiencing warming across all depth intervals. The temperature trend in the upper mixed layer (0-25 m) is approximately 0.055 \u00b0C/year, decreasing to 0.045 \u00b0C/year within the seasonal thermocline layer. A peak temperature trend of 0.065 \u00b0C/year is observed at a depth of 70 m, aligning with the base of the cold intermediate layer. Beyond this depth, the trend stabilizes, closely matching the 0.065 \u00b0C/year value. At a 95% confidence level, it can be stated that the Baltic Sea's warming is consistent with depth, averaging around 0.06 \u00b0C/year. Notably, recent trends show a significant increase; for instance, Savchuk's 2018 measurements indicate an average temperature trend of 0.04 \u00b0C/year in the Baltic Proper's deep layers (>60m) from 1979 to 2016.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00208\n\nReferences:\n\n* Chubarenko, I., Stepanova, N. 2018. Cold intermediate layer of the Baltic Sea: Hypothesis of the formation of its core. Progress in Oceanography, 167, 1-10, doi: 10.1016/j.pocean.2018.06.012\n* Kahru, M., Elmgren, R., and Savchuk, O. P. 2016. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009\u20131018. doi: 10.5194/bg-13-1009-2016\n* Lepp\u00e4ranta, M., Myrberg, K. 2008. Physical Oceanography of the Baltic Sea. Springer, Praxis Publishing, Chichester, UK, pp. 370\n* Liblik, T., Lips, U. 2011. Characteristics and variability of the vertical thermohaline structure in the Gulf of Finland in summer. Boreal Environment Research, 16, 73-83.\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Savchuk, .P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00208", "instrument": null, "keywords": "baltic-omi-tempsal-ttz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Temperature trend from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"abstract": "DEFINITION\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the Baltic Sea- Bornholm basin, Gdansk basin and Gotland basin. The Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona basin and in the Bornholm basin and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland basin (BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Bottom salinity increase in the Arkona basin is the first indication of the saline water inflow, but not necessarily Major Baltic Inflow. Abrupt increase of bottom salinity of 2-3 units in the more downstream Bornholm basin is a solid indicator that Major Baltic Inflow has occurred.\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic \n\nCONTEXT\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). The saline and oxygenated water inflows to the Baltic Sea through the Danish straits, especially the Major Baltic Inflows, occur only intermittently (e.g. Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and the overall weakening of vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s Lehmann et al., 2022). Bottom salinity variations in the Arkona Basin represent water exchange between the Baltic Sea and Skagerrak-Kattegat area. The increasing salinity signal in that area does not indicate that a Major Baltic Inflow has occurred. The mean sea level of the Baltic Sea derived from satellite altimetry data can be used as a proxy for the detection of saline water inflows to the Baltic Sea from the North Sea (Raudsepp et al., 2018). The medium and strong inflow events increase oxygen concentration in the near-bottom layer of the Bornholm Basin while some medium size inflows have no impact on deep water salinity (Mohrholz, 2018). \n\nKEY FINDINGS\n\nTime series data of bottom salinity variations in the Arkona Basin are instrumental for monitoring the sporadic nature of water inflow and outflow events. The bottom salinity in the Arkona Basin fluctuates between 11 and 25 g/kg. The highest recorded bottom salinity value is associated with the Major Baltic Inflow of 2014, while other significant salinity peaks align with the Major Baltic Inflows of 1993 and 2002. Low salinity episodes in the Arkona Basin mark the occasions of barotropic outflows of brackish water from the Baltic Sea. In the Bornholm Basin, the bottom salinity record indicates three Major Baltic Inflow events: the first in 1993, followed by 2002, and the most recent in 2014. Following the last Major Baltic Inflow, the bottom salinity in the Bornholm Basin rose to 20 g/kg. Over the subsequent nine years, it has declined to 16 g/kg. The winter of 2023/24 did not experience a Major Baltic Inflow.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00209\n\nReferences:\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz V, 2018, Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, doi: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, doi: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00209", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-bottom-salinity-arkona-bornholm,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: bottom salinity from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"abstract": "\"_DEFINITION_'\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the central Baltic Sea, i.e. the Gotland Basin. These Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona Basin and in the Bornholm Basin (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland Basin. Temperature, salinity and dissolved oxygen profiles in the Gotland Basin enable us to estimate the amount of the Major Baltic Inflow water that has reached central Baltic, the depth interval of which has been the most affected, and how much the oxygen conditions have been improved. \n\nCONTEXT\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). This implies that fresher water lies on top of water with higher salinity. The saline water inflows to the Baltic Sea through the Danish Straits, especially the Major Baltic Inflows, shape hydrophysical conditions in the Gotland Basin of the central Baltic Sea, which in turn have a substantial influence on marine ecology on different trophic levels (Bergen et al., 2018; Raudsepp et al.,2019). In the absence of the Major Baltic Inflows, oxygen in the deeper layers of the Gotland Basin is depleted and replaced by hydrogen sulphide (e.g., Savchuk, 2018). As the Baltic Sea is connected to the North Sea only through very narrow and shallow channels in the Danish Straits, inflows of high salinity and oxygenated water into the Baltic occur only intermittently (e.g., Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and overall weakening of the vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s (Lehmann et al., 2022).\n\nKEY FINDINGS\n\nThe Major Baltic Inflows of 1993, 2002, and 2014 (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) present a distinct signal in the Gotland Basin, influencing water salinity, temperature, and dissolved oxygen up to a depth of 100 meters. Following each event, deep layer salinity in the Gotland Basin increases, reaching peak bottom salinities approximately 1.5 years later, with elevated salinity levels persisting for about three years. Post-2017, salinity below 150 meters has declined, while the halocline has risen, suggesting saline water movement to the Gotland Basin's intermediate layers. Typically, temperatures fall immediately after a Major Baltic Inflow, indicating the descent of cold water from nearby upstream regions to the Gotland Deep's bottom. From 1993 to 1997, deep water temperatures remained relatively low (below 6 \u00b0C). Since 1998, these waters have warmed, with even moderate inflows in 1997/98, 2006/07, and 2018/19 introducing warmer water to the Gotland Basin's bottom layer. From 2019 onwards, water warmer than 7 \u00b0C has filled the layer beneath 100 meters depth. The water temperature below the halocline has risen by approximately 2 \u00b0C since 1993, and the cold intermediate layer's temperature has also increased from 1993 to 2023. Oxygen levels begin to drop sharply after the temporary reoxygenation of the bottom waters. The decline in 2014 was attributed to a shortage of smaller inflows that could bring oxygen-rich water to the Gotland Basin (Neumann et al., 2017) and an increase in biological oxygen demand (Savchuk, 2018; Meier et al., 2018). Additionally, warmer water has accelerated oxygen consumption in the deep layer, leading to increased anoxia. By 2023, oxygen was completely depleted below the depth of 75 metres.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00210\n\nReferences:\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Bergen, B., Naumann, M., Herlemann, D.P.R., Gr\u00e4we, U., Labrenz, M., J\u00fcrgens, K., 2018. Impact of a Major inflow event on the composition and distribution of bacterioplankton communities in the Baltic Sea. Frontiers in Marine Science, 5:383, doi: 10.3389/fmars.2018.00383\n* Meier, H.E.M., V\u00e4li, G., Naumann, M., Eilola, K., Frauen, C., 2018. Recently Accelerated Oxygen Consumption Rates Amplify Deoxygenation in the Baltic Sea. , J. Geophys. Res. Oceans, doi:10.1029/2017JC013686\|\n* Mohrholz, V., 2018. Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, DOI: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* Savchuk, P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00210", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-sto2tz-gotland,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: time/depth evolution S,T,O2 from Observations Reprocessing"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"abstract": "BLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.2-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, zooplankton and phytoplankton carbon biomass, oxygen-demand-units, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\nDOI (product): \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\n\nReferences:\n\n* Gr\u00e9goire, M., Vandenbulcke, L. and Capet, A. (2020) \u201cBlack Sea Biogeochemical Analysis and Forecast (CMEMS Near-Real Time BLACKSEA Biogeochemistry).\u201d Copernicus Monitoring Environment Marine Service (CMEMS). doi: 10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\"\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-bgc-007-010,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"abstract": "The BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 3 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\nDOI (Product): \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6\n\nReferences:\n\n* Jansen, E., Martins, D., Stefanizzi, L., Ciliberti, S. A., Gunduz, M., Ilicak, M., Lecci, R., Cret\u00ed, S., Causio, S., Aydo\u011fdu, A., Lima, L., Palermo, F., Peneva, E. L., Coppini, G., Masina, S., Pinardi, N., Palazov, A., and Valchev, N. (2022). Black Sea Physical Analysis and Forecast (Copernicus Marine Service BS-Currents, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS5\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-phy-007-001,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"abstract": "The wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\nCitation: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n\nDOI (Product): \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5\n\nReferences:\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n* Ricker, M., Behrens, A., & Staneva, J. (2024). The operational CMEMS wind wave forecasting system of the Black Sea. Journal of Operational Oceanography, 1\u201322. https://doi.org/10.1080/1755876X.2024.2364974\n", "doi": "10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-wav-007-003,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-04-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Analysis and Forecast"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"abstract": "The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\nDOI (product):\nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI\n\nReferences:\n\n* Gr\u00e9goire, M., Vandenbulcke, L., & Capet, A. (2020). Black Sea Biogeochemical Reanalysis (CMEMS BS-Biogeochemistry) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_REANALYSIS_BIO_007_005_BAMHBI\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,blksea-multiyear-bgc-007-005,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Reanalysis"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"abstract": "The BLKSEA_MULTIYEAR_PHY_007_004 product provides ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamic core is based on the NEMOv4.0 general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/40\u00ba and 121 vertical levels. NEMO is forced by atmospheric fluxes computed from a bulk formulation applied to ECMWF ERA5 atmospheric fields at the resolution of 1/4\u00ba in space and 1-h in time. A heat flux correction through sea surface temperature (SST) relaxation is employed using the ESA-CCI SST-L4 product. This version has an open lateral boundary, a new model characteristic that allows a better inflow/outflow representation across the Bosphorus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly (SLA) along-track observations from Copernicus and available in situ vertical profiles of temperature and salinity from both SeaDataNet and Copernicus datasets. Upgrades on data assimilation include an improved background error covariance matrix and an observation-based mean dynamic topography for the SLA assimilation.\n\nDOI (Product): \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n\nReferences:\n\n* Lima, L., Aydogdu, A., Escudier, R., Masina, S., Ciliberti, S. A., Azevedo, D., Peneva, E. L., Causio, S., Cipollone, A., Clementi, E., Cret\u00ed, S., Stefanizzi, L., Lecci, R., Palermo, F., Coppini, G., Pinardi, N., & Palazov, A. (2020). Black Sea Physical Reanalysis (CMEMS BS-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,blksea-multiyear-phy-007-004,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Reanalysis"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"abstract": "The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.\n\nCitation: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). \n\nDOI (Product): \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4\n\nReferences:\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_WAV_007_006_EAS4\n", "doi": "10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,blksea-multiyear-wav-007-006,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Reanalysis"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"abstract": "DEFINITION\n\nThe oxygenation status of the Black Sea open basin is described by three complementary indicators, derived from vertical profiles and spatially averaged over the Black Sea open basin (depth > 50m). (1) The oxygen penetration depth is the depth at which [O2] < 20\u00b5M, expressed in [m]. (2) The oxygen penetration density is the potential density anomaly at the oxygen penetration depth [kg/m\u00b3]. (3) The oxygen inventory is the vertically integrated oxygen content [mol O2/m\u00b2]. The 20\u00b5M threshold was chosen to minimize the indicator sensitivity to sensor\u2019s precision. Those three metrics are complementary: Oxygen penetration depth is more easily understood, but present more spatial variability. Oxygen penetration density helps in dissociating biogeochemical processes from shifts in the physical structure. Although less intuitive, the oxygen inventory is a more integrative diagnostic and its definition is more easily transposed to other areas.\n\nCONTEXT\n\nThe Black Sea is permanently stratified, due to the contrast in density between large riverine and Mediterranean inflows. This stratification restrains the ventilation of intermediate and deep waters and confines, within a restricted surface layer, the waters that are oxygenated by photosynthesis and exchanges with the atmosphere. The vertical extent of the oxic layer determines the volume of habitat available for pelagic populations (Ostrovskii and Zatsepin 2011, Sak\u0131nan and G\u00fcc\u00fc 2017) and present spatial and temporal variations (Murray et al. 1989; Tugrul et al. 1992; Konovalov and Murray 2001). At long and mid-term, these variations can be monitored with three metrics (Capet et al. 2016), derived from the vertical profiles that can obtained from traditional ship casts or autonomous Argo profilers (Stanev et al., 2013). A large source of uncertainty associated with the spatial and temporal average of those metrics stems from the small number of Argo floats, scarcely adequate to sample the known spatial variability of those metrics.\n\nCMEMS KEY FINDINGS\n\nDuring the past 60 years, the vertical extent of the Black Sea oxygenated layer has narrowed from 140m to 90m (Capet et al. 2016). The Argo profilers active for 2016 suggested an ongoing deoxygenation trend and indicated an average oxygen penetration depth of 72m at the end of 2016, the lowest value recorded during the past 60 years. The oxygenation of subsurface water is closely related to the intensity of cold water formation, an annual ventilation processes which has been recently limited by warmer-than-usual winter air temperature (Capet et al. 2020). In 2017, 2018 and 2020, cold waters formation resulted in a partial reoxygenation of the intermediate layer. Yet, such ventilation has been lacking in winter 2020-2021, and the updated 2021 indicators reveals the lowest oxygen inventory ever reported in this OMI time series. This results in significant detrimental trends now depicted also over the Argo period (2012-2021).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00213\n\nReferences:\n\n* Capet, A., Vandenbulcke, L., & Gr\u00e9goire, M. (2020). A new intermittent regime of convective ventilation threatens the Black Sea oxygenation status. Biogeosciences , 17(24), 6507\u20136525.\n* Capet A, Stanev E, Beckers JM, Murray J, Gr\u00e9goire M. (2016). Decline of the Black Sea oxygen inventory. Biogeosciences. 13:1287-1297.\n* Capet Arthur, Vandenbulcke Luc, Veselka Marinova, Gr\u00e9goire Marilaure. (2018). Decline of the Black Sea oxygen inventory. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Konovalov S, Murray JW. (2001). Variations in the chemistry of the Black Sea on a time scale of decades (1960\u20131995). J Marine Syst. 31: 217\u2013243.\n* Murray J, Jannasch H, Honjo S, Anderson R, Reeburgh W, Top Z, Friederich G, Codispoti L, Izdar E. (1989). Unexpected changes in the oxic/anoxic interface in the Black Sea. Nature. 338: 411\u2013413.\n* Ostrovskii A and Zatsepin A. (2011). Short-term hydrophysical and biological variability over the northeastern Black Sea continental slope as inferred from multiparametric tethered profiler surveys, Ocean Dynam., 61, 797\u2013806, 2011.\n* \u00d6zsoy E and \u00dcnl\u00fcata \u00dc. (1997). Oceanography of the Black Sea: a review of some recent results. Earth-Science Reviews. 42(4):231-72.\n* Sak\u0131nan S, G\u00fcc\u00fc AC. (2017). Spatial distribution of the Black Sea copepod, Calanus euxinus, estimated using multi-frequency acoustic backscatter. ICES J Mar Sci. 74(3):832-846. doi:10.1093/icesjms/fsw183\n* Stanev E, He Y, Grayek S, Boetius A. (2013). Oxygen dynamics in the Black Sea as seen by Argo profiling floats. Geophys Res Lett. 40(12), 3085-3090.\n* Tugrul S, Basturk O, Saydam C, Yilmaz A. (1992). Changes in the hydrochemistry of the Black Sea inferred from water density profiles. Nature. 359: 137-139.\n* von Schuckmann, K. et al. Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography 11, S1\u2013S142 (2018).\n", "doi": "10.48670/moi-00213", "instrument": null, "keywords": "black-sea,blksea-omi-health-oxygen-trend,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,ocean-mole-content-of-dissolved-molecular-oxygen,oceanographic-geographical-features,sea-water-sigma-theta-defined-by-mole-concentration-of-dissolved-molecular-oxygen-in-sea-water-above-threshold,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1955-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Oxygen Trend from Observations Reprocessing"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS BLKSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_WAV_007_006) and the Analysis product (BLKSEA_ANALYSISFORECAST_WAV_007_003).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1979-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\nCONTEXT\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, including biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015, IPCC, 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (IPCC, 2021).\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence demonstrating that the highest values of the average annual 99th percentiles are in the areas where high winds and long fetch are simultaneously present. The largest values of the mean 99th percentile in the Black Sea in the southewestern Black Sea are around 3.5 m, while in the eastern part of the basin are around 2.5 m (Staneva et al., 2019a and 2019b).\n\nCMEMS KEY FINDINGS\n\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence with largest values in the southwestern Black Sea, with values as high as 3.5 m, while the 99th percentile values in the eastern part of the basin are around 2.5 m. The Black Sea, the 99th mean percentile for 2002-2019 shows a similar pattern demonstrating that the highest values of the mean annual 99th percentile are in the western Black Sea. This pattern is consistent with the previous studies, e.g. of (Akp\u0131nar and K\u00f6m\u00fcrc\u00fc, 2012; and Akpinar et al., 2016).\nThe anomaly of the 99th percentile in 2020 is mostly negative with values down to ~-45 cm. The highest negative anomalies for 2020 are observed in the southeastern area where the multi-year mean 99th percentile is the lowest. The highest positive anomalies of the 99th percentile in 2020 are located in the southwestern Black Sea and along the eastern coast. The map of anomalies for 2020, presenting alternate bands of positive and negative values depending on latitude, is consistent with the yearly west-east displacement of the tracks of the largest storms. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00214\n\nReferences:\n\n* Akp\u0131nar, A.; K\u00f6m\u00fcrc\u00fc, M.\u02d9I. Wave energy potential along the south-east coasts of the Black Sea. Energy 2012, 42, 289\u2013302.\n* Akp\u0131nar, A., Bing\u00f6lbali, B., Van Vledder, G., 2016. Wind and wave characteristics in the Black Sea based on the SWAN wave model forced with the CFSR winds. Ocean Eng. 126, 276\u2014298, http://dx. doi.org/10.1016/j.oceaneng.2016.09.026.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Staneva, J. Behrens, A., Gayer G, Ricker M. (2019a) Black sea CMEMS MYP QUID Report\n* Staneva J, Behrens A., Gayer G, Aouf A., (2019b). Synergy between CMEMS products and newly available data from SENTINEL, Section 3.3, In: Schuckmann K,et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, doi: 10.1080/1755876X.2019.1633075.\n", "doi": "10.48670/moi-00214", "instrument": null, "keywords": "black-sea,blksea-omi-seastate-extreme-var-swh-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Significant Wave Height extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS BLKSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_PHY_007_004) and the Analysis product (BLKSEA_ANALYSIS_FORECAST_PHYS_007_001).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. Particularly in the Black Sea, ocean-atmospheric processes together with its general cyclonic circulation (Rim Current) play an important role on the sea surface temperature variability (Capet et al. 2012). As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. The 99th mean percentile of sea surface temperature provides a worth information about the variability of the sea surface temperature and warming trends but has not been investigated with details in the Black Sea.\nWhile the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013). Recent studies indicated a warming trend of the sea surface temperature in the Black Sea in the latest years (Mulet et al., 2018; Sakali and Ba\u015fusta, 2018). A specific analysis on the interannual variability of the basin-averaged sea surface temperature revealed a higher positive trend in its eastern region (Ginzburg et al., 2004). For the past three decades, Sakali and Ba\u015fusta (2018) presented an increase in sea surface temperature that varied along both east\u2013west and south\u2013north directions in the Black Sea. \n\nCMEMS KEY FINDINGS\n\nThe mean annual 99th percentile in the period 1993\u20132019 exhibits values ranging from 25.50 to 26.50 oC in the western and central regions of the Black Sea. The values increase towards the east, exceeding 27.5 oC. This contrasting west-east pattern may be linked to the basin wide cyclonic circulation. There are regions showing lower values, below 25.75 oC, such as a small area west of Crimean Peninsula in the vicinity of the Sevastopol anticyclone, the Northern Ukraine region, in particular close to the Odessa and the Karkinytska Gulf due to the freshwaters from the land and a narrow area along the Turkish coastline in the south. Results for 2020 show negative anomalies in the area of influence of the Bosporus and the Bulgarian offshore region up to the Crimean peninsula, while the North West shelf exhibits a positive anomaly as in the Eastern basin. The highest positive value is occurring in the Eastern Tukish coastline nearest the Batumi gyre area. This may be related to the variously increase of sea surface temperature in such a way the southern regions have experienced a higher warming.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00216\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Capet, A., Barth, A., Beckers, J. M., & Marilaure, G. (2012). Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns. Deep Sea Research Part II: Topical Studies in Oceanography, 77, 128-142. https://doi.org/10.1016/j.dsr2.2012.04.010\n* Ginzburg, A. I.; Kostianoy, A. G.; Sheremet, N. A. (2004). Seasonal and interannual variability of the Black Sea surface temperature as revealed from satellite data (1982\u20132000), Journal of Marine Systems, 52, 33-50. https://doi.org/10.1016/j.jmarsys.2004.05.002.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sakalli A, Ba\u015fusta N. 2018. Sea surface temperature change in the Black Sea under climate change: A simulation of the sea surface temperature up to 2100. International Journal of Climatology, 38(13), 4687-4698. https://doi.org/10.1002/joc.5688\n", "doi": "10.48670/moi-00216", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-extreme-var-temp-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "\"_DEFINITION_'\n\nThe blksea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Black Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure. This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\n\nKEY FINDINGS\n\nDuring 2023, the Black Sea basin average SST anomaly was ~1.1 \u00b0C above the 1991-2020 climatology, doubling that of previous year (~0.5 \u00b0C). The Black Sea SST monthly anomalies ranged between -1.0/+1.0 \u00b0C. The highest temperature anomaly (~1.8 \u00b0C) was reached in January 2023, while the lowest (~-0.28 \u00b0C) in May. This year, along with 2022, was characterized by milder temperature anomalies with respect to the previous three consecutive years (2018-2020) marked by peaks of ~3 \u00b0C occurred in May 2018, June 2019, and October 2020.\nOver the period 1982-2023, the Black Sea SST has warmed at a rate of 0.065 \u00b1 0.002 \u00b0C/year, which corresponds to an average increase of about 2.7 \u00b0C during these last 42 years.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00217\n\nReferences:\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n", "doi": "10.48670/moi-00217", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-sst-area-averaged-anomalies,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature time series and trend from Observations Reprocessing"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"abstract": "DEFINITION\n\nThe blksea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Black Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the timeseries (42). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\nKEY FINDINGS\n\nOver the past four decades (1982-2023), the Black Sea surface temperature (SST) warmed at a rate of 0.065 \u00b1 0.002 \u00b0C per year, corresponding to a mean surface temperature warming of about 2.7 \u00b0C. The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.053 \u00b0C/year to 0.080 \u00b0C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas.\nDOI (product): \nhttps://doi.org/10.48670/moi-00218\n\nReferences:\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00218", "instrument": null, "keywords": "baltic-sea,blksea-omi-tempsal-sst-trend,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"abstract": "The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related [QuID](https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-028.pdf)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00015", "doi": "10.48670/moi-00015", "instrument": null, "keywords": "cell-height,cell-thickness,cell-width,coastal-marine-environment,forecast,global-analysisforecast-bgc-001-028,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"abstract": "The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00016", "doi": "10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,forecast,global-analysisforecast-phy-001-024,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"abstract": "The operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00017\n\nReferences:\n\n* F. Ardhuin, R. Magne, J-F. Filipot, A. Van der Westhyusen, A. Roland, P. Quefeulou, J. M. Lef\u00e8vre, L. Aouf, A. Babanin and F. Collard : Semi empirical dissipation source functions for wind-wave models : Part I, definition and calibration and validation at global scales. Journal of Physical Oceanography, March 2010.\n* P. Janssen, L. Aouf, A. Behrens, G. Korres, L. Cavalieri, K. Christiensen, O. Breivik : Final report of work-package I in my wave project. December 2014.\n", "doi": "10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,forecast,global-analysisforecast-wav-001-027,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T03:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Analysis and Forecast"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"abstract": "The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4 ocean physics produced at Mercator-Ocean and ERA-Interim atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00019", "doi": "10.48670/moi-00019", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-bgc-001-029,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Hindcast"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"abstract": "The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [CLS](https://www.cls.fr) on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\nForcings sources:\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\nVertical coverage:\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00020\n\nReferences:\n\n* Lehodey P., Murtugudde R., Senina I. (2010). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanography, 84, p. 69-84.\n* Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R. (2015) Optimization of a micronekton model with acoustic data. ICES Journal of Marine Science, 72(5), p. 1399-1412.\n* Conchon A. (2016). Mod\u00e9lisation du zooplancton et du micronecton marins. Th\u00e8se de Doctorat, Universit\u00e9 de La Rochelle, 136 p.\n", "doi": "10.48670/moi-00020", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,euphotic-zone-depth,global-multiyear-bgc-001-033,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"abstract": "The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00021", "doi": "10.48670/moi-00021", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,global-multiyear-phy-001-030,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Reanalysis"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"abstract": "You can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution: monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00024", "doi": "10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-multiyear-phy-ens-001-031,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Ensemble Physics Reanalysis"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"abstract": "GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\nDOI (product):\nhttps://doi.org/10.48670/moi-00022", "doi": "10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-wav-001-032,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"abstract": "\"_DEFINITION_'\n\nNINO34 sub surface temperature anomaly (\u00b0C) is defined as the difference between the subsurface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031). Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. \n\nCONTEXT\n\nEl Nino Southern Oscillation (ENSO) is one of the most important sources of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 sub-surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific el Nino conditions and enable to monitor the evolution the ENSO phase.\n\nCMEMS KEY FINDINGS \n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO (el nino) phases in particular during the 1997/1998 winter and the 2015/2016 winter, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like during the 1998/1999 winter and during the 2010/2011 winter. \nThe NINO34 subsurface indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of atmospheric conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00220\n\nReferences:\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00220", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-tzt-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Temporal Evolution of Vertical Profile of Temperature from Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"abstract": "DEFINITION\n\nNINO34 sea surface temperature anomaly (\u00b0C) is defined as the difference between the sea surface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031) . Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. El Nino or La Nina events are defined when the NINO3.4 SST anomalies exceed +/- 0.5\u00b0C during a period of six month.\n\nCONTEXT\n\nEl Nino Southern Oscillation (ENSO) is one of the most important source of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 Sea surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific El Nino conditions and enable to monitor the evolution the ENSO phase.\n\nCMEMS KEY FINDINGS\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO phases in particular in 1998 and 2016, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like in 2000 and 2008. \nThe NINO34 indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of meteorological conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00219\n\nReferences:\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Sea Surface Temperature time series from Reanalysis"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"abstract": "DEFINITION\n\nThe global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble.\n\nCONTEXT\n\nSince the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277\u00b13 ppm (Joos and Spahni, 2008) to 412.44\u00b10.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 \u00b1 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). \n\nCMEMS KEY FINDINGS\n\nThe rate of change of the integrated yearly surface downward flux has increased by 0.04\u00b10.03e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06\u00b10.04e-1 PgC/yr2. In 2021 (resp. 2020), the global ocean CO2 sink was 2.41\u00b10.13 (resp. 2.50\u00b10.12) PgC/yr. The average over the full period is 1.61\u00b10.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr. In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of 0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45\u00b10.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78\u00b10.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00223\n\nReferences:\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Que\u0301re\u0301, C., Myneni, R., Piao, S., and Thorn- ton, P.: Chapter 6: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge University Press, Cambridge, 2013.\n* Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl. noaa.gov/gmd/ccgg/trends/global.html, last access: 11 March 2022.\n* Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425\u20131430, https://doi.org/10.1073/pnas.0707386105, 2008.\n* Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Qu\u00e9r\u00e9, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., G\u00fcrses, \u00d6., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., K\u00f6rtzinger, A., Landsch\u00fctzer, P., Lauvset, S. K., Lef\u00e8vre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., R\u00f6denbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., S\u00e9f\u00e9rian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917\u20132005, https://doi.org/10.5194/essd-14-1917-2022, 2022.\n* Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and Gloor, M. (2007), A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* Resplandy, L., Keeling, R. F., R\u00f6denbeck, C., Stephens, B. B., Khatiwala, S., Rodgers, K. B., Long, M. C., Bopp, L. and Tans, P. P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nature Geoscience, 11(7), p.504, 2018.\n", "doi": "10.48670/moi-00223", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-co2-flux-integrated,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Yearly CO2 Sink from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"abstract": "DEFINITION\n\nOcean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, \n* Obtain values for alkalinity based on the so called \u201clocally interpolated alkalinity regression (LIAR)\u201d method after Carter et al., 2016; 2018. \n* Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/)\n* Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity.\nThe global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1\u03c3 ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017\u00b10.0004e-1 pH units per year.\nThe indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time. Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements. \nA full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).\n\nCONTEXT\n\nThe decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as \u201ca reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean\u201d. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14). \n\nCMEMS KEY FINDINGS\n\nSince the year 1985, global ocean surface pH is decreasing at a rate of -0.0017\u00b10.0004e-1 per year. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00224\n\nReferences:\n\n* Bakker, D. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383-413, https://doi.org/10.5194/essd-8-383-2016, 2016.\n* Bopp, L. et al.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225\u20136245, doi: 10.5194/bg-10-6225-2013, 2013.\n* Carter, B.R., et al.: Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnol. Oceanogr.: Methods 16, 119\u2013131, 2018.\n* Carter, B. R., et al.: Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr.: Methods 14: 268\u2013277. doi:10.1002/lom3.10087, 2016.\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022. Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M.D. Mastrandrea, M. Tignor and K.L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, California, United States of America, pp.164.\n* Kroeker, K. J. et al.: Meta- analysis reveals negative yet variable effects of ocean acidifica- tion on marine organisms, Ecol. Lett., 13, 1419\u20131434, 2010.\n* Orr, J. C. et al.: Anthropogenic ocean acidification over the twenty-first century and its impact on cal- cifying organisms, Nature, 437, 681\u2013686, 2005.\n* van Heuven, S., et al.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., US DOE, Oak Ridge, Tenn., 2011.\n", "doi": "10.48670/moi-00224", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-area-averaged,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"abstract": "DEFINITION\n\nThis ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 0.25\u00b0\u00d70.25\u00b0 resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (\u03bc) and residual standard deviation (\u03c3) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e., \u03c3-to-\u00b5 ratio over a threshold of 1 0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean.\n\nCONTEXT\n\nA decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; P\u00e9rez et al, 2021). As projected in Gattuso et al (2015), \u201cunder our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050\u201d. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; P\u00f6rtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and P\u00e9rez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1\u03c3-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 0.25\u00b0\u00d70.25\u00b0 grid cells over the global ocean.\n\nCMEMS KEY FINDINGS\n\nSince 1985, pH has been decreasing at a rate between -0.0008 yr-1 and -0.0022 yr-1 over most of the global ocean basins. Tropical and subtropical regions, the eastern equatorial Pacific excepted, show pH trends falling in the interquartile range of all the trend estimates (between -0.0012 yr-1 and -0.0018 yr-1). pH over the eastern equatorial Pacific decreases much faster, reaching a growth rate larger than -0.0024 yr-1. Such a high rate of change in pH is also observed over a sector south of the Indian Ocean. Part of the polar and subpolar North Atlantic and the Southern Ocean has no significant trend. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00277\n\nReferences:\n\n* Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383\u2013413, DOI:10.5194/essd-8-383- 2016, 2016.\n* Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J., and Magdalena Santana-Casiano, J.: A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27, 126\u2013141, 2014.\n* Chau, T. T. T., Gehlen, M., Chevallier, F. : Global Ocean Surface Carbon: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information, DOI:10.48670/moi-00047, 2022a.\n* Chau, T. T. T., Gehlen, M., Chevallier, F.: Global mean seawater pH (GLOBAL_OMI_HEALTH_carbon_ph_area_averaged), E.U. Copernicus Marine Service Information, DOI: 10.48670/moi-00224, 2022b.\n* Doney, S. C., Balch, W. M., Fabry, V. J., and Feely, R. A.: Ocean Acidification: A critical emerging problem for the ocean sciences, Oceanography, 22, 16\u201325, 2009.\n* Gattuso, J-P., Alexandre Magnan, Rapha\u00ebl Bill\u00e9, William WL Cheung, Ella L. Howes, Fortunat Joos, Denis Allemand et al. \"\"Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios.\"\" Science 349, no. 6243 (2015).\n* Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Chau T T T., Conchon A., Denvil-Sommer A., Chevallier F., Vrac M., Mejia C. : Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI:10.1080/1755876X.2020.1785097, 2020.\n* Lauvset, S. K., Gruber, N., Landsch\u00fctzer, P., Olsen, A., and Tjiputra, J.: Trends and drivers in global surface ocean pH over the past 3 decades, Biogeosciences, 12, 1285\u20131298, DOI:10.5194/bg-12-1285-2015, 2015.\n* Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., \u00c1lvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterstr\u00f6m, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., P\u00e9rez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565\u20135589, DOI:10.5194/essd-13-5565-2021, 2021.\n* P\u00f6rtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Wiley IPCC Intergovernmental Panel on Climate Change, Geneva, 2019).\n* P\u00e9rez FF, Olafsson J, \u00d3lafsd\u00f3ttir SR, Fontela M, Takahashi T. Contrasting drivers and trends of ocean acidification in the subarctic Atlantic. Sci Rep 11, 13991, DOI:10.1038/s41598-021-93324-3, 2021.\n", "doi": "10.48670/moi-00277", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-trend,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,trend-of-surface-ocean-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"abstract": "DEFINITION\n\nThe Atlantic Meridional Overturning profile at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth. A climatological mean is then taken over time. This is done by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 over the whole time period (1993-2023) and over the period for which there are comparable observations (Apr 2004-Mar 2023). The observations come from the RAPID array (Smeed et al, 2017). \n\nCONTEXT\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). There are uncertainties in the deep profile associated with how much transport is returned in the upper (1-3km) or lower (3-5km) North Atlantic deep waters (Roberts et al 2013, Sinha et al 2018).\n\nCMEMS KEY FINDINGS \n\nThe AMOC strength at 1000m is found to be 17.0 \u00b1 3.2 Sv (1 Sverdrup=106m3/s; range is 2 x standard deviation of multi-product). See also Jackson et al (2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00231\n\nReferences:\n\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* Roberts, C. D., J. Waters, K. A. Peterson, M. D. Palmer, G. D. McCarthy, E. Frajka\u2010Williams, K. Haines, D. J. Lea, M. J. Martin, D. Storkey, E. W. Blockley and H. Zuo (2013), Atmosphere drives recent interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N, Geophys. Res. Lett., 40, 5164\u20135170 doi: 10.1002/grl.50930.\n* Sinha, B., Smeed, D.A., McCarthy, G., Moat, B.I., Josey, S.A., Hirschi, J.J.-M., Frajka-Williams, E., Blaker, A.T., Rayner, D. and Madec, G. (2018), The accuracy of estimates of the overturning circulation from basin-wide mooring arrays. Progress in Oceanography, 160. 101-123\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00231", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-26n-profile,marine-resources,marine-safety,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"abstract": "DEFINITION\n\nThe Atlantic Meridional Overturning strength at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 . The maximum value in depth is then taken as the strength in Sverdrups (Sv=1x106m3/s). The observations come from the RAPID array (Smeed et al, 2017).\n\nCONTEXT\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). The observations have revealed variability at monthly to decadal timescales including a temporary weakening in 2009/10 (McCarthy et al, 2012) and a decrease from 2005-2012 (Smeed et al, 2014; Smeed et al, 2018). Other studies have suggested that this weakening may be a result of variability (Smeed et al, 2014; Jackson et al 2017).\n\nCMEMS KEY FINDINGS \n\nThe AMOC strength exhibits significant variability on many timescales with a temporary weakening in 2009/10. There has been a weakening from 2005-2012 (-0.67 Sv/year, (p=0.03) in the observations and -0.53 Sv/year (p=0.04) in the multi-product mean). The multi-product suggests an earlier increase from 2001-2006 (0.48 Sv/yr, p=0.04), and a weakening in 1998-99, however before this period there is significant uncertainty. This indicates that the changes observed are likely to be variability rather than an ongoing trend (see also Jackson et al, 2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00232\n\nReferences:\n\n* Jackson, L. C., Peterson, K. A., Roberts, C. D. & Wood, R. A. (2016). Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nature Geosci, 9, 518\u2014522\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* McCarthy, G., Frajka-Williams, E., Johns, W. E., Baringer, M. O., Meinen, C. S., Bryden, H. L., Rayner, D., Duchez, A., Roberts, C. & Cunningham, S. A. (2012). Observed interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N. Geophys. Res. Lett., 39, L19609+\n* Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A. & Bryden, H. L. (2014). Observed decline of the Atlantic meridional overturning circulation 2004&2012. Ocean Science, 10, 29--38.\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Smeed, D. A., Josey, S. A., Beaulieu, C., Johns, W. E., Moat, B. I., Frajka-Williams, E., Rayner, D., Meinen, C. S., Baringer, M. O., Bryden, H. L. & McCarthy, G. D. (2018). The North Atlantic Ocean Is in a State of Reduced Overturning. Geophys. Res. Lett., 45, 2017GL076350+. doi: 10.1002/2017gl076350\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00232", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-max26n-timeseries,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005, the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 1.0 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00235\n\nReferences:\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00235", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-2000,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005, the near-surface (0-300m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.4 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00233\n\nReferences:\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00233", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-300,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005, the upper (0-700m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.6 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00234\n\nReferences:\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00234", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-700,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-700m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_trend": {"abstract": "DEFINITION\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\nCMEMS KEY FINDINGS\n\nRegional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show warming at rates ranging from the global mean average up to more than 8 W/m2 in some specific regions (e.g. northern hemisphere western boundary current regimes). There are specific regions where a negative trend is observed above noise at rates up to about -5 W/m2 such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00236\n\nReferences:\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00236", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-trend,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"abstract": "DEFINITION\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\nCONTEXT\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005 the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 1.3\u00b10.2 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00240\n\nReferences:\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00240", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-2000,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"abstract": "DEFINITION\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\nCONTEXT\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\nCMEMS KEY FINDINGS\n\nSince the year 2005 the upper (0-700m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 0.9\u00b10.1 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00239\n\nReferences:\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00239", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-700,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_trend": {"abstract": "DEFINITION\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m).\n\nCONTEXT\n\nMost of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60\u00b0 latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. \n\nCMEMS KEY FINDINGS\n\nSignificant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -8 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00241\n\nReferences:\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Stammer D, Cazenave A, Ponte RM, Tamisiea ME (2013) Causes for contemporary regional sea level changes. Ann Rev Mar Sci 5:21\u201346. doi:10.1146/annurev-marine-121211-172406\n* Meyssignac, B., C. G. Piecuch, C. J. Merchant, M.-F. Racault, H. Palanisamy, C. MacIntosh, S. Sathyendranath, R. Brewin, 2016: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011\n", "doi": "10.48670/moi-00241", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"abstract": "DEFINITION\n\nThe linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in \u00b0C.\nThe linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details.\n\nCONTEXT\n\nLarge-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017).\n\nCMEMS KEY FINDINGS\n\nThe results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40\u00b0S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called \u2018cold event\u2019 prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00244\n\nReferences:\n\n* Dubois, C., K. von Schuckmann, S. Josey, A. Ceschin, 2018: Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Gattuso, J-P., et al. (2015): Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, no. 6243.\n* Gourrion, J., J. Deshayes, M. Juza, T. Szekely, J. Tontore, 2018: A persisting regional cold and fresh water anomaly in the Northern Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Hoegh-Guldberg, O., et al., 2018: Impacts of 1.5\u00baC Global Warming on Natural and Human Systems. In: Global Warming of 1.5\u00b0C. An IPCC Special Report on the impacts of global warming of 1.5\u00b0C above preindustrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. P\u00f6rtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma- Okia, C. P\u00e9an, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.\n* IPCC, 2019: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Molinos, J.G., et al. (2016): Climate velocity and the future global redistribution of marine biodiversity, NATURE Climate Change 6 doi:10.10383/NCLIMATE2769.\n* Mulet S, Buongiorno Nardelli B, Good S, A. Pisano A, E. Greiner, Monier M, 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Ram\u00edrez, F., I. Af\u00e1n, L.S. Davis, and A. Chiaradia (2017): Climate impacts on global hot spots of marine biodiversity. Science Advances 3, no. 2 : e1601198.\n* Sun, Y., Z. Zhong, T. Li, L. Yi, Y. Hu, H. Wan, H. Chen, Q. Liao, C. Ma and Q. Li, 2017: Impact of Ocean Warming on Tropical Cyclone Size and Its Destructiveness, Nature Scientific Reports, Volume 7 (8154), https://doi.org/10.1038/s41598-017-08533-6.\n* Trenberth, K. E., L. J. Cheng, P. Jacobs, Y. X. Zhang, and J. Fasullo (2018): Hurricane Harvey links to ocean heat content and climate change adaptation. Earth's Future, 6, 730--744, https://doi.org/10.1029/2018EF000825.\n", "doi": "10.48670/moi-00244", "instrument": null, "keywords": "change-over-time-in-sea-water-temperature,coastal-marine-environment,global-ocean,global-omi-tempsal-tyz-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Zonal Mean Subsurface Temperature cumulative trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "DEFINITION\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tAn area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. \n5.\tThe time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n6.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\nCONTEXT\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\nCMEMS KEY FINDINGS\n\nOver the period 1993 to 2021, the global average linear trend was 0.015 \u00b1 0.001\u00b0C / year (95% confidence interval). 2021 is nominally the sixth warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Ni\u00f1o and La Ni\u00f1a conditions. For example, peaks in the time series coincide with the strong El Ni\u00f1o events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00242\n\nReferences:\n\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Gasparin, F., von Schuckmann, K., Desportes, C., Sathyendranath, S. and Pardo, S. 2018. El Ni\u00f1o southern oscillation. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n", "doi": "10.48670/moi-00242", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"abstract": "DEFINITION\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tThe time series for each grid cell was passed through the X11 seasonal adjustment procedure, which decomposes a time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n5.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\nCONTEXT\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\nCMEMS KEY FINDINGS\n\nWarming trends occurred over most of the globe between 1993 and 2021. One of the exceptions is the North Atlantic, which has a region south of Greenland where a cooling trend is found. The cooling in this area has been previously noted as occurring on centennial time scales (IPCC, 2013; Caesar et al., 2018; Sevellee et al., 2017).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00243\n\nReferences:\n\n* Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. and Saba, V., 2018. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), p.191. DOI: 10.1038/s41586-018-0006-5.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n* S\u00e9vellec, F., Fedorov, A.V. and Liu, W., 2017. Arctic sea-ice decline weakens the Atlantic meridional overturning circulation. Nature Climate Change, 7(8), p.604, doi: 10.1038/nclimate3353.\n", "doi": "10.48670/moi-00243", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature trend map from Observations Reprocessing"}, "GLOBAL_OMI_WMHE_heattrp": {"abstract": "DEFINITION\n\nHeat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\nCONTEXT\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\nCMEMS KEY FINDINGS\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00245\n\nReferences:\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n* Bricaud C, Drillet Y, Garric G. 2016. Ocean mass and heat transport. In CMEMS Ocean State Report, Journal of Operational Oceanography, 9, http://dx.doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00245", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-heattrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Heat Transport across sections from Reanalysis"}, "GLOBAL_OMI_WMHE_northward_mht": {"abstract": "DEFINITION\n\nMeridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. \nThey are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\nCONTEXT\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\nCMEMS KEY FINDINGS\n\nAfter an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 \u00b0 N in the Pacific Ocean during the El Ni\u00f1o event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00246\n\nReferences:\n\n* Crosnier L, Barnier B, Treguier AM, 2001. Aliasing inertial oscillations in a 1/6\u00b0 Atlantic circulation model: impact on the mean meridional heat transport. Ocean Modelling, vol 3, issues 1-2, pp21-31. https://doi.org/10.1016/S1463-5003(00)00015-9\n* Ganachaud, A. , Wunsch C. 2003. Large-Scale Ocean Heat and Freshwater Transports during the World Ocean Circulation Experiment. J. Climate, 16, 696\u2013705, https://doi.org/10.1175/1520-0442(2003)016<0696:LSOHAF>2.0.CO;2\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00246", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-northward-mht,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis"}, "GLOBAL_OMI_WMHE_voltrp": {"abstract": "DEFINITION\n\nVolume transport across lines are obtained by integrating the volume fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in Sverdrup (Sv).\n\nCONTEXT\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\nCMEMS KEY FINDINGS\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdi\u00e8re and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00247\n\nReferences:\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00247", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-voltrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Volume Transport across sections from Reanalysis"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"abstract": "The IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00026\n\nReferences:\n\n* Gutknecht, E. and Reffray, G. and Mignot, A. and Dabrowski, T. and Sotillo, M. G. Modelling the marine ecosystem of Iberia-Biscay-Ireland (IBI) European waters for CMEMS operational applications. Ocean Sci., 15, 1489\u20131516, 2019. https://doi.org/10.5194/os-15-1489-2019\n", "doi": "10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-bgc-005-004,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"abstract": "The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00027\n\nReferences:\n\n* Sotillo, M.G.; Campuzano, F.; Guihou, K.; Lorente, P.; Olmedo, E.; Matulka, A.; Santos, F.; Amo-Baladr\u00f3n, M.A.; Novellino, A. River Freshwater Contribution in Operational Ocean Models along the European Atlantic Fa\u00e7ade: Impact of a New River Discharge Forcing Data on the CMEMS IBI Regional Model Solution. J. Mar. Sci. Eng. 2021, 9, 401. https://doi.org/10.3390/jmse9040401\n* Mason, E. and Ruiz, S. and Bourdalle-Badie, R. and Reffray, G. and Garc\u00eda-Sotillo, M. and Pascual, A. New insight into 3-D mesoscale eddy properties from CMEMS operational models in the western Mediterranean. Ocean Sci., 15, 1111\u20131131, 2019. https://doi.org/10.5194/os-15-1111-2019\n* Lorente, P. and Garc\u00eda-Sotillo, M. and Amo-Baladr\u00f3n, A. and Aznar, R. and Levier, B. and S\u00e1nchez-Garrido, J. C. and Sammartino, S. and de Pascual-Collar, \u00c1. and Reffray, G. and Toledano, C. and \u00c1lvarez-Fanjul, E. Skill assessment of global, regional, and coastal circulation forecast models: evaluating the benefits of dynamical downscaling in IBI (Iberia-Biscay-Ireland) surface waters. Ocean Sci., 15, 967\u2013996, 2019. https://doi.org/10.5194/os-15-967-2019\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n* Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Reffray, G., Amo-Baladr\u00f3n, A., Benkiran, M., and Alvarez Fanjul, E.: The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products and roadmap to the future Copernicus Service, J. Oper. Oceanogr., 8, 63\u201379, https://doi.org/10.1080/1755876X.2015.1014663, 2015.\n", "doi": "10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-phy-005-001,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"abstract": "The IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 1/36\u00ba of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00025\n\nReferences:\n\n* Toledano, C.; Ghantous, M.; Lorente, P.; Dalphinet, A.; Aouf, L.; Sotillo, M.G. Impacts of an Altimetric Wave Data Assimilation Scheme and Currents-Wave Coupling in an Operational Wave System: The New Copernicus Marine IBI Wave Forecast Service. J. Mar. Sci. Eng. 2022, 10, 457. https://doi.org/10.3390/jmse10040457\n", "doi": "10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-wav-005-005,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "IBI_MULTIYEAR_BGC_005_003": {"abstract": "The IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, zooplankton and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\nFor all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00028\n\nReferences:\n\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n", "doi": "10.48670/moi-00028", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,iberian-biscay-irish-seas,ibi-multiyear-bgc-005-003,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "IBI_MULTIYEAR_PHY_005_002": {"abstract": "The IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Besides, daily means of air-sea fluxes are provided. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. For all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00029", "doi": "10.48670/moi-00029", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,iberian-biscay-irish-seas,ibi-multiyear-phy-005-002,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "IBI_MULTIYEAR_WAV_005_006": {"abstract": "The IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1980 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product and with the same horizontal resolution (1/36\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Besides, air-sea fluxes are provided. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\nDOI (Product): \nhttps://doi.org/10.48670/moi-00030", "doi": "10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,iberian-biscay-irish-seas,ibi-multiyear-wav-005-006,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "IBI_OMI_CURRENTS_cui": {"abstract": "DEFINITION\n\nThe Coastal Upwelling Index (CUI) is computed along the African and the Iberian Peninsula coasts. For each latitudinal point from 27\u00b0N to 42\u00b0N the Coastal Upwelling Index is defined as the temperature difference between the maximum and minimum temperature in a range of distance from the coast up to 3.5\u00ba westwards.\n\u3016CUI\u3017_lat=max\u2061(T_lat )-min\u2061(T_lat)\nA high Coastal Upwelling Index indicates intense upwelling conditions.\nThe index is computed from the following Copernicus Marine products:\n\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (1993-2019)\n\tIBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001 (2020 onwards)\n\nCONTEXT\n\nCoastal upwelling process occurs along coastlines as the result of deflection of the oceanic water away from the shore. Such deflection is produced by Ekman transport induced by persistent winds parallel to the coastline (Sverdrup, 1938). When this transported water is forced, the mass balance is maintained by pumping of ascending intermediate water. This water is typically denser, cooler and richer in nutrients. The Iberia-Biscay-Ireland domain contains two well-documented Eastern Boundary Upwelling Ecosystems, they are hosted under the same system known as Canary Current Upwelling System (Fraga, 1981; Hempel, 1982). This system is one of the major coastal upwelling regions of the world and it is produced by the eastern closure of the Subtropical Gyre. The North West African (NWA) coast presents an intense upwelling region that extends from Morocco to south of Senegal, likewise the western coast of the Iberian Peninsula (IP) shows a seasonal upwelling behavior. These two upwelling domains are separated by the presence of the Gulf of Cadiz, where the coastline does not allow the formation of upwelling conditions from 34\u00baN up to 37\u00baN.\nThe Copernicus Marine Service Coastal Upwelling Index is defined following the steps of several previous upwelling indices described in literature. More details and full scientific evaluation can be found in the dedicated section in the first issue of the Copernicus Marine Service Ocean State report (Sotillo et al., 2016).\n\nCMEMS KEY FINDINGS\n\nThe NWA coast (latitudes below 34\u00baN) shows a quite constantlow variability of the periodicity and the intensity of the upwelling, few periods of upwelling intensifications are found in years 1993-1995, and 2003-2004.\nIn the IP coast (latitudes higher than 37\u00baN) the interannual variability is more remarkable showing years with high upwelling activity (1994, 2004, and 2015-2017) followed by periods of lower activity (1996-1998, 2003, 2011, and 2013).\nAccording to the results of the IBI-NRT system, 2020 was a year with weak upwelling in the IP latitudes. \nWhile in the NWA coast the upwelling activity was more intense than the average.\nThe analysis of trends in the period 1993-2019 shows significant positive trend of the upwelling index in the IP latitudes. This trend implies an increase of temperature differences between the coastal and offshore waters of approximately 0.02 \u00b0C/Year.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00248\n\nReferences:\n\n* Fraga F. 1981. Upwelling off the Galician Coast, Northwest Spain. In: Richardson FA, editor. Coastal Upwelling. Washington (DC): Am Geoph Union; p. 176\u2013182.\n* Hempel G. 1982. The Canary current: studies of an upwelling system. Introduction. Rapp. Proc. Reun. Cons. Int. Expl. Mer., 180, 7\u20138.\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016 Iberian-Biscay-Irish Sea. In von Scuckmann et al. (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Sverdrup HV. 1938. On the process of upwelling. J Mar Res. 1:155\u2013164.\n", "doi": "10.48670/moi-00248", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-currents-cui,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Coastal Upwelling Index from Reanalysis"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSIS_FORECAST_WAV_005_005).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2022.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\nCONTEXT\n\nThe sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world\u2019s greatest wave generating regions (M\u00f8rk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes.\nThe North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Mart\u00ednez-Asensio et al. 2016; Bacon and Carter, 1991; Bouws et al., 1996; Bauer, 2001; Wolf et al., 2002; Gleeson et al., 2017). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006). \nAdditionally a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mart\u00ednez-Asensio et al., 2016). \nIn this context, long\u2010term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences.\n\nCMEMS KEY FINDINGS\n\nThe climatic mean of 99th percentile (1993-2021) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described.\nThe 99th percentile anomalies in 2023 show that during this period, the central latitudes of the domain (between 37 \u00baN and 50 \u00baN) were affected by extreme wave events that exceeded up to twice the standard deviation of the anomalies. These events impacted not only the open waters of the Northeastern Atlantic but also European coastal areas such as the west coast of Portugal, the Spanish Atlantic coast, and the French coast, including the English Channel.\nAdditionally, the impact of significant wave extremes exceeding twice the standard deviation of anomalies was detected in the Mediterranean region of the Balearic Sea and the Algerian Basin. This pattern is commonly associated with the impact of intense Tramontana winds originating from storms that cross the Iberian Peninsula from the Gulf of Biscay.\n\nFigure caption\n\nIberia-Biscay-Ireland Significant Wave Height extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00249\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: .Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Marshall, J., Kushnir, Y., Battisti, D., Chang, P., Czaja, A., Dickson, R., ... & Visbeck, M. (2001). North Atlantic climate variability: phenomena, impacts and mechanisms. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(15), 1863-1898.\n", "doi": "10.48670/moi-00249", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-extreme-var-swh-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Significant Wave Height extreme from Reanalysis"}, "IBI_OMI_SEASTATE_swi": {"abstract": "DEFINITION\n\nThe Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art.\nTwo CMEMS products are used to compute the Strong Wave Incidence index:\n\u2022\tIBI-WAV-MYP: IBI_REANALYSIS_WAV_005_006\n\u2022\tIBI-WAV-NRT: IBI_ANALYSIS_FORECAST_WAV_005_005\nThe Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product:\nSWI = Fobs(SWH > ThP90) \u2013 Fclim(SWH > ThP90)\nSince the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively.\n\nCONTEXT\n\nOcean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. The Strong Wave Incidence index based on the CMEMS regional analysis and reanalysis product provides information on the frequency of severe wave events.\nThe IBI-MFC covers the Europe\u2019s Atlantic coast in a region bounded by the 26\u00baN and 56\u00baN parallels, and the 19\u00baW and 5\u00baE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (M\u00f8rk et al., 2010), one of the world\u2019s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kursnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995; Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mat\u00ednez-Asensio et al., 2016).\nThe Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions (Figure 1.A) are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands.\nMore details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020).\n\nCMEMS KEY FINDINGS\n\nThe analysis of the index in the last decades do not show significant trends of the strong wave conditions over the period 1992-2021 with 99% confidence. The maximum wave event reported in region WAV1 (B) occurred in February 2014, producing an increment of 25% of strong wave conditions in the region. Two maximum wave events are found in WAV2 (C) with an increment of 15% of high wave conditions in November 2009 and February 2014. As in regions WAV1 and WAV2, in the region WAV3 (D), a strong wave event took place in February 2014, this event is one of the maximum events reported in the region with an increment of strong wave conditions of 20%, two months before (December 2013) there was a storm of similar characteristics affecting this region, other events of similar magnitude are detected on October 2000 and November 2009. The region WAV4 (E) present its maximum wave event in January 1996, such event produced a 25% of increment of strong wave conditions in the region. Despite of each monitoring region is affected by independent wave events; the analysis shows several past higher-than-average wave events that were propagated though several monitoring regions: November-December 2010 (WAV3 and WAV2); February 2014 (WAV1, WAV2, and WAV3); and February-March 2018 (WAV1 and WAV4).\nThe analysis of the NRT period (January 2022 onwards) depicts a significant event that occurred in November 2022, which affected the WAV2 and WAV3 regions, resulting in a 15% and 25% increase in maximum wave conditions, respectively. In the case of the WAV3 region, this event was the strongest event recorded in this region.\nIn the WAV4 region, an event that occurred in February 2024 was the second most intense on record, showing an 18% increase in strong wave conditions in the region.\nIn the WAV1 region, the NRT period includes two high-intensity events that occurred in February 2024 (21% increase in strong wave conditions) and April 2022 (18% increase in maximum wave conditions).\n\nFigure caption\n\n(A) Mean 90th percentile of Sea Wave Height computed from IBI_REANALYSIS_WAV_005_006 product at an hourly basis. Gray dotted lines denote the four monitoring areas where the Strong Wave Incidence index is computed. (B, C, D, and E) Strong Wave Incidence index averaged in monitoring regions WAV1 (A), WAV2 (B), WAV3 (C), and WAV4 (D). Panels show merged results of two CMEMS products: IBI_REANALYSIS_WAV_005_006 (blue), IBI_ANALYSIS_FORECAST_WAV_005_005 (orange). The trend and 99% confidence interval of IBI-MYP product is included (bottom right).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00251\n\nReferences:\n\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Dodet G, Bertin X, Taborda R. 2010. Wave climate variability in the North-East Atlantic Ocean over the last six decades, Ocean Modelling, 31, 120\u2013131.\n* Dupuis H, Michel D, Sottolichio A. 2006. Wave climate evolution in the Bay of Biscay over two decades. Journal of Marine Systems, 63, 105\u2013114.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation, Science, 269:676\u2013679.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* Pascual A., Levier B., Aznar R., Toledano C., Garc\u00eda-Valdecasas JM., Garc\u00eda M., Sotillo M., Aouf L., \u00c1lvarez E. (2020) Monitoring of wave sea state in the Iberia-Biscay-Ireland regional seas. In von Scuckmann et al. (2020) Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097\n* Tsimplis M N, Woolf D K, Osborn T J, Wakelin S, Wolf J, Flather R, Shaw A G P, Woodworth P, Challenor P, Blackman D, Pert F, Yan Z, Jevrejeva S. 2005. Towards a vulnerability assessment of the UK and northern European coasts: the role of regional climate variability. Phil. Trans. R. Soc. A, Vol. 363, 1329\u20131358 doi:10.1098/rsta.2005.1571.\n* Wang X, Swail V. 2004. Historical and possible future changes of wave heights in northern hemisphere oceans. In: Perrie W (ed), Atmosphere ocean interactions, vol 2. Wessex Institute of Technology Press, Ashurst.\n* WASA-Group. 1998. Changing waves and storms in the Northeast Atlantic?, Bull. Am. Meteorol. Soc., 79:741\u2013760.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Young I R, Zieger S, Babanin A V. 2011. Global Trends in Wind Speed and Wave Height. Science, Vol. 332, Issue 6028, 451-455, doi: 10.1126/science.1197219.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n", "doi": "10.48670/moi-00251", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-swi,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Strong Wave Incidence index from Reanalysis"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThe Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018).\n\nThe IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003).\nAlso relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980).\n\nCMEMS KEY FINDINGS\n\nIn the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in \u00c1lvarez Fanjul (2019) for the period 1993-2016.\nThe analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43\u00baN parallel.\nExtreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer.\nIt is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis.\n\nFigure caption\n\nIberia-Biscay-Ireland Surface Temperature extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00254\n\nReferences:\n\n* Alvarez I, Gomez-Gesteira M, DeCastro M, Lorenzo MN, Crespo AJC, Dias JM. 2011. Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Continental Shelf Research, 31(5), 388-399.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Czaja A, Frankignoul C. 2002. Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. Journal of Climate, 15(6), 606-623.\n* Dickson RR, Gurbutt PA, Pillai VN. 1980. Satellite evidence of enhanced upwelling along the European continental slope. Journal of Physical Oceanography, 10(5), 813-819.\n* Dubois C, von Schuckmann K, Josey S. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 2.9, s66\u2013s70, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Edwards A, Jones K, Graham JM, Griffiths CR, MacDougall N, Patching J, Raine R. 1996. Transient coastal upwelling and water circulation in Bantry Bay, a ria on the south-west coast of Ireland. Estuarine, Coastal and Shelf Science, 42(2), 213-230.\n* Flatau MK, Talley L, Niiler PP. 2003. The North Atlantic Oscillation, surface current velocities, and SST changes in the subpolar North Atlantic. Journal of Climate, 16(14), 2355-2369.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016. Iberian-Biscay-Irish Sea. In von Schuckmann et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report No.1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00254", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-extreme-var-temp-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis"}, "IBI_OMI_WMHE_mow": {"abstract": "DEFINITION\n\nVariations of the Mediterranean Outflow Water at 1000 m depth are monitored through area-averaged salinity anomalies in specifically defined boxes. The salinity data are extracted from several CMEMS products and averaged in the corresponding monitoring domain: \n* IBI-MYP: IBI_MULTIYEAR_PHY_005_002\n* IBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001\n* GLO-MYP: GLOBAL_REANALYSIS_PHY_001_030\n* CORA: INSITU_GLO_TS_REP_OBSERVATIONS_013_002_b\n* ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012\n\nThe anomalies of salinity have been computed relative to the monthly climatology obtained from IBI-MYP. Outcomes from diverse products are combined to deliver a unique multi-product result. Multi-year products (IBI-MYP, GLO,MYP, CORA, and ARMOR) are used to show an ensemble mean and the standard deviation of members in the covered period. The IBI-NRT short-range product is not included in the ensemble, but used to provide the deterministic analysis of salinity anomalies in the most recent year.\n\nCONTEXT\n\nThe Mediterranean Outflow Water is a saline and warm water mass generated from the mixing processes of the North Atlantic Central Water and the Mediterranean waters overflowing the Gibraltar sill (Daniault et al., 1994). The resulting water mass is accumulated in an area west of the Iberian Peninsula (Daniault et al., 1994) and spreads into the North Atlantic following advective pathways (Holliday et al. 2003; Lozier and Stewart 2008, de Pascual-Collar et al., 2019).\nThe importance of the heat and salt transport promoted by the Mediterranean Outflow Water flow has implications beyond the boundaries of the Iberia-Biscay-Ireland domain (Reid 1979, Paillet et al. 1998, van Aken 2000). For example, (i) it contributes substantially to the salinity of the Norwegian Current (Reid 1979), (ii) the mixing processes with the Labrador Sea Water promotes a salt transport into the inner North Atlantic (Talley and MacCartney, 1982; van Aken, 2000), and (iii) the deep anti-cyclonic Meddies developed in the African slope is a cause of the large-scale westward penetration of Mediterranean salt (Iorga and Lozier, 1999).\nSeveral studies have demonstrated that the core of Mediterranean Outflow Water is affected by inter-annual variability. This variability is mainly caused by a shift of the MOW dominant northward-westward pathways (Bozec et al. 2011), it is correlated with the North Atlantic Oscillation (Bozec et al. 2011) and leads to the displacement of the boundaries of the water core (de Pascual-Collar et al., 2019). The variability of the advective pathways of MOW is an oceanographic process that conditions the destination of the Mediterranean salt transport in the North Atlantic. Therefore, monitoring the Mediterranean Outflow Water variability becomes decisive to have a proper understanding of the climate system and its evolution (e.g. Bozec et al. 2011, Pascual-Collar et al. 2019).\nThe CMEMS IBI-OMI_WMHE_mow product is aimed to monitor the inter-annual variability of the Mediterranean Outflow Water in the North Atlantic. The objective is the establishment of a long-term monitoring program to observe the variability and trends of the Mediterranean water mass in the IBI regional seas. To do that, the salinity anomaly is monitored in key areas selected to represent the main reservoir and the three main advective spreading pathways. More details and a full scientific evaluation can be found in the CMEMS Ocean State report Pascual et al., 2018 and de Pascual-Collar et al. 2019.\n\nCMEMS KEY FINDINGS\n\nThe absence of long-term trends in the monitoring domain Reservoir (b) suggests the steadiness of water mass properties involved on the formation of Mediterranean Outflow Water.\nResults obtained in monitoring box North (c) present an alternance of periods with positive and negative anomalies. The last negative period started in 2016 reaching up to the present. Such negative events are linked to the decrease of the northward pathway of Mediterranean Outflow Water (Bozec et al., 2011), which appears to return to steady conditions in 2020 and 2021. \nResults for box West (d) reveal a cycle of negative (2015-2017) and positive (2017 up to the present) anomalies. The positive anomalies of salinity in this region are correlated with an increase of the westward transport of salinity into the inner North Atlantic (de Pascual-Collar et al., 2019), which appear to be maintained for years 2020-2021.\nResults in monitoring boxes North and West are consistent with independent studies (Bozec et al., 2011; and de Pascual-Collar et al., 2019), suggesting a westward displacement of Mediterranean Outflow Water and the consequent contraction of the northern boundary.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00258\n\nReferences:\n\n* Bozec A, Lozier MS, Chassignet EP, Halliwell GR. 2011. On the variability of the Mediterranean outflow water in the North Atlantic from 1948 to 2006. J Geophys Res 116:C09033. doi:10.1029/2011JC007191.\n* Daniault N, Maze JP, Arhan M. 1994. Circulation and mixing of MediterraneanWater west of the Iberian Peninsula. Deep Sea Res. Part I. 41:1685\u20131714.\n* de Pascual-Collar A, Sotillo MG, Levier B, Aznar R, Lorente P, Amo-Baladr\u00f3n A, \u00c1lvarez-Fanjul E. 2019. Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019.\n* Holliday NP. 2003. Air-sea interaction and circulation changes in the northeast Atlantic. J Geophys Res. 108(C8):3259. doi:10.1029/2002JC001344.\n* Iorga MC, Lozier MS. 1999. Signatures of the Mediterranean outflow from a North Atlantic climatology: 1. Salinity and density fields. Journal of Geophysical Research: Oceans, 104(C11), 25985-26009.\n* Lozier MS, Stewart NM. 2008. On the temporally varying northward penetration of Mediterranean overflow water and eastward penetration of Labrador Sea water. J Phys Oceanogr. 38(9):2097\u20132103. doi:10.1175/2008JPO3908.1.\n* Paillet J, Arhan M, McCartney M. 1998. Spreading of labrador Sea water in the eastern North Atlantic. J Geophys Res. 103 (C5):10223\u201310239.\n* Pascual A, Levier B, Sotillo M, Verbrugge N, Aznar R, Le Cann B. 2018. Characterisation of Mediterranean outflow w\u00e1ter in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Braseur, P., Fennel, K., Djavidnia, S.: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11, sup1, s1-s142, doi:10.1080/1755876X.2018.1489208, 2018.\n* Reid JL. 1979. On the contribution of the Mediterranean Sea outflow to the Norwegian\u2010Greenland Sea, Deep Sea Res., Part A, 26, 1199\u20131223, doi:10.1016/0198-0149(79)90064-5.\n* Talley LD, McCartney MS. 1982. Distribution and circulation of Labrador Sea water. Journal of Physical Oceanography, 12(11), 1189-1205.\n* van Aken HM. 2000. The hydrography of the mid-latitude northeast Atlantic Ocean I: the deep water masses. Deep Sea Res. Part I. 47:757\u2013788.\n", "doi": "10.48670/moi-00258", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-wmhe-mow,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterrranean Outflow Water Index from Reanalysis & Multi-Observations Reprocessing"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"abstract": "Arctic Oceans - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00031", "doi": "10.48670/moi-00031", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-arc-phybgcwav-discrete-mynrt-013-031,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean- In Situ Near Real Time Observations"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"abstract": "Baltic Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00032", "doi": "10.48670/moi-00032", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-bal-phybgcwav-discrete-mynrt-013-032,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- In Situ Near Real Time Observations"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"abstract": "Black Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00033", "doi": "10.48670/moi-00033", "instrument": null, "keywords": "black-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-blk-phybgcwav-discrete-mynrt-013-034,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea- In-Situ Near Real Time Observations"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"abstract": "Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2024 and GLobal Ocean Data Analysis Project GLODAPv2.2023. \nThe SOCATv2024-OBS dataset contains >38 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2024. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2024-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean.\nGLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. \nSOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.\n\nDOI (product): \nhttps://doi.org/10.17882/99089\n\nReferences:\n\n* Bakker et al., 2016. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth Syst. Sci. Data, 8, 383\u2013413, https://doi.org/10.5194/essd-8-383-2016.\n* Lauvset et al. 2024. The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product. Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-468.\n* Lauvset et al., 2016. A new global interior ocean mapped climatology: t\u202f\u00d7\u2009\u202f1\u00b0 GLODAP version 2. Earth Syst. Sci. Data, 8, 325\u2013340, https://doi.org/10.5194/essd-8-325-2016.\n", "doi": "10.17882/99089", "instrument": null, "keywords": "coastal-marine-environment,fugacity-of-carbon-dioxide-in-sea-water,global-ocean,in-situ-observation,insitu-glo-bgc-carbon-discrete-my-013-050,level-3,marine-resources,marine-safety,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,multi-year,oceanographic-geographical-features,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1957-10-22T22:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - In Situ reprocessed carbon observations - SOCATv2024 / GLODAPv2.2023"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"abstract": "For the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data.\n\nDOI (product): \nhttps://doi.org/10.17882/86207", "doi": "10.17882/86207", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-bgc-discrete-my-013-046,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-silicate-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Biogeochemical product"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"abstract": "Global Ocean - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00036", "doi": "10.48670/moi-00036", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phybgcwav-discrete-mynrt-013-030,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- In-Situ Near-Real-Time Observations"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"abstract": "This product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices.\n\nDOI (product): \nhttps://doi.org/10.17882/93670", "doi": "10.17882/93670", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ssh-discrete-my-013-053,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1821-05-25T05:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Sea level product"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"abstract": "For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. This version is a merged product between the previous verion of CORA and EN4 distributed by the Met Office for the period 1950-1990.\n\nDOI (product): \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-discrete-my-013-001,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"abstract": "Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\nDOI (product): \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-my-013-052,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1960-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"abstract": "For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00037", "doi": "10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-nrt-013-002,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Real time in-situ observations objective analysis"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"abstract": "Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed:\n* The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided.\n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program.\n* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms\n* The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories\n\nDOI (product):\nhttps://doi.org/10.17882/86236", "doi": "10.17882/86236", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-my-013-044,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1979-12-11T04:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"abstract": "This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed:\n* The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP\u2019s Global Drifter Program. These data are delivered together with wind stress components, surface temperature and a wind-slippage correction for drogue-off and drogue-on drifters trajectories. \n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth and in surface, calculated along the trajectories of the floats which are part of the Argo Program.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00041", "doi": "10.48670/moi-00041", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-nrt-013-048,level-2,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,surface-eastward-sea-water-velocity,surface-northward-sea-water-velocity,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1986-06-02T09:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- in-situ Near real time observations of ocean currents"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"abstract": "These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.\n\nDOI (product): \nhttps://doi.org/10.17882/70345", "doi": "10.17882/70345", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-wav-discrete-my-013-045,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-04-27T18:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Wave product"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"abstract": "IBI Seas - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00043", "doi": "10.48670/moi-00043", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,iberian-biscay-irish-seas,in-situ-observation,insitu-ibi-phybgcwav-discrete-mynrt-013-033,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Irish Ocean- In-Situ Near Real Time Observations"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"abstract": "Mediterranean Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00044", "doi": "10.48670/moi-00044", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-med-phybgcwav-discrete-mynrt-013-035,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea- In-Situ Near Real Time Observations"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"abstract": "NorthWest Shelf area - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00045", "doi": "10.48670/moi-00045", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-nws-phybgcwav-discrete-mynrt-013-036,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean In-Situ Near Real Time observations"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"abstract": "The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\nSalon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997\u20131022. DOI: https://doi.org/10.5194/os-15-997-2019\n\n_DOI (Product)_: \nhttps://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4\n\nReferences:\n\n* Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Teruzzi, A., Di Biagio, V., Coidessa, G., Alvarez, E., Amadio, C., & Cossarini, G. (2022). Mediterranean Sea Biogeochemical Analysis and Forecast (CMEMS MED-Biogeochemistry, MedBFM4 system) (Version 1) [Data set]. Copernicus Marine Service. https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_BGC_006_014_MEDBFM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,medsea-analysisforecast-bgc-006-014,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"abstract": "The physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\"\n\n_DOI (Product)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n\nReferences:\n\n* Clementi, E., Aydogdu, A., Goglio, A. C., Pistoia, J., Escudier, R., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Coppini, G., Masina, S., & Pinardi, N. (2021). Mediterranean Sea Physical Analysis and Forecast (CMEMS MED-Currents, EAS6 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n", "doi": "10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-phy-006-013,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"abstract": "MEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\nDOI (product): https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4\n\nReferences:\n\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Analysis and Forecast (Copernicus Marine Service MED-Waves, MEDWA\u039c4 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-wav-006-017,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-04-19T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Analysis and Forecast"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"abstract": "The Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n_DOI (Interim dataset)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n\nReferences:\n\n* Teruzzi, A., Di Biagio, V., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3\n* Teruzzi, A., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Di Biagio, V., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis INTERIM (CMEMS MED-Biogeochemistry, MedBFM3i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS) https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n", "doi": "10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,medsea-multiyear-bgc-006-008,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"abstract": "The Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\nProduct Citation: \nPlease refer to our Technical FAQ for citing products\n\nDOI (Product): \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\nDOI (Interim dataset):\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n\nReferences:\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini G., Pinardi, N. (2021). A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, 9, 1060, https://www.frontiersin.org/article/10.3389/feart.2021.702285, DOI=10.3389/feart.2021.702285\n* Nigam, T., Escudier, R., Pistoia, J., Aydogdu, A., Omar, M., Clementi, E., Cipollone, A., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2021). Mediterranean Sea Physical Reanalysis INTERIM (CMEMS MED-Currents, E3R1i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n", "doi": "10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-phy-006-004,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Reanalysis"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"abstract": "MEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 \n\n_DOI (Interim dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I \n \n_DOI (climatological dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM \n\nDOI (Interim dataset):\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n\nReferences:\n\n* Korres, G., Ravdas, M., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis INTERIM (CMEMS Med-Waves, MedWAM3I system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Monthly Climatology (CMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM\n* Korres, G., Ravdas, M., Zacharioudaki, A., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis (CMEMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012\n", "doi": "10.25423/cmcc/medsea_multiyear_wav_006_012", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-wav-006-012,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Reanalysis"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_yr-T_clim )dz \t\t\t\t\t\t\t\t[1]\nwith a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009).\nTime series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and is evaluated for topography deeper than 300m.\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nThe quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). \nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4 degree horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031): \nGLORYS, C-GLORS, ORAS5, FOAM\n\tTwo observation based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\nCMEMS KEY FINDINGS\n\nThe ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2019 at rate of 1.4\u00b10.3 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00261\n\nReferences:\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Iona, A., A. Theodorou, S. Sofianos, S. Watelet, C. Troupin, J.-M. Beckers, 2018: Mediterranean Sea climatic indices: monitoring long term variability and climate changes, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-51, in review.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841. DOI: 10.1007/s00382-015-2728-5\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, s1-s142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00261", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,medsea-omi-ohc-area-averaged-anomalies,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS MEDSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_WAV_006_012) and the Analysis product (MEDSEA_ANALYSIS_FORECAST_WAV_006_017).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\nCONTEXT\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2014). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Mediterranean Sea is an almost enclosed basin where the complexity of its orographic characteristics deeply influences the atmospheric circulation at local scale, giving rise to strong regional wind regimes (Drobinski et al. 2018). Therefore, since waves are primarily driven by winds, high waves are present over most of the Mediterranean Sea and tend to reach the highest values where strong wind and long fetch (i.e. the horizontal distance over which wave-generating winds blow) are simultaneously present (Lionello et al. 2006). Specifically, as seen in figure and in agreement with other studies (e.g. Sartini et al. 2017), the highest values (5 \u2013 6 m in figure, top) extend from the Gulf of Lion to the southwestern Sardinia through the Balearic Sea and are sustained southwards approaching the Algerian coast. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that become stronger due to orographic effects (Menendez et al. 2014), and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves (4-5 m in figure, top). In the Aegean and Levantine Seas, high waves (4-5 m in figure, top) are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014). In general, northerly winds are responsible for most high waves in the Mediterranean (e.g. Chronis et al. 2011; Menendez et al. 2014). In agreement with figure (top), studies on the eastern Mediterranean and the Hellenic Seas have found that the typical wave height range in the Aegean Sea is similar to the one observed in the Ionian Sea despite the shorter fetches characterizing the former basin (Zacharioudaki et al. 2015). This is because of the numerous islands in the Aegean Sea which cause wind funneling and enhance the occurrence of extreme winds and thus of extreme waves (Kotroni et al. 2001). Special mention should be made of the high waves, sustained throughout the year, observed east and west of the island of Crete, i.e. around the exiting points of the northerly airflow in the Aegean Sea (Zacharioudaki et al. 2015). This airflow is characterized by consistently high magnitudes that are sustained during all seasons in contrast to other airflows in the Mediterranean Sea that exhibit a more pronounced seasonality (Chronis et al. 2011). \n\nCMEMS KEY FINDINGS\n\nIn 2020 (bottom panel), higher-than-average values of the 99th percentile of Significant Wave Height are seen over most of the northern Mediterranean Sea, in the eastern Alboran Sea, and along stretches of the African coast (Tunisia, Libya and Egypt). In many cases they exceed the climatic standard deviation. Regions where the climatic standard deviation is exceeded twice are the European and African coast of the eastern Alboran Sea, a considerable part of the eastern Spanish coast, the Ligurian Sea and part of the east coast of France as well as areas of the southern Adriatic. These anomalies correspond to the maximum positive anomalies computed in the Mediterranean Sea for year 2020 with values that reach up to 1.1 m. Spatially constrained maxima are also found at other coastal stretches (e.g. Algeri, southeast Sardinia). Part of the positive anomalies found along the French and Spanish coast, including the coast of the Balearic Islands, can be associated with the wind storm \u201cGloria\u201d (19/1 \u2013 24/1) during which exceptional eastern winds originated in the Ligurian Sea and propagated westwards. The storm, which was of a particularly high intensity and long duration, caused record breaking wave heights in the region, and, in return, great damage to the coast (Amores et al., 2020; de Alfonso et al., 2021). Other storms that could have contributed to the positive anomalies observed in the western Mediterranean Sea include: storm Karine (25/2 \u2013 5/4), which caused high waves from the eastern coast of Spain to the Balearic Islands (Copernicus, Climate Change Service, 2020); storm Bernardo (7/11 \u2013 18/11) which also affected the Balearic islands and the Algerian coast and; storm Herv\u00e9 (2/2 \u2013 8/2) during which the highest wind gust was recorded at north Corsica (Wikiwand, 2021). In the eastern Mediterranean Sea, the medicane Ianos (14/9 \u2013 21/9) may have contributed to the positive anomalies shown in the central Ionian Sea since this area coincides with the area of peak wave height values during the medicane (Copernicus, 2020a and Copernicus, 2020b). Otherwise, higher-than-average values in the figure are the result of severe, yet not unusual, wind events, which occurred during the year. Negative anomalies occur over most of the southern Mediterranean Sea, east of the Alboran Sea. The maximum negative anomalies reach about -1 m and are located in the southeastern Ionian Sea and west of the south part of mainland Greece as well as in coastal locations of the north and east Aegean They appear to be quite unusual since they are greater than two times the climatic standard deviation in the region. They could imply less severe southerly wind activity during 2020 (Drobinski et al., 2018). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00262\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Amores, A., Marcos, M., Carri\u00f3, Di.S., Gomez-Pujol, L., 2020. Coastal impacts of Storm Gloria (January 2020) over the north-western Mediterranean. Nat. Hazards Earth Syst. Sci. 20, 1955\u20131968. doi:10.5194/nhess-20-1955-2020\n* Chronis T, Papadopoulos V, Nikolopoulos EI. 2011. QuickSCAT observations of extreme wind events over the Mediterranean and Black Seas during 2000-2008. Int J Climatol. 31: 2068\u20132077.\n* Copernicus: Climate Change Service. 2020a (Last accessed July 2021): URL: https://surfobs.climate.copernicus.eu/stateoftheclimate/march2020.php\n* Copernicus, Copernicus Marine Service. 2020b (Last accessed July 2021): URL: https://marine.copernicus.eu/news/following-cyclone-ianos-across-mediterranean-sea\n* de Alfonso, M., Lin-Ye, J., Garc\u00eda-Valdecasas, J.M., P\u00e9rez-Rubio, S., Luna, M.Y., Santos-Mu\u00f1oz, D., Ruiz, M.I., P\u00e9rez-G\u00f3mez, B., \u00c1lvarez-Fanjul, E., 2021. Storm Gloria: Sea State Evolution Based on in situ Measurements and Modeled Data and Its Impact on Extreme Values. Front. Mar. Sci. 8, 1\u201317. doi:10.3389/fmars.2021.646873\n* Drobinski P, Alpert P, Cavicchia L, Flaoumas E, Hochman A, Kotroni V. 2018. Strong winds Observed trends, future projections, Sub-chapter 1.3.2, p. 115-122. In: Moatti JP, Thi\u00e9bault S (dir.). The Mediterranean region under climate change: A scientific update. Marseille: IRD \u00c9ditions.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2014. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2014 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Kotroni V, Lagouvardos K, Lalas D. 2001. The effect of the island of Crete on the Etesian winds over the Aegean Sea. Q J R Meteorol Soc. 127: 1917\u20131937. doi:10.1002/qj.49712757604\n* Lionello P, Rizzoli PM, Boscolo R. 2006. Mediterranean climate variability, developments in earth and environmental sciences. Elsevier.\n* Menendez M, Garc\u00eda-D\u00edez M, Fita L, Fern\u00e1ndez J, M\u00e9ndez FJ, Guti\u00e9rrez JM. 2014. High-resolution sea wind hindcasts over the Mediterranean area. Clim Dyn. 42:1857\u20131872.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sartini L, Besio G, Cassola F. 2017. Spatio-temporal modelling of extreme wave heights in the Mediterranean Sea. Ocean Modelling, 117, 52-69.\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Wikiwand: 2019 - 20 European windstorm season. URL: https://www.wikiwand.com/en/2019%E2%80%9320_European_windstorm_season\n* Zacharioudaki A, Korres G, Perivoliotis L, 2015. Wave climate of the Hellenic Seas obtained from a wave hindcast for the period 1960\u20132001. Ocean Dynamics. 65: 795\u2013816. https://doi.org/10.1007/s10236-015-0840-z\n", "doi": "10.48670/moi-00262", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-seastate-extreme-var-swh-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Significant Wave Height extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid \u201880s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend.\nThe Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14\u00b0C in the Northwestern part of the basin to ~23\u00b0C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80\u2019s as shown in Marb\u00e0 et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018).\n\nCMEMS KEY FINDINGS\n\nThe Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 \u00b0C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23\u201325 \u00b0C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016.\nThe 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3\u00b0C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00266\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Darmaraki S, Somot S, Sevault F, Nabat P, Cabos W, Cavicchia L, et al. 2019. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371\u20131392. doi: 10.1007/s00382-019-04661-z\n* Ducrocq V., Drobinski P., Gualdi S., Raimbault P. 2016. The water cycle in the Mediterranean. Chapter 1.2.1 in The Mediterranean region under climate change. IRD E\u0301ditions. DOI : 10.4000/books.irdeditions.22908.\n* Marb\u00e0 N, Jord\u00e0 G, Agust\u00ed S, Girard C, Duarte CM. 2015. Footprints of climate change on Mediterranean Sea biota. Front.Mar.Sci.2:56. doi: 10.3389/fmars.2015.00056\n* Mariotti A and Dell\u2019Aquila A. 2012. Decadal climate variability in the Mediterranean region: roles of large-scale forcings and regional processes. Clim Dyn. 38,1129\u20131145. doi:10.1007/s00382-011-1056-7\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-zP\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Buongiorno Nardelli B. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sens. 2020, 12, 132.\n", "doi": "10.48670/moi-00266", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-extreme-var-temp-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe medsea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Mediterranean Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST satellite product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\nKEY FINDINGS\n\nDuring 2023, the Mediterranean Sea continued experiencing the intense sea surface temperatures\u2019 warming (marine heatwave event) that started in May 2022 (Marullo et al., 2023). The basin average SST anomaly was 0.9 \u00b1 0.1 \u00b0C in 2023, the highest in this record. The Mediterranean SST warming started in May 2022, when the mean anomaly increased abruptly from 0.01 \u00b0C (April) to 0.76 \u00b0C (May), reaching the highest values during June (1.66 \u00b0C) and July (1.52 \u00b0C), and persisting until summer 2023 with anomalies around 1 \u00b0C above the 1991-2020 climatology. The peak of July 2023 (1.76 \u00b0C) set the record of highest SST anomaly ever recorded since 1982. The 2022/2023 Mediterranean marine heatwave is comparable to that occurred in 2003 (see e.g. Olita et al., 2007) in terms of anomaly magnitude but longer in duration.\nOver the period 1982-2023, the Mediterranean SST has warmed at a rate of 0.041 \u00b1 0.001 \u00b0C/year, which corresponds to an average increase of about 1.7 \u00b0C during these last 42 years. Within its error (namely, the 95% confidence interval), this warming trend is consistent with recent trend estimates in the Mediterranean Sea (Pisano et al., 2020; Pastor et al., 2020). However, though the linear trend being constantly increasing during the whole period, the picture of the Mediterranean SST trend in 2022 seems to reveal a restarting after the pause occurred in the last years (since 2015-2021).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00268\n\nReferences:\n\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Olita, A., Sorgente, R., Natale, S., Gaber\u0161ek, S., Ribotti, A., Bonanno, A., & Patti, B. (2007). Effects of the 2003 European heatwave on the Central Mediterranean Sea: surface fluxes and the dynamical response. Ocean Science, 3(2), 273-289.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00268", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-area-averaged-anomalies,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"abstract": "DEFINITION\n\nThe medsea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Mediterranean Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the time series (42 years). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the SST trend over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterize the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\nKEY FINDINGS\n\nOver the past four decades (1982-2023), the Mediterranean Sea surface temperature (SST) warmed at a rate of 0.041 \u00b1 0.001 \u00b0C per year, corresponding to a mean surface temperature warming of about 1.7 \u00b0C. The spatial pattern of the Mediterranean SST trend shows a general warming tendency, ranging from 0.002 \u00b0C/year to 0.063 \u00b0C/year. Overall, a higher SST trend intensity characterizes the Eastern and Central Mediterranean basin with respect to the Western basin. In particular, the Balearic Sea, Tyrrhenian and Adriatic Seas, as well as the northern Ionian and Aegean-Levantine Seas show the highest SST trends (from 0.04 \u00b0C/year to 0.05 \u00b0C/year on average). Trend patterns of warmer intensity characterize some of main sub-basin Mediterranean features, such as the Pelops Anticyclone, the Cretan gyre and the Rhodes Gyre. On the contrary, less intense values characterize the southern Mediterranean Sea (toward the African coast), where the trend attains around 0.025 \u00b0C/year. The SST warming rate spatial change, mostly showing an eastward increase pattern (see, e.g., Pisano et al., 2020, and references therein), i.e. the Levantine basin getting warm faster than the Western, appears now to have tilted more along a North-South direction.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00269\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734 Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Kirtman, B., Power, S. B, Adedoyin, J. A., Boer, G. J., Bojariu, R. et al., 2013. Near-term climate change: Projections and Predictability. In: Stocker, T.F., et al. (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00269", "instrument": null, "keywords": "change-over-time-in-sea-surface-temperature,coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-trend,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"abstract": "This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor.\nThe method called CANYON (Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) is based on a neural network trained using high-quality nutrient data collected over the last 30 years (GLODAPv2 database, https://www.glodap.info/). The method is applied to each Argo float equipped with an oxygen sensor using as input the properties measured by the float (pressure, temperature, salinity, oxygen), and its date and position.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00048\n\nReferences:\n\n* Sauzede R., H. C. Bittig, H. Claustre, O. Pasqueron de Fommervault, J.-P. Gattuso, L. Legendre and K. S. Johnson, 2017: Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A novel Approach Based on Neural Networks. Front. Mar. Sci. 4:128. doi: 10.3389/fmars.2017.00128.\n* Bittig H. C., T. Steinhoff, H. Claustre, B. Fiedler, N. L. Williams, R. Sauz\u00e8de, A. K\u00f6rtzinger and J.-P. Gattuso,2018: An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Front. Mar. Sci. 5:328. doi: 10.3389/fmars.2018.00328.\n", "doi": "10.48670/moi-00048", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,moles-of-nitrate-per-unit-mass-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,moles-of-phosphate-per-unit-mass-in-sea-water,moles-of-silicate-per-unit-mass-in-sea-water,multi-year,multiobs-glo-bgc-nutrients-carbon-profiles-mynrt-015-009,none,oceanographic-geographical-features,partial-pressure-of-carbon-dioxide-in-sea-water,sea-water-ph-reported-on-total-scale,sea-water-pressure,sea-water-salinity,sea-water-temperature,total-alkalinity-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nutrient and carbon profiles vertical distribution"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"abstract": "This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\nDOI (product):\nhttps://doi.org/10.48670/moi-00046\n\nReferences:\n\n* Sauzede R., H. Claustre, J. Uitz, C. Jamet, G. Dall\u2019Olmo, F. D\u2019Ortenzio, B. Gentili, A. Poteau, and C. Schmechtig, 2016: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC011408.\n", "doi": "10.48670/moi-00046", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,multi-year,multiobs-glo-bio-bgc-3d-rep-015-010,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"abstract": "This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00047\n\nReferences:\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO2 fugacity and air-sea fluxes with low latency. Geophysical Research Letters, 51, e2023GL106670. https://doi.org/10.1029/2023GL106670\n* Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25\u00b0, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121\u2013160, https://doi.org/10.5194/essd-16-121-2024, 2024.\n", "doi": "10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-bio-carbon-surface-mynrt-015-008,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Surface ocean carbon fields"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"abstract": "This product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00327\n\nReferences:\n\n* Rio, M.-H., S. Mulet, and N. Picot: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, doi:10.1002/2014GL061773, 2014.\n", "doi": "10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-mynrt-015-003,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\nDOI (product): \nhttps://doi.org/10.1016/j.rse.2016.02.061\n\nReferences:\n\n* Boutin, J., J. L. Vergely, S. Marchand, F. D'Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin, and J. Vialard (2018), New SMOS Sea Surface Salinity with reduced systematic errors and improved variability, Remote Sensing of Environment, 214, 115-134. doi:https://doi.org/10.1016/j.rse.2018.05.022\n* Kolodziejczyk, N., J. Boutin, J.-L. Vergely, S. Marchand, N. Martin, and G. Reverdin (2016), Mitigation of systematic errors in SMOS sea surface salinity, Remote Sensing of Environment, 180, 164-177. doi:https://doi.org/10.1016/j.rse.2016.02.061\n", "doi": "10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l3-mynrt-015-014,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-01-12T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version: https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version: https://archimer.ifremer.fr/doc/00665/77702/\n\nDOI (product): \nhttps://doi.org/10.1175/JTECH-D-20-0093.1\n\nReferences:\n\n* Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n", "doi": "10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l4-my-015-015,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-06-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"abstract": "This product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\nProduct citation: \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00051\n\nReferences:\n\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2016: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface. J. Atmos. Oceanic Technol. doi:10.1175/JTECH-D-15-0194.1.\n* Buongiorno Nardelli, B., R. Droghei, and R. Santoleri, 2016: Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data. Rem. Sens. Environ., doi:10.1016/j.rse.2015.12.052.\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2018: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993\u20132016), Front. Mar. Sci., 5(March), 1\u201313, doi:10.3389/fmars.2018.00084.\n* Sammartino, Michela, Salvatore Aronica, Rosalia Santoleri, and Bruno Buongiorno Nardelli. (2022). Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data, Remote Sensing 14, 2502: https://doi.org/10.3390/rs14102502.\n", "doi": "10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-s-surface-mynrt-015-013,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"abstract": "You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 5 datasets: \n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1D-m, which delivers near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1M-m, which delivers near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy_my_0.125deg_P1D-m, which delivers multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy_my_0.125deg_P1M-m, which delivers multi-year reprocessed (REP) monthly data\n* cmems_obs-mob_glo_phy_mynrt_0.125deg-climatology-uncertainty_P1M-m, which delivers monthly static uncertainty data\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00052\n\nReferences:\n\n* Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845\u2013857.\n* Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77\u201380(0):70\u201381.\n", "doi": "10.48670/moi-00052", "instrument": null, "keywords": "coastal-marine-environment,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-tsuv-3d-mynrt-015-012,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"abstract": "You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\nDOI (product): \nhttps://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007\n\nReferences:\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n", "doi": "10.25423/cmcc/multiobs_glo_phy_w_rep_015_007", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-w-3d-rep-015-007,northward-sea-water-velocity,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "DEFINITION\n\nThe CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013).\nTwo parameters are included on this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\nCONTEXT\n\nThis domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself.\n\nCMEMS KEY FINDINGS\n\nThe 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\nDOI (product)\nhttps://doi.org/10.48670/moi-00273\n\nReferences:\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Elliott, A.J., Clarke, T., Li, ., 1990: Monthly distributions of surface and bottom temperatures in the northwest European shelf seas. Continental Shelf Research, Vol 11, no 5, pp 453-466, http://doi.org/10.1016/0278-4343(91)90053-9\n* Holt, J., Hughes, S., Hopkins, J., Wakelin, S., Holliday, P.N., Dye, S., Gonz\u00e1lez-Pola, C., Hj\u00f8llo, S., Mork, K., Nolan, G., Proctor, R., Read, J., Shammon, T., Sherwin, T., Smyth, T., Tattersall, G., Ward, B., Wiltshire, K., 2012: Multi-decadal variability and trends in the temperature of the northwest European continental shelf: A model-data synthesis. Progress in Oceanography, 96-117, 106, http://doi.org/10.1016/j.pocean.2012.08.001\n* Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., Sparks, T. and Garforth, J. (2021), State of the UK Climate 2020. Int J Climatol, 41 (Suppl 2): 1-76. https://doi.org/10.1002/joc.7285\n* Liu, M., Tanhua, T., 2021: Water masses in the Atlantic Ocean: characteristics and distributions. Ocean Sci, 17, 463-486, http://doi.org/10.5194/os-17-463-2021\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00273", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northwestshelf-omi-tempsal-extreme-var-temp-mean-and-anomaly,numerical-model,oceanographic-geographical-features,temp-percentile99-anom,temp-percentile99-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Sea Surface Temperature extreme from Reanalysis"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"abstract": "The NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00056", "doi": "10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-bgc-004-002,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"abstract": "The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 10-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00054", "doi": "10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,nwshelf-analysisforecast-phy-004-013,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"abstract": "The NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00055\n\nReferences:\n\n* The impact of ocean-wave coupling on the upper ocean circulation during storm events (Bruciaferri, D., Tonani, M., Lewis, H., Siddorn, J., Saulter, A., Castillo, J.M., Garcia Valiente, N., Conley, D., Sykes, P., Ascione, I., McConnell, N.) in Journal of Geophysical Research, Oceans, 2021, 126, 6. https://doi.org/10.1029/2021JC017343\n", "doi": "10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-wav-004-014,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-10-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"abstract": "Short Description:\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009). Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [CMEMS-NWS-QUID-004-011](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for details.\n\nAssociated products:\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009).\nAn analysis-forecast product is available from: [NWSHELF_MULTIYEAR_BGC_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00058\n\nReferences:\n\n* Ciavatta, S., Brewin, R. J. W., Sk\u00e1kala, J., Polimene, L., de Mora, L., Artioli, Y., & Allen, J. I. (2018). [https://doi.org/10.1002/2017JC013490 Assimilation of ocean\u2010color plankton functional types to improve marine ecosystem simulations]. Journal of Geophysical Research: Oceans, 123, 834\u2013854. https://doi.org/10.1002/2017JC013490\n", "doi": "10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,nwshelf-multiyear-bgc-004-011,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"abstract": "Short Description:\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [GLOBAL_REANALYSIS_PHY_001_026](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026) and at the Baltic boundary by the [BALTICSEA_REANALYSIS_PHY_003_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011). The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [CMEMS-NWS-QUID-004-009](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for further details.\n\nAssociated products:\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011). An analysis-forecast product is available from [NWSHELF_ANALYSISFORECAST_PHY_LR_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001).\nThe product is updated biannually provinding six-month extension of the time series.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00059", "doi": "10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,nwshelf-multiyear-phy-004-009,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "NWSHELF_REANALYSIS_WAV_004_015": {"abstract": "Short description:\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [ECMWF ERA-5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [CMEMS-NWS-PUM-004-015](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf) for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [CMEMS-NWS-QUID-004-015](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf).\nThe product is updated biannually provinding six-month extension of the time series.\n\nAssociated products:\n\n[NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00060", "doi": "10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,nwshelf-reanalysis-wav-004-015,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m\ncmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m\ncmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00061\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00061", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l3-nrt-009-201,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\nDataset names: \ncmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00062\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00062", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l4-nrt-009-207,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the \"multi\" products and S3A & S3B only for the \"OLCI\" products.\n* Variables: Chlorophyll-a (CHL), Diffuse Attenuation (KD490) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 4 km (multi) or 300 m (OLCI).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00292", "doi": "10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-arc-bgc-l3-my-009-123,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Suspended Matter (SPM), Diffuse Attenuation (KD490), Detrital and Dissolved Material Absorption Coef. (ADG443_), Phytoplankton Absorption Coef. (APH443_), Total Absorption Coef. (ATOT443_) and Reflectance (RRS_').\n\n* Temporal resolutions: daily, monthly.\n* Spatial resolutions: 300 meters (olci).\n* Recent products are organized in datasets called Near Real Time (NRT).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00290", "doi": "10.48670/moi-00290", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l3-nrt-009-121,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the \"multi\" products , and S3A & S3B only for the \"OLCI\" products.\n* Variables: Chlorophyll-a (CHL), Diffuse Attenuation (KD490)\n\n\n* Temporal resolutions: monthly.\n* Spatial resolutions: 4 km (multi) or 300 meters (OLCI).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00293", "doi": "10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-arc-bgc-l4-my-009-124,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"abstract": "For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR \u2013 Rome, Italy) is providing Bio-Geo_Chemical (BGC) products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL) and Diffuse Attenuation (KD490).\n\n* Temporal resolutions:monthly.\n* Spatial resolutions: 300 meters (olci).\n* Recent products are organized in datasets called Near Real Time (NRT).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00291", "doi": "10.48670/moi-00291", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l4-nrt-009-122,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton and Transparency L4 NRT monthly observations"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 1 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00286", "doi": "10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-atl-bgc-l3-my-009-113,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 1 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00284", "doi": "10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-atl-bgc-l3-nrt-009-111,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and S3A & S3B only for the \"olci\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: 1 km.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"GlobColour\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00289", "doi": "10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-atl-bgc-l4-my-009-118,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and S3A & S3B only for the \"olci\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: 1 km.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"GlobColour\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00288", "doi": "10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-atl-bgc-l4-nrt-009-116,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00079\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l3-nrt-009-202,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00080\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l4-nrt-009-208,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-08T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolution: daily\n\nSpatial resolution: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00296\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-bal-bgc-l3-my-009-133,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: OLCI-S3A & S3B \n\nTemporal resolution: daily\n\nSpatial resolution: 300 meters \n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00294\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n", "doi": "10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-bal-bgc-l3-nrt-009-131,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly\n\nSpatial resolution: 1 km for \"\"multi\"\" and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00308\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-bal-bgc-l4-my-009-134,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"abstract": "For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\nUpstreams: OLCI-S3A & S3B \n\nTemporal resolution: monthly \n\nSpatial resolution: 300 meters \n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00295\n\nReferences:\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-bal-bgc-l4-nrt-009-132,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00086\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l3-nrt-009-206,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\nDOI (product): \nhttps://doi.org/10.48670/moi-00087\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l4-nrt-009-212,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and OLCI-S3A & S3B for the \"olci\" products\n\nTemporal resolution: daily\n\nSpatial resolution: 1 km for \"multi\" and 300 meters for \"olci\"\n\nTo find this product in the catalogue, use the search keyword \"OCEANCOLOUR_BLK_BGC_L3_MY\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00303\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-blk-bgc-l3-my-009-153,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolution: daily\n\nSpatial resolutions: 1 km for \"\"multi\"\" and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00301\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-blk-bgc-l3-nrt-009-151,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated gap-free Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly and daily (for \"\"gap-free\"\", \"\"pp\"\" and climatology data)\n\nSpatial resolution: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BLK_BGC_L4_MY\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00304\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-blk-bgc-l4-my-009-154,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"abstract": "For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated gap-free Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"\"multi\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly and daily (for \"\"gap-free\"\" and \"\"pp\"\" data)\n\nSpatial resolutions: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00302\n\nReferences:\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-blk-bgc-l4-nrt-009-152,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily.\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\"\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00280", "doi": "10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-103,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"abstract": "For the Global Ocean Satellite Observations, Brockmann Consult (BC) is providing Bio-Geo_Chemical (BGC) products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the \"\"multi\"\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT) and Reflectance (RRS).\n\n* Temporal resolutions: daily, monthly.\n* Spatial resolutions: 4 km (multi).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find these products in the catalogue, use the search keyword \"\"ESA-CCI\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00282", "doi": "10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-107,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Gradient of Chlorophyll-a (CHL_gradient), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: daily\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00278", "doi": "10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l3-nrt-009-101,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and S3A & S3B only for the \"\"olci\"\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"\"GlobColour\"\".\"\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00281", "doi": "10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-104,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"abstract": "For the Global Ocean Satellite Observations, Brockmann Consult (BC) is providing Bio-Geo_Chemical (BGC) products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the \"\"multi\"\" products.\n* Variables: Chlorophyll-a (CHL).\n\n* Temporal resolutions: monthly.\n* Spatial resolutions: 4 km (multi).\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find these products in the catalogue, use the search keyword \"\"ESA-CCI\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00283", "doi": "10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-108,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"abstract": "For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and S3A & S3B only for the \"olci\" products.\n* Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Primary Production (PP), Suspended Matter (SPM), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Particulate Backscattering (BBP), Absorption Coef. (CDM) and Reflectance (RRS).\n\n* Temporal resolutions: monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: 4 km and a finer resolution based on olci 300 meters inputs.\n* Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY).\n\nTo find the Copernicus-GlobColour products in the catalogue, use the search keyword \"GlobColour\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00279", "doi": "10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l4-nrt-009-102,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "2023-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00107\n\nReferences:\n\n Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l3-nrt-009-204,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00108\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l4-nrt-009-210,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00109\n\nReferences:\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l3-nrt-009-205,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00110\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l4-nrt-009-211,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"multi\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* _pp_ with the Integrated Primary Production (PP)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and OLCI-S3A & S3B for the \"olci\" products\n\nTemporal resolution: daily\n\nSpatial resolution: 1 km for \"multi\" and 300 meters for \"olci\"\n\nTo find this product in the catalogue, use the search keyword \"OCEANCOLOUR_MED_BGC_L3_MY\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00299\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l3-my-009-143,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* _reflectance_ with the spectral Remote Sensing Reflectance (RRS)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"\"multi\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _optics_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolution: daily\n\nSpatial resolutions: 1 km for \"\"multi\"\" and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00297\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l3-nrt-009-141,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated gap-free Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"multi\" products, and OLCI-S3A & S3B for the \"olci\" products\n\nTemporal resolutions: monthly and daily (for \"gap-free\" and climatology data)\n\nSpatial resolution: 1 km for \"multi\" and 300 meters for \"olci\"\n\nTo find this product in the catalogue, use the search keyword \"OCEANCOLOUR_MED_BGC_L4_MY\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00300\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l4-my-009-144,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"abstract": "For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets:\n* _plankton_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated gap-free Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* _transparency_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for \"\"multi\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* _pp_ with the Integrated Primary Production (PP).\n\nUpstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the \"\"multi\"\" products, and OLCI-S3A & S3B for the \"\"olci\"\" products\n\nTemporal resolutions: monthly and daily (for \"\"gap-free\"\" and \"\"pp\"\" data)\n\nSpatial resolutions: 1 km for \"\"multi\"\" (4 km for \"\"pp\"\") and 300 meters for \"\"olci\"\"\n\nTo find this product in the catalogue, use the search keyword \"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\".\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00298\n\nReferences:\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l4-nrt-009-142,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \n\ncmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00118\n\nReferences:\n\n Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-nws-bgc-hr-l3-nrt-009-203,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\nProcessing information:\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\nDescription of observation methods/instruments:\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\nQuality / Accuracy / Calibration information:\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\nSuitability, Expected type of users / uses:\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\nDataset names: \ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\nFiles format:\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00119\n\nReferences:\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,oceancolour-nws-bgc-hr-l4-nrt-009-209,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"abstract": "DEFINITION\n\nThe Black Sea Rim Current index (BSRCI) reflects the intensity of the Rim current, which is a main feature of the Black Sea circulation, a basin scale cyclonic current. The index was computed using sea surface current speed averaged over two areas of intense currents based on reanalysis data. The areas are confined between the 200 and 1800 m isobaths in the northern section 33-39E (from the Caucasus coast to the Crimea Peninsula), and in the southern section 31.5-35E (from Sakarya region to near Sinop Peninsula). Thus, three indices were defined: one for the northern section (BSRCIn), for the southern section (BSRCIs) and an average for the entire basin (BSRCI).\nBSRCI=(V \u0305_ann-V \u0305_cl)/V \u0305_cl \nwhere V \u0305 denotes the representative area average, the \u201cann\u201d denotes the annual mean for each individual year in the analysis, and \u201ccl\u201d indicates the long-term mean over the whole period 1993-2020. In general, BSRCI is defined as the relative annual anomaly from the long-term mean speed. An index close to zero means close to the average conditions a positive index indicates that the Rim current is more intense than average, or negative - if it is less intense than average. In other words, positive BSRCI would mean higher circumpolar speed, enhanced baroclinicity, enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, intensification of the heat redistribution, etc.\nThe BSRCI is introduced in the fifth issue of the Ocean State Report (von Schuckmann et al., 2021). The Black Sea Physics Reanalysis (BLKSEA_REANALYSIS_PHYS_007_004) has been used as a data base to build the index. Details on the products are delivered in the PUM and QUID of this OMI.\n\nCONTEXT\n\nThe Black Sea circulation is driven by the regional winds and large freshwater river inflow in the north-western part (including the main European rivers Danube, Dnepr and Dnestr). The major cyclonic gyre encompasses the sea, referred to as Rim current. It is quasi-geostrophic and the Sverdrup balance approximately applies to it. \nThe Rim current position and speed experiences significant interannual variability (Stanev and Peneva, 2002), intensifying in winter due to the dominating severe northeastern winds in the region (Stanev et al., 2000). Consequently, this impacts the vertical stratification, Cold Intermediate Water formation, the biological activity distribution and the coastal mesoscale eddies\u2019 propagation along the current and their evolution. The higher circumpolar speed leads to enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, enhanced baroclinicity, intensification of the heat redistribution which is important for the winter freezing in the northern zones (Simonov and Altman, 1991). Fach (2015) finds that the anchovy larval dispersal in the Black Sea is strongly controlled at the basin scale by the Rim Current and locally - by mesoscale eddies. \nSeveral recent studies of the Black Sea pollution claim that the understanding of the Rim Current behavior and how the mesoscale eddies evolve would help to predict the transport of various pollution such as oil spills (Korotenko, 2018) and floating marine litter (Stanev and Ricker, 2019) including microplastic debris (Miladinova et al., 2020) raising a serious environmental concern today. \nTo summarize, the intensity of the Black Sea Rim Current could give valuable integral measure for a great deal of physical and biogeochemical processes manifestation. Thus our objective is to develop a comprehensive index reflecting the annual mean state of the Black Sea general circulation to be used by policy makers and various end users. \n\nCMEMS KEY FINDINGS\n\nThe Black Sea Rim Current Index is defined as the relative annual anomaly of the long-term mean speed. The BSRCI value characterizes the annual circulation state: a value close to zero would mean close to average conditions, positive value indicates enhanced circulation, and negative value \u2013 weaker circulation than usual. The time-series of the BSRCI suggest that the Black Sea Rim current speed varies within ~30% in the period 1993-2020 with a positive trend of ~0.1 m/s/decade. In the years 2005 and 2014 there is evidently higher mean velocity, and on the opposite end are the years \u20132004, 2013 and 2016. The time series of the BSRCI gives possibility to check the relationship with the wind vorticity and validate the Sverdrup balance hypothesis. \n\nFigure caption\n\nTime series of the Black Sea Rim Current Index (BSRCI) at the north section (BSRCIn), south section (BSRCIs), the average (BSRCI) and its tendency for the period 1993-2020.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00326\n\nReferences:\n\n* Capet, A., A. Barth, J.-M. Beckers, and M. Gr\u00e9goire (2012), Interannual variability of Black Sea\u2019s hydrodynamics and connection to atmospheric patterns, Deep-Sea Res. Pt. II, 77 \u2013 80, 128\u2013142, doi:10.1016/j.dsr2.2012.04.010\n* Fach, B., (2015), Modeling the Influence of Hydrodynamic Processes on Anchovy Distribution and Connectivity in the Black Sea, Turkish Journal of Fisheries and Aquatic Sciences 14: 1-2, doi: 10.4194/1303-2712-v14_2_06\n* Ivanov V.A., Belokopytov V.N. (2013) Oceanography of the Black Sea. Editorial publishing board of Marine Hydrophysical Institute, 210 p, Printed by ECOSY-Gidrofizika, Sevastopol Korotaev, G., T. Oguz, A. Nikiforov, and C. Koblinsky. Seasonal, interannual, and mesoscale variability of the Black Sea upper layer circulation derived from altimeter data. Journal of Geophysical Research (Oceans). 108. C4. doi: 10. 1029/2002JC001508, 2003\n* Korotenko KA. Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: an assessment of the environmental impacts. PeerJ. 2018 Aug 29;6:e5448. doi: 10.7717/peerj.5448. PMID: 30186680; PMCID: PMC6119461.\n* Kubryakov, A. A., and S.V. Stanichny (2015), Seasonal and interannual variability of the Black Sea eddies and its dependence on characteristics of the large-scale circulation, Deep-Sea Res. Pt. I, 97, 80-91, https://doi.org/10.1016/j.dsr.2014.12.002\n* Miladinova S., A. Stips, D. Macias Moy, E. Garcia-Gorriz, (2020a) Pathways and mixing of the north western river waters in the Black Sea Estuarine, Coastal and Shelf Science, Volume 236, 5 May 2020, https://doi\n", "doi": "10.48670/mds-00326", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-boundary-blksea-rim-current-index,rim-current-intensity-index,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Rim Current Intensity Index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"abstract": "DEFINITION\n\nThe indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149\u00b0E and 32-37\u00b0N and computed from the DUACS (https://duacs.cls.fr) delayed-time (reprocessed version DT-2021, CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047, including \u201cmy\u201d (multi-year) & \u201cmyint\u201d (multi-year interim) datasets) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the EKE (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the Kuroshio extension (correlation coefficient of 0.988 for the total period, 0.994 for the delayed time period only). \n\nCONTEXT\n\nThe Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35\u00b0N, 140\u00b0E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014).\n\nCMEMS KEY FINDINGS\n\nThe different states of the Kuroshio extension phase have been presented and validated by (Bessi\u00e8res et al., 2013) and further reported by Dr\u00e9villon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an \u2018elongated state\u2019 (also called \u2018strong state\u2019) corresponding to a narrow strong steady jet, and a \u2018contracted state\u2019 (also called \u2018weak state\u2019) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then.\n\nFigure caption\n\nStandardized Eddy Kinetic Energy over the Kuroshio region (following Bessi\u00e8res et al., 2013) Blue shaded areas correspond to well established strong elongated states periods, while orange shaded areas fit weak contracted states periods. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red line shows the date of the transition between \u201cmyint\u201d and \u201cnrt\u201d products used.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00222\n\nReferences:\n\n* Bessi\u00e8res, L., Rio, M.H., Dufau, C., Boone, C., Pujol, M.I., 2013. Ocean state indicators from MyOcean altimeter products. Ocean Sci. 9, 545\u2013560. https://doi.org/10.5194/os-9-545-2013\n* Dr\u00e9villon, M., Legeais, J.-F., Peterson, A., Zuo, H., Rio, M.-H., Drillet, Y., Greiner, E., 2018. Western boundary currents. J. Oper. Oceanogr., Copernicus Marine Service Ocean State Report Issue 2, s60\u2013s65. https://doi.org/10.1080/1755876X.2018.1489208\n* Jayne, S.R., Hogg, N.G., Waterman, S.N., Rainville, L., Donohue, K.A., Randolph Watts, D., Tracey, K.L., McClean, J.L., Maltrud, M.E., Qiu, B., Chen, S., Hacker, P., 2009. The Kuroshio Extension and its recirculation gyres. Deep Sea Res. Part Oceanogr. Res. Pap. 56, 2088\u20132099. https://doi.org/10.1016/j.dsr.2009.08.006\n* Kelly, K.A., Small, R.J., Samelson, R.M., Qiu, B., Joyce, T.M., Kwon, Y.-O., Cronin, M.F., 2010. Western Boundary Currents and Frontal Air\u2013Sea Interaction: Gulf Stream and Kuroshio Extension. J. Clim. 23, 5644\u20135667. https://doi.org/10.1175/2010JCLI3346.1\n* Niiler, P.P., Maximenko, N.A., Panteleev, G.G., Yamagata, T., Olson, D.B., 2003. Near-surface dynamical structure of the Kuroshio Extension. J. Geophys. Res. Oceans 108. https://doi.org/10.1029/2002JC001461\n* Qiu, B., 2003. Kuroshio Extension Variability and Forcing of the Pacific Decadal Oscillations: Responses and Potential Feedback. J. Phys. Oceanogr. 33, 2465\u20132482. https://doi.org/10.1175/2459.1\n* Qiu, B., 2002. The Kuroshio Extension System: Its Large-Scale Variability and Role in the Midlatitude Ocean-Atmosphere Interaction. J. Oceanogr. 58, 57\u201375. https://doi.org/10.1023/A:1015824717293\n* Qiu, B., Chen, S., 2010. Eddy-mean flow interaction in the decadally modulating Kuroshio Extension system. Deep Sea Res. Part II Top. Stud. Oceanogr., North Pacific Oceanography after WOCE: A Commemoration to Nobuo Suginohara 57, 1098\u20131110. https://doi.org/10.1016/j.dsr2.2008.11.036\n* Qiu, B., Chen, S., 2005. Variability of the Kuroshio Extension Jet, Recirculation Gyre, and Mesoscale Eddies on Decadal Time Scales. J. Phys. Oceanogr. 35, 2090\u20132103. https://doi.org/10.1175/JPO2807.1\n", "doi": "10.48670/moi-00222", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-circulation-boundary-pacific-kuroshio-phase-area-averaged,satellite-observation,specific-turbulent-kinetic-energy-of-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Kuroshio Phase from Observations Reprocessing"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"abstract": "DEFINITION\n\nThis ocean monitoring indicator (OMI) provides a time series of Meridional Overturning Circulation (MOC) Strength in density coordinates, area-averaged and calculated for the period from 1993 to the most recent year with the availability of reanalysis data in the Black Sea (BS). It contains 1D (time dimension) maximum MOC data computed from the Black Sea Reanalysis (BLK-REA; BLKSEA_MULTIYEAR_PHY_007_004) (Ilicak et al., 2022). The MOC is calculated by summing the meridional transport provided by the Copernicus Marine BLK-REA within density bins. The Black Sea MOC indicator represents the maximum MOC value across the basin for a density range between 22.45 and 23.85 kg/m\u00b3, which corresponds approximately to a depth interval of 25 to 80 m. To understand the overturning circulation of the Black Sea, we compute the residual meridional overturning circulation in density space. Residual overturning as a function of latitude (y) and density (\u03c3 \u0305) bins can be computed as follows:\n\u03c8^* (y,\u03c3 \u0305 )=-1/T \u222b_(t_0)^(t_1)\u2592\u222b_(x_B1)^(x_B2)\u2592\u3016\u222b_(-H)^0\u2592H[\u03c3 \u0305-\u03c3(x,y,z,t)] \u00d7\u03bd(x,y,z,t)dzdxdt,\u3017\nwhere H is the Heaviside function and \u03bd is the meridional velocity. We used 100 \u03c3_2 (potential density anomaly with reference pressure of 2000 dbar) density bins to remap the mass flux fields.\n\nCONTEXT\n\nThe BS meridional overturning circulation (BS-MOC) is a clockwise circulation in the northern part up to 150 m connected to cold intermediate layer (CIL) and an anticlockwise circulation in the southern part that could be connected to the influence of the Mediterranean Water inflow into the BS. In contrast to counterparts observed in the deep Atlantic and Mediterranean overturning circulations, the BS-MOC is characterized by shallowness and relatively low strength. However, its significance lies in its capacity to monitor the dynamics and evolution of the CIL which is crucial for the ventilation of the subsurface BS waters. The monitoring of the BS-MOC evolution from the BLK-REA can support the understanding how the CIL formation is affected due to climate change. The study of Black Sea MOC is relatively new. For more details, see Ilicak et al., (2022).\n\nKEY FINDINGS\n\nThe MOC values show a significant decline from 1994 to 2009, corresponding to the reduction in the CIL during that period. However, after 2010, the MOC in the Black Sea increased from 0.07 Sv (1 Sv = 106 m3/s) to 0.10 Sv. The CIL has nearly disappeared in recent years, as discussed by Stanev et al. (2019) and Lima et al. (2021) based on observational data and reanalysis results. The opposite pattern observed since 2010 suggests that mechanisms other than the CIL may be influencing the Black Sea MOC.\nFor the OMI we have used an updated version of the reanalysis (version E4R1) which has a different spinup compared to the OSR6 (version E3R1).\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00349\n\nReferences:\n\n* Ilicak, M., Causio, S., Ciliberti, S., Coppini, G., Lima, L., Aydogdu, A., Azevedo, D., Lecci, R., Cetin, D. U., Masina, S., Peneva, E., Gunduz, M., Pinardi, N. (2022). The Black Sea overturning circulation and its indicator of change. In: Copernicus Ocean State Report, issue 6, Journal of Operational Oceanography, 15:sup1, s64:s71; DOI: doi.org/10.1080/1755876X.2022.2095169\n* Lima, L., Ciliberti, S.A., Aydo\u011fdu, A., Masina, S., Escudier, R., Cipollone, A., Azevedo, D., Causio, S., Peneva, E., Lecci, R., Clementi, E., Jansen, E., Ilicak, M., Cret\u00ec, S., Stefanizzi, L., Palermo, F., Coppini, G. (2021). Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis. Front. Mar. Sci. 8:710973. doi: 10.3389/fmars.2021.710973\n* Stanev, E. V., Peneva, E., Chtirkova, B. (2019). Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans 124, 4803\u20134819. doi: 10.1029/2019JC015076\n", "doi": "10.48670/mds-00349", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-blksea-area-averaged-mean,s,sla,t,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"abstract": "DEFINITION\n\nTime mean meridional Eulerian streamfunctions are computed using the velocity field estimate provided by the Copernicus Marine Mediterranean Sea reanalysis over the last 35 years (1987\u20132021). The Eulerian meridional streamfunction is evaluated by integrating meridional velocity daily data first in a vertical direction, then in a meridional direction, and finally averaging over the reanalysis period.\nThe Mediterranean overturning indices are derived for the eastern and western Mediterranean Sea by computing the annual streamfunction in the two areas separated by the Strait of Sicily around 36.5\u00b0N, and then considering the associated maxima. \nIn each case a geographical constraint focused the computation on the main region of interest. For the western index, we focused on deep-water formation regions, thus excluding both the effect of shallow physical processes and the Gibraltar net inflow. For the eastern index, we investigate the Levantine and Cretan areas corresponding to the strongest meridional overturning cell locations, thus only a zonal constraint is defined.\nTime series of annual mean values is provided for the Mediterranean Sea using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021).\nMore details can be found in the Copernicus Marine Ocean State Report issue 4 (OSR4, von Schuckmann et al., 2020) Section 2.4 (Lyubartsev et al., 2020).\n\nCONTEXT\n\nThe western and eastern Mediterranean clockwise meridional overturning circulation is connected to deep-water formation processes. The Mediterranean Sea 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021) is used to show the interannual variability of the Meridional Overturning Index. Details on the product are delivered in the PUM and QUID of this OMI. \nThe Mediterranean Meridional Overturning Index is defined here as the maxima of the clockwise cells in the eastern and western Mediterranean Sea and is associated with deep and intermediate water mass formation processes that occur in specific areas of the basin: Gulf of Lion, Southern Adriatic Sea, Cretan Sea and Rhodes Gyre (Pinardi et al., 2015).\nAs in the global ocean, the overturning circulation of the western and eastern Mediterranean are paramount to determine the stratification of the basins (Cessi, 2019). In turn, the stratification and deep water formation mediate the exchange of oxygen and other tracers between the surface and the deep ocean (e.g., Johnson et al., 2009; Yoon et al., 2018). In this sense, the overturning indices are potential gauges of the ecosystem health of the Mediterranean Sea, and in particular they could instruct early warning indices for the Mediterranean Sea to support the Sustainable Development Goal (SDG) 13 Target 13.3.\n\nCMEMS KEY FINDINGS\n\nThe western and eastern Mediterranean overturning indices (WMOI and EMOI) are synthetic indices of changes in the thermohaline properties of the Mediterranean basin related to changes in the main drivers of the basin scale circulation. The western sub-basin clockwise overturning circulation is associated with the deep-water formation area of the Gulf of Lion, while the eastern clockwise meridional overturning circulation is composed of multiple cells associated with different intermediate and deep-water sources in the Levantine, Aegean, and Adriatic Seas. \nOn average, the EMOI shows higher values than the WMOI indicating a more vigorous overturning circulation in eastern Mediterranean. The difference is mostly related to the occurrence of the eastern Mediterranean transient (EMT) climatic event, and linked to a peak of the EMOI in 1992. In 1999, the difference between the two indices started to decrease because EMT water masses reached the Sicily Strait flowing into the western Mediterranean Sea (Schroeder et al., 2016). The western peak in 2006 is discussed to be linked to anomalous deep-water formation during the Western Mediterranean Transition (Smith, 2008; Schroeder et al., 2016). Thus, the WMOI and EMOI indices are a useful tool for long-term climate monitoring of overturning changes in the Mediterranean Sea. \n\nFigure caption\n\nTime series of Mediterranean overturning indices [Sverdrup] calculated from the annual average of the meridional streamfunction over the period 1987 to 2021. Blue: Eastern Mediterranean Overturning Index (lat<36.5\u00b0N); Red: Western Mediterranean Overturning Index (lat\u226540\u00b0N, z>300m). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00317\n\nReferences:\n\n* Cessi, P. 2019. The global overturning circulation. Ann Rev Mar Sci. 11:249\u2013270. DOI:10.1146/annurev-marine- 010318-095241. Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini, G., Pinardi, N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Gertman, I., Pinardi, N., Popov, Y., Hecht, A. 2006. Aegean Sea water masses during the early stages of the eastern Mediterranean climatic Transient (1988\u20131990). J Phys Oceanogr. 36(9):1841\u20131859. DOI:10.1175/JPO2940.1.\n* Johnson, K.S., Berelson, W.M., Boss, E.S., Chase, Z., Claustre, H., Emerson, S.R., Gruber, N., Ko\u0308rtzinger, A., Perry, M.J., Riser, S.C. 2009. Observing biogeochemical cycles at global scales with profiling floats and gliders: prospects for a global array. Oceanography. 22:216\u2013225. DOI:10.5670/oceanog. 2009.81.\n* Lyubartsev, V., Borile, F., Clementi, E., Masina, S., Drudi, M/. Coppini, G., Cessi, P., Pinardi, N. 2020. Interannual variability in the Eastern and Western Mediterranean Overturning Index. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097.\n* Pinardi, N., Cessi, P., Borile, F., Wolfe, C.L.P. 2019. The Mediterranean Sea overturning circulation. J Phys Oceanogr. 49:1699\u20131721. DOI:10.1175/JPO-D-18-0254.1.\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., Tonani, M., Lyubartsev, V., Dobricic, S., Bonaduce, A. 2015. Mediterranean Sea large-scale, low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Prog Oceanogr. 132:318\u2013332. DOI:10.1016/j.pocean.2013.11.003.\n* Roether, W., Klein, B., Hainbucher, D. 2014. Chap 6. The eastern Mediterranean transient. In: GL Eusebi Borzelli, M Gacic, P Lionello, P Malanotte-Rizzoli, editors. The Mediterranean Sea. American Geophysical Union (AGU); p. 75\u201383. DOI:10.1002/9781118847572.ch6.\n* Roether, W., Manca, B.B., Klein, B., Bregant, D., Georgopoulos, D., Beitzel, V., Kovac\u030cevic\u0301, V., Luchetta, A. 1996. Recent changes in the eastern Mediterranean deep waters. Science. 271:333\u2013335. DOI:10.1126/science.271.5247.333.\n* Schroeder, K., Chiggiato, J., Bryden, H., Borghini, M., Ismail, S.B. 2016. Abrupt climate shift in the western Mediterranean Sea. Sci Rep. 6:23009. DOI:10.1038/srep23009.\n* Smith, R.O., Bryden, H.L., Stansfield, K. 2008. Observations of new western Mediterranean deep water formation using Argo floats 2004-2006. Ocean Science, 4 (2), 133-149.\n* Von Schuckmann, K. et al. 2020. Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097.\n* Yoon, S., Chang, K., Nam, S., Rho, T.K., Kang, D.J., Lee, T., Park, K.A., Lobanov, V., Kaplunenko, D., Tishchenko, P., Kim, K.R. 2018. Re-initiation of bottom water formation in the East Sea (Japan Sea) in a warming world. Sci Rep. 8:1576. DOI:10. 1038/s41598-018-19952-4.\n", "doi": "10.48670/mds-00317", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-medsea-area-averaged-mean,sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Meridional Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"abstract": "DEFINITION\n\nNet (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1): Faroe Shetland Channel (Water mass criteria, T > 5 \u00b0C); Barents Sea Opening (T > 3 \u00b0C) and the Fram Strait (T > 2 \u00b0C). Net volume transport of Overflow Waters (\u03c3\u03b8 >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).\n\nCONTEXT\n\nThe poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sand\u00f8 et al., 2010; \u00c5rthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).\n\nCMEMS KEY FINDINGS\n\nThe flow of Atlantic Water through the F\u00e6r\u00f8y-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.5 Sv for the period 1993-2021. \nIn the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 1.8 Sv (Smedsrud et al., 2013).\nIn the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 \u00b0C).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00189\n\nReferences:\n\n* Berx B. Hansen B, \u00d8sterhus S, Larsen KM, Sherwin T, Jochumsen K. 2013. Combining in situ measurements and altimetry to estimate volume, heat and salt transport variability through the F\u00e6r\u00f8y-Shetland Channel. Ocean Sci. 9, 639-654\n* Dickson RR, Brown J. 1994. The production of North-Atlantic deep-water \u2013 sources, rates, and pathways. J Geophys Res Oceans. 99(C6), 12319-12341\n* Eldevik T, Nilsen JE\u00d8, Iovino D, Olsson KA, Sand\u00f8 AB, Drange H. 2009. Observed sources and variability of Nordic seas overflow. Nature Geosci. 2(6), 405-409\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan M.M, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Climate Change. 5, 673-678.\n* Mauritzen C. 1996. Production of dense overflow waters feeding the North Atlantic across the Greenland-Scotland Ridge. 1. Evidence for a revised circulation scheme. Deep-Sea Res Part I. 43(6), 769-806\n* Mauritzen C, Hansen E, Andersson M, Berx B, Beszczynzka-M\u00f6ller A, Burud I, Christensen KH, Debernard J, de Steur L, Dodd P, et al. 2011. Closing the loop \u2013 Approaches to monitoring the state of the Arctic Mediterranean during the International Polar Year 2007-2008. Prog Oceanogr. 90, 62-89\n* Onarheim IH, Eldevik T, \u00c5rthun M, Ingvaldsen RB, Smedsrud LH. 2015. Skillful prediction of Barents Sea ice cover. Geophys Res Lett. 42(13), 5364-5371\n* Raj RP, Johannessen JA, Eldevik T, Nilsen JE\u00d8, Halo I. 2016. Quantifying mesoscale eddies in the Lofoten basin. J Geophys Res Oceans. 121. doi:10.1002/2016JC011637\n* Rudels B. 2012. Arctic Ocean circulation and variability \u2013 advection and external forcing encounter constraints and local processes. Ocean Sci. 8(2), 261-286\n* Sand\u00f8, A.B., J.E.\u00d8. Nilsen, Y. Gao and K. Lohmann, 2010: Importance of heat transport and local air-sea heat fluxes for Barents Sea climate variability. J Geophys Res. 115, C07013\n* von Schuckmann K, et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. J Oper Oceanogr. 9, 235-320\n* von Schuckmann K. 2018. Copernicus Marine Service Ocean State Report, J Oper Oceanogr. 11, sup1, S1-S142. Smedsrud LH, Esau I, Ingvaldsen RB, Eldevik T, Haugan PM, Li C, Lien VS, Olsen A, Omar AM, Otter\u00e5 OH, Risebrobakken B, Sand\u00f8 AB, Semenov VA, Sorokina SA. 2013. The role of the Barents Sea in the climate system. Rev Geophys. 51, 415-449\n* Sundby, S., 2000. Recruitment of Atlantic cod stocks in relation to temperature and advection of copepod populations. Sarsia. 85, 277-298.\n* Walczowski W, Piechura J, Goszczko I, Wieczorek P. 2012. Changes in Atlantic water properties: an important factor in the European Arctic marine climate. ICES J Mar Sys. 69(5), 864-869.\n* Piechura J, Walczowski W. 2009. Warming of the West Spitsbergen Current and sea ice north of Svalbard. Oceanol. 51(2), 147-164\n* \u00c5rthun, M., Eldevik, T., Smedsrud, L.H., Skagseth, \u00d8., Ingvaldsen, R.B., 2012. Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat. J. Climate. 25, 4736-4743.\n", "doi": "10.48670/moi-00189", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,omi-circulation-voltrans-arctic-averaged,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nordic Seas Volume Transports from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"abstract": "DEFINITION\n\nThe product OMI_IBI_CURRENTS_VOLTRANS_section_integrated_anomalies is defined as the time series of annual mean volume transport calculated across a set of vertical ocean sections. These sections have been chosen to be representative of the temporal variability of various ocean currents within the IBI domain.\nThe currents that are monitored include: transport towards the North Sea through Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), Canary Current (CC) (Knoll et al. 2002, Mason et al. 2011), Azores Current (AC) (Mason et al., 2011), Algerian Current (ALG) (Tintor\u00e9 et al, 1988; Benzohra and Millot, 1995; Font et al., 1998), and net transport along the 48\u00baN latitude parallel (N48) (see OMI Figure).\nTo provide ensemble-based results, four Copernicus products have been used. Among these products are three reanalysis products (GLO-REA, IBI-REA and MED-REA) and one product obtained from reprocessed observations (GLO-ARM).\n\u2022\tGLO-REA: GLOBAL_MULTIYEAR_PHY_001_030 (Reanalysis)\n\u2022\tIBI-REA: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tMED-REA: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\n\u2022\tMED-REA: MEDSEA_MULTIYEAR_PHY_006_004MEDSEA_MULTIYEAR_PHY_006_004 (Reanalysis)\nThe time series comprises the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with the sign reversed (red dashed line) to indicate the threshold of anomaly values that would entail a reversal of the current transport. Additionally, the analysis of trends in the time series at the 95% confidence interval is included in the bottom right corner of each diagram.\nDetails on the product are given in the corresponding Product User Manual (de Pascual-Collar et al., 2024a) and QUality Information Document (de Pascual-Collar et al., 2024b) as well as the CMEMS Ocean State Report: de Pascual-Collar et al., 2024c.\n\nCONTEXT\n\nThe IBI area is a very complex region characterized by a remarkable variety of ocean currents. Among them, Podemos destacar las que se originan como resultado del closure of the North Atlantic Drift (Mason et al., 2011; Holliday et al., 2008; Peliz et al., 2007; Bower et al., 2002; Knoll et al., 2002; P\u00e9rez et al., 2001; Jia, 2000), las corrientes subsuperficiales que fluyen hacia el norte a lo largo del talud continental (de Pascual-Collar et al., 2019; Pascual et al., 2018; Machin et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Maz\u00e9 et al., 1997; White & Bowyer, 1997). Y las corrientes de intercambio que se producen en el Estrecho de Gibraltar y el Mar de Alboran (Sotillo et al., 2016; Font et al., 1998; Benzohra and Millot, 1995; Tintor\u00e9 et al., 1988).\nThe variability of ocean currents in the IBI domain is relevant to the global thermohaline circulation and other climatic and environmental issues. For example, as discussed by Fasullo and Trenberth (2008), subtropical gyres play a crucial role in the meridional energy balance. The poleward salt transport of Mediterranean water, driven by subsurface slope currents, has significant implications for salinity anomalies in the Rockall Trough and the Nordic Seas, as studied by Holliday (2003), Holliday et al. (2008), and Bozec et al. (2011). The Algerian current serves as the sole pathway for Atlantic Water to reach the Western Mediterranean.\n\nCMEMS KEY FINDINGS\n\nThe volume transport time series show periods in which the different monitored currents exhibited significantly high or low variability. In this regard, we can mention the periods 1997-1998 and 2014-2015 for the RTE current, the period 2012-2014 in the N48 section, the years 2006 and 2017 for the ALG current, the year 2021 for the AC current, and the period 2009-2012 for the CC current.\nAdditionally, periods are detected where the anomalies are large enough (in absolute value) to indicate a reversal of the net transport of the current. This is the case for the years 1999, 2003, and 2012-2014 in the N48 section (with a net transport towards the north), the year 2017 in the ALC current (with net transport towards the west), and the year 2010 in the CC current (with net transport towards the north).\nThe trend analysis of the monitored currents does not detect any significant trends over the analyzed period (1993-2022). However, the confidence interval for the trend in the RTE section is on the verge of rejecting the hypothesis of no trend.\n\nFigure caption\n\nAnnual anomalies of cross-section volume transport in monitoring sections RTE, N48, AC, ALC, and CC. Time series computed and averaged from different Copernicus Marine products for each window (see section Definition) providing a multi-product result. The blue line represents the ensemble mean, and shaded grey areas represent the standard deviation of the ensemble. Red dashed lines depict the velocity value at which the direction of the current reverses. This aligns with the average transport value (with sign reversed) and the point where absolute transport becomes zero. The analysis of trends (at 95% confidence interval) computed in the period 1993\u20132021 is included (bottom right box). Trend lines (gray dashed line) are only included in the figures when a significant trend is obtained.\n\nDOI (product):\nhttps://doi.org/10.48670/mds-00351\n\nReferences:\n\n* Benzohra, M., Millot, C.: Characteristics and circulation of the surface and intermediate water masses off Algeria. Deep Sea Research Part I: Oceanographic Research Papers, 42(10), 1803-1830, https://doi.org/10.1016/0967-0637(95)00043-6, 1995.\n* Bower, A. S., Le Cann, B., Rossby, T., Zenk, T., Gould, J., Speer, K., Richardson, P. L., Prater, M. D., Zhang, H.-M.: Directly measured mid-depth circulation in the northeastern North Atlantic Ocean: Nature, 419, 6907, 603\u2013607, https://doi.org/10.1038/nature01078, 2002.\n* Bozec, A., Lozier, M. S., Chasignet, E. P., Halliwel, G. R.: On the variability of the Mediterranean Outflow Water in the North Atlantic from 1948 to 2006, J. Geophys. Res.-Oceans, 116, C09033, https://doi.org/10.1029/2011JC007191, 2011.\n* Fasullo, J. T., Trenberth, K. E.: The annual cycle of the energy budget. Part II: Meridional structures and poleward transports. Journal of Climate, 21(10), 2313-2325, https://doi.org/10.1175/2007JCLI1936.1, 2008.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Friocourt Y, Levier B, Speich S, Blanke B, Drijfhout SS. A regional numerical ocean model of the circulation in the Bay of Biscay, J. Geophys. Res.,112:C09008, https://doi.org/10.1029/2006JC003935, 2007.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Holliday, N. P., Hughes, S. L., Bacon, S., Beszczynska-M\u00f6ller, A., Hansen, B., Lav\u00edn, A., Loeng, H., Mork, K. A., \u00d8sterhus, S., Sherwin, T., Walczowski, W.: Reversal of the 1960s to 1990s freshening trend in the northeast North Atlantic and Nordic Seas, Geophys. Res. Lett., 35, L03614, https://doi.org/10.1029/2007GL032675, 2008.\n* Holliday, N. P.: Air\u2010sea interactionand circulation changes in the north- east Atlantic, J. Geophys. Res., 108(C8), 3259, https://doi.org/10.1029/2002JC001344, 2003.\n* Jia, Y.: Formation of an Azores Current Due to Mediterranean Overflow in a Modeling Study of the North Atlantic. J. Phys. Oceanogr., 30, 9, 2342\u20132358, https://doi.org/10.1175/1520-0485(2000)030<2342:FOAACD>2.0.CO;2, 2000.\n* Knoll, M., Hern\u00e1ndez-Guerra, A., Lenz, B., L\u00f3pez Laatzen, F., Mach\u0131\u0301n, F., M\u00fcller, T. J., Siedler, G.: The Eastern Boundary Current system between the Canary Islands and the African Coast, Deep-Sea Research. 49-17, 3427-3440, https://doi.org/10.1016/S0967-0645(02)00105-4, 2002.\n* Lozier, M. S., Stewart, N. M.: On the temporally varying penetration ofMediterranean overflowwaters and eastward penetration ofLabrador Sea Water, J. Phys. Oceanogr., 38,2097\u20132103, https://doi.org/10.1175/2008JPO3908.1, 2008.\n* Mach\u00edn, F., Pelegr\u00ed, J. L., Fraile-Nuez, E., V\u00e9lez-Belch\u00ed, P., L\u00f3pez-Laatzen, F., Hern\u00e1ndez-Guerra, A., Seasonal Flow Reversals of Intermediate Waters in the Canary Current System East of the Canary Islands. J. Phys. Oceanogr, 40, 1902\u20131909, https://doi.org/10.1175/2010JPO4320.1, 2010.\n* Mason, E., Colas, F., Molemaker, J., Shchepetkin, A. F., Troupin, C., McWilliams, J. C., Sangra, P.: Seasonal variability of the Canary Current: A numerical study. Journal of Geophysical Research: Oceans, 116(C6), https://doi.org/10.1029/2010JC006665, 2011.\n* Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997. Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997.\n* Pascual, A., Levier ,B., Sotillo, M., Verbrugge, N., Aznar, R., Le Cann, B.: Characterization of Mediterranean Outflow Water in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann et al. (2018) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product Quality Information Document for Global Reanalysis Products, OMI_CURRENTS_VOLTRANS_section_integrated_anomalies, Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-IBI-OMI-QUID-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024a.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product User Manual for OMI_CURRENTS_VOLTRANS_section_integrated_anomalies. Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-IBI-OMI-PUM-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024b.\n* de Pascual-Collar, A., Aznar, R., Levier, B., Garc\u00eda-Sotillo, M.: Monitoring Main Ocean Currents of the IBI Region, in: 8th edition of the Copernicus Ocean State Report (OSR8), accepted pending of publication, 2004c.\n* de Pascual-Collar, A., Sotillo, M. G., Levier, B., Aznar, R., Lorente, P., Amo-Baladr\u00f3n, A., \u00c1lvarez-Fanjul E.: Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019, 2019.\n* Peliz, A., Dubert, J., Marchesiello, P., Teles\u2010Machado, A.: Surface circulation in the Gulf of Cadiz: Model and mean flow structure. Journal of Geophysical Research: Oceans, 112, C11, https://doi.org/10.1029/2007JC004159, 2007.\n* Perez, F. F., Castro, C. G., \u00c1lvarez-Salgado, X. A., R\u00edos, A. F.: Coupling between the Iberian basin-scale circulation and the Portugal boundary current system: a chemical study, Deep-Sea Research. I 48,1519 -1533, https://doi.org/10.1016/S0967-0637(00)00101-1, 2001.\n* Sotillo, M. G., Amo-Baladr\u00f3n, A., Padorno, E., Garcia-Ladona, E., Orfila, A., Rodr\u00edguez-Rubio, P., Conti, D., Jim\u00e9nez Madrid, J. A., de los Santos, F. J., Alvarez Fanjul E.: How is the surface Atlantic water inflow through the Gibraltar Strait forecasted? A lagrangian validation of operational oceanographic services in the Alboran Sea and the Western Mediterranean, Deep-Sea Research. 133, 100-117, https://doi.org/10.1016/j.dsr2.2016.05.020, 2016.\n* Tintore, J., La Violette, P. E., Blade, I., Cruzado, A.: A study of an intense density front in the eastern Alboran Sea: the Almeria\u2013Oran front. Journal of Physical Oceanography, 18, 10, 1384-1397, https://doi.org/10.1175/1520-0485(1988)018%3C1384:ASOAID%3E2.0.CO;2, 1988.\n* White, M., Bowyer, P.: The shelf-edge current north-west of Ireland. Annales Geophysicae 15, 1076\u20131083. https://doi.org/10.1007/s00585-997-1076-0, 1997.\n", "doi": "10.48670/mds-00351", "instrument": null, "keywords": "coastal-marine-environment,cur-armor,cur-glo-myp,cur-ibi-myp,cur-mean,cur-med-myp,cur-nws-myp,cur-std,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-voltrans-ibi-section-integrated-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Volume Transport Anomaly in Selected Vertical Sections"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe Ocean Freshwater Content (OFC) is calculated according to Boyer et al. (2007)\nOFC = \u03c1(Sref, Tref, p) / \u03c1(0, Tref, p ) \u00b7 ( Sref - S) / Sref\nwhere S(x, y, z, t) and Sref (x, y, z) are actual salinity and reference salinity, respectively, and x,y,z,t are zonal, meridional, vertical and temporal coordinates, respectively. The density, \u03c1, is calculated according to the TEOS10 (IOC et al., 2010). The key issue of OFC calculations lies in how the reference salinity is defined. The climatological range of salinity in the Baltic Sea varies from the freshwater conditions in the northern and eastern parts to the oceanic water conditions in the Kattegat. We follow the Boyer et al. (2007) formulation and calculate the climatological OFC from the three-dimensional temperature (Tref) and salinity (Sref) fields averaged over the period of 1993\u20132014.\nThe method for calculating the ocean freshwater content anomaly is based on the daily mean sea water salinity fields (S) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total freshwater content anomaly is determined using the following formula:\nOFC(t) = \u222dV OFC(x, y, z, t) dx dy dz\nThe vertical integral is computed using the static cell vertical thicknesses (dz) sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial integration is performed over the Baltic Sea spatial domain, defined as the region between 9\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N using product grid definition in cmems_mod_bal_phy_my_static. \nWe evaluate the uncertainty from the mean standard deviation of monthly mean OFC. The shaded area in the figure corresponds to the annual standard deviation of monthly mean OFC. \nLinear trend (km3y-1) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\nCONTEXT\nClimate warming has resulted in the intensification of the global hydrological cycle but not necessarily on the regional scale (Pratap and Markonis, 2022). The increase of net precipitation over land and sea areas, decrease of ice cover, and increase of river runoff are the main components of the global hydrological cycle that increase freshwater content in the ocean (Boyer et al., 2007) and decrease ocean salinity.\nThe Baltic Sea is one of the marginal seas where water salinity and OFC are strongly influenced by the water exchange with the North Sea. The Major Baltic Inflows (MBIs) are the most voluminous event-type sources of saline water to the Baltic Sea (Mohrholz, 2018). The frequency and intensity of the MBIs and other large volume inflows have no long-term trends but do have a multidecadal variability of about 30 years (Mohrholz, 2018; Lehmann and Post, 2015; Lehmann et al., 2017; Radtke et al., 2020). Smaller barotropic and baroclinically driven inflows transport saline water into the halocline or below it, depending on the density of the inflow water (Reissmann et al., 2009). \n\nKEY FINDINGS\n\nThe Baltic Sea's ocean freshwater content is exhibiting a declining trend of -37\u00b18.8 km\u00b3/year, along with decadal fluctuations as also noted by Lehmann et al. (2022). Elevated freshwater levels were recorded prior to the Major Baltic Inflows of 1993, 2002, and 2013, which subsequently led to a swift decrease in freshwater content. The lowest ocean freshwater content was recorded in 2019. Over the past four years, the freshwater content anomaly has remained comparatively stable. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00347\n\nReferences:\n\n* Boyer, T., Levitus, S., Antonov, J., Locarnini, R., Mishonov, A., Garcia, H., Josey, S.A., 2007. Changes in freshwater content in the North Atlantic Ocean 1955\u20132006. Geophysical Research Letters, 34(16), L16603. Doi: 10.1029/2007GL030126\n* IOC, SCOR and IAPSO, 2010: The international thermodynamic equation of seawater - 2010: Calculation and use of thermodynamic properties. Intergovernmental Oceanographic Commission, Manuals and Guides No. 56, UNESCO (English), 196 pp. Available from http://www.TEOS-10.org (11.10.2021).\n* Lehmann, A., Post, P., 2015. Variability of atmospheric circulation patterns associated with large volume changes of the Baltic Sea. Advances in Science and Research, 12, 219\u2013225, doi:10.5194/asr-12-219-2015\n* Lehmann, A., H\u00f6flich, K., Post, P., Myrberg, K., 2017. Pathways of deep cyclones associated with large volume changes (LVCs) and major Baltic inflows (MBIs). Journal of Marine Systems, 167, pp.11-18. doi:10.1016/j.jmarsys.2016.10.014\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz, V., 2018. Major Baltic inflow statistics\u2013revised. Frontiers in Marine Science, 5, p.384. doi:10.3389/fmars.2018.00384\n* Pratap, S., Markonis, Y., 2022. The response of the hydrological cycle to temperature changes in recent and distant climatic history, Progress in Earth and Planetary Science 9(1),30. doi:10.1186/s40645-022-00489-0\n* Radtke, H., Brunnabend, S.-E., Gr\u00e4we, U., Meier, H. E. M., 2020. Investigating interdecadal salinity changes in the Baltic Sea in a 1850\u20132008 hindcast simulation, Climate of the Past, 16, 1617\u20131642, doi:10.5194/cp-16-1617-2020\n* Reissmann, J. H., Burchard, H., Feistel,R., Hagen, E., Lass, H. U., Mohrholz, V., Nausch, G., Umlauf, L., Wiecczorek, G., 2009. Vertical mixing in the Baltic Sea and consequences for eutrophication a review, Progress in Oceanography, 82, 47\u201380. doi:10.1016/j.pocean.2007.10.004\n", "doi": "10.48670/mds-00347", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ofc-balrean,ofc-balrean-lower-rmsd,ofc-balrean-upper-rmsd,omi-climate-ofc-baltic-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Freshwater Content Anomaly (0-300m) from Reanalysis"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"abstract": "DEFINITION\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 300 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_m-T_clim )dz [1]\nwith a reference density = 1020 kg m-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009; Lima et al., 2020); T_m corresponds to the monthly average temperature and T_clim is the climatological temperature of the corresponding month that varies according to each individual product.\nTime series of monthly mean values area averaged ocean heat content is provided for the Black Sea (40.86\u00b0N, 46.8\u00b0N; 27.32\u00b0E, 41.96\u00b0E) and is evaluated in areas where the topography is deeper than 300m. The Azov and Marmara Seas are not considered.\nThe quality evaluation of OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). Three global products and one regional (Black Sea) product have been used to build an ensemble mean, and its associated ensemble spread. Details on the products are delivered in the PUM and QUID of this OMI.\n\nCONTEXT\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nSeveral studies discuss a warming in the Black Sea using either observations or model results (Akpinar et al., 2017; Stanev et al. 2019; Lima et al. 2020). Using satellite sea surface temperature observations (SST), Degtyarev (2000) detected a positive temperature trend of 0.016 \u00baC years-1 in the 50-100 m layer from 1985 to 1997. From Argo floats Stanev et al. (2019) found a warming trend in the cold intermediate layer (CIL; at approximately 25 \u2013 70 m) of about 0.05 oC year-1 in recent years. The warming signal was also present in ocean heat content analyses conducted by Lima et al. (2020). Their results from the Black Sea regional reanalysis showed an increase rate of 0.880\u00b10.181 W m-2 in the upper layers (0 \u2013 200 m), which has been reflected in the disappearance of Black Sea cold intermediate layer in recent years. The newest version of reanalysis also presents a warming of 0.814\u00b10.045 W m-2 in 0 \u2013 200 m (Lima et al. (2021). This warming has been reflected in a more incidence of marine heat waves in the Black Sea over the past few years (Mohammed et al. 2022).\n\nCMEMS KEY FINDINGS\n\nTime series of ocean heat content anomalies present a significant interannual variability, altering between cool and warm events. This important characteristic becomes evident over the years 2012 to 2015: a minimum of ocean heat content anomaly is registered close to \u2013 2.00 x 108 J m-2 in 2012, followed by positive values around 2.00 x 108 J m-2 in 2013 and above 2.0 x 108 J m-2 most of time in 2014 and 2015. Since 2005 the Black Sea experienced an increase in ocean heat content (0-300 m), and record OHC values are noticed in 2020. The Black Sea is warming at a rate of 0.995\u00b10.084 W m-2, which is higher than the global average warming rate.\nThe increase in ocean heat content weakens the CIL, whereas its decreasing favours the CIL restoration (Akpinar et al., 2017). The years 2012 and 2017 exhibited a more evident warming interruption that induced a replenishment of the CIL (Lima et al. 2021).\n\nFigure caption\n\nTime series of the ensemble mean and ensemble spread (shaded area) of the monthly Black Sea averaged ocean heat content anomalies integrated over the 0-300m depth layer (J m\u20132) during Jan 2005 \u2013 December 2020. The monthly ocean heat content anomalies are defined as the deviation from the climatological ocean heat content mean (1993\u20132014) of each corresponding month. Mean trend values are also reported at the bottom right corner. The ensemble is based on different data products, i.e. Black Sea Reanalysis, global ocean reanalysis GLORYS12V1; global observational based products CORA5.2, ARMOR3D. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\nDOI (product): \n\u00a0https://doi.org/10.48670/moi-00306\n\nReferences:\n\n* Akpinar, A., Fach, B. A., Oguz, T., 2017: Observing the subsurface thermal signature of the Black Sea cold intermediate layer with Argo profiling floats. Deep Sea Res. I Oceanogr. Res. Papers 124, 140\u2013152. doi: 10.1016/j.dsr.2017.04.002.\n* Lima, L., Peneva, E., Ciliberti, S., Masina, S., Lemieux, B., Storto, A., Chtirkova, B., 2020: Ocean heat content in the Black Sea. In: Copernicus marine service Ocean State Report, issue 4, Journal of Operational Oceanography, 13:Sup1, s41\u2013s47, doi: 10.1080/1755876X.2020.1785097.\n* Lima L., Ciliberti S. A., Aydo\u011fdu A., Masina S., Escudier R., Cipollone A., Azevedo D., Causio S., Peneva E., Lecci R., Clementi E., Jansen E., Ilicak M., Cret\u00ec S., Stefanizzi L., Palermo F., Coppini G., 2021: Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis, Frontier in Marine Science, 8:710973, doi: 10.3389/fmars.2021.710973.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841, DOI: 10.1007/s00382-015-2728-5.\n* Stanev, E. V., Peneva, E., and Chtirkova, B. 2019: Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans, 124, 4803\u20134819, doi: 10.1029/2019JC015076.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:Sup1, s1-s142, doi: 10.1080/1755876X.2018.1489208.\n* Degtyarev, A. K., 2000: Estimation of temperature increase of the Black Sea active layer during the period 1985\u2013 1997, Meteorilogiya i Gidrologiya, 6, 72\u2013 76 (in Russian).\n", "doi": "10.48670/moi-00306", "instrument": null, "keywords": "black-sea,coastal-marine-environment,in-situ-observation,integral-wrt-depth-of-sea-water-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-blksea-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Ocean Heat Content Anomaly (0-300m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"abstract": "DEFINITION\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-20210) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 2000 m depth:\n \n With a reference density of \u03c10 = 1030 kgm-3 and a specific heat capacity of cp = 3980 J/kg\u00b0C (e.g. von Schuckmann et al., 2009)\nAveraged time series for ocean heat content and their error bars are calculated for the Iberia-Biscay-Ireland region (26\u00b0N, 56\u00b0N; 19\u00b0W, 5\u00b0E).\nThis OMI is computed using IBI-MYP, GLO-MYP reanalysis and CORA, ARMOR data from observations which provide temperatures. Where the CMEMS product for each acronym is:\n\u2022\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tGLO-MYP: GLOBAL_REANALYSIS_PHY_001_031 (Reanalysis)\n\u2022\tCORA: INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b (Observations)\n\u2022\tARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\nThe figure comprises ensemble mean (blue line) and the ensemble spread (grey shaded). Details on the product are given in the corresponding PUM for this OMI as well as the CMEMS Ocean State Report: von Schuckmann et al., 2016; von Schuckmann et al., 2018.\n\nCONTEXT\n\nChange in OHC is a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and can impact marine ecosystems and human livelihoods (IPCC, 2019). Additionally, OHC is one of the six Global Climate Indicators recommended by the World Meterological Organisation (WMO, 2017). \nIn the last decades, the upper North Atlantic Ocean experienced a reversal of climatic trends for temperature and salinity. While the period 1990-2004 is characterized by decadal-scale ocean warming, the period 2005-2014 shows a substantial cooling and freshening. Such variations are discussed to be linked to ocean internal dynamics, and air-sea interactions (Fox-Kemper et al., 2021; Collins et al., 2019; Robson et al 2016). Together with changes linked to the connectivity between the North Atlantic Ocean and the Mediterranean Sea (Masina et al., 2022), these variations affect the temporal evolution of regional ocean heat content in the IBI region.\nRecent studies (de Pascual-Collar et al., 2023) highlight the key role that subsurface water masses play in the OHC trends in the IBI region. These studies conclude that the vertically integrated trend is the result of different trends (both positive and negative) contributing at different layers. Therefore, the lack of representativeness of the OHC trends in the surface-intermediate waters (from 0 to 1000 m) causes the trends in intermediate and deep waters (from 1000 m to 2000 m) to be masked when they are calculated by integrating the upper layers of the ocean (from surface down to 2000 m).\n\nCMEMS KEY FINDINGS\n\nThe ensemble mean OHC anomaly time series over the Iberia-Biscay-Ireland region are dominated by strong year-to-year variations, and an ocean warming trend of 0.41\u00b10.4 W/m2 is barely significant.\n\nFigure caption\n\nTime series of annual mean area averaged ocean heat content in the Iberia-Biscay-Ireland region (basin wide) and integrated over the 0-2000m depth layer during 1993-2022: ensemble mean (blue line) and ensemble spread (shaded area). The ensemble mean is based on different data products i.e., the IBI Reanalysis, global ocean reanalysis, and the global observational based products CORA, and ARMOR3D. Trend of ensemble mean (dashed line and bottom-right box) with 95% confidence interval computed in the period 1993-2022. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00316\n\nReferences:\n\n* Collins M., M. Sutherland, L. Bouwer, S.-M. Cheong, T. Fr\u00f6licher, H. Jacot Des Combes, M. Koll Roxy, I. Losada, K. McInnes, B. Ratter, E. Rivera-Arriaga, R.D. Susanto, D. Swingedouw, and L. Tibig, 2019: Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 589\u2013655. https://doi.org/10.1017/9781009157964.008.\n* Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. A\u00f0algeirsd\u00f3ttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sall\u00e9e, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211\u20131362, doi: 10.1017/9781009157896.011.\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Masina, S., Pinardi, N., Cipollone, A., Banerjee, D. S., Lyubartsev, V., von Schuckmann, K., Jackson, L., Escudier, R., Clementi, E., Aydogdu, A. and Iovino D., (2022). The Atlantic Meridional Overturning Circulation forcing the mean se level in the Mediterranean Sea through the Gibraltar transport. In: Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography,15:sup1, s119\u2013s126; DOI: 10.1080/1755876X.2022.2095169\n* Potter, R. A., and Lozier, M. S. 2004: On the warming and salinification of the Mediterranean outflow waters in the North Atlantic, Geophys. Res. Lett., 31, 1\u20134, doi:10.1029/2003GL018161.\n* Robson, J., Ortega, P., Sutton, R., 2016: A reversal of climatic trends in the North Atlantic since 2005. Nature Geosci 9, 513\u2013517. https://doi.org/10.1038/ngeo2727.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/mds-00316", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,integral-wrt-depth-of-sea-water-potential-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-ibi-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Ocean Heat Content Anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"abstract": "DEFINITION\n\nOcean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:\n\u00afS=1/V \u222bV S dV\nTime series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.\n\nCONTEXT\n\nThe freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link \u2028to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.\nThe OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the \u201cmulti-product\u201d approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.\nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24\u00b0horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4\u00b0horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, \nGLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)\n\tTwo observation-based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI: https://doi.org/10.17882/46219, Szekely et al., 2022) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\nCMEMS KEY FINDINGS\n\nThe Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2019 at rate of 5.610-3 \u00b13.510-4 psu yr-1. \nThe overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993\u20132019 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.\n\nFigure caption\n\nTime series of annual mean volume ocean salt content in the Mediterranean Sea (basin wide), integrated over the 0-300m depth layer during 1993-2019 (or longer according to data availability) including ensemble mean and ensemble spread (shaded area). The ensemble mean and associated ensemble spread are based on different data products, i.e., Mediterranean Sea Reanalysis (MED-REA), global ocean reanalysis (GLORYS, C-GLORS, ORAS5, and FOAM) and global observational based products (CORA and ARMOR3D). Details on the products are given in the corresponding PUM and QUID for this OMI.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00325\n\nReferences:\n\n* Aydogdu, A., Miraglio, P., Escudier, R., Clementi, E., Masina, S.: The dynamical role of upper layer salinity in the Mediterranean Sea, State of the Planet, accepted, 2023.\n* Desportes, C., Garric, G., R\u00e9gnier, C., Dr\u00e9villon, M., Parent, L., Drillet, Y., Masina, S., Storto, A., Mirouze, I., Cipollone, A., Zuo, H., Balmaseda, M., Peterson, D., Wood, R., Jackson, L., Mulet, S., Grenier, E., and Gounou, A.: EU Copernicus Marine Service Quality Information Document for the Global Ocean Ensemble Physics Reanalysis, GLOBAL_REANALYSIS_PHY_001_031, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-031.pdf (last access: 3 May 2023), 2022.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020).\n* Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Grenier, E., Verbrugge, N., Mulet, S., and Guinehut, S.: EU Copernicus Marine Service Quality Information Document for the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-MOB-QUID-015-012.pdf (last access: 3 May 2023), 2021.\n* Szekely, T.: EU Copernicus Marine Service Quality Information Document for the Global Ocean-Delayed Mode gridded CORA \u2013 In-situ Observations objective analysis in Delayed Mode, INSITU_GLO_PHY_TS_OA_MY_013_052, issue 1.2, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-INS-QUID-013-052.pdf (last access: 4 April 2023), 2022.\n", "doi": "10.48670/mds-00325", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,integral-wrt-depth-of-sea-water-salinity-expressed-as-salt-content,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-osc-medsea-volume-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Salt Content (0-300m)"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Baltic Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nThe Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gr\u00e4we et al., 2019).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Baltic Sea rises at a rate of 4.1 \uf0b1 0.8 mm/year with an acceleration of 0.10 \uf0b1\uf0200.07 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00202\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Gr\u00e4we, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Clim., 32, 3089\u20133108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00202", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-baltic-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Black Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \nIn the Black Sea, major drivers of change have been attributed to anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Black Sea rises at a rate of 1.00 \u00b1 0.80 mm/year with an acceleration of -0.47 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00215\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Volkov, D. L. and Landerer, F. W.: Internal and external forcing of sea level variability in the Black Sea, Clim. Dyn., 45, 2633\u20132646, https://doi.org/10.1007/s00382-015-2498-0, 2015.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00215", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-blksea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Northeast Atlantic Ocean and adjacent seas Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nUncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, sea level variations are influenced by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016) and the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. This region encompasses the Mediterranean, IBI, North-West shelf and Baltic regions with different sea level dynamics detailed in the regional indicators.\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Northeast Atlantic Ocean and adjacent seas area rises at a rate of 3.2 \u00b1 0.80 mm/year with an acceleration of 0.10 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00335\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/mds-00335", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,oceanographic-geographical-features,omi-climate-sl-europe-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European Seas Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Global Ocean weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and global GIA correction of -0.3mm/yr (common global GIA correction, see Spada, 2017). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty of 0.3 mm/yr is provided at 90% confidence interval using altimeter error budget (Gu\u00e9rou et al., 2022). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, global mean sea level rises at a rate of 3.4 \u00b1 0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada, 2017) to consider the ongoing movement of land. The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Ni\u00f1o Southern Oscillation (Cazenave and Cozannet, 2014).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00237\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Oppenheimer, M., Glavovic, B. C., Hinkel, J., Van de Wal, R., Magnan, A. K., Abd-Elgaward, A., Cai, R., Cifuentes Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities \u2014 Special Report on the Ocean and Cryosphere in a Changing Climate: Chapter 4, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Gu\u00e9rou, A., Meyssignac, B., Prandi, P., Ablain, M., Ribes, A., and Bignalet-Cazalet, F.: Current observed global mean sea level rise and acceleration estimated from satellite altimetry and the associated uncertainty, EGUsphere, 1\u201343, https://doi.org/10.5194/egusphere-2022-330, 2022.\n* Cazenave, A. and Cozannet, G. L.: Sea level rise and its coastal impacts, Earths Future, 2, 15\u201334, https://doi.org/10.1002/2013EF000188, 2014.\n", "doi": "10.48670/moi-00237", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"abstract": "DEFINITION\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. The product is distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). At each grid point, the trends/accelerations are estimated on the time series corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional GIA correction (GIA map of a 27 ensemble model following Spada et Melini, 2019) and adjusted from annual and semi-annual signals. Regional uncertainties on the trends estimates can be found in Prandi et al., 2021.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and regional sea level change is also influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2019, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \n\nKEY FINDINGS\n\nThe altimeter sea level trends over the [1993/01/01, 2023/07/06] period exhibit large-scale variations with trends up to +10 mm/yr in regions such as the western tropical Pacific Ocean. In this area, trends are mainly of thermosteric origin (Legeais et al., 2018; Meyssignac et al., 2017) in response to increased easterly winds during the last two decades associated with the decreasing Interdecadal Pacific Oscillation (IPO)/Pacific Decadal Oscillation (e.g., McGregor et al., 2012; Merrifield et al., 2012; Palanisamy et al., 2015; Rietbroek et al., 2016).\nPrandi et al. (2021) have estimated a regional altimeter sea level error budget from which they determine a regional error variance-covariance matrix and they provide uncertainties of the regional sea level trends. Over 1993-2019, the averaged local sea level trend uncertainty is around 0.83 mm/yr with local values ranging from 0.78 to 1.22 mm/yr. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00238\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nature Clim Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press., 2019.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., von Schuckmann, K., Melet, A., Storto, A., and Meyssignac, B.: Sea Level, Journal of Operational Oceanography, 11, s13\u2013s16, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Journal of Operational Oceanography, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* McGregor, S., Gupta, A. S., and England, M. H.: Constraining Wind Stress Products with Sea Surface Height Observations and Implications for Pacific Ocean Sea Level Trend Attribution, 25, 8164\u20138176, https://doi.org/10.1175/JCLI-D-12-00105.1, 2012.\n* Merrifield, M. A., Thompson, P. R., and Lander, M.: Multidecadal sea level anomalies and trends in the western tropical Pacific, 39, https://doi.org/10.1029/2012GL052032, 2012.\n* Meyssignac, B., Piecuch, C. G., Merchant, C. J., Racault, M.-F., Palanisamy, H., MacIntosh, C., Sathyendranath, S., and Brewin, R.: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011, in: Integrative Study of the Mean Sea Level and Its Components, edited by: Cazenave, A., Champollion, N., Paul, F., and Benveniste, J., Springer International Publishing, Cham, 191\u2013219, https://doi.org/10.1007/978-3-319-56490-6_9, 2017.\n* Palanisamy, H., Cazenave, A., Delcroix, T., and Meyssignac, B.: Spatial trend patterns in the Pacific Ocean sea level during the altimetry era: the contribution of thermocline depth change and internal climate variability, Ocean Dynamics, 65, 341\u2013356, https://doi.org/10.1007/s10236-014-0805-7, 2015.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Rietbroek, R., Brunnabend, S.-E., Kusche, J., Schr\u00f6ter, J., and Dahle, C.: Revisiting the contemporary sea-level budget on global and regional scales, 113, 1504\u20131509, https://doi.org/10.1073/pnas.1519132113, 2016.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat Commun, 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00238", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-regional-trends,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level trend map from Observations Reprocessing"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Irish-Biscay-Iberian (IBI) Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT \n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the IBI area rises at a rate of 4.00 \uf0b1 0.80 mm/year with an acceleration of 0.14 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00252\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00252", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-ibi-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator of regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Mediterranean Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nBeside a clear long-term trend, the regional mean sea level variation in the Mediterranean Sea shows an important interannual variability, with a high trend observed between 1993 and 1999 (nearly 8.4 mm/y) and relatively lower values afterward (nearly 2.4 mm/y between 2000 and 2022). This variability is associated with a variation of the different forcing. Steric effect has been the most important forcing before 1999 (Fenoglio-Marc, 2002; Vigo et al., 2005). Important change of the deep-water formation site also occurred in the 90\u2019s. Their influence contributed to change the temperature and salinity property of the intermediate and deep water masses. These changes in the water masses and distribution is also associated with sea surface circulation changes, as the one observed in the Ionian Sea in 1997-1998 (e.g. Ga\u010di\u0107 et al., 2011), under the influence of the North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases (Incarbona et al., 2016). These circulation changes may also impact the sea level trend in the basin (Vigo et al., 2005). In 2010-2011, high regional mean sea level has been related to enhanced water mass exchange at Gibraltar, under the influence of wind forcing during the negative phase of NAO (Landerer and Volkov, 2013).The relatively high contribution of both sterodynamic (due to steric and circulation changes) and gravitational, rotational, and deformation (due to mass and water storage changes) after 2000 compared to the [1960, 1989] period is also underlined by (Calafat et al., 2022).\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Mediterranean Sea rises at a rate of 2.5 \u00b1 0.8 mm/year with an acceleration of 0.01 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00264\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Ga\u010di\u0107, M., Civitarese, G., Eusebi Borzelli, G. L., Kova\u010devi\u0107, V., Poulain, P.-M., Theocharis, A., Menna, M., Catucci, A., and Zarokanellos, N.: On the relationship between the decadal oscillations of the northern Ionian Sea and the salinity distributions in the eastern Mediterranean, J. Geophys. Res. Oceans, 116, https://doi.org/10.1029/2011JC007280, 2011.\n* Incarbona, A., Martrat, B., Mortyn, P. G., Sprovieri, M., Ziveri, P., Gogou, A., Jord\u00e0, G., Xoplaki, E., Luterbacher, J., Langone, L., Marino, G., Rodr\u00edguez-Sanz, L., Triantaphyllou, M., Di Stefano, E., Grimalt, J. O., Tranchida, G., Sprovieri, R., and Mazzola, S.: Mediterranean circulation perturbations over the last five centuries: Relevance to past Eastern Mediterranean Transient-type events, Sci. Rep., 6, 29623, https://doi.org/10.1038/srep29623, 2016.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Landerer, F. W. and Volkov, D. L.: The anatomy of recent large sea level fluctuations in the Mediterranean Sea, Geophys. Res. Lett., 40, 553\u2013557, https://doi.org/10.1002/grl.50140, 2013.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J. Geophys. Res. Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n", "doi": "10.48670/moi-00264", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-medsea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the North-West Shelf Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\nCONTEXT\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, the time series shows decadal variations. As observed over the global ocean, the main actors of the long-term sea level trend are associated with anthropogenic global/regional warming (IPCC WGII, 2021). Decadal variability is mainly linked to the Strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. They also underscored the influence of the inverse barometer forcing in some coastal regions.\n\nKEY FINDINGS\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the NWS area rises at a rate of 3.2 \uf0b1 0.8 mm/year with an acceleration of 0.09 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\nFigure caption\n\nRegional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the North-West Shelf region, derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) altimeter sea level gridded products distributed by the Copernicus Climate Change Service (C3S), and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off), and the curve is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00271\n\nReferences:\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00271", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Atlantic Shelf Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2023, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product. \n\nCONTEXT\n\nSea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 oC when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\nCMEMS KEY FINDINGS\n\nThe basin-average trend of SST anomalies for Baltic Sea region amounts to 0.038\u00b10.004\u00b0C/year over the period 1982-2023 which corresponds to an average warming of 1.60\u00b0C. Adding the North Sea area, the average trend amounts to 0.029\u00b10.002\u00b0C/year over the same period, which corresponds to an average warming of 1.22\u00b0C for the entire region since 1982. \n\nFigure caption\n\nTime series of monthly mean (turquoise line) and annual mean (blue line) of sea surface temperature anomalies for January 1982 to December 2023, relative to the 1991-2020 mean, combined for the Baltic Sea and North Sea SST (OMI_CLIMATE_SST_BAL_area_averaged_anomalies). The data are based on the multi-year Baltic Sea L4 satellite SST reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00205\n\nReferences:\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Mar-ta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda La-fuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Mari-nova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n", "doi": "10.48670/moi-00205", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_trend": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_SST_BAL_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\nCMEMS KEY FINDINGS\n\nSST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1982 to December 2023. The average trend for the Baltic Sea domain (east of 9\u00b0E longitude) is 0.038\u00b0C/year, which represents an average warming of 1.60\u00b0C for the 1982-2023 period considered here. When the North Sea domain is included, the trend decreases to 0.029\u00b0C/year corresponding to an average warming of 1.22\u00b0C for the 1982-2023 period. \nFigure caption\n\nFigure caption\n\nCumulative trends in sea surface temperature anomalies calculated from 1982 to 2023 for the Baltic Sea (OMI_CLIMATE_SST_BAL_trend). Trend calculations are based on the multi-year Baltic Sea L4 SST satellite product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00206\n\nReferences:\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Marta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda Lafuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Marinova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00206", "instrument": null, "keywords": "baltic-sea,change-over-time-in-sea-surface-foundation-temperature,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe omi_climate_sst_ibi_area_averaged_anomalies product for 2022 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Iberia-Biscay-Irish Seas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v2.1.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberia-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019), Copernicus Climate Change Service (C3S) initiatives and Eumetsat data. Anomalies are computed against the 1993-2014 reference period.\n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018).\n\nCMEMS KEY FINDINGS\n\nThe overall trend in the SST anomalies in this region is 0.013 \u00b10.001 \u00b0C/year over the period 1993-2022. \n\nFigure caption\n\nTime series of monthly mean and 12-month filtered sea surface temperature anomalies in the Iberia-Biscay-Irish Seas during the period 1993-2022. Anomalies are relative to the climatological period 1993-2014 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-IBI-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00256\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00256", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_trend": {"abstract": "DEFINITION\n\nThe omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\nCMEMS KEY FINDINGS\n\nThe overall trend in the SST anomalies in this region is 0.022 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\nFigure caption\nSea surface temperature trend over the period 1982-2023 in the Iberia-Biscay-Irish areas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00257\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389\n", "doi": "10.48670/moi-00257", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_anomaly product includes the 2D annual mean surface temperature anomaly for the Arctic Ocean for 2023. The annual mean surface temperature anomaly is calculated from the climatological mean estimated from 1991 to 2020, defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). The SST/IST Level 4 analysis that provides the input to the climatology and mean anomaly calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\nKEY FINDINGS\n\nThe area average anomaly of 2023 is 1.70\u00b11.08\u00b0C (\u00b1 means 1 standard deviation in this case). The majority of anomalies are positive and exceed 2\u00b0C for most areas of the Arctic Ocean, while the largest regional anomalies exceeded 6\u00b0C. Near zero and slightly negative anomalies are observed in some areas of the Barents, Norwegian and Greenland Sea and around the Bering Strait. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00353\n\nReferences:\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42\n", "doi": "10.48670/mds-00353", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-anomaly,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"abstract": "DEFINITION \n\nThe OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_area_averaged_anomalies product includes time series of monthly mean SST/IST anomalies over the period 1982-2023, relative to the 1991-2020 climatology (30 years), averaged for the Arctic Ocean. The SST/IST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. The climatological period used is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\nKEY FINDINGS\n\nThe basin-average trend of SST/IST anomalies for the Arctic Ocean region amounts to 0.104\u00b10.005 \u00b0C/year over the period 1982-2023 (42 years) which corresponds to an average warming of 4.37\u00b0C. The 2-d map of warming trends indicates these are highest for the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. The 2d map of Arctic anomalies for 2023 reveals regional peak warming exceeding 6\u00b0C.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00323\n\nReferences:\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00323", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-area-averaged-anomalies,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly time series based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"abstract": "DEFINITION\n\nThe OMI_CLIMATE_sst_ist_ARCTIC_sst_ist_trend product includes the cumulative/net trend in combined sea and ice surface temperature anomalies for the Arctic Ocean from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST/IST Level 4 analysis that provides the input to the trend calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude.\n\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the reference climatology defined over the period 1991-2020, according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate), using daily level 4 SST/IST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\nCONTEXT\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al., 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\nKEY FINDINGS\n\nSST/IST trends were calculated for the Arctic Ocean over the period January 1982 to December 2023. The cumulative trends are upwards of 2\u00b0C for the greatest part of the Arctic Ocean, with the largest trends occur in the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. Zero to slightly negative trends are found at the North Atlantic part of the Arctic Ocean. The combined sea and sea ice surface temperature trend is 0.104+/-0.005\u00b0C/yr, i.e. an increase by around 4.37\u00b0C between 1982 and 2023. The 2d map of Arctic anomalies reveals regional peak warming exceeding 6\u00b0C.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00324\n\nReferences:\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00324", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-trend,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature 2D trend from climatology based on reprocessed observations"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "DEFINITION\n\nThe omi_climate_sst_northwestshelf_area_averaged_anomalies product for 2023 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the European North West Shelf Seas. The NORTHWESTSHELF SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish areas(SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST- NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1991-2020 reference period. \n\nCONTEXT\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\nCMEMS KEY FINDINGS \n\nThe overall trend in the SST anomalies in this region is 0.024 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\nFigure caption\n\nTime series of monthly mean and 24-month filtered sea surface temperature anomalies in the European North West Shelf Seas during the period 1982-2023. Anomalies are relative to the climatological period 1991-2020 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-NORTHWESTSHELF-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00275\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00275", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"abstract": "DEFINITION\n\nThe omi_climate_sst_northwestshelf_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the SST trend over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\nCONTEXT \n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\nCMEMS KEY FINDINGS \n\nOver the period 1982-2023, the European North West Shelf Seas mean Sea Surface Temperature (SST) increased at a rate of 0.024 \u00b1 0.002 \u00b0C/Year.\n\nFigure caption\n\nSea surface temperature trend over the period 1982-2023 in the European North West Shelf Seas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005;) and Sen\u2019s method (Sen 1968).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00276\n\nReferences:\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00276", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"abstract": "DEFINITION\n\nThe North Pacific Gyre Oscillation (NPGO) is a climate pattern introduced by Di Lorenzo et al. (2008) and further reported by Tranchant et al. (2019) in the CMEMS Ocean State Report #3. The NPGO is defined as the second dominant mode of variability of Sea Surface Height (SSH) anomaly and SST anomaly in the Northeast Pacific (25\u00b0\u2013 62\u00b0N, 180\u00b0\u2013 250\u00b0E). The spatial and temporal pattern of the NPGO has been deduced over the [1950-2004] period using an empirical orthogonal function (EOF) decomposition on sea level and sea surface temperature fields produced by the Regional Ocean Modeling System (ROMS) (Di Lorenzo et al., 2008; Shchepetkin and McWilliams, 2005). Afterward, the sea level spatial pattern of the NPGO is used/projected with satellite altimeter delayed-time sea level anomalies to calculate and update the NPGO index.\nThe NPGO index disseminated on CMEMS was specifically updated from 2004 onward using up-to-date altimeter products (DT2021 version; SEALEVEL_GLO_PHY_L4_MY _008_047 CMEMS product, including \u201cmy\u201d & \u201cmyint\u201d datasets, and the near-real time SEALEVEL_GLO_PHY_L4_NRT _008_046 CMEMS product). Users that previously used the index disseminated on www.o3d.org/npgo/ web page will find slight differences induced by this update. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the SSH anomaly (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the NPGO. \n\nCONTEXT\n\nNPGO mode emerges as the leading mode of decadal variability for surface salinity and upper ocean nutrients (Di Lorenzo et al., 2009). The North Pacific Gyre Oscillation (NPGO) term is used because its fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyres circulations (Chhak et al., 2009). This index measures change in the North Pacific gyres circulation and explains key physical-biological ocean variables including temperature, salinity, sea level, nutrients, chlorophyll-a. A positive North Pacific Gyre Oscillation phase is a dipole pattern with negative SSH anomaly north of 40\u00b0N and the opposite south of 40\u00b0N. (Di Lorenzo et al., 2008) suggested that the North Pacific Gyre Oscillation is the oceanic expression of the atmospheric variability of the North Pacific Oscillation (Walker and Bliss, 1932), which has an expression in both the 2nd EOFs of SSH and Sea Surface Temperature (SST) anomalies (Ceballos et al., 2009). This finding is further supported by the recent work of (Yi et al., 2018) showing consistent pattern features between the atmospheric North Pacific Oscillation and the oceanic North Pacific Gyre Oscillation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) database.\n\nCMEMS KEY FINDINGS\n\nThe NPGO index is presently in a negative phase, associated with a positive SSH anomaly north of 40\u00b0N and negative south of 40\u00b0N. This reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients. \n\nFigure caption\n\nNorth Pacific Gyre Oscillation (NPGO) index monthly averages. The NPGO index has been projected on normalized satellite altimeter sea level anomalies. The NPGO index is derived from (Di Lorenzo et al., 2008) before 2004, the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red lines show the date of the transition between the historical Di Lorenzo\u2019s series and the DUACS product, then between the DUACS \u201cmyint\u201d and \u201cnrt\u201d products used.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00221\n\nReferences:\n\n* Ceballos, L. I., E. Di Lorenzo, C. D. Hoyos, N. Schneider and B. Taguchi, 2009: North Pacific Gyre Oscillation Synchronizes Climate Fluctuations in the Eastern and Western Boundary Systems. Journal of Climate, 22(19) 5163-5174, doi:10.1175/2009jcli2848.1\n* Chhak, K. C., E. Di Lorenzo, N. Schneider and P. F. Cummins, 2009: Forcing of Low-Frequency Ocean Variability in the Northeast Pacific. Journal of Climate, 22(5) 1255-1276, doi:10.1175/2008jcli2639.1.\n* Di Lorenzo, E., N. Schneider, K.M. Cobb, K. Chhak, P.J.S. Franks, A.J. Miller, J.C. McWilliams, S.J. Bograd, H. Arango, E. Curchister, and others. 2008. North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophysical Research Letters 35, L08607, https://doi.org/10.1029/2007GL032838.\n* Di Lorenzo, E., J. Fiechter, N. Schneider, A. Bracco, A. J. Miller, P. J. S. Franks, S. J. Bograd, A. M. Moore, A. C. Thomas, W. Crawford, A. Pena and A. J. Hermann, 2009: Nutrient and salinity decadal variations in the central and eastern North Pacific. Geophysical Research Letters, 36, doi:10.1029/2009gl038261.\n* Di Lorenzo, E., K. M. Cobb, J. C. Furtado, N. Schneider, B. T. Anderson, A. Bracco, M. A. Alexander and D. J. Vimont, 2010: Central Pacific El Nino and decadal climate change in the North Pacific Ocean. Nature Geoscience, 3(11) 762-765, doi:10.1038/ngeo984\n* Tranchant, B. I. Pujol, E. Di Lorenzo and JF Legeais (2019). The North Pacific Gyre Oscillation. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Yi, D. L., Gan, B. Wu., L., A.J. Miller, 2018. The North Pacific Gyre Oscillation and Mechanisms of Its Decadal Variability in CMIP5 Models: Journal of Climate: Vol 31, No 6, 2487-2509.\n", "doi": "10.48670/moi-00221", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-climvar-pacific-npgo-sla-eof-mode-projection,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Oscillation from Observations Reprocessing"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"abstract": "DEFINITION\n\nTemperature deviation from the 30-year (1991-2020) daily climatological mean temperature for the Barents Sea region (68\u00b0N - 80\u00b0N, 18\u00b0E - 55\u00b0E), relative to the difference between the daily climatological average and the 90th percentile above the climatological mean. Thus, when the index is above 1 the area is in a state of a marine heatwave, and when the index is below -1 the area is in a state of a marine cold spell, following the definition by Hobday et al. (2016). For further details, see Lien et al. (2024).\"\"\n\nCONTEXT\nAnomalously warm oceanic events, often termed marine heatwaves, can potentially impact the ecosystem in the affected region. The marine heatwave concept and terminology was systemized by Hobday et al. (2016), and a generally adopted definition of a marine heatwave is a period of more than five days where the temperature within a region exceeds the 90th percentile of the seasonally varying climatological average temperature for that region. The Barents Sea region has warmed considerably during the most recent climatological average period (1991-2020) due to a combination of climate warming and positive phase of regional climate variability (e.g., Lind et al., 2018 ; Skagseth et al., 2020 ; Smedsrud et al., 2022), with profound consequences for marine life where boreal species are moving northward at the expense of arctic species (e.g., Fossheim et al., 2015; Oziel et al., 2020; Husson et al., 2022).\n\nKEY FINDINGS\n\nThere is a clear tendency of reduced frequency and intensity of marine cold spells, and a tendency towards increased frequency and intensity of marine heat waves in the Barents Sea. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00346\n\nReferences:\n\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Hobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Gupta AS, Wernberg T. 2016. A hierarchical approach to defining marine heatwaves. Progr. Oceanogr., 141, 227-238\n* Husson B, Lind S, Fossheim M, Kato-Solvang H, Skern-Mauritzen M, P\u00e9cuchet L, Ingvaldsen RB, Dolgov AV, Primicerio R. 2022. Successive extreme climatic events lead to immediate, large-scale, and diverse responses from fish in the Arctic. Global Change Biol, 28, 3728-3744\n* Lien VS, Raj RP, Chatterjee S. 2024. Surface and bottom marine heatwave characteristics in the Barents Sea: a model study. State of the Planet (in press)\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import. Nat Clim Change, 8, 634-639\n* Oziel L, Baudena A, Ardyna M, Massicotte P, Randelhoff A, Sallee J-B, Ingvaldsen RB, Devred E, Babin M. 2020. Faster Atlantic currents drive poleward expansion of temperate phytoplankton in the Arctic Ocean. Nat Commun., 11(1), 1705, doi:10.1038/s41467-020-15485-5\n* Skagseth \u00d8, Eldevik T, \u00c5rthun M, Asbj\u00f8rnsen H, Lien VS, Smedsrud LH. 2020. Reduced efficiency of the Barents Sea cooling machine. Nat Clim Change, doi.org/10.1038/s41558-020-0772-6\n* Smedsrud LH, Muilwijk M, Brakstad A, Madonna E, Lauvset SK, Spensberger C, Born A, Eldevik T, Drange H, Jeansson E, Li C, Olsen A, Skagseth \u00d8, Slater DA, Straneo F, V\u00e5ge K, \u00c5rthun M. 2022.\n* Nordic Seas heat loss, Atlantic inflow, and Arctic sea ice cover over the last century. Rev Geophys., 60, e2020RG000725\n", "doi": "10.48670/mds-00346", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mhw-index-bottom,mhw-index-surface,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-mhw-arctic-area-averaged-anomalies,temperature-bottom,temperature-surface,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Marine Heatwave Index in the Barents Sea from Reanalysis"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"abstract": "DEFINITION\n\nSignificant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr.\n\nCONTEXT\n\nGrasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends.\n\nKEY FINDINGS\n\nOver the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 \u00b1 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 \u00b1 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 \u00b1 1.5 cm/year (a), with the mean SWH increasing by 0.7 \u00b1 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 \u00b1 2.15 cm/year (a) and the mean SWH trend is 1.4 \u00b1 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024).\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00352\n\nReferences:\n\n* Barnard, P. L., Hoover, D., Hubbard, D. M., Snyder, A., Ludka, B. C., Allan, J., Kaminsky, G. M., Ruggiero, P., Gallien, T. W., Gabel, L., McCandless, D., Weiner, H. M., Cohn, N., Anderson, D. L., and Serafin, K. A.: Extreme oceanographic forcing and coastal response due to the 2015\u20132016 El Ni\u00f1o, Nature Communications, 8, https://doi.org/10.1038/ncomms14365, 2017.\n* Donelan, M. A., Drennan, W. M., and Katsaros, K. B.: The air\u2013sea momentum flux in conditions of wind sea and swell, Journal of Physical Oceanography, 27, 2087\u20132099, https://doi.org/10.1175/1520-0485(1997)0272.0.co;2, 1997.\n* Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Dosio, A., and Feyen, L.: Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns, Geophysical Research Letters, 44, 2416\u20132426, https://doi.org/10.1002/2016gl072488, 2017\n* Thomas, S., Babanin, A. V., Walsh, K. J. E., Stoney, L., and Heil, P.: Effect of wave-induced mixing on Antarctic sea ice in a high-resolution ocean model, Ocean Dynamics, 69, 737\u2013746, https://doi.org/10.1007/s10236-019-01268-0, 2019.\n* Gommenginger, C. P., Srokosz, M. A., Challenor, P. G., and Cotton, P. D.: Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset, IEEE Transactions on Geoscience and Remote Sensing, 40, 251\u2013260, https://doi.org/10.1109/36.992782, 2002.\n* Storlazzi, C. D., Gingerich, S. B., van Dongeren, A., Cheriton, O. M., Swarzenski, P. W., Quataert, E., Voss, C. I., Field, D. W., Annamalai, H., Piniak, G. A., and McCall, R.: Most atolls will be uninhabitable by the mid-21st century because of sea level rise exacerbating wave-driven flooding, Science Advances, 4, https://doi.org/10.1126/sciadv.aap9741, 2018.\n* Husson, R., Charles, E.: EU Copernicus Marine Service Product User Manual for the Global Ocean L 4 Significant Wave Height From Reprocessed Satellite Measurements Product, WAVE_GLO_PHY_SWH_L4_MY_014_007, Issue 2.0, Mercator Ocean International, https://documentation.marine.copernicus.eu/PUM/CMEMS-WAV-PUM-014-005-006-007.pdf, last access: 21 July 2023, 2021 Laloue, A., Ghantous, M., Faug\u00e8re, Y., Dalphinet. A., Aouf, L.: Statistical analysis of global ocean significant wave heights from satellite altimetry over the past two decades. OSR-8 (under review)\n", "doi": "10.48670/mds-00352", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-seastate-global-swh-mean-and-p95-obs,satellite-observation,sea-surface-significant-height,sea-surface-significant-height-seasonal-number-of-observations,sea-surface-significant-height-trend-uncertainty-95percentile,sea-surface-significant-height-trend-uncertainty-mean,sea-surface-wave-significant-height-95percentile-trend,sea-surface-wave-significant-height-mean-trend,sea-surface-wave-significant-height-seasonal-95percentile,sea-surface-wave-significant-height-seasonal-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean, extreme and mean significant wave height trends from satellite observations - seasonal trends"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_baltic_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Baltic Sea is affected by vertical land motion due to the Glacial Isostatic Adjustment (Ludwigsen et al., 2020) and consequently relative sea level trends (as measured by tide gauges) have been shown to be strongly negative, especially in the northern part of the basin. On the other hand, Baltic Sea absolute sea level trends (from altimetry-based observations) show statistically significant positive trends (Passaro et al., 2021).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\nUp to 45 stations fulfill the completeness index criteria in this region, a few less than in 2020 (51). The spatial variation of the mean 99th percentiles follow the tidal range pattern, reaching its highest values in the northern end of the Gulf of Bothnia (e.g.: 0.81 and 0.78 m above mean sea level at the Finnish stations Kemi and Oulu, respectively) and the inner part of the Gulf of Finland (e.g.: 0.83 m above mean sea level in St. Petersburg, Russia). Smaller tides and therefore 99th percentiles are found along the southeastern coast of Sweden, between Stockholm and Gotland Island (e.g.: 0.42 m above mean sea level in Visby, Gotland Island-Sweden). Annual 99th percentiles standard deviation ranges between 3-5 cm in the South (e.g.: 3 cm in Korsor, Denmark) to 10-13 cm in the Gulf of Finland (e.g.: 12 cm in Hamina). Negative anomalies of 2022 99th percentile are observed in the northern part of the basin, in the Gulf of Bothnia, in the inner part of the Gulf of Finland and in Lolland Island stations (Denmark) reaching maximum values of -12 cm in Kemi, -9 cm in St. Petersburg and -8 cm in Rodby, respectively.. Positive anomalies of 2022 99th percentile are however found in the central and southeastern parts of the basin, with maximum values reaching 7 cm in Paldisky (Estonia) and Slipshavn (Denmark). \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00203\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Passaro M, M\u00fcller F L, Oelsmann J, Rautiainen L, Dettmering D, Hart-Davis MG, Abulaitijiang A, Andersen, OB, H\u00f8yer JL, Madsen, KS, Ringgaard IM, S\u00e4rkk\u00e4 J, Scarrott R, Schwatke C, Seitz F, Tuomi L, Restano M, and Benveniste J. 2021. Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A Revisit, Front Mar Sci, 8, 647607, https://doi.org/10.3389/FMARS.2021.647607/BIBTEX.\n", "doi": "10.48670/moi-00203", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-baltic-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe completeness index criteria is fulfilled by 57 stations in 2021, two more than those available in 2021 (55), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol and English Channels (e.g.: 6.26, 5.58 and 5.17 m above mean sea level in Newport, St. Malo and St. Helier, respectively). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-2 cm in the Canary Islands to 12 cm in Newport (Bristol Channel). Although less pronounced and general than in 2021, negative or close to zero anomalies of 2022 99th percentile still prevail throughout the region this year reaching up to -14 cm in St.Helier (Jersey Island, Channel Islands), or -12 cm in St. Malo. Positive anomalies of 2022 99th percentile are found in the northern part of the region (Irish eastern coast and west Scotland coast) and at a couple of stations in Southern England, with values reaching 9 cm in Bangor (Northern Ireland) and 6 cm in Portsmouth (South England). \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00253\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Chafik L, Nilsen JE\u00d8, Dangendorf S et al. 2019. North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era. Sci Rep 9, 1041. https://doi.org/10.1038/s41598-018-37603-6\n", "doi": "10.48670/moi-00253", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-ibi-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_medsea_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Mediterranean Sea shows statistically significant positive sea level trends over the whole basin. However, at sub-basin scale sea level trends show spatial variability arising from local circulation (Calafat et al., 2022; Meli et al., 2023).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe completeness index criteria is fulfilled in this region by 38 stations, 26 more than in 2021, significantly increasing spatial coverage with new in situ data in the central Mediterranean Sea, primarily from Italian stations. The mean 99th percentiles reflect the spatial variability of the tide, a microtidal regime, along the Spanish, French and Italian coasts, ranging from around 0.20 m above mean sea level in Sicily and the Balearic Islands (e.g.: 0.22 m in Porto Empedocle, 0.23 m in Ibiza)) to around 0.60 m above mean sea level in the Northern Adriatic Sea (e.g.: 0.63 m in Trieste, 0.61 m in Venice). . The annual 99th percentiles standard deviation ranges between 2 cm in M\u00e1laga and Motril (South of Spain) to 8 cm in Marseille. . The 2022 99th percentile anomalies present negative values mainly along the Spanish coast (as in 2021) and in the islands of Corsica and Sardinia (Western part of the region), while positive values are observed along the Eastern French Mediterranean coast and at most of the Italian stations (closer to the central part of the region), with values ranging from -4 cm in M\u00e1laga and Motril (Spain) to +5 cm in Ancona (Italy). \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00265\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J Geophys Res Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n* Legeais J-F, Llovel W, Melet A, and Meyssignac B. 2020. Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, s77\u2013s82, https://doi.org/10.1080/1755876X.2020.1785097.\n* Meli M, Camargo CML, Olivieri M, Slangen ABA, and Romagnoli C. 2023. Sea-level trend variability in the Mediterranean during the 1993\u20132019 period, Front Mar Sci, 10, 1150488, https://doi.org/10.3389/FMARS.2023.1150488/BIBTEX.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n", "doi": "10.48670/moi-00265", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sl-medsea-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe North West Shelf area presents positive sea level trends with higher trend estimates in the German Bight and around Denmark, and lower trends around the southern part of Great Britain (Dettmering et al., 2021).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe completeness index criteria is fulfilled in this region by 34 stations, eight more than in 2021 (26), most of them from Norway. The mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in East England and at the entrance of the English channel, and lowest values along the Danish and Swedish coasts, ranging from the 3.08 m above mean sea level in Immingan (East England) to 0.57 m above mean sea level in Ringhals (Sweden) and Helgeroa (Norway). The standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in Harwich, 3 cm in Dunkerke) and 7-8 cm in the eastern part and the Kattegat (e.g. 8 cm in Stenungsund, Sweden).. The 99th percentile anomalies for 2022 show positive values in Southeast England, with a maximum value of +8 cm in Lowestoft, and negative values in the eastern part of the Kattegat, reaching -8 cm in Oslo. The remaining stations exhibit minor positive or negative values. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00272\n\nReferences:\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Dettmering D, M\u00fcller FL, Oelsmann J, Passaro M, Schwatke C, Restano M, Benveniste J, and Seitz F. 2021. North SEAL: A new dataset of sea level changes in the North Sea from satellite altimetry, Earth Syst Sci Data, 13, 3733\u20133753, https://doi.org/10.5194/ESSD-13-3733-2021.\n", "doi": "10.48670/moi-00272", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sl-northwestshelf-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Baltic Sea has showed in the last two decades a warming trend across the whole basin with more frequent and severe heat waves (IPCC, 2022). This trend is significantly higher when considering only the summer season, which would affect the high extremes (e.g. H\u00f8yer and Karagali, 2016).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area go from 19.6\u00baC in Tallinn station to 21.4\u00baC in Rohukula station, and the standard deviation ranges between 1\u00baC and 5.4\u00baC reached in the Estonian Coast.\nResults for this year show either positive or negative low anomalies in the Coast of Sweeden (-0.7/+0.5\u00baC) within the standard deviation margin and a general positive anomaly in the rest of the region. This anomaly is noticeable in Rohukula and Virtsu tide gauges (Estonia) with +3.9\u00baC, but inside the standard deviation in both locations. In the South Baltic two stations, GreifswalderOie and Neustadt, reach an anomaly of +2\u00baC, but around the standard deviation.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00204\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00204", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-baltic-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Iberia Biscay Ireland area is characterized by a great complexity in terms of processes that take place in it. The sea surface temperature varies depending on the latitude with higher values to the South. In this area, the clear warming trend observed in other European Seas is not so evident. The northwest part is influenced by the refreshing trend in the North Atlantic, and a mild warming trend has been observed in the last decade (Pisano et al. 2020).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present a range from 16-20\u00baC in the Southwest of the British Isles and the English Channel, 19-21\u00baC in the West of Galician Coast, 21-23\u00baC in the south of Bay of Biscay, 23.5\u00baC in the Gulf of Cadiz to 24.5\u00baC in the Canary Island. The standard deviations are between 0.5\u00baC and 1.3\u00baC in the region except in the English Channel where the standard deviation is higher, reaching 3\u00baC.\nResults for this year show either positive or negative low anomalies below the 45\u00ba parallel, with a slight positive anomaly in the Gulf of Cadiz and the Southeast of the Bay of Biscay over 1\u00baC. In the Southwest of the British Isles and the English Channel, the anomaly is clearly positive, with some stations with an anomaly over 2\u00baC, but inside the standard deviation in the area. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00255\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00255", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-ibi-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann et al., 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Mediterranean Sea has showed a constant increase of the SST in the last three decades across the whole basin with more frequent and severe heat waves (Juza et al., 2022). Deep analyses of the variations have displayed a non-uniform rate in space, being the warming trend more evident in the eastern Mediterranean Sea with respect to the western side. This variation rate is also changing in time over the three decades with differences between the seasons (e.g. Pastor et al. 2018; Pisano et al. 2020), being higher in Spring and Summer, which would affect the extreme values.\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present values from 25\u00baC in Ionian Sea and 26\u00ba in the Alboran sea and Gulf of Lion to 27\u00baC in the East of Iberian Peninsula. The standard deviation ranges from 0.6\u00baC to 1.2\u00baC in the Western Mediterranean and is around 2.2\u00baC in the Ionian Sea.\nResults for this year show a slight negative anomaly in the Ionian Sea (-1\u00baC) inside the standard deviation and a clear positive anomaly in the Western Mediterranean Sea reaching +2.2\u00baC, almost two times the standard deviation in the area.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00267\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-z.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00267", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sst-medsea-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\nCONTEXT\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. H\u00f8yer and Karagali, 2016).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\nThe mean 99th percentiles showed in the area present a range from 14-16\u00baC in the North of the British Isles, 16-19\u00baC in the Southwest of the North Sea to 19-21\u00baC around Denmark (Helgoland Bight, Skagerrak and Kattegat Seas). The standard deviation ranges from 0.5-1\u00baC in the North of the British Isles, 0.5-2\u00baC in the Southwest of the North Sea to 1-3\u00baC in the buoys around Denmark.\nResults for this year show either positive or negative low anomalies around their corresponding standard deviation in in the North of the British Isles (-0.5/+0.6\u00baC) and a clear positive anomaly in the other two areas reaching +2\u00baC even when they are around the standard deviation margin.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00274\n\nReferences:\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer JL, Karagali I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00274", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sst-northwestshelf-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the Baltic Sea, the particular bathymetry and geography of the basin intensify the seasonal and spatial fluctuations in wave conditions. No clear statistically significant trend in the sea state has been appreciated except a rising trend in significant wave height in winter season, linked with the reduction of sea ice coverage (Soomere, 2023; Tuomi et al., 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles shown in the area are from 3 to 4 meters and the standard deviation ranges from 0.2 m to 0.4 m. \nResults for this year show a slight positive or negative anomaly in all the stations, from -0.24 m to +0.36 m, inside the margin of the standard deviation.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00199\n\nReferences:\n\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n", "doi": "10.48670/moi-00199", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-wave-baltic-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"abstract": "DEFINITION\n\nExtreme wave characteristics are computed by analysing single storm events and their long-term means and trends based on the product BLKSEA_MULTIYEAR_WAV_007_006. These storm events were detected using the method proposed by Weisse and G\u00fcnther (2007). The basis of the method is the definition of a severe event threshold (SET), which we define as the 99th percentile of the significant wave height (SWH). Then, the exceedance and shortfall of the SWH at every grid point was determined and counted as a storm event. The analysis of extreme wave events also comprises the following three parameters but are not part of this OMI. The time period between each exceedance and shortfall of the SET is the lifetime of an event. The difference in the maximum SWH of each event and the SET is defined as the event intensity. The geographic area of storm events and exceedance of the SET are defined as the maximum event area. The number, lifetime, and intensity of events were averaged over each year. Finally, the yearly values were used to compute the long-term means. In addition to these parameters, we estimated the difference (anomaly) of the last available year in the multiyear dataset compared against the long-term average as well as the linear trend. To show multiyear variability, each event, fulfilling the above-described definition, is considered in the statistics. This was done independent of the events\u2019 locations within the domain. To obtain long-term trends, a linear regression was applied to the yearly time series. The statistics are based on the period 1950 to -1Y. This approach has been presented in Staneva et al. (2022) for the area of the Black Sea and was later adapted to the South Atlantic in Gramcianinov et al. (2023a, 2023b).\n\nCONTEXT\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect and in the frame of climate change, which also affects regional wind patterns and therewith the wave climate, it is important for coastal regions to gain insights into wave extreme characteristics and the related trends. These insights are crucial to initiate necessary abatement strategies especially in combination with extreme wave power statistics (see OMI OMI_EXTREME_WAVE_BLKSEA_wave_power).\n\nKEY FINDINGS\n\nThe yearly mean number of storm events is rather low in regions where the average annual lifetime and intensity of storms are high. In contrast, the number of events is high where their lifetime and intensity are low. While the southwest Black Sea is exposed to yearly mean storm event numbers of below the long-term spatial averages (7.3 events), it is observed that the yearly mean lifetime of the events in the same region is higher than the long-term averages. The extreme wave statistics based on the 99th percentile threshold of the significant wave height (SWH) are very similar to the wind sea wave parameter, and the swell contribution is much lower. On overall, the yearly trend of the storm events is slightly negative (-0.01 events/year) with two areas showing positive trends located in the very east and west. In terms of the mean number of storm events in 2022, a pronounced area with positive values is located along the eastern coast and another in the western basin. The rest of the Black Sea area mostly experienced less events in 2022.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00348\n\nReferences:\n\n* Gramcianinov, C.B., Staneva, J., de Camargo, R., & da Silva Dias, P.L. (2023a): Changes in extreme wave events in the southwestern South Atlantic Ocean. Ocean Dynamics, doi:10.1007/s10236-023-01575-7\n* Gramcianinov, C.B., Staneva, J., Souza, C.R.G., Linhares, P., de Camargo, R., & da Silva Dias, P.L. (2023b): Recent changes in extreme wave events in the south-western South Atlantic. In: von Schuckmann, K., Moreira, L., Le Traon, P.-Y., Gr\u00e9goire, M., Marcos, M., Staneva, J., Brasseur, P., Garric, G., Lionello, P., Karstensen, J., & Neukermans, G. (eds.): 7th edition of the Copernicus Ocean State Report (OSR7). Copernicus Publications, State Planet, 1-osr7, 12, doi:10.5194/sp-1-osr7-12-2023\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n* Weisse, R., & G\u00fcnther, H. (2007): Wave climate and long-term changes for the Southern North Sea obtained from a high-resolution hindcast 1958\u20132002. Ocean Dynamics, 57(3), 161\u2013172, doi:10.1007/s10236-006-0094-x\n", "doi": "10.48670/mds-00348", "instrument": null, "keywords": "2022-anomaly-of-yearly-mean-number-of-wave-storm-events,black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-wave-blksea-recent-changes,swh,weather-climate-and-seasonal-forecasting,wind-speed,yearly-mean-number-of-wave-storm-events,yearly-trend-of-mean-number-of-wave-storm-events", "license": "proprietary", "missionStartDate": "1986-01-30T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea extreme wave events"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"abstract": "DEFINITION\n\nThe Wave Power P is defined by:\nP=(\u03c1g^2)/64\u03c0 H_s^2 T_e\nWhere \u03c1 is the surface water density, g the acceleration due to gravity, Hs the significant wave height (VHM0), and Te the wave energy period (VTM10) also abbreviated with Tm-10. The extreme statistics and related recent changes are defined by (1) the 99th percentile of the Wave Power, (2) the linear trend of 99th percentile of the Wave Power, and (3) the difference (anomaly) of the 99th percentile of the last available year in the multiyear dataset BLKSEA_MULTIYEAR_WAV_007_006 compared against the long-term average. The statistics are based on the period 1950 to -1Y and are obtained from yearly averages. This approach has been presented in Staneva et al. (2022).\n\nCONTEXT\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect, the Wave Power is a crucial quantity to plan and operate wave energy converters (WEC) and for coastal and offshore structures. For both reliable estimates of long-term Wave Power extremes are important to secure a high efficiency and to guarantee a robust and secure design, respectively.\n\nKEY FINDINGS\n\nThe 99th percentile of wave power mean patterns are overall consistent with the respective significant wave height pattern. The maximum 99th percentile of wave power is observed in the southwestern Black Sea. Typical values of in the eastern basin are ~20 kW/m and in the western basin ~45 kW/m. The trend of the 99th percentile of the wave power is decreasing with typical values of 50 W/m/year and a maximum of 120 W/m/year, which is equivalent to a ~25% decrease over whole period with respect to the mean. The pattern of the anomaly of the 99th percentile of wave power in 2022 correlates well with that of the wind speed anomaly in 2022, revealing a negative wave-power anomaly in the western Black Sea (P_2020<P_average) and a mix of positive (P_2020<P_average) and negative anomalies in the eastern basin, where the positive anomalies are mainly present in coastal regions.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00350\n\nReferences:\n\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n", "doi": "10.48670/mds-00350", "instrument": null, "keywords": "2022-anomaly-of-yearly-average-of-99th-percentile-of-wave-power,black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-wave-blksea-wave-power,swh,weather-climate-and-seasonal-forecasting,wind-speed,yearly-average-of-99th-percentile-of-wave-power,yearly-trend-of-99th-percentile-of-wave-power", "license": "proprietary", "missionStartDate": "1986-01-30T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea wave power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al. (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young, 2011; Young & Ribal, 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present a wide range from 2-3.5m in the Canary Island with 0.1-0.3 m of standard deviation (std), 3.5m in the Gulf of Cadiz with 0.5m of std, 3-6m in the English Channel and the Irish Sea with 0.5-0.6m of std, 4-7m in the Bay of Biscay with 0.4-0.9m of std to 8-10m in the West of the British Isles with 0.7-1.4m of std. \nResults for this year show close to zero anomalies in the Canary Island (-0.2/+0.1m), the Gulf of Cadiz (-0.2m) and the English Channel and the Irish Sea (-0.1/+0.1), a general slight negative anomaly in the Bay of Biscay reaching -0.7m but inside the range of the standard deviation, and a positive anomaly (+1.0/+1.55m) in the West of the British Isles, barely out of the standard deviation range in the area. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00250\n\nReferences:\n\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Young IR, Zieger S, and Babanin AV. 2011. Global Trends in Wind Speed and Wave Height, Science, 332, 451\u2013455, https://doi.org/10.1126/science.1197219\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "doi": "10.48670/moi-00250", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-wave-ibi-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nFor the Mediterranean Sea an interesting publication (De Leo et al., 2024) analyses recent studies in this basin showing the variability in the different results and the difficulties to reach a consensus, especially in the mean wave conditions. The only significant conclusion is the positive trend in extreme values for the western Mediterranean Sea and in particular in the Gulf of Lion and in the Tyrrhenian Sea.\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS\n\nThe mean 99th percentiles showed in the area present a range from 1.5-3.5 in the Gibraltar Strait and Alboran Sea with 0.25-0.55 of standard deviation (std), 2-5m in the East coast of the Iberian Peninsula and Balearic Islands with 0.2-0.4m of std, 3-4m in the Aegean Sea with 0.4-0.6m of std to 3-5m in the Gulf of Lyon with 0.3-0.5m of std. \nResults for this year show a positive anomaly in the Gibraltar Strait (+0.8m), and a negative anomaly in the Aegean Sea (-0.8m), the East Coast of the Iberian Peninsula (-0.7m) and in the Gulf of Lyon (-0.6), all of them slightly over the standard deviation in the respective areas.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00263\n\nReferences:\n\n* De Leo F, Briganti R & Besio G. 2024. Trends in ocean waves climate within the Mediterranean Sea: a review. Clim Dyn 62, 1555\u20131566. https://doi.org/10.1007/s00382-023-06984-4\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "doi": "10.48670/moi-00263", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-wave-medsea-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"abstract": "DEFINITION\n\nThe OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\nCONTEXT\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al. (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young et al., 2011; Young & Ribal, 2019).\n\nCOPERNICUS MARINE SERVICE KEY FINDINGS \n\nThe mean 99th percentiles showed in the area present a wide range from 2.5 meters in the English Channel with 0.3m of standard deviation (std), 3-5m in the southern and central North Sea with 0.3-0.6m of std, 4 meters in the Skagerrak Strait with 0.6m of std, 7.5m in the northern North Sea with 0.6m of std to 8 meters in the North of the British Isles with 0.6m of std. \nResults for this year show either low positive or negative anomalies between -0.6m and +0.4m, inside the margin of the standard deviation for all the stations. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00270\n\nReferences:\n\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Timmermans BW, Gommenginger CP, Dodet G, Bidlot JR. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys, Geophys. Res. Lett., \u2116 47. https://doi.org/10.1029/2019GL086880\n* Young IR, Zieger S, and Babanin AV. 2011. Global Trends in Wind Speed and Wave Height, Science, 332, 451\u2013455, https://doi.org/10.1126/science.1197219\n* Young IR & Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548\u2013552. https://doi.org/10.1126/science.aav9527\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n", "doi": "10.48670/moi-00270", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-wave-northwestshelf-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf significant wave height extreme variability mean and anomaly (observations)"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms to remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deasonalised time series is derived by subtracting the seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. \n\nCONTEXT\n\nPhytoplankton \u2013 and chlorophyll concentration , which is a measure of phytoplankton concentration \u2013 respond rapidly to changes in environmental conditions. Chlorophyll concentration is highly seasonal in the Arctic Ocean region due to a strong dependency on light and nutrient availability, which in turn are driven by seasonal sunlight and sea-ice cover dynamics, as well as changes in mixed layer. In the past two decades, an increase in annual net primary production by Arctic Ocean phytoplankton has been observed and linked to sea-ice decline (Arrigo and van Dijken, 2015); in the same line Kahru et al. (2011) have showed that chlorophyll concentration peaks are appearing increasingly earlier in the year in parts of the Arctic. It is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales in the area, in order to be able to separate potential long-term climate signals from natural variability in the short term.\n\nCMEMS KEY FINDINGS\n\nWhile the overall trend average for the 1997-2021 period in the Arctic Sea is positive (0.86 \u00b1 0.17 % per year), a continued plateau in the linear trend, initiated in 2013 is observed in the time series extension, with both the amplitude and the baseline of the cycle continuing to decrease during 2021 as reported for previous years (Sathyendranath et al., 2018). In particular, the annual average for the region in 2021 is 1.05 mg m-3 - a 30% reduction on 2020 values. There appears to be no appreciable changes in the timings or amplitude of the 2021 spring and autumn blooms. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00188\n\nReferences:\n\n* Arrigo, K. R., & van Dijken, G. L., 2015. Continued increases in Arctic Ocean primary production. Progress in Oceanography, 136, 60\u201370. doi: 10.1016/j.pocean.2015.05.002.\n* Kahru, M., Brotas, V., Manzano\u2010Sarabia, M., Mitchell, B. G., 2011. Are phytoplankton blooms occurring earlier in the Arctic? Global Change Biology, 17(4), 1733\u20131739. doi:10.1111/j.1365\u20102486.2010.02312.x.\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018. 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. 10.1093/icesjms/fsp107.\n", "doi": "10.48670/moi-00188", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,multi-year,oceanographic-geographical-features,omi-health-chl-arctic-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deseasonalised time series is derived by subtracting the mean seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. \n\nCONTEXT\n\nPhytoplankton \u2013 and chlorophyll concentration as a proxy for phytoplankton \u2013 respond rapidly to changes in environmental conditions, such as temperature, light and nutrients availability, and mixing. The response in the North Atlantic ranges from cyclical to decadal oscillations (Henson et al., 2009); it is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the North Atlantic are known to respond to climate variability associated with the North Atlantic Oscillation (NAO), with the initiation of the spring bloom showing a nominal correlation with sea surface temperature and the NAO index (Zhai et al., 2013).\n\nCMEMS KEY FINDINGS\n\nWhile the overall trend average for the 1997-2021 period in the North Atlantic Ocean is slightly positive (0.16 \u00b1 0.12 % per year), an underlying low frequency harmonic signal can be seen in the deseasonalised data. The annual average for the region in 2021 is 0.25 mg m-3. Though no appreciable changes in the timing of the spring and autumn blooms have been observed during 2021, a lower peak chlorophyll concentration is observed in the timeseries extension. This decrease in peak concentration with respect to the previous year is contributing to the reduction trend.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00194\n\nReferences:\n\n* Henson, S. A., Dunne, J. P. , and Sarmiento, J. L., 2009, Decadal variability in North Atlantic phytoplankton blooms, J. Geophys. Res., 114, C04013, doi:10.1029/2008JC005139.\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n* Zhai, L., Platt, T., Tang, C., Sathyendranath, S., Walne, A., 2013. The response of phytoplankton to climate variability associated with the North Atlantic Oscillation, Deep Sea Research Part II: Topical Studies in Oceanography, 93, 159-168, doi: 10.1016/j.dsr2.2013.04.009.\n", "doi": "10.48670/moi-00194", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-atlantic-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication": {"abstract": "DEFINITION\n\nWe have derived an annual eutrophication and eutrophication indicator map for the North Atlantic Ocean using satellite-derived chlorophyll concentration. Using the satellite-derived chlorophyll products distributed in the regional North Atlantic CMEMS MY Ocean Colour dataset (OC- CCI), we derived P90 and P10 daily climatologies. The time period selected for the climatology was 1998-2017. For a given pixel, P90 and P10 were defined as dynamic thresholds such as 90% of the 1998-2017 chlorophyll values for that pixel were below the P90 value, and 10% of the chlorophyll values were below the P10 value. To minimise the effect of gaps in the data in the computation of these P90 and P10 climatological values, we imposed a threshold of 25% valid data for the daily climatology. For the 20-year 1998-2017 climatology this means that, for a given pixel and day of the year, at least 5 years must contain valid data for the resulting climatological value to be considered significant. Pixels where the minimum data requirements were met were not considered in further calculations.\n We compared every valid daily observation over 2021 with the corresponding daily climatology on a pixel-by-pixel basis, to determine if values were above the P90 threshold, below the P10 threshold or within the [P10, P90] range. Values above the P90 threshold or below the P10 were flagged as anomalous. The number of anomalous and total valid observations were stored during this process. We then calculated the percentage of valid anomalous observations (above/below the P90/P10 thresholds) for each pixel, to create percentile anomaly maps in terms of % days per year. Finally, we derived an annual indicator map for eutrophication levels: if 25% of the valid observations for a given pixel and year were above the P90 threshold, the pixel was flagged as eutrophic. Similarly, if 25% of the observations for a given pixel were below the P10 threshold, the pixel was flagged as oligotrophic.\n\nCONTEXT\n\nEutrophication is the process by which an excess of nutrients \u2013 mainly phosphorus and nitrogen \u2013 in a water body leads to increased growth of plant material in an aquatic body. Anthropogenic activities, such as farming, agriculture, aquaculture and industry, are the main source of nutrient input in problem areas (Jickells, 1998; Schindler, 2006; Galloway et al., 2008). Eutrophication is an issue particularly in coastal regions and areas with restricted water flow, such as lakes and rivers (Howarth and Marino, 2006; Smith, 2003). The impact of eutrophication on aquatic ecosystems is well known: nutrient availability boosts plant growth \u2013 particularly algal blooms \u2013 resulting in a decrease in water quality (Anderson et al., 2002; Howarth et al.; 2000). This can, in turn, cause death by hypoxia of aquatic organisms (Breitburg et al., 2018), ultimately driving changes in community composition (Van Meerssche et al., 2019). Eutrophication has also been linked to changes in the pH (Cai et al., 2011, Wallace et al. 2014) and depletion of inorganic carbon in the aquatic environment (Balmer and Downing, 2011). Oligotrophication is the opposite of eutrophication, where reduction in some limiting resource leads to a decrease in photosynthesis by aquatic plants, reducing the capacity of the ecosystem to sustain the higher organisms in it. \nEutrophication is one of the more long-lasting water quality problems in Europe (OSPAR ICG-EUT, 2017), and is on the forefront of most European Directives on water-protection. Efforts to reduce anthropogenically-induced pollution resulted in the implementation of the Water Framework Directive (WFD) in 2000. \n\nCMEMS KEY FINDINGS\n\nThe coastal and shelf waters, especially between 30 and 400N that showed active oligotrophication flags for 2020 have reduced in 2021 and a reversal to eutrophic flags can be seen in places. Again, the eutrophication index is positive only for a small number of coastal locations just north of 40oN in 2021, however south of 40oN there has been a significant increase in eutrophic flags, particularly around the Azores. In general, the 2021 indicator map showed an increase in oligotrophic areas in the Northern Atlantic and an increase in eutrophic areas in the Southern Atlantic. The Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area (OSPAR ICG-EUT, 2017) reported an improvement from 2008 to 2017 in eutrophication status across offshore and outer coastal waters of the Greater North Sea, with a decrease in the size of coastal problem areas in Denmark, France, Germany, Ireland, Norway and the United Kingdom.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00195\n\nReferences:\n\n* Anderson, D.M., Glibert, P.M. & Burkholder, J.M. (2002). Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences. Estuaries 25, 704\u2013726 /10.1007/BF02804901.\n* Balmer, M.B., Downing, J.A. (2011), Carbon dioxide concentrations in eutrophic lakes: undersaturation implies atmospheric uptake, Inland Waters, 1:2, 125-132, 10.5268/IW-1.2.366.\n* Breitburg, D., Levin, L.A., Oschlies, A., Gr\u00e9goire, M., Chavez, F.P., Conley, D.J., Gar\u00e7on, V., Gilbert, D., Guti\u00e9rrez, D., Isensee, K. and Jacinto, G.S. (2018). Declining oxygen in the global ocean and coastal waters. Science, 359 (6371), p.eaam7240.\n* Cai, W., Hu, X., Huang, W. (2011) Acidification of subsurface coastal waters enhanced by eutrophication. Nature Geosci 4, 766\u2013770, 10.1038/ngeo1297.\n* Galloway, J.N., Townsend, A.R., Erisman, J.W., Bekunda, M., Cai, Z., Freney, J. R., Martinelli, L. A., Seitzinger, S. P., Sutton, M. A. (2008). Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions, Science 320, 5878, 889-892, 10.1126/science.1136674.\n* Howarth, R.W., Anderson, D., Cloern, J., Elfring, C., Hopkinson, C., Lapointe, B., Malone, T., & Marcus, N., McGlathery, K., Sharpley, A., Walker, D. (2000). Nutrient pollution of coastal rivers, bays and seas. Issues in Ecology, 7.\n* Howarth, R.W., Marino, R. (2006). Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades, Limnology and Oceanography, 51(1, part 2), 10.4319/lo.2006.51.1_part_2.0364.\n* Jickells, T. D. (1998). Nutrient biogeochemistry of the coastal zone. Science 281, 217\u2013222. doi: 10.1126/science.281.5374.217\n* OSPAR ICG-EUT. Axe, P., Clausen, U., Leujak, W., Malcolm, S., Ruiter, H., Prins, T., Harvey, E.T. (2017). Eutrophication Status of the OSPAR Maritime Area. Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area.\n* Schindler, D. W. (2006) Recent advances in the understanding and management of eutrophication. Limnology and Oceanography, 51, 356-363.\n* Smith, V.H. (2003). Eutrophication of freshwater and coastal marine ecosystems a global problem. Environmental Science and Pollution Research, 10, 126\u2013139, 10.1065/espr2002.12.142.\n* Van Meerssche, E., Pinckney, J.L. (2019) Nutrient Loading Impacts on Estuarine Phytoplankton Size and Community Composition: Community-Based Indicators of Eutrophication. Estuaries and Coasts 42, 504\u2013512, 10.1007/s12237-018-0470-z.\n* Wallace, R.B., Baumann, H., Grear, J.S., Aller, R.B., Gobler, C.J. (2014). Coastal ocean acidification: The other eutrophication problem, Estuarine, Coastal and Shelf Science, 148, 1-13, 10.1016/j.ecss.2014.05.027.\n", "doi": "10.48670/moi-00195", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-atlantic-oceancolour-eutrophication,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Eutrophication from Observations Reprocessing"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi-Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b). \nMonthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\nCONTEXT\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, in the Baltic Sea phytoplankton is known to respond to the variations of SST in the basin associated with climate variability (Kabel et al. 2012).\n\nKEY FINDINGS\n\nThe Baltic Sea shows a slight positive trend over the 1997-2023 period, with a slope of 0.30\u00b10.49% per year, indicating a decrease compared to the previous release. The maxima and minima values are relatively consistent year-to-year, with the absolute maximum occurring in 2008 and the minima observed in 2004 and 2014. A decrease in the chlorophyll signal has been evident over the past two years.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00197\n\nReferences:\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D\u2019Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D\u2019Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b (accepted). Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals. Remote Sens. 2021, 13, x.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kabel K, Moros M, Porsche C, Neumann T, Adolphi F, Andersen TJ, Siegel H, Gerth M, Leipe T, Jansen E, Sinninghe Damste\u0301 JS. 2012. Impact of climate change on the health of the Baltic Sea ecosystem over the last 1000 years. Nat Clim Change. doi:10.1038/nclimate1595.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., et al., 2018. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1. Technical Report Centre for Environmental Data Analysis. doi:10.5285/9c334fbe6d424a708cf3c4cf0c6a53f5.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., Me\u0301lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n", "doi": "10.48670/moi-00197", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,multi-year,oceanographic-geographical-features,omi-health-chl-baltic-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThis product includes the Baltic Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory (PML) using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b).\nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014) and anthropogenic climate change. Eutrophication is one of the most important issues for the Baltic Sea (HELCOM, 2018), therefore the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nKEY FINDINGS\n\nOn average, the trend for the Baltic Sea over the 1997-2023 period is relatively flat (0.08%). The pattern of positive and negative trends is quite similar to the previous release, indicating a general decrease in absolute values. This result aligns with the findings of Sathyendranath et al. (2018), which show an increasing trend in chlorophyll concentration in most of the European Seas.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00198\n\nReferences:\n\n* Brando, V.E., A. Di Cicco, M. Sammartino, S. Colella, D D\u2019Alimonte, T. Kajiyama, S. Kaitala, J. Attila, 2021a. OCEAN COLOUR PRODUCTION CENTRE, Baltic Sea Observation Products. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-131to134.pdf).\n* Brando, V.E.; Sammartino, M; Colella, S.; Bracaglia, M.; Di Cicco, A; D\u2019Alimonte, D.; Kajiyama, T., Kaitala, S., Attila, J., 2021b. Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* HELCOM (2018): HELCOM Thematic assessment of eutrophication 2011-2016. Baltic Sea Environment Proceedings No. 156.\n* Kahru, M. and Elmgren, R.: Multidecadal time series of satellite- detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619 3633, doi:10.5194/bg-11-3619-2014, 2014.\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Zibordi, G., Berthon, J.-F., M\u00e9lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n", "doi": "10.48670/moi-00198", "instrument": null, "keywords": "baltic-sea,change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-baltic-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\nCONTEXT\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014, Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003).\n\nKEY FINDINGS\n\nIn the Black Sea, the trend average for the 1997-2023 period is negative (-1.13\u00b11.07% per year). Nevertheless, this negative trend is lower than the one estimated in the previous release (both 1997-2021 and 1997-2022). The negative trend is mainly due to the marked change on chlorophyll concentrations between 2002 and 2004. From 2004 onwards, minima and maxima are strongly variable year by year. However, on average, the minima/maxima variability can be considered quite constant with a continuous decrease of maxima from 2015 up to mid 2020 where signal seems to change again with relative high chlorophyll values in 2021, 2022 and especially in the last year (2023). The general negative trend in the Black Sea is also confirmed by the analysis of Sathyendranath et al. (2018), that reveals an increasing trend in chlorophyll concentration in all the European Seas, except for the Black Sea.\nDOI (product): \nhttps://doi.org/10.48670/moi-00211\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf)\n* Gregg, W. W., and C. S. Rousseaux, 2014. Decadal Trends in Global Pelagic Ocean Chlorophyll: A New Assessment Integrating Multiple Satellites, in Situ Data, and Models. Journal of Geophysical Research Oceans 119. doi:10.1002/2014JC010158.\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1109/\u00acLGRS.2018.2883539\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Oguz, T., Cokacar, T., Malanotte\u2010Rizzoli, P., & Ducklow, H. W. (2003). Climatic warming and accompanying changes in the ecological regime of the Black Sea during 1990s. Global Biogeochemical Cycles, 17(3).\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Zibordi, G., Berthon, J.-F., M\u00e9lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n* Zibordi, G., F. M\u00e9lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286, 2015.\n", "doi": "10.48670/moi-00211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,multi-year,oceanographic-geographical-features,omi-health-chl-blksea-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThis product includes the Black Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003). Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nKEY FINDINGS\n\nThe average Black Sea trend for the 1997-2023 period is absolutely similar to the previous ones (1997-2021 or 1997-2022) showing, on average, a trend of -1.24% per year. The trend is negative overall the basin, with weaker values in the central area, up to no significant trend percentages. The western side of the basin highlights markable negative trend. Negative values are shown in the Azov Sea with a strong inversion offshore the Don River. The overall negative trend in the map is in accordance with the results of Bengil and Mavruk (2018), that revealed a decreasing trend of chlorophyll during the post-eutrophication phase in the years 1997-2017.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00212\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Bengil, F., & Mavruk, S. (2018). Bio-optical trends of seas around Turkey: An assessment of the spatial and temporal variability. Oceanologia, 60(4), 488-499.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf)\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1109/\u00acLGRS.2018.2883539\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Oguz, T., Cokacar, T., Malanotte\u2010Rizzoli, P., & Ducklow, H. W. (2003). Climatic warming and accompanying changes in the ecological regime of the Black Sea during 1990s. Global Biogeochemical Cycles, 17(3).\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Zibordi, G., Berthon, J.-F., Me\u0301lin, F., and D\u2019Alimonte, D.: Cross- site consistent in situ measurements for satellite ocean color ap- plications: the BiOMaP radiometric dataset, Rem. Sens. Environ., 115, 2104\u20132115, 2011.\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286, 2015.\n", "doi": "10.48670/moi-00212", "instrument": null, "keywords": "black-sea,change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-blksea-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al. 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the North Atlantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.14% year-1 increase in area relative to 2000-01-01 values. This trend has decreased compared with the 1997-2019 trend of 0.39%, and is no longer statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.21% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This is a slightly lower rate of change compared with the -0.24% trend for the 1997-2020 period but is still statistically significant (p<0.05).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00226\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00226", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-nag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the North Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 1.75% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 2.17% reported for the 1997-2020 period. The trend is statistically significant (p<0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.26% year-1) in the North Pacific gyre relative to 2000-01-01 values. This trend is slightly less negative than the trend of -0.31% year-1 for the 1997-2020 period, though the sign of the trend remains unchanged and is statistically significant (p<0.05). It must be noted that the difference is small and within the uncertainty of the calculations, indicating that the trend is significant, however there may be no change associated with the timeseries extension.\nFor 2016, The Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00227\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00227", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-npg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the South Altantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.01% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.09% rate for the 1997-2020 trend (though within the uncertainties associated with the two estimates) and is not statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.73% year-1) relative to 2000-01-01 values. This is a significant increase from the trend of 0.35% year-1 for the 1997-2020 period and is statistically significant (p<0.05). The last two years of the timeseries show an increased deviation from the mean.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00228\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00228", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-sag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"abstract": "DEFINITION\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\nCONTEXT\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\nCMEMS KEY FINDINGS\n\nThe trend in the South Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.04% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.16% change for the 1997-2020 period, with the sign of the trend remaining unchanged and is not statistically significant (p<0.05). An underlying low frequency signal is observed with a period of approximately a decade.\nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.66% year-1) in the South Pacific gyre relative to 2000-01-01 values. This rate has increased compared to the rate of 0.45% year-1 for the 1997-2020 period and remains statistically significant (p<0.05). In the last two years of the timeseries, an increase in the variation from the mean is observed.\nFor 2016, the Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00229\n\nReferences:\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00229", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-spg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThe trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. \n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nCMEMS KEY FINDINGS\n\nThe average global trend for the 1997-2021 period was 0.51% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension. The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00230\n\nReferences:\n\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018b, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n", "doi": "10.48670/moi-00230", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"abstract": "DEFINITION\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\nCONTEXT\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Nin\u0303o Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016).\n\nKEY FINDINGS\n\nIn the Mediterranean Sea, the trend average for the 1997-2023 period is slightly negative (-0.73\u00b10.65% per year) emphasising the results obtained from previous release (1997-2022). This result is in contrast with the analysis of Sathyendranath et al. (2018) that reveals an increasing trend in chlorophyll concentration in all the European Seas. Starting from 2010-2011, except for 2018-2019, the decrease of chlorophyll concentrations is quite evident in the deseasonalized timeseries (in green), and in the maxima of the observations (grey line), starting from 2015. This attenuation of chlorophyll values of the last decade, results in an overall negative trend for the Mediterranean Sea.\nDOI (product): \nhttps://doi.org/10.48670/moi-00259\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G. (2004). Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., Santoleri, R., 2016. Mediterranean ocean colour chlorophyll trends. PLoS One 11, 1 16. https://doi.org/10.1371/journal.pone.0155756.\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00259", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"abstract": "DEFINITION\n\nThis product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) (at 1 km resolution) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\nCONTEXT\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). The Mediterranean Sea is an oligotrophic basin, where chlorophyll concentration decreases following a specific gradient from West to East (Colella et al. 2016). The highest concentrations are observed in coastal areas and at the river mouths, where the anthropogenic pressure and nutrient loads impact on the eutrophication regimes (Colella et al. 2016). The the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\nKEY FINDINGS\n\nChlorophyll trend in the Mediterranean Sea, for the period 1997-2023, generally confirm trend results of the previous release with negative values over most of the basin. In Ligurian Sea, negative trend is slightly emphasized. As for the previous release, the southern part of the western Mediterranean basin, Rhode Gyre and in the northern coast of the Aegean Sea show weak positive trend areas but they seems weaker than previous ones. On average the trend in the Mediterranean Sea is about -0.83% per year, emphasizing the mean negative trend achieved in the previous release. Contrary to what shown by Salgado-Hernanz et al. (2019) in their analysis (related to 1998-2014 satellite observations), western and eastern part of the Mediterranean Sea do not show differences. In the Ligurian Sea, the trend switch to negative values, differing from the positive regime observed in the trend maps of both Colella et al. (2016) and Salgado-Hernanz et al. (2019), referred, respectively, to 1998-2009 and 1998-2014 period, respectively. The waters offshore the Po River mouth show weak negative trend values, partially differing from the markable negative regime observed in the 1998-2009 period (Colella et al., 2016), and definitely moving from the positive trend observed by Salgado-Hernanz et al. (2019).\nDOI (product): \nhttps://doi.org/10.48670/moi-00260\n\nReferences:\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 200.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Salgado-Hernanz, P. M., Racault, M. F., Font-Mu\u00f1oz, J. S., & Basterretxea, G. (2019). Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing. Remote Sensing of Environment, 221, 50-64.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00260", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"abstract": "DEFINITION\n\nThe Mediterranean water mass formation rates are evaluated in 4 areas as defined in the Ocean State Report issue 2 section 3.4 (Simoncelli and Pinardi, 2018) as shown in Figure 2: (1) the Gulf of Lions for the Western Mediterranean Deep Waters (WMDW); (2) the Southern Adriatic Sea Pit for the Eastern Mediterranean Deep Waters (EMDW); (3) the Cretan Sea for Cretan Intermediate Waters (CIW) and Cretan Deep Waters (CDW); (4) the Rhodes Gyre, the area of formation of the so-called Levantine Intermediate Waters (LIW) and Levantine Deep Waters (LDW).\nAnnual water mass formation rates have been computed using daily mixed layer depth estimates (density criteria \u0394\u03c3 = 0.01 kg/m3, 10 m reference level) considering the annual maximum volume of water above mixed layer depth with potential density within or higher the specific thresholds specified in Table 1 then divided by seconds per year.\nAnnual mean values are provided using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al. 2020, 2021).\n\nCONTEXT\n\nThe formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation. This circulation varies at interannual and multidecadal time scales and it is composed of an upper zonal cell (Zonal Overturning Circulation) and two main meridional cells in the western and eastern Mediterranean (Pinardi and Masetti 2000).\nThe objective is to monitor the main water mass formation events using the eddy resolving Mediterranean Sea Reanalysis (Escudier et al. 2020, 2021) and considering Pinardi et al. (2015) and Simoncelli and Pinardi (2018) as references for the methodology. The Mediterranean Sea Reanalysis can reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in the literature. This will permit constant monitoring of the open ocean deep convection process in the Mediterranean Sea and a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales. \nDeep and intermediate water formation events reveal themselves by a deep mixed layer depth distribution in four Mediterranean areas (Table 1 and Figure 2): Gulf of Lions, Southern Adriatic Sea Pit, Cretan Sea and Rhodes Gyre. \n\nCMEMS KEY FINDINGS\n\nThe Western Mediterranean Deep Water (WMDW) formation events in the Gulf of Lion appear to be larger after 1999 consistently with Schroeder et al. (2006, 2008) related to the Eastern Mediterranean Transient event. This modification of WMDW after 2005 has been called Western Mediterranean Transition. WMDW formation events are consistent with Somot et al. (2016) and the event in 2009 is also reported in Houpert et al. (2016). \nThe Eastern Mediterranean Deep Water (EMDW) formation in the Southern Adriatic Pit region displays a period of water mass formation between 1988 and 1993, in agreement with Pinardi et al. (2015), in 1996, 1999 and 2000 as documented by Manca et al. (2002). Weak deep water formation in winter 2006 is confirmed by observations in Vilibi\u0107 and \u0160anti\u0107 (2008). An intense deep water formation event is detected in 2012-2013 (Ga\u010di\u0107 et al., 2014). Last years are characterized by large events starting from 2017 (Mihanovic et al., 2021).\nCretan Intermediate Water formation rates present larger peaks between 1989 and 1993 with the ones in 1992 and 1993 composing the Eastern Mediterranean Transient phenomena. The Cretan Deep Water formed in 1992 and 1993 is characterized by the highest densities of the entire period in accordance with Velaoras et al. (2014).\nThe Levantine Deep Water formation rate in the Rhode Gyre region presents the largest values between 1992 and 1993 in agreement with Kontoyiannis et al. (1999). \n\nFigure caption\n\nWater mass formation rates [Sverdrup] computed in 4 regions: in the Gulf of Lion for the Western Mediterranean Deep Waters (WMDW); in the Southern Adriatic region for the Eastern Mediterranean Deep Waters (EMDW); in the Cretan Sea for the Cretan Intermediate Waters (CIW) and the Cretan Deep Waters (CDW); in the Rhode Gyre area for the Levantine Intermediate Waters (LIW) and the Levantine Deep Waters (LDW). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00318\n\nReferences:\n\n* Escudier R., Clementi E., Cipollone A., Pistoia J., Drudi M., Grandi A., Lyubartsev V., Lecci R., Aydogdu A., Delrosso D., Omar M., Masina S., Coppini G., Pinardi N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Ga\u010di\u0107, M., Civitarese, G., Kova\u010devi\u0107, V., Ursella, L., Bensi, M., Menna, M., et al. 2014. Extreme winter 2012 in the Adriatic: an example of climatic effect on the BiOS rhythm. Ocean Sci. 10, 513\u2013522. doi: 10.5194/os-10-513-2014\n* Houpert, L., de Madron, X.D., Testor, P., Bosse, A., D\u2019Ortenzio, F., Bouin, M.N., Dausse, D., Le Goff, H., Kunesch, S., Labaste, M., et al. 2016. Observations of open-ocean deep convection in the northwestern Mediterranean Sea: seasonal and inter- annual variability of mixing and deep water masses for the 2007-2013 period. J Geophys Res Oceans. 121:8139\u20138171. doi:10.1002/ 2016JC011857.\n* Kontoyiannis, H., Theocharis, A., Nittis, K. 1999. Structures and characteristics of newly formed water masses in the NW levantine during 1986, 1992, 1995. In: Malanotte-Rizzoli P., Eremeev V.N., editor. The eastern Mediterranean as a laboratory basin for the assessment of contrasting ecosys- tems. NATO science series (series 2: environmental secur- ity), Vol. 51. Springer: Dordrecht.\n* Manca, B., Kovacevic, V., Gac\u030cic\u0301, M., Viezzoli, D. 2002. Dense water formation in the Southern Adriatic Sea and spreading into the Ionian Sea in the period 1997\u20131999. J Mar Sys. 33/ 34:33\u2013154.\n* Mihanovi\u0107, H., Vilibi\u0107, I., \u0160epi\u0107, J., Mati\u0107, F., Ljube\u0161i\u0107, Z., Mauri, E., Gerin, R., Notarstefano, G., Poulain, P.-M.. 2021. Observation, preconditioning and recurrence of exceptionally high salinities in the Adriatic Sea. Frontiers in Marine Science, Vol. 8, https://www.frontiersin.org/article/10.3389/fmars.2021.672210\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., ... & Bonaduce, A. 2015. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Progress in Oceanography, 132, 318-332\n* Schroeder, K., Gasparini, G.P., Tangherlini, M., Astraldi, M. 2006. Deep and intermediate water in the western Mediterranean under the influence of the eastern Mediterranean transient. Geophys Res Lett. 33. doi:10. 1028/2006GL02712.\n* Schroeder, K., Ribotti, A., Borghini, M., Sorgente, R., Perilli, A., Gasparini, G.P. 2008. An extensive western Mediterranean deep water renewal between 2004 and 2006. Geophys Res Lett. 35(18):L18605. doi:10.1029/2008GL035146.\n* Simoncelli, S. and Pinardi, N. 2018. Water mass formation processes in the Mediterranean sea over the past 30 years. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208.\n* Somot, S., Houpert, L., Sevault, F., Testor, P., Bosse, A., Taupier-Letage, I., Bouin, M.N., Waldman, R., Cassou, C., Sanchez-Gomez, E., et al. 2016. Characterizing, modelling and under- standing the climate variability of the deep water formation in the North-Western Mediterranean Sea. Clim Dyn. 1\u201332. doi:10.1007/s00382-016-3295-0.\n* Velaoras, D., Krokos, G., Nittis, K., Theocharis, A. 2014. Dense intermediate water outflow from the Cretan Sea: a salinity driven, recurrent phenomenon, connected to thermohaline circulation changes. J Geophys Res Oceans. 119:4797\u20134820. doi:10.1002/2014JC009937.\n* Vilibic\u0301, I., S\u030cantic\u0301, D. 2008. Deep water ventilation traced by Synechococcus cyanobacteria. Ocean Dyn 58:119\u2013125. doi:10.1007/s10236-008-0135-8.\n* Von Schuckmann K. et al. (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/mds-00318", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-var-extreme-wmf-medsea-area-averaged-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Water Mass Formation Rates from Reanalysis"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"abstract": "For the Antarctic Sea - A sea ice concentration product based on satellite SAR imagery and microwave radiometer data: The algorithm uses SENTINEL-1 SAR EW and IW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00320", "doi": "10.48670/mds-00320", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,sea-ice-edge,seaice-ant-phy-auto-l3-nrt-011-012,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean - High Resolution Sea Ice Information"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"abstract": "Antarctic sea ice displacement during winter from medium resolution sensors since 2002\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00120", "doi": "10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,seaice-ant-phy-l3-my-011-018,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"abstract": "Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00343", "doi": "10.48670/mds-00343", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,floe-size;,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l3-mynrt-011-023,stage-of-development,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L3"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"abstract": "Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid. \n\nDOI (product): \nhttps://doi.org/10.48670/mds-00344", "doi": "10.48670/mds-00344", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l4-mynrt-011-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L4"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"abstract": "For the European Arctic Sea - A sea ice concentration product based on SAR data and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. A sea ice type product covering the same area is produced from SENTINEL-1 SAR EW mode dual-polarized HH/HV data.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00122", "doi": "10.48670/moi-00122", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,seaice-arc-phy-auto-l4-nrt-011-015,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High resolution Sea Ice Concentration and Sea Ice Type"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"abstract": "Arctic Sea and Ice surface temperature\nDetailed description: Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00315", "doi": "10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l3-my-011-021,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"abstract": "Arctic Sea and Ice surface temperature\n\nDetailed description:\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00123", "doi": "10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l4-my-011-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"abstract": "For the Arctic Ocean - multiple Sentinel-1 scenes, Sigma0 calibrated and noise-corrected, with individual geographical map projections over Svalbard and Greenland Sea regions.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00124", "doi": "10.48670/moi-00124", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,seaice-arc-phy-l3m-nrt-011-017,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ARCTIC Ocean and Sea-Ice Sigma-Nought"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"abstract": "Arctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer:\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00126", "doi": "10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-arc-seaice-l3-rep-observations-011-010,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-12-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"abstract": "For the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview chart is produced twice weekly.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00128", "doi": "10.48670/moi-00128", "instrument": null, "keywords": "arctic-ocean,ca,cb,cc,cd,cf,cn,coastal-marine-environment,concentration-range,ct,data-quality,fa,fb,fc,ice-poly-id-grid,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,polygon-id,polygon-type,sa,satellite-observation,sb,sc,sea-ice-area-fraction,seaice-arc-seaice-l4-nrt-observations-011-002,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"abstract": "The iceberg product contains 4 datasets (IW and EW modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.\n\nThe iceberg product also contains two additional datasets of individual iceberg positions in Greenland-Newfoundland-Labrador Waters. These datasets are in shapefile format to allow the best representation of the icebergs (the 1st dataset contains the iceberg point observations, the 2nd dataset contains the polygonized satellite coverage). These are also derived by applying a Constant False Alarm Rate (CFAR) algorithm on Sentinel-1 SAR imagery.\nDespite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00129", "doi": "10.48670/moi-00129", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,seaice-arc-seaice-l4-nrt-observations-011-007,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T04:11:59Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"abstract": "Arctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00130", "doi": "10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-seaice-l4-nrt-observations-011-008,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00131", "doi": "10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,seaice-bal-phy-l4-my-011-019,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-11-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea ice concentration, extent, and classification time series"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"abstract": "For the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00132\n\nReferences:\n\n* J. Karvonen, M. Simila, SAR-Based Estimation of the Baltic Sea Ice Motion, Proc. of the International Geoscience and Remote Sensing Symposium 2007 (IGARSS 07), pp. 2605-2608, 2007. (Unfortunately there is no publication of the new algorithm version yet).\n", "doi": "10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,seaice-bal-seaice-l4-nrt-observations-011-004,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"abstract": "For the Baltic Sea - The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The products are based on SAR images and are produced on pass-by-pass basis during the Baltic Sea ice season, and show the ice thickness and drift in a 500 m and 800m grid, respectively. The Baltic sea ice concentration product is based on data from SAR and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00133\n\nReferences:\n\n* J. Karvonen, Operational SAR-based sea ice drift monitoring over the Baltic Sea, Ocean Science, v. 8, pp. 473-483, (http://www.ocean-sci.net/8/473/2012/os-8-473-2012.html) 2012.\n", "doi": "10.48670/moi-00133", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-thickness,sea-ice-x-displacement,sea-ice-y-displacement,seaice-bal-seaice-l4-nrt-observations-011-011,target-application#seaiceclimate,target-application#seaiceforecastingapplication,target-application#seaiceinformation,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"abstract": "Arctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00127", "doi": "10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-climate-l3-my-011-013,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1995-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"abstract": "The product contains a reprocessed multi year version of the daily composite dataset from SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 covering the Sentinel1 years from autumn 2014 until 1 year before present\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00328", "doi": "10.48670/mds-00328", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-phy-l4-my-011-020,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift Time Series"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"abstract": "Arctic sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and Aprilt to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00125", "doi": "10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-l4-nrt-011-014,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Thickness derived from merging of L-Band radiometry and radar altimeter derived sea ice thickness"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"abstract": "For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00134", "doi": "10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"abstract": "DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00135", "doi": "10.48670/moi-00135", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-006,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"abstract": "The CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00136\n\nReferences:\n\n* [http://osisaf.met.no/docs/osisaf_cdop2_ss2_pum_sea-ice-conc-reproc_v2p2.pdf]\n", "doi": "10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,seaice-glo-seaice-l4-rep-observations-011-009,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-10-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_BLK is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Black Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00138", "doi": "10.48670/moi-00138", "instrument": null, "keywords": "black-sea,coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-blk-phy-mdt-l4-static-008-067,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "BLACK SEA MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n\u201c\u2019Associated products\u201d\u2019\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00139", "doi": "10.48670/moi-00139", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-my-008-061,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T07:53:03Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Seas. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\nAssociated products\n\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00140", "doi": "10.48670/moi-00140", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-nrt-008-059,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T03:04:52Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA LEVEL ANOMALIES NRT"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00141", "doi": "10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-my-008-068,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00142", "doi": "10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-nrt-008-060,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2024_EUR is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the European Seas. This is consistent with the reference time period also used in the SSALTO DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00337", "doi": "10.48670/mds-00337", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-eur-phy-mdt-l4-static-008-070,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"abstract": "DUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00145", "doi": "10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-climate-l4-my-008-057,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\nAssociated products\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00146", "doi": "10.48670/moi-00146", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-my-008-062,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T01:42:25Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\nAssociated products\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\nDOI (product):\nhttps://doi.org/10.48670/moi-00147", "doi": "10.48670/moi-00147", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-nrt-008-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00148", "doi": "10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-my-008-047,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00149", "doi": "10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-nrt-008-046,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"abstract": "Mean Dynamic Topography that combines the global CNES-CLS-2022 MDT, the Black Sea CMEMS2020 MDT and the Med Sea CMEMS2020 MDT. It is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid. This is consistent with the reference time period also used in the DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00150", "doi": "10.48670/moi-00150", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,invariant,level-4,marine-resources,marine-safety,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-glo-phy-mdt-008-063,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"abstract": "In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00144", "doi": "10.48670/moi-00144", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-noise-l4-static-008-033,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00151", "doi": "10.48670/moi-00151", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-med-phy-mdt-l4-static-008-066,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY"}, "SST_ATL_PHY_L3S_MY_010_038": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00311", "doi": "10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sst-atl-phy-l3s-my-010-038,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "SST_ATL_PHY_L3S_NRT_010_037": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00310", "doi": "10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-atl-phy-l3s-nrt-010-037,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"abstract": "For the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00152", "doi": "10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-nrt-observations-010-025,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"abstract": "For the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00153", "doi": "10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-rep-observations-010-026,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_L3S_MY_010_040": {"abstract": "For the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00312\n\nReferences:\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-l3s-my-010-040,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"abstract": "For the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00309\n\nReferences:\n\n* Karagali I. and H\u00f8yer, J. L. (2014). Characterisation and quantification of regional diurnal cycles from SEVIRI. Ocean Science, 10 (5), 745-758.\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-subskin-l4-nrt-010-034,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00154\n\nReferences:\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l3s-nrt-observations-010-032,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-03-11T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00155", "doi": "10.48670/moi-00155", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l4-nrt-observations-010-007-b,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-12-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Analysis L4"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00156\n\nReferences:\n\n* H\u00f8yer, J. L., & Karagali, I. (2016). Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-bal-sst-l4-rep-observations-010-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_L3S_MY_010_041": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00313\n\nReferences:\n\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18. Pisano, A., Buongiorno Nardelli, B., Tronconi, C. & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sens. Environ. 176, 107\u2013116.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n", "doi": "10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sst-bs-phy-l3s-my-010-041,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"abstract": "For the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00157\n\nReferences:\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-bs-phy-subskin-l4-nrt-010-035,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00158\n\nReferences:\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-bs-sst-l3s-nrt-observations-010-013,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00159\n\nReferences:\n\n* Buongiorno Nardelli B., S. Colella, R. Santoleri, M. Guarracino, A. Kholod, 2009: A re-analysis of Black Sea Surface Temperature, J. Mar. Sys.., doi:10.1016/j.jmarsys.2009.07.001\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-nrt-observations-010-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00160\n\nReferences:\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-rep-observations-010-022,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_GLO_PHY_L3S_MY_010_039": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00329", "doi": "10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-phy-l3s-my-010-039,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_PHY_L4_MY_010_044": {"abstract": "For the global ocean. The IFREMER/ODYSSEA Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.10deg. x 0.10deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00345", "doi": "10.48670/mds-00345", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-my-010-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean ODYSSEA L4 Sea Surface Temperature"}, "SST_GLO_PHY_L4_NRT_010_043": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00321", "doi": "10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-nrt-010-043,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00164", "doi": "10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-sst-l3s-nrt-observations-010-010,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"abstract": "For the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of: Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00165\n\nReferences:\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n* Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n* John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. McCulloch, 2007, OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system., Oceans 07 IEEE Aberdeen, conference proceedings. Marine challenges: coastline to deep sea. Aberdeen, Scotland.IEEE.\n", "doi": "10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-nrt-observations-010-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"abstract": "The OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00168\n\nReferences:\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720\n", "doi": "10.48670/moi-00168", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-rep-observations-010-011,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"abstract": "The ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00169\n\nReferences:\n\n* Good, S., Fiedler, E., Mao, C., Martin, M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720.\n* Lavergne, T., S\u00f8rensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkj\u00e6r, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\n", "doi": "10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,sst-glo-sst-l4-rep-observations-010-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "SST_MED_PHY_L3S_MY_010_042": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00314\n\nReferences:\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sst-med-phy-l3s-my-010-042,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"abstract": "For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[How to cite](https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services)\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00170\n\nReferences:\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-med-phy-subskin-l4-nrt-010-036,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00171\n\nReferences:\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-med-sst-l3s-nrt-observations-010-012,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. Since November 2024, the L4 MED UHR processing chain makes use of an improved background field as initial guess for the Optimal Interpolation of this product. The improvement is obtained in terms of the effective spatial resolution via the application of a convolutional neural network (CNN). These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00172\n\nReferences:\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n* Fanelli, C., Ciani, D., Pisano, A., & Buongiorno Nardelli, B. (2024). Deep Learning for Super-Resolution of Mediterranean Sea Surface Temperature Fields. EGUsphere, 2024, 1-18 (pre-print)\n", "doi": "10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-nrt-observations-010-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00173\n\nReferences:\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-rep-observations-010-021,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"abstract": "Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra.\nUsing linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered.\nThe dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land).\nThe integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.\nThis product is processed by the WAVE-TAC multi-mission SAR data processing system.\nIt processes near-real-time data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM.\nOne file is produced for each mission and is available in two formats depending on the user needs: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00178", "doi": "10.48670/moi-00178", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-fwk-l3-nrt-014-002,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"abstract": "Multi-Year mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00174", "doi": "10.48670/moi-00174", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-my-014-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"abstract": "Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. \n\nDOI (product): \nN/A", "doi": null, "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,global-ocean,iberian-biscay-irish-seas,level-3,mediterranean-sea,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-nrt-014-009,wave-spectrum", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"abstract": "Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\nDOI (product): \nhttps://doi.org/10.48670/moi-00175", "doi": "10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l4-nrt-014-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"abstract": "Multi-Year mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission and in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nThis product is based on the ESA Sea State Climate Change Initiative data Level 3 product (version 2) and is formatted by the WAVE-TAC to be homogeneous with the CMEMS Level 3 Near-real-time product. It is based on the reprocessing of GDR data from the following altimeter missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa and Jason-3. CFOSAT Multi-Year dataset is based on the reprocessing of CFOSAT Level-2P products (CNES/CLS), inter-calibrated on Jason-3 reference mission issued from the CCI Sea State dataset.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH) and the unfiltered SWH (VAVH_UNFILTERED).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00176", "doi": "10.48670/moi-00176", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-my-014-005,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T06:29:22Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"abstract": "Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00179", "doi": "10.48670/moi-00179", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-nrt-014-001,weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"abstract": "Multi-Year gridded multi-mission merged satellite significant wave height based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2\u00b0 horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5\u00b0 horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00177", "doi": "10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,wave-glo-phy-swh-l4-my-014-007,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"abstract": "Near-Real-Time gridded multi-mission merged satellite significant wave height, based on CMEMS level-3 SWH datasets. Onyl valid data are included. It merges multiple along-track SWH data (Sentinel-6A,\u00a0 Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C) and produces daily gridded data at a 2\u00b0 horizontal resolution. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00180", "doi": "10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l4-nrt-014-003,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00338", "doi": "10.48670/mds-00338", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-my-012-105,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.'\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00330", "doi": "10.48670/mds-00330", "instrument": null, "keywords": "arctic-ocean,eastward-wind,level-3,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-nrt-012-100,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Arctic Sea"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00339", "doi": "10.48670/mds-00339", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-my-012-106,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00331", "doi": "10.48670/mds-00331", "instrument": null, "keywords": "eastward-wind,iberian-biscay-irish-seas,level-3,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-nrt-012-101,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Atlantic Sea"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00340", "doi": "10.48670/mds-00340", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-my-012-107,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00332", "doi": "10.48670/mds-00332", "instrument": null, "keywords": "baltic-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-nrt-012-102,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Baltic Sea"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00341", "doi": "10.48670/mds-00341", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-my-012-108,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00333", "doi": "10.48670/mds-00333", "instrument": null, "keywords": "black-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-nrt-012-103,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Black Sea"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"abstract": "For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments. The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00181", "doi": "10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-4,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-climate-l4-my-012-003,wind-speed", "license": "proprietary", "missionStartDate": "1994-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L3_MY_012_005": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\nDOI (product):* \nhttps://doi.org/10.48670/moi-00183", "doi": "10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-my-012-005,wind-speed,wind-to-direction,wvc-index", "license": "proprietary", "missionStartDate": "1991-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L3_NRT_012_002": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\nThe ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\nThe OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \nThe HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00182", "doi": "10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-nrt-012-002,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": "proprietary", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L4_MY_012_006": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00185", "doi": "10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-my-012-006", "license": "proprietary", "missionStartDate": "1994-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L4_NRT_012_004": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\nDOI (product): \nhttps://doi.org/10.48670/moi-00305", "doi": "10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-nrt-012-004", "license": "proprietary", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00342", "doi": "10.48670/mds-00342", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-my-012-109,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "2021-06-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\nDOI (product): \nhttps://doi.org/10.48670/mds-00334", "doi": "10.48670/mds-00334", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-nrt-012-104,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Mediterranean Sea"}}, "providers_config": {"ANTARCTIC_OMI_SI_extent": {"collection": "ANTARCTIC_OMI_SI_extent"}, "ANTARCTIC_OMI_SI_extent_obs": {"collection": "ANTARCTIC_OMI_SI_extent_obs"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"collection": "ARCTIC_ANALYSISFORECAST_BGC_002_004"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_002_001"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"collection": "ARCTIC_ANALYSIS_FORECAST_WAV_002_014"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"collection": "ARCTIC_MULTIYEAR_BGC_002_005"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"collection": "ARCTIC_MULTIYEAR_PHY_002_003"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"collection": "ARCTIC_MULTIYEAR_PHY_ICE_002_016"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"collection": "ARCTIC_MULTIYEAR_WAV_002_013"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"collection": "ARCTIC_OMI_SI_Transport_NordicSeas"}, "ARCTIC_OMI_SI_extent": {"collection": "ARCTIC_OMI_SI_extent"}, "ARCTIC_OMI_SI_extent_obs": {"collection": "ARCTIC_OMI_SI_extent_obs"}, "ARCTIC_OMI_TEMPSAL_FWC": {"collection": "ARCTIC_OMI_TEMPSAL_FWC"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"collection": "BALTICSEA_ANALYSISFORECAST_BGC_003_007"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"collection": "BALTICSEA_ANALYSISFORECAST_PHY_003_006"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"collection": "BALTICSEA_ANALYSISFORECAST_WAV_003_010"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"collection": "BALTICSEA_MULTIYEAR_BGC_003_012"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"collection": "BALTICSEA_MULTIYEAR_PHY_003_011"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"collection": "BALTICSEA_MULTIYEAR_WAV_003_015"}, "BALTIC_OMI_HEALTH_codt_volume": {"collection": "BALTIC_OMI_HEALTH_codt_volume"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"collection": "BALTIC_OMI_OHC_area_averaged_anomalies"}, "BALTIC_OMI_SI_extent": {"collection": "BALTIC_OMI_SI_extent"}, "BALTIC_OMI_SI_volume": {"collection": "BALTIC_OMI_SI_volume"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Stz_trend"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Ttz_trend"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"collection": "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"collection": "BALTIC_OMI_WMHE_mbi_sto2tz_gotland"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"collection": "BLKSEA_ANALYSISFORECAST_BGC_007_010"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"collection": "BLKSEA_ANALYSISFORECAST_PHY_007_001"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"collection": "BLKSEA_ANALYSISFORECAST_WAV_007_003"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"collection": "BLKSEA_MULTIYEAR_BGC_007_005"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"collection": "BLKSEA_MULTIYEAR_PHY_007_004"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"collection": "BLKSEA_MULTIYEAR_WAV_007_006"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"collection": "BLKSEA_OMI_HEALTH_oxygen_trend"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"collection": "BLKSEA_OMI_TEMPSAL_sst_trend"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"collection": "GLOBAL_ANALYSISFORECAST_BGC_001_028"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"collection": "GLOBAL_ANALYSISFORECAST_PHY_001_024"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"collection": "GLOBAL_ANALYSISFORECAST_WAV_001_027"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"collection": "GLOBAL_MULTIYEAR_BGC_001_029"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"collection": "GLOBAL_MULTIYEAR_BGC_001_033"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"collection": "GLOBAL_MULTIYEAR_PHY_001_030"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"collection": "GLOBAL_MULTIYEAR_PHY_ENS_001_031"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"collection": "GLOBAL_MULTIYEAR_WAV_001_032"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"collection": "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"collection": "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_trend"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_26N_profile"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_OHC_trend": {"collection": "GLOBAL_OMI_OHC_trend"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_SL_thsl_trend": {"collection": "GLOBAL_OMI_SL_thsl_trend"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"collection": "GLOBAL_OMI_TEMPSAL_Tyz_trend"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"collection": "GLOBAL_OMI_TEMPSAL_sst_trend"}, "GLOBAL_OMI_WMHE_heattrp": {"collection": "GLOBAL_OMI_WMHE_heattrp"}, "GLOBAL_OMI_WMHE_northward_mht": {"collection": "GLOBAL_OMI_WMHE_northward_mht"}, "GLOBAL_OMI_WMHE_voltrp": {"collection": "GLOBAL_OMI_WMHE_voltrp"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"collection": "IBI_ANALYSISFORECAST_BGC_005_004"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"collection": "IBI_ANALYSISFORECAST_PHY_005_001"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"collection": "IBI_ANALYSISFORECAST_WAV_005_005"}, "IBI_MULTIYEAR_BGC_005_003": {"collection": "IBI_MULTIYEAR_BGC_005_003"}, "IBI_MULTIYEAR_PHY_005_002": {"collection": "IBI_MULTIYEAR_PHY_005_002"}, "IBI_MULTIYEAR_WAV_005_006": {"collection": "IBI_MULTIYEAR_WAV_005_006"}, "IBI_OMI_CURRENTS_cui": {"collection": "IBI_OMI_CURRENTS_cui"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "IBI_OMI_SEASTATE_swi": {"collection": "IBI_OMI_SEASTATE_swi"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "IBI_OMI_WMHE_mow": {"collection": "IBI_OMI_WMHE_mow"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"collection": "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"collection": "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"collection": "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"collection": "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"collection": "INSITU_GLO_BGC_DISCRETE_MY_013_046"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"collection": "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"collection": "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"collection": "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"collection": "INSITU_GLO_PHY_TS_OA_MY_013_052"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"collection": "INSITU_GLO_PHY_TS_OA_NRT_013_002"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"collection": "INSITU_GLO_WAV_DISCRETE_MY_013_045"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"collection": "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"collection": "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"collection": "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"collection": "MEDSEA_ANALYSISFORECAST_BGC_006_014"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"collection": "MEDSEA_ANALYSISFORECAST_PHY_006_013"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"collection": "MEDSEA_ANALYSISFORECAST_WAV_006_017"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"collection": "MEDSEA_MULTIYEAR_BGC_006_008"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"collection": "MEDSEA_MULTIYEAR_PHY_006_004"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"collection": "MEDSEA_MULTIYEAR_WAV_006_012"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"collection": "MEDSEA_OMI_OHC_area_averaged_anomalies"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"collection": "MEDSEA_OMI_TEMPSAL_sst_trend"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"collection": "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"collection": "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"collection": "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"collection": "MULTIOBS_GLO_PHY_MYNRT_015_003"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"collection": "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"collection": "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"collection": "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"collection": "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"collection": "MULTIOBS_GLO_PHY_W_3D_REP_015_007"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"collection": "NWSHELF_ANALYSISFORECAST_BGC_004_002"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"collection": "NWSHELF_ANALYSISFORECAST_PHY_004_013"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"collection": "NWSHELF_ANALYSISFORECAST_WAV_004_014"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"collection": "NWSHELF_MULTIYEAR_BGC_004_011"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"collection": "NWSHELF_MULTIYEAR_PHY_004_009"}, "NWSHELF_REANALYSIS_WAV_004_015": {"collection": "NWSHELF_REANALYSIS_WAV_004_015"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"collection": "OCEANCOLOUR_ARC_BGC_L3_MY_009_123"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"collection": "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"collection": "OCEANCOLOUR_ARC_BGC_L4_MY_009_124"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"collection": "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"collection": "OCEANCOLOUR_ATL_BGC_L3_MY_009_113"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"collection": "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"collection": "OCEANCOLOUR_ATL_BGC_L4_MY_009_118"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"collection": "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"collection": "OCEANCOLOUR_BAL_BGC_L3_MY_009_133"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"collection": "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"collection": "OCEANCOLOUR_BAL_BGC_L4_MY_009_134"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"collection": "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"collection": "OCEANCOLOUR_BLK_BGC_L3_MY_009_153"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"collection": "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"collection": "OCEANCOLOUR_BLK_BGC_L4_MY_009_154"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"collection": "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_103"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_107"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"collection": "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_104"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_108"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"collection": "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"collection": "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"collection": "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"collection": "OCEANCOLOUR_MED_BGC_L3_MY_009_143"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"collection": "OCEANCOLOUR_MED_BGC_L3_NRT_009_141"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"collection": "OCEANCOLOUR_MED_BGC_L4_MY_009_144"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"collection": "OCEANCOLOUR_MED_BGC_L4_NRT_009_142"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"collection": "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"collection": "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"collection": "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"collection": "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"collection": "OMI_CLIMATE_OSC_MEDSEA_volume_mean"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"collection": "OMI_CLIMATE_SL_GLOBAL_regional_trends"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_BAL_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_trend": {"collection": "OMI_CLIMATE_SST_BAL_trend"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IBI_trend": {"collection": "OMI_CLIMATE_SST_IBI_trend"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_anomaly"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_trend"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_trend"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"collection": "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"collection": "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"collection": "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"collection": "OMI_EXTREME_WAVE_BLKSEA_recent_changes"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"collection": "OMI_EXTREME_WAVE_BLKSEA_wave_power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"collection": "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"collection": "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"collection": "SEAICE_ANT_PHY_L3_MY_011_018"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"collection": "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"collection": "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"collection": "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"collection": "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"collection": "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"collection": "SEAICE_ARC_PHY_L3M_NRT_011_017"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"collection": "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"collection": "SEAICE_BAL_PHY_L4_MY_011_019"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"collection": "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"collection": "SEAICE_GLO_PHY_L4_MY_011_020"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"collection": "SEAICE_GLO_PHY_L4_NRT_011_014"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"collection": "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"collection": "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"collection": "SEALEVEL_EUR_PHY_L3_MY_008_061"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"collection": "SEALEVEL_EUR_PHY_L3_NRT_008_059"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"collection": "SEALEVEL_EUR_PHY_L4_MY_008_068"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"collection": "SEALEVEL_EUR_PHY_L4_NRT_008_060"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"collection": "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"collection": "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"collection": "SEALEVEL_GLO_PHY_L3_MY_008_062"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"collection": "SEALEVEL_GLO_PHY_L3_NRT_008_044"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"collection": "SEALEVEL_GLO_PHY_L4_MY_008_047"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"collection": "SEALEVEL_GLO_PHY_L4_NRT_008_046"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"collection": "SEALEVEL_GLO_PHY_MDT_008_063"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"collection": "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"collection": "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066"}, "SST_ATL_PHY_L3S_MY_010_038": {"collection": "SST_ATL_PHY_L3S_MY_010_038"}, "SST_ATL_PHY_L3S_NRT_010_037": {"collection": "SST_ATL_PHY_L3S_NRT_010_037"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"collection": "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"collection": "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026"}, "SST_BAL_PHY_L3S_MY_010_040": {"collection": "SST_BAL_PHY_L3S_MY_010_040"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"collection": "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"collection": "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"collection": "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"collection": "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016"}, "SST_BS_PHY_L3S_MY_010_041": {"collection": "SST_BS_PHY_L3S_MY_010_041"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"collection": "SST_BS_PHY_SUBSKIN_L4_NRT_010_035"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"collection": "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"collection": "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"collection": "SST_BS_SST_L4_REP_OBSERVATIONS_010_022"}, "SST_GLO_PHY_L3S_MY_010_039": {"collection": "SST_GLO_PHY_L3S_MY_010_039"}, "SST_GLO_PHY_L4_MY_010_044": {"collection": "SST_GLO_PHY_L4_MY_010_044"}, "SST_GLO_PHY_L4_NRT_010_043": {"collection": "SST_GLO_PHY_L4_NRT_010_043"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"collection": "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"collection": "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024"}, "SST_MED_PHY_L3S_MY_010_042": {"collection": "SST_MED_PHY_L3S_MY_010_042"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"collection": "SST_MED_PHY_SUBSKIN_L4_NRT_010_036"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"collection": "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"collection": "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"collection": "SST_MED_SST_L4_REP_OBSERVATIONS_010_021"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"collection": "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"collection": "WAVE_GLO_PHY_SPC_L3_MY_014_006"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"collection": "WAVE_GLO_PHY_SPC_L3_NRT_014_009"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"collection": "WAVE_GLO_PHY_SPC_L4_NRT_014_004"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"collection": "WAVE_GLO_PHY_SWH_L3_MY_014_005"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"collection": "WAVE_GLO_PHY_SWH_L3_NRT_014_001"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"collection": "WAVE_GLO_PHY_SWH_L4_MY_014_007"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"collection": "WAVE_GLO_PHY_SWH_L4_NRT_014_003"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"collection": "WIND_ARC_PHY_HR_L3_MY_012_105"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"collection": "WIND_ARC_PHY_HR_L3_NRT_012_100"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"collection": "WIND_ATL_PHY_HR_L3_MY_012_106"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"collection": "WIND_ATL_PHY_HR_L3_NRT_012_101"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"collection": "WIND_BAL_PHY_HR_L3_MY_012_107"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"collection": "WIND_BAL_PHY_HR_L3_NRT_012_102"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"collection": "WIND_BLK_PHY_HR_L3_MY_012_108"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"collection": "WIND_BLK_PHY_HR_L3_NRT_012_103"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"collection": "WIND_GLO_PHY_CLIMATE_L4_MY_012_003"}, "WIND_GLO_PHY_L3_MY_012_005": {"collection": "WIND_GLO_PHY_L3_MY_012_005"}, "WIND_GLO_PHY_L3_NRT_012_002": {"collection": "WIND_GLO_PHY_L3_NRT_012_002"}, "WIND_GLO_PHY_L4_MY_012_006": {"collection": "WIND_GLO_PHY_L4_MY_012_006"}, "WIND_GLO_PHY_L4_NRT_012_004": {"collection": "WIND_GLO_PHY_L4_NRT_012_004"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"collection": "WIND_MED_PHY_HR_L3_MY_012_109"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"collection": "WIND_MED_PHY_HR_L3_NRT_012_104"}}}, "earth_search": {"product_types_config": {"cop-dem-glo-30": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme.", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters.", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "landsat-c2-l2": {"abstract": "Atmospherically corrected global Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "sentinel-1-grd": {"abstract": "Sentinel-1 is a pair of Synthetic Aperture Radar (SAR) imaging satellites launched in 2014 and 2016 by the European Space Agency (ESA). Their 6 day revisit cycle and ability to observe through clouds makes this dataset perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "sentinel-1", "platformSerialIdentifier": "sentinel-1a,sentinel-1b", "processingLevel": null, "title": "Sentinel-1 Level-1C Ground Range Detected (GRD)"}, "sentinel-2-c1-l2a": {"abstract": "Sentinel-2 Collection 1 Level-2A, data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Collection 1 Level-2A"}, "sentinel-2-l1c": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l1c,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-1C"}, "sentinel-2-l2a": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-2-pre-c1-l2a": {"abstract": "Sentinel-2 Pre-Collection 1 Level-2A (baseline < 05.00), with data and metadata matching collection sentinel-2-c1-l2a", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-pre-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Pre-Collection 1 Level-2A "}}, "providers_config": {"cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "naip": {"productType": "naip"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-2-c1-l2a": {"productType": "sentinel-2-c1-l2a"}, "sentinel-2-l1c": {"productType": "sentinel-2-l1c"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-2-pre-c1-l2a": {"productType": "sentinel-2-pre-c1-l2a"}}}, "eumetsat_ds": {"product_types_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszfr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszor02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszrr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0088", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0142", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0143", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0236", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0237", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0238", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0239", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0240", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0241", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0274", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0300", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0301", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0302", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0303", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0305", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0343", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0344", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0345", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0348", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0349", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0374", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0394", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0398", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0405", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0406", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0407", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0408", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0409", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0410", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0411", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0412", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0413", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0414", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0415", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0416", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0417", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0533", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0556", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0557", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0558", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0576", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0577", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0578", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0579", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0581", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0582", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0583", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0584", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0585", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0586", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0601", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0615", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0617", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0645", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0647", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0662", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0665", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0676": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0676", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0676"}, "EO:EUM:DAT:0677": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0677", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0677"}, "EO:EUM:DAT:0678": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0678", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0678"}, "EO:EUM:DAT:0683": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0683", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0683"}, "EO:EUM:DAT:0684": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0684", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0684"}, "EO:EUM:DAT:0685": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0685", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0685"}, "EO:EUM:DAT:0686": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0686", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0687", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0688", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0690", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0691", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0758", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0782", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0799": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0799", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0799"}, "EO:EUM:DAT:0833": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0833", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0834", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0835", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0836", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0837", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0838", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0839", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0840", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0841", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0842", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0850", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0851", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0852", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0853", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0854", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0855", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0856", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0857", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0858", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0859", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0862", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0863", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0880": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0880", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0881", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0882", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0894", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0895", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0959", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0960", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0961", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0962", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0963", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0964", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0986", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0987", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:0998": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0998", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0998"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:dmsp:osi-401-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:amsul1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszf1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszo1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszr1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:avhrrl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:glb-sst-nc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:gomel1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil1c-all", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil2cox", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iassnd02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:lsa-002", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:mhsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:nto", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:oas025", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-104", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-a", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo12", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo25", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:CTH": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:cth", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CTH"}, "EO:EUM:DAT:MSG:CTH-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:cth-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CTH-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:msg15-rss", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:rss-clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:mult:hirsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MULT:HIRSL1"}}, "providers_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"parentIdentifier": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"parentIdentifier": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"parentIdentifier": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"parentIdentifier": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"parentIdentifier": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"parentIdentifier": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"parentIdentifier": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"parentIdentifier": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"parentIdentifier": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"parentIdentifier": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"parentIdentifier": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"parentIdentifier": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"parentIdentifier": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"parentIdentifier": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"parentIdentifier": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"parentIdentifier": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"parentIdentifier": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"parentIdentifier": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"parentIdentifier": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"parentIdentifier": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"parentIdentifier": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"parentIdentifier": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"parentIdentifier": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"parentIdentifier": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"parentIdentifier": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"parentIdentifier": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"parentIdentifier": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"parentIdentifier": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"parentIdentifier": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"parentIdentifier": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"parentIdentifier": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"parentIdentifier": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"parentIdentifier": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"parentIdentifier": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"parentIdentifier": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"parentIdentifier": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"parentIdentifier": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"parentIdentifier": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"parentIdentifier": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"parentIdentifier": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"parentIdentifier": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"parentIdentifier": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"parentIdentifier": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"parentIdentifier": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"parentIdentifier": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"parentIdentifier": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"parentIdentifier": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"parentIdentifier": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"parentIdentifier": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"parentIdentifier": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"parentIdentifier": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"parentIdentifier": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"parentIdentifier": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"parentIdentifier": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"parentIdentifier": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"parentIdentifier": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"parentIdentifier": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0676": {"parentIdentifier": "EO:EUM:DAT:0676"}, "EO:EUM:DAT:0677": {"parentIdentifier": "EO:EUM:DAT:0677"}, "EO:EUM:DAT:0678": {"parentIdentifier": "EO:EUM:DAT:0678"}, "EO:EUM:DAT:0683": {"parentIdentifier": "EO:EUM:DAT:0683"}, "EO:EUM:DAT:0684": {"parentIdentifier": "EO:EUM:DAT:0684"}, "EO:EUM:DAT:0685": {"parentIdentifier": "EO:EUM:DAT:0685"}, "EO:EUM:DAT:0686": {"parentIdentifier": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"parentIdentifier": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"parentIdentifier": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"parentIdentifier": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"parentIdentifier": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"parentIdentifier": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"parentIdentifier": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0799": {"parentIdentifier": "EO:EUM:DAT:0799"}, "EO:EUM:DAT:0833": {"parentIdentifier": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"parentIdentifier": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"parentIdentifier": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"parentIdentifier": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"parentIdentifier": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"parentIdentifier": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"parentIdentifier": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"parentIdentifier": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"parentIdentifier": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"parentIdentifier": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"parentIdentifier": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"parentIdentifier": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"parentIdentifier": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"parentIdentifier": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"parentIdentifier": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"parentIdentifier": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"parentIdentifier": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"parentIdentifier": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"parentIdentifier": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"parentIdentifier": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"parentIdentifier": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"parentIdentifier": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0880": {"parentIdentifier": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"parentIdentifier": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"parentIdentifier": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"parentIdentifier": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"parentIdentifier": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"parentIdentifier": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"parentIdentifier": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"parentIdentifier": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"parentIdentifier": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"parentIdentifier": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"parentIdentifier": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"parentIdentifier": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"parentIdentifier": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:0998": {"parentIdentifier": "EO:EUM:DAT:0998"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"parentIdentifier": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"parentIdentifier": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"parentIdentifier": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"parentIdentifier": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"parentIdentifier": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"parentIdentifier": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"parentIdentifier": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"parentIdentifier": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:CTH": {"parentIdentifier": "EO:EUM:DAT:MSG:CTH"}, "EO:EUM:DAT:MSG:CTH-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:CTH-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"parentIdentifier": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"parentIdentifier": "EO:EUM:DAT:MULT:HIRSL1"}}}, "geodes": {"product_types_config": {"FLATSIM_AFAR_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, etc.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-afar-auxiliarydata-public,ground-to-radar,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar interferograms"}, "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-afar-interferogram-public,ground-geometry,insar,interferogram,landslides,radar-geometry,tectonics,unwrapped,volcanology,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar interferograms"}, "FLATSIM_AFAR_TIMESERIE_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-afar-timeserie-public,ground-geometry,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar time series"}, "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, etc.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-andes-auxiliarydata-public,ground-to-radar,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes auxiliary data"}, "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-andes-interferogram-public,ground-geometry,insar,interferogram,landslides,radar-geometry,tectonics,unwrapped,volcanology,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes interferograms"}, "FLATSIM_ANDES_TIMESERIE_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-andes-timeserie-public,ground-geometry,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes time series"}, "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,anthropogenic-and-climatic-hazards,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-balkans-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans auxiliary data"}, "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "anthropogenic-and-climatic-hazards,atmospheric-phase-screen,coherence,deformation,flatsim-balkans-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans interferograms"}, "FLATSIM_BALKANS_TIMESERIE_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "anthropogenic-and-climatic-hazards,data-cube,deformation,flatsim-balkans-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans time series"}, "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-caucase-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus auxiliary data"}, "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-caucase-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus interferograms"}, "FLATSIM_CAUCASE_TIMESERIE_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-caucase-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus time series"}, "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range, (2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes, (3) understand the link between the seismic cycle and relief building, (4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-chili-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile auxiliary data"}, "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range,(2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes,(3) understand the link between the seismic cycle and relief building,(4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-chili-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile interferograms"}, "FLATSIM_CHILI_TIMESERIE_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range, (2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes, (3) understand the link between the seismic cycle and relief building, (4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-chili-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile time series"}, "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.)This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-corse-auxiliarydata-public,ground-to-radar,hydrology,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,subsidences,tectonic,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse auxiliary data"}, "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.) Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-corse-interferogram-public,ground-geometry,hydrology,insar,interferogram,landslides,radar-geometry,subsidences,tectonic,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse interferograms"}, "FLATSIM_CORSE_TIMESERIE_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog. The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.) This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-corse-timeserie-public,ground-geometry,hydrology,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,subsidences,tectonic,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse time series"}, "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-france-auxiliarydata-public,ground-to-radar,hydrology,insar,landslides,lookup-tables,radar-to-ground,rock-glaciers,spectral-diversity,subsidence,tectonic,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France auxiliary data"}, "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform. After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog. The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-france-interferogram-public,ground-geometry,hydrology,insar,interferogram,landslides,radar-geometry,rock-glaciers,subsidence,tectonic,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France interferograms"}, "FLATSIM_FRANCE_TIMESERIE_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-france-timeserie-public,ground-geometry,hydrology,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,rock-glaciers,stack-list,subsidence,tectonic,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France time series"}, "FLATSIM_INDE_AUXILIARYDATA_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-inde-auxiliarydata-public,ground-to-radar,insar,landslide-instability,lookup-tables,radar-to-ground,spectral-diversity,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India auxiliary data"}, "FLATSIM_INDE_INTERFEROGRAM_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-inde-interferogram-public,ground-geometry,insar,interferogram,landslide-instability,radar-geometry,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India interferograms"}, "FLATSIM_INDE_TIMESERIE_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-inde-timeserie-public,ground-geometry,insar,landslide-instability,mean-los-velocity,quality-maps,radar-geometry,stack-list,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India time series"}, "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity).This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-levant-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant auxiliary data"}, "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-levant-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant interferograms"}, "FLATSIM_LEVANT_TIMESERIE_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-levant-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant time series"}, "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-maghreb-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb auxiliary data"}, "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-maghreb-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb interferograms"}, "FLATSIM_MAGHREB_TIMESERIE_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-maghreb-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb time series"}, "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-makran-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran auxiliary data"}, "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-makran-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran interferograms"}, "FLATSIM_MAKRAN_TIMESERIE_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-makran-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran time series"}, "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-mexique-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico auxiliary data"}, "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-mexique-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico interferograms"}, "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-mexique-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico time series"}, "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-mozambique-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique auxiliary data"}, "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-mozambique-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique interferograms"}, "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-mozambique-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique time series"}, "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-okavango-auxiliarydata-public,ground-to-radar,hydrology,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango auxiliary data"}, "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-okavango-interferogram-public,ground-geometry,hydrology,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango interferograms"}, "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-okavango-timeserie-public,ground-geometry,hydrology,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango time series"}, "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-ozark-auxiliarydata-public,ground-to-radar,hydrology,insar,lookup-tables,radar-to-ground,spectral-diversity,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark auxiliary data"}, "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-ozark-interferogram-public,ground-geometry,hydrology,insar,interferogram,radar-geometry,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark interferograms"}, "FLATSIM_OZARK_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-ozark-timeserie-public,ground-geometry,hydrology,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark time series"}, "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-showcase-auxiliarydata-public,ground-to-radar,insar,junit-vector,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase auxiliary data"}, "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-showcase-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase interferograms"}, "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-showcase-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase time series"}, "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tarim-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim auxiliary data"}, "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tarim-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim interferograms"}, "FLATSIM_TARIM_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-tarim-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim time series"}, "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults,(2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range,and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tianshan-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan auxiliary data"}, "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults, (2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range, and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tianshan-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan interferograms"}, "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults, (2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range, and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-tianshan-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan time series"}, "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tibethim-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet auxiliary data"}, "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tibethim-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet interferograms"}, "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-tibethim-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet time series"}, "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-turquie-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey auxiliary data"}, "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-turquie-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey interferograms"}, "FLATSIM_TURQUIE_TIMESERIE_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-turquie-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey time series"}, "MUSCATE_LANDSAT_LANDSAT8_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects along with a mask of clouds and their shadows, as well as a mask of water and snow. Landsat products are provided by Theia in surface reflectance (level 2A) with cloud masks, the processing being performed with the MAJA algorithm. They are orthorectified and cut on the same tiles as Sentinel-2 products.", "instrument": null, "keywords": "boa-reflectance,l2a,l8,landsat,landsat8,muscate-landsat-landsat8-l2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2013-04-11T10:13:56Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT8 L2A"}, "MUSCATE_Landsat57_LANDSAT5_N2A": {"abstract": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "instrument": null, "keywords": "boa-reflectance,l2a,l5,landsat,landsat5,muscate-landsat57-landsat5-n2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2009-01-09T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT5 L2A"}, "MUSCATE_Landsat57_LANDSAT7_N2A": {"abstract": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "instrument": null, "keywords": "boa-reflectance,l2a,l7,landsat,landsat7,muscate-landsat57-landsat7-n2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2009-01-03T10:42:49Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT7 L2A"}, "MUSCATE_OSO_RASTER_L3B-OSO": {"abstract": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The classification raster is a single raster covering the whole French metropolitan territory. It has a spatial resolution of 10 m. It results from the processing of the complete Sentinel-2 time series of the reference year using the iota\u00b2 processing chain. A Random Forest classification model is calibrated using a training dataset derived from a combination of several national and international vector data sources (BD TOPO IGN, Corine Land Cover, Urban Atlas, R\u00e9f\u00e9rentiel Parcellaire Graphique, etc.).", "instrument": null, "keywords": "l3b,l3b-oso,muscate-oso-raster-l3b-oso,oso,raster", "license": "Apache-2.0", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE OSO RASTER"}, "MUSCATE_OSO_VECTOR_L3B-OSO": {"abstract": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The Vector format is a product with a minimum collection unit of 0.1 ha derived from the 20 m raster with a procedure of regularization and a simplification of the polygons obtained. In order to preserve as much information as possible from the raster product, each polygon is characterized by a set of attributes: - The majority class, with the same nomenclature of the raster product. - The average number of cloud-free images used for classification and the standard deviation. These attributes are named validmean and validstd. - The confidence of the majority class obtained from the Random Forest classifier (value between 0 and 100). - The percentage of the area covered by each class of the classification. This percentage is calculated on the 10m raster, even if the simplified polygons are derived from the 20m raster. - The area of the polygon. - The product is clipped according to the administrative boundaries of the departments and stored in a zip archive containing the 4 files that make up the \u201cESRI Shapefile\u201d format.", "instrument": null, "keywords": "l3b,l3b-oso,muscate-oso-vector-l3b-oso,oso,vector", "license": "Apache-2.0", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE OSO VECTOR"}, "MUSCATE_PLEIADES_PLEIADES_ORTHO": {"abstract": "After the successful launch of Pleiades 1A (17 December 2011) and Pleiades 1B (1 December 2012), a Thematic Acceptance Phase (RTU) was set up by CNES, in cooperation with Airbus Defence and Space. The RTU took place over two years, from March 2012 to March 2014, with the objective of: - test THR imagery and the capabilities of Pleiades satellites (agility, stereo/tri-stereo,...) - benefit from the dedicated access policy for French institutions within the framework of the Delegation of Public Service (DSP) - thematically \u00abevaluate/validate\u00bb the value-added products and services defined through 130 thematic studies proposed by the various Working Groups. These studies covered 171 geographical sites, covering several fields (coastline, sea, cartography, geology, risks, hydrology, forestry, agriculture). - evaluate the algorithms and tools developed through the Methodological Component of the ORFEO programme. More than 650 Pleiades images representing a volume of nearly 170,000 km2 that were acquired by CNES and made available free of charge to some sixty French scientific and institutional laboratories. All images acquired within the specific framework of the RTU are considered as demonstration products for non-commercial use only.", "instrument": null, "keywords": "l1c,muscate-pleiades-pleiades-ortho,pleiades,reflectance,satellite-image,toa-reflectance", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Pleiades RTU L1C"}, "MUSCATE_PLEIADES_PLEIADES_PRIMARY": {"abstract": "After the successful launch of Pleiades 1A (17 December 2011) and Pleiades 1B (1 December 2012), a Thematic Acceptance Phase (RTU) was set up by CNES, in cooperation with Airbus Defence and Space. The RTU took place over two years, from March 2012 to March 2014, with the objective of: - test THR imagery and the capabilities of Pleiades satellites (agility, stereo/tri-stereo,...) - benefit from the dedicated access policy for French institutions within the framework of the Delegation of Public Service (DSP) - thematically \u00abevaluate/validate\u00bb the value-added products and services defined through 130 thematic studies proposed by the various Working Groups. These studies covered 171 geographical sites, covering several fields (coastline, sea, cartography, geology, risks, hydrology, forestry, agriculture). - evaluate the algorithms and tools developed through the Methodological Component of the ORFEO programme. More than 650 Pleiades images representing a volume of nearly 170,000 km2 that were acquired by CNES and made available free of charge to some sixty French scientific and institutional laboratories. All images acquired within the specific framework of the RTU are considered as demonstration products for non-commercial use only.", "instrument": null, "keywords": "l1a,muscate-pleiades-pleiades-primary,pleiades,pleiades-1a,pleiades-1b,primary,rtu,satellite-image", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Pleiades RTU L1A"}, "MUSCATE_SENTINEL2_SENTINEL2_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA (before called MACCS) for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-sentinel2-sentinel2-l2a,reflectance,s2,satellite-image,sentinel,sentinel2,surface", "license": "Apache-2.0", "missionStartDate": "2015-07-04T10:10:35.881Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SENTINEL2 L2A"}, "MUSCATE_SENTINEL2_SENTINEL2_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations.", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-sentinel2-sentinel2-l3a,reflectance,s2,satellite-image,sentinel,sentinel2,surface", "license": "Apache-2.0", "missionStartDate": "2017-07-15T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SENTINEL2 L3A"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot1-l1c,reflectance,satellite-image,spot,spot-1,spot1,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1986-03-18T20:21:50Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT1 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot2-l1c,reflectance,satellite-image,spot,spot-2,spot2,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1990-02-23T08:22:06Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT2 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot3-l1c,reflectance,satellite-image,spot,spot-3,spot3,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1993-10-02T13:56:34Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT3 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot4-l1c,reflectance,satellite-image,spot,spot-4,spot4,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1998-03-27T11:19:29Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT4 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Nowadays, two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot5-l1c,reflectance,satellite-image,spot,spot-5,spot5,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2002-06-21T09:52:57Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT5 L1C"}, "MUSCATE_Snow_LANDSAT8_L2B-SNOW": {"abstract": "The Theia snow product indicates the snow presence or absence on the land surface every fifth day if there is no cloud. The product is distributed by Theia as a raster file (8 bits GeoTIFF) of 20 m resolution and a vector file (Shapefile polygons). Level 2 offers monotemporal data, i.e. from ortho-rectified Sentinel-2 mono-date images, expressed in surface reflectance and accompanied by a cloud mask.", "instrument": null, "keywords": "cover,cryosphere,l2b,l2b-snow,l8,landsat,landsat8,muscate-snow-landsat8-l2b-snow,presence,snow,snow-mask", "license": "Apache-2.0", "missionStartDate": "2013-04-11T10:13:56Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Snow LANDSAT8 L2B"}, "MUSCATE_Snow_MULTISAT_L3B-SNOW": {"abstract": "Level 3 snow products offers annual syntheses of snow cover duration per pixel between September 1 and August 31. The L3B SNOW maps generated on September 1 are made from Sentinel-2 and Landsat-8 data. Those generated on September 2 will only be made from Sentinel-2. 4 raster files are provided in the product : 1- the snow cover duration map (SCD), pixel values within [0-number of days] corresponding the number of snow days, 2- the date of snow disappearance (Snow Melt-Out Date), defined as the last date of the longest snow period, 3- the date of snow appearance (Snow Onset Date), defined as the first date of the longest snow period, 4- the number of clear observations (NOBS) to compute the 3 other files.", "instrument": null, "keywords": "l3b,landsat,landsat8,muscate-snow-multisat-l3b-snow,presence,sentinel,sentinel2,snow,snow-cover", "license": "Apache-2.0", "missionStartDate": "2017-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE L3B Snow"}, "MUSCATE_Snow_SENTINEL2_L2B-SNOW": {"abstract": "Theia Snow product is generated from Sentinel-2 (20m resolution, every 5 days or less) and Landsat-8 images over selected areas of the globe. The processing chain used is Let-it-snow (LIS).", "instrument": null, "keywords": "cover,cryosphere,l2b,l2b-snow,muscate-snow-sentinel2-l2b-snow,presence,s2,sentinel2,snow,snow-mask", "license": "Apache-2.0", "missionStartDate": "2015-08-18T17:42:49Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Snow SENTINEL2 L2B"}, "MUSCATE_Spirit_SPOT5_L1A": {"abstract": "SPOT 5 stereoscopic survey of polar ice. The objectives of the SPIRIT project were to build a large archive of Spot 5 HRS images of polar ice and, for certain regions, to produce digital terrain models (DEMs) and high-resolution images for free distribution to the community. . scientist. The target areas were the coastal regions of Greenland and Antarctica as well as all other glacial masses (Alaska, Iceland, Patagonia, etc.) surrounding the Arctic Ocean and Antarctica. The SPIRIT project made it possible to generate an archive of DEMs at 40m planimetric resolution from the HRS instrument.", "instrument": null, "keywords": "1a,dem,glacier,ice,muscate-spirit-spot5-l1a,spirit,spot,spot5", "license": "Apache-2.0", "missionStartDate": "2003-08-06T12:54:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Spirit SPOT5 L1A"}, "MUSCATE_VENUSVM05_VM5_L1C": {"abstract": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13, bad pixel mask - channel 14, and cloudy pixels - channel 15).", "instrument": null, "keywords": "l1c,muscate-venusvm05-vm5-l1c,reflectance,satellite-image,toa-reflectance,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2022-03-09T11:42:13Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L1C"}, "MUSCATE_VENUSVM05_VM5_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-venusvm05-vm5-l2a,reflectance,satellite-image,surface,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2022-03-09T11:42:13Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L2A"}, "MUSCATE_VENUSVM05_VM5_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-venusvm05-vm5-l3a,reflectance,synthesis,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2023-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L3A"}, "MUSCATE_VENUS_VM1_L1C": {"abstract": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13, bad pixel mask - channel 14, and cloudy pixels - channel 15).", "instrument": null, "keywords": "l1c,muscate-venus-vm1-l1c,reflectance,satellite-image,toa-reflectance,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-11-01T10:06:54Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L1C"}, "MUSCATE_VENUS_VM1_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-venus-vm1-l2a,reflectance,satellite-image,surface,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-11-01T10:06:54Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L2A"}, "MUSCATE_VENUS_VM1_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-venus-vm1-l3a,reflectance,synthesis,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L3A"}, "MUSCATE_WaterQual_SENTINEL2_L2B-WATER": {"abstract": "The processing chain outputs rasters of the concentration of SPM estimated in the Bands B4 and B8. The concentration is given in mg/L. So a pixel value of 21.34 corresponds to 21.34 mg/L estimated at this point. The value -10000 signifies that there is no- or invalid data available. The concentration is always calculated only over the pixels classified as water. An RGB raster for the given ROI is also included. The values correspond to reflectance TOC (Top-Of-Canopy), which is unitless. Several masks generated by the Temporal-Synthesis are also included.", "instrument": null, "keywords": "l2b,l2b-water,muscate-waterqual-sentinel2-l2b-water,s2,sentinel,sentinel2,sentinel2a,sentinel2b", "license": "Apache-2.0", "missionStartDate": "2016-01-10T10:30:07Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE WaterQual SENTINEL2 L2B"}, "PEPS_S1_L1": {"abstract": "Sentinel-1 Level-1 products are the baseline products for the majority of users from which higher levels are derived. From data in each acquisition mode, the Instrument Processing Facility (IPF) generates focused Level-1 Single Look Complex (SLC) products and Level-1 Ground Range Detected (GRD) products. SAR parameters that vary with the satellite position in orbit, such as azimuth FM rate, Doppler centroid frequency and terrain height, are periodically updated to ensure the homogeneity of the scene when processing a complete data take. Similarly, products generated from WV data can contain any number of vignettes, potentially up to an entire orbit's worth. All Level-1 products are geo-referenced and time tagged with zero Doppler time at the centre of the swath. Geo-referencing is corrected for the azimuth bi-static bias by taking into account the pulse travel time delta between the centre of the swath and the range of each geo-referenced point. A Level-1 product can be one of the following two types: Single Look Complex (SLC) products or Ground Range Detected (GRD) products Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Level-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.", "instrument": null, "keywords": "backscatter,csar,grd,imagingradars,level1,peps-s1-l1,s1,sar,sentinel-1,sentinel1,slc", "license": "Apache-2.0", "missionStartDate": "2014-06-15T03:44:44.792Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-1 Level1"}, "PEPS_S1_L2": {"abstract": "Sentinel-1 Level-2 consists of geolocated geophysical products derived from Level-1. There is only one standard Level-2 product for wind, wave and currents applications - the Level-2 Ocean (OCN) product. The OCN product may contain the following geophysical components derived from the SAR data: - Ocean Wind field (OWI) - Ocean Swell spectra (OSW) - Surface Radial Velocity (RVL). OCN products are generated from all four Sentinel-1 imaging modes. From SM mode, the OCN product will contain all three components. From IW and EW modes, the OCN product will only contain OWI and RVL. From WV modes, the OCN product will only contain OSW and RVL.", "instrument": null, "keywords": "csar,oceans,oceanswellspectra,oceanwindfield,ocn,peps-s1-l2,s1,sar,sentinel1,surfaceradialvelocity,wavedirection,waveheight,wavelength,waveperiod,windstress", "license": "Apache-2.0", "missionStartDate": "2014-12-30T13:31:51.933Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-1 Level2"}, "PEPS_S2_L1C": {"abstract": "Sentinel-2 L1C tiles acquisition and storage from PEPS. Data are provided per S2 tile.", "instrument": null, "keywords": "l1c,peps-s2-l1c,reflectance,s2,sentinel2,toareflectance", "license": "Apache-2.0", "missionStartDate": "2015-07-04T10:10:06.027Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-2 L1C tiles"}, "PEPS_S2_L2A": {"abstract": "Sentinel-2 L2A tiles acquisition and storage from PEPS. Data are provdided per S2 tile.", "instrument": null, "keywords": "cloudcover,l2a,peps-s2-l2a,reflectance,s2,sentinel2,surfacereflectance", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-2 L2A tiles"}, "PEPS_S3_L1": {"abstract": "Sea surface topography measurements to at least the level of quality of the ENVISAT altimetry system, including an along track SAR capability of CRYOSAT heritage for improved measurement quality in coastal zones and over sea-ice", "instrument": null, "keywords": "altimetry,l1,level1,peps-s3-l1,s3,sentinel3,sral,ssh,stm,swh,windspeed", "license": "Apache-2.0", "missionStartDate": "2022-04-20T18:46:06.819Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GDH Sentinel-3 L1 STM Level-1 products"}, "POSTEL_LANDCOVER_GLOBCOVER": {"abstract": "A land cover map associates to each pixel of the surface a labelling characterizing the surface (ex : deciduous forest, agriculture area, etc) following a predefined nomenclature. A commonly used nomenclature is the LCCS (Land Cover Classification System) used by FAO and UNEP, and comprising 22 classes. POSTEL produces and makes available the global land cover map at 300 m resolution of the GLOBCOVER / ESA project, which can be viewed with a zooming capacity. Regional maps are also available with classes adapted to each bioclimatic area.", "instrument": null, "keywords": "classification,land-cover,land-surface,postel,postel-landcover-globcover", "license": "Apache-2.0", "missionStartDate": "2004-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL global land cover"}, "POSTEL_RADIATION_BRDF": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Bidirectional Reflectance Distribution Function (FDRB) describes how terrestrial surfaces reflect the sun radiation. Its potential has been demonstrated for several applications in land surface studies (see Bicheron and Leroy, 2000). The space-borne POLDER-1/ADEOS-1 instrument (November 1996 \u2013 June 1997) has provided the first opportunity to sample the BRDF of every point on Earth for viewing angles up to 60\u00b0-70\u00b0, and for the full azimuth range, at a spatial resolution of about 6km, when the atmospheric conditions are favorable (Hautecoeur et Leroy, 1998). From April to October 2003, the land surface BRDF was sampled by the POLDER-2/ADEOS-2 sensor. From March 2005, the POLDER-3 sensor onboard the PARASOL microsatellite measures the bi-driectional reflectance of the continental ecosystems. These successive observations allowed building : 1- a BRDF database from the 8 months of POLDER-1 mesurements : The POLDER-1 BRDF data base compiles 24,857 BRDFs acquired by ADEOS-1/POLDER-1 during 8 months, from November, 1996 to June, 1997, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-1 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms developed for the processing line of the ADEOS-2/POLDER-2 data. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000. 2- a BRDF database from the 7 months of POLDER-2 measurements : The POLDER-2 BRDF data base compiles 24,090 BRDFs acquired by ADEOS-2/POLDER-2 from April ro October 2003, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-2 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms described on the CNES scientific Web site. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000 land cover map. 3- 4 BRDF databases from one year of POLDER-3 measurements :The LSCE, one of the POSTEL Expertise Centre, defined a new method to select the BRDFs from POLDER-3/PARASOL data acquired from November 2005 to October 2006 in order to build 4 BRDF databases. 2 MONTHLY databases gathering the best quality BRDFs for each month, independently : one based upon the IGBP land cover map, the second based upon the GLC2000 land cover map. 2 YEARLY databases designed to monitor the annual cycle of surface reflectance and its directional signature. The selection of high quality pixels is based on the full year. The first database is based upon the IGBP land cover map, the second one is based upon the GLC2000 land cover map.", "instrument": null, "keywords": "bidirectional-reflectance-distribution-function,brdf,land,land-surface,polder,postel,postel-radiation-brdf,radiation,reflectance", "license": "Apache-2.0", "missionStartDate": "1996-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation BRDF"}, "POSTEL_RADIATION_DLR": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Downwelling Longwave Radiation (W.m-2) (DLR) is defined as the thermal irradiance reaching the surface in the thermal infrared spectrum (4 \u2013 100 \u00b5m). It is determined by the radiation that originates from a shallow layer close to the surface, about one third being emitted by the lowest 10 meters and 80% by the 500-meter layer. The DLR is derived from several sensors (Meteosat, MSG) using various approaches, in the framework of the Geoland project.", "instrument": null, "keywords": "geoland,irradiance,land,land-surface,long-wave-radiation-descending-flux,longwave,postel,postel-radiation-dlr,radiation,thermal", "license": "Apache-2.0", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Downwelling Longwave Radiation"}, "POSTEL_RADIATION_SURFACEALBEDO": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The albedo is the fraction of the incoming solar radiation reflected by the land surface, integrated over the whole viewing directions. The albedo can be directional (calculated for a given sun zenith angle, also called \u201cblack-sky albedo\u201d) or hemispheric (integrated over all illumination directions, also called \u201cwhite-sky albedo\u201d), spectral (for each narrow band of the sensor) or broadband (integrated over the solar spectrum). The surface albedos are derived from many sensors (Vegetation, Polder, Meteosat) in the frame of different projects, namely Geoland and Amma.", "instrument": null, "keywords": "albedo,amma,bio,geoland,land-surface-albedo,polder,postel,postel-radiation-surfacealbedo,radiation,surface", "license": "Apache-2.0", "missionStartDate": "1996-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation Surface Albedo"}, "POSTEL_RADIATION_SURFACEREFLECTANCE": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "instrument": null, "keywords": "boa-reflectance,land,parasol,polder,postel,postel-radiation-surfacereflectance,radiation,reflectance,surface,surface-reflectance", "license": "Apache-2.0", "missionStartDate": "1996-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation Surface Reflectance"}, "POSTEL_VEGETATION_FAPAR": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FAPAR is defined as the fraction of photosynthetically active radiation absorbed by vegetation for photosynthesis activity. The FAPAR can be instantaneous or daily. FAPAR is assessed using various approaches and algorithms applied to many sensors (Vegetation, Polder, Modis, AVHRR) in the frame of Polder and Amma projects.", "instrument": null, "keywords": "amma,bio,biosphere,fapar,fraction-of-absorbed-photosynthetically-active-radiation,geoland,polder,postel,postel-vegetation-fapar,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation FAPAR"}, "POSTEL_VEGETATION_FCOVER": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FCover is the fraction of ground surface covered by vegetation. Fcover is assessed using various approaches and algorithms applied to Vegetation, and Polder, data in the frame of the Cyclopes project.", "instrument": null, "keywords": "bio,biosphere,cyclopes,fcover,geoland,postel,postel-vegetation-fcover,vegetation,vegetation-cover-fraction", "license": "Apache-2.0", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation FCover"}, "POSTEL_VEGETATION_LAI": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation LAI is defined as half the total foliage area per unit of ground surface (Chen and Black, 1992). It is assessed using various approaches and algorithms applied to many sensors data (Vegetation, Polder, Modis, AVHRR) in the frame of the Amma project.", "instrument": null, "keywords": "amma,bio,biosphere,geoland,lai,leaf-area-index,postel,postel-vegetation-lai,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation LAI"}, "POSTEL_VEGETATION_NDVI": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. Postel Vegetation NDVI (Normalized Difference Vegetation Index) is calculated as the normalized ratio of the difference between the reflectances measured in the red and near-infrared sensor bands. The NDVI is the most frequently used vegetation index to assess the quantity of vegetation on the surface, and to monitor the temporal ecosystems variations. Postel provides NDVI, derived from observations of various sensors (Polder, AVHRR, Seviri) in the frame of different projects : Polder \u2013 Parasol and Amma.", "instrument": null, "keywords": "amma,bio,biosphere,ndvi,normalized-difference-vegetation-index,parasol,postel,postel-vegetation-ndvi,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation NDVI"}, "POSTEL_VEGETATION_SURFACEREFLECTANCE": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "instrument": null, "keywords": "boa-reflectance,cyclopes,geoland,globcover,land,postel,postel-vegetation-surfacereflectance,reflectance,surface,surface-reflectance,vegetation", "license": "Apache-2.0", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation Surface Reflectance"}, "POSTEL_WATER_PRECIP": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, IMP (University of Vienna) and EARS assess the precipitation amount from geo-stationnary sensors images using various approaches for applications of the Observatory Natural Carbon (ONC) and of the Observatory Food Security and Crop Monitoring (OFM). Postel Water PRECIP is global scale daily precipitation product based on existing multi-satellite products and bias-corrected precipitation gauge analyses.", "instrument": null, "keywords": "athmosphere,geoland,postel,postel-water-precip,precipitation,water", "license": "Apache-2.0", "missionStartDate": "1997-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Precipitation"}, "POSTEL_WATER_SOILMOISTURE": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, University of Bonn assess soil moisture parameters from passive micro-wave sensors measurements. Postel Water Soil Moisture is water column in mm, in the upper meter of soil.", "instrument": null, "keywords": "geoland,humidity,moisture,postel,postel-water-soilmoisture,soil,water,water-surface", "license": "Apache-2.0", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Soil Moisture"}, "POSTEL_WATER_SURFWET": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel SurfWet (Surface Wetness) is Soil moisture content in the 1-5 centimetre layer of the soil in relative units ranging between 0 wetness and total water capacity.", "instrument": null, "keywords": "geoland,postel,postel-water-surfwet,soil,soil-moisture,surface,surface-wetness,surfwet,water,water-surface", "license": "Apache-2.0", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Surface wet"}, "POSTEL_WATER_SWI": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel Water SWI (Soil Water Index) is soil moisture content in the 1st meter of the soil in relative units ranging between wilting level and field capacity.", "instrument": null, "keywords": "geoland,humidity,moisture,postel,postel-water-swi,soil,soil-water-index,swi,water,water-surface", "license": "Apache-2.0", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Soil Water Index"}, "SWH_SPOT123_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot123-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": "1986-02-23T08:53:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT1-2-3 Level1A"}, "SWH_SPOT4_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot4-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT4 Level1A"}, "SWH_SPOT5_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot5-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT5 Level1A"}, "TAKE5_SPOT4_L1C": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L1C products are data orthorectified reflectance at the top of the atmosphere.", "instrument": null, "keywords": "image,l1c,reflectance,satellite,spot,spot-4,spot4,take5,take5-spot4-l1c,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2013-01-31T01:57:43Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT4 LEVEL1C"}, "TAKE5_SPOT4_L2A": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "instrument": null, "keywords": "boa-reflectance,image,l2a,satellite,spot,spot-4,spot4,surface-reflectance,take5,take5-spot4-l2a", "license": "Apache-2.0", "missionStartDate": "2013-01-31T07:07:32Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT4 LEVEL2A"}, "TAKE5_SPOT5_L1C": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L1C products are data orthorectified reflectance at the top of the atmosphere.", "instrument": null, "keywords": "image,l1c,reflectance,satellite,spot,spot5,take5,take5-spot5-l1c,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2015-04-08T00:31:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT5 LEVEL1C"}, "TAKE5_SPOT5_L2A": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "instrument": null, "keywords": "boa-reflectance,image,l2a,reflectance,satellite,spot,spot5,take5,take5-spot5-l2a", "license": "Apache-2.0", "missionStartDate": "2015-04-08T00:31:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT5 LEVEL2A"}}, "providers_config": {"FLATSIM_AFAR_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_AFAR_AUXILIARYDATA_PUBLIC"}, "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC"}, "FLATSIM_AFAR_TIMESERIE_PUBLIC": {"productType": "FLATSIM_AFAR_TIMESERIE_PUBLIC"}, "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC"}, "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC"}, "FLATSIM_ANDES_TIMESERIE_PUBLIC": {"productType": "FLATSIM_ANDES_TIMESERIE_PUBLIC"}, "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC"}, "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC"}, "FLATSIM_BALKANS_TIMESERIE_PUBLIC": {"productType": "FLATSIM_BALKANS_TIMESERIE_PUBLIC"}, "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CAUCASE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CAUCASE_TIMESERIE_PUBLIC"}, "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CHILI_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CHILI_TIMESERIE_PUBLIC"}, "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CORSE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CORSE_TIMESERIE_PUBLIC"}, "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_FRANCE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_FRANCE_TIMESERIE_PUBLIC"}, "FLATSIM_INDE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_INDE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_INDE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_INDE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_INDE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_INDE_TIMESERIE_PUBLIC"}, "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC"}, "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC"}, "FLATSIM_LEVANT_TIMESERIE_PUBLIC": {"productType": "FLATSIM_LEVANT_TIMESERIE_PUBLIC"}, "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MAGHREB_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MAGHREB_TIMESERIE_PUBLIC"}, "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MAKRAN_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MAKRAN_TIMESERIE_PUBLIC"}, "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC"}, "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC"}, "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC"}, "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC"}, "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC": {"productType": "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC"}, "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC"}, "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC"}, "FLATSIM_OZARK_TIMESERIE_PUBLIC": {"productType": "FLATSIM_OZARK_TIMESERIE_PUBLIC"}, "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC"}, "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TARIM_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TARIM_TIMESERIE_PUBLIC"}, "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC"}, "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC"}, "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TURQUIE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TURQUIE_TIMESERIE_PUBLIC"}, "MUSCATE_LANDSAT_LANDSAT8_L2A": {"productType": "MUSCATE_LANDSAT_LANDSAT8_L2A"}, "MUSCATE_Landsat57_LANDSAT5_N2A": {"productType": "MUSCATE_Landsat57_LANDSAT5_N2A"}, "MUSCATE_Landsat57_LANDSAT7_N2A": {"productType": "MUSCATE_Landsat57_LANDSAT7_N2A"}, "MUSCATE_OSO_RASTER_L3B-OSO": {"productType": "MUSCATE_OSO_RASTER_L3B-OSO"}, "MUSCATE_OSO_VECTOR_L3B-OSO": {"productType": "MUSCATE_OSO_VECTOR_L3B-OSO"}, "MUSCATE_PLEIADES_PLEIADES_ORTHO": {"productType": "MUSCATE_PLEIADES_PLEIADES_ORTHO"}, "MUSCATE_PLEIADES_PLEIADES_PRIMARY": {"productType": "MUSCATE_PLEIADES_PLEIADES_PRIMARY"}, "MUSCATE_SENTINEL2_SENTINEL2_L2A": {"productType": "MUSCATE_SENTINEL2_SENTINEL2_L2A"}, "MUSCATE_SENTINEL2_SENTINEL2_L3A": {"productType": "MUSCATE_SENTINEL2_SENTINEL2_L3A"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C"}, "MUSCATE_Snow_LANDSAT8_L2B-SNOW": {"productType": "MUSCATE_Snow_LANDSAT8_L2B-SNOW"}, "MUSCATE_Snow_MULTISAT_L3B-SNOW": {"productType": "MUSCATE_Snow_MULTISAT_L3B-SNOW"}, "MUSCATE_Snow_SENTINEL2_L2B-SNOW": {"productType": "MUSCATE_Snow_SENTINEL2_L2B-SNOW"}, "MUSCATE_Spirit_SPOT5_L1A": {"productType": "MUSCATE_Spirit_SPOT5_L1A"}, "MUSCATE_VENUSVM05_VM5_L1C": {"productType": "MUSCATE_VENUSVM05_VM5_L1C"}, "MUSCATE_VENUSVM05_VM5_L2A": {"productType": "MUSCATE_VENUSVM05_VM5_L2A"}, "MUSCATE_VENUSVM05_VM5_L3A": {"productType": "MUSCATE_VENUSVM05_VM5_L3A"}, "MUSCATE_VENUS_VM1_L1C": {"productType": "MUSCATE_VENUS_VM1_L1C"}, "MUSCATE_VENUS_VM1_L2A": {"productType": "MUSCATE_VENUS_VM1_L2A"}, "MUSCATE_VENUS_VM1_L3A": {"productType": "MUSCATE_VENUS_VM1_L3A"}, "MUSCATE_WaterQual_SENTINEL2_L2B-WATER": {"productType": "MUSCATE_WaterQual_SENTINEL2_L2B-WATER"}, "PEPS_S1_L1": {"productType": "PEPS_S1_L1"}, "PEPS_S1_L2": {"productType": "PEPS_S1_L2"}, "PEPS_S2_L1C": {"productType": "PEPS_S2_L1C"}, "PEPS_S2_L2A": {"productType": "PEPS_S2_L2A"}, "PEPS_S3_L1": {"productType": "PEPS_S3_L1"}, "POSTEL_LANDCOVER_GLOBCOVER": {"productType": "POSTEL_LANDCOVER_GLOBCOVER"}, "POSTEL_RADIATION_BRDF": {"productType": "POSTEL_RADIATION_BRDF"}, "POSTEL_RADIATION_DLR": {"productType": "POSTEL_RADIATION_DLR"}, "POSTEL_RADIATION_SURFACEALBEDO": {"productType": "POSTEL_RADIATION_SURFACEALBEDO"}, "POSTEL_RADIATION_SURFACEREFLECTANCE": {"productType": "POSTEL_RADIATION_SURFACEREFLECTANCE"}, "POSTEL_VEGETATION_FAPAR": {"productType": "POSTEL_VEGETATION_FAPAR"}, "POSTEL_VEGETATION_FCOVER": {"productType": "POSTEL_VEGETATION_FCOVER"}, "POSTEL_VEGETATION_LAI": {"productType": "POSTEL_VEGETATION_LAI"}, "POSTEL_VEGETATION_NDVI": {"productType": "POSTEL_VEGETATION_NDVI"}, "POSTEL_VEGETATION_SURFACEREFLECTANCE": {"productType": "POSTEL_VEGETATION_SURFACEREFLECTANCE"}, "POSTEL_WATER_PRECIP": {"productType": "POSTEL_WATER_PRECIP"}, "POSTEL_WATER_SOILMOISTURE": {"productType": "POSTEL_WATER_SOILMOISTURE"}, "POSTEL_WATER_SURFWET": {"productType": "POSTEL_WATER_SURFWET"}, "POSTEL_WATER_SWI": {"productType": "POSTEL_WATER_SWI"}, "SWH_SPOT123_L1": {"productType": "SWH_SPOT123_L1"}, "SWH_SPOT4_L1": {"productType": "SWH_SPOT4_L1"}, "SWH_SPOT5_L1": {"productType": "SWH_SPOT5_L1"}, "TAKE5_SPOT4_L1C": {"productType": "TAKE5_SPOT4_L1C"}, "TAKE5_SPOT4_L2A": {"productType": "TAKE5_SPOT4_L2A"}, "TAKE5_SPOT5_L1C": {"productType": "TAKE5_SPOT5_L1C"}, "TAKE5_SPOT5_L2A": {"productType": "TAKE5_SPOT5_L2A"}}}, "planetary_computer": {"product_types_config": {"3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Classification"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Cloud"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Surface Model"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model (Native)"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Height above Ground"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Intensity"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Source"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Returns"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "missionStartDate": "1925-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Seamless DEMs"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "missionStartDate": "2016-12-07T00:00:00Z", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "title": "ALOS World 3D-30m"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS Forest/Non-Forest Annual Mosaic"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS PALSAR Annual Mosaic"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "missionStartDate": "2000-03-04T12:00:00Z", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "title": "ASTER L1T"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (13-class)"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (7-class)"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Use"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "missionStartDate": "2003-07-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chloris Biomass"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40] \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40] \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-SA-40] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)"}, "conus404": {"abstract": "[CONUS404](https://www.usgs.gov/data/conus404-four-kilometer-long-term-regional-hydroclimate-reanalysis-over-conterminous-united) is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the conterminous United States. CONUS404, so named because it covers the CONterminous United States for over 40 years at 4 km resolution, was produced by the Weather Research and Forecasting (WRF) model simulations run by NCAR as part of a collaboration with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water years 1980-2021) and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for CONUS surface waters.\n\nThe CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in [ds612.0](https://rda.ucar.edu/datasets/ds612.0/) (Liu, et al., 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the NOAH-Multiparameterization land surface model. It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5) as input and covers a longer period than CONUS1.", "instrument": null, "keywords": "climate,conus404,hydroclimate,hydrology,inland-waters,precipitation,weather", "license": "CC-BY-4.0", "missionStartDate": "1979-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CONUS404"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Hawaii"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual North America"}, "daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Puerto Rico"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Hawaii"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily North America"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Puerto Rico"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Hawaii"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly North America"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Puerto Rico"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Flood Maps"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n\| Name \| Scale \| Period \| Number of basins \|\n\|--------\|--------------------------\|-----------\|------------------\|\n\| ERA5 \| Global \| 1967-2020 \| 3236 \|\n\| CHIRPS \| Global (+/- 50 latitude) \| 1981-2020 \| 2951 \|\n\| EOBS \| Europe/North Africa \| 1979-2020 \| 682 \|\n\| NLDAS \| USA \| 1979-2020 \| 1090 \|\n\| BOM \| Australia \| 1979-2020 \| 116 \|\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "missionStartDate": "1970-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Water Availability"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Denver Regional Council of Governments Land Use Land Cover"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Urban Innovation Eclipse Sensor Data"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows forecast times* in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* stream is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* type is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* reference time is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the reference time changes and `step` as the forecast time changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast was made, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ECMWF Open Data (real-time)"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An analysis, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A forecast starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ERA5 - PDS"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "title": "ESA WorldCover"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "missionStartDate": "2020-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Forest Inventory and Analysis"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\nNOTE: Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "missionStartDate": "2022-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FWS National Wetlands Inventory"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS Gap Land Cover"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "missionStartDate": "2021-04-13T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Biodiversity Information Facility (GBIF)"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Rasters"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Tables"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "missionStartDate": "2017-02-28T00:16:52Z", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "title": "GOES-R Cloud & Moisture Imagery"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "missionStartDate": "2018-02-13T16:10:00Z", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "title": "GOES-R Lightning Detection"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "missionStartDate": "2000-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GPM IMERG"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gridMET"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "missionStartDate": "2010-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HGB: Harmonized Global Biomass for 2010"}, "hls2-l30": {"abstract": "Harmonized Landsat Sentinel-2 (HLS) Version 2.0 provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard the ESA (European Space Agency) Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30 meter (m) spatial resolution. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling and are \u201cstackable\u201d for time series analysis. This dataset is in the form of [cloud-optimized GeoTIFFs](https://www.cogeo.org/). The HLS v2.0 data is generated by NASA's IMPACT team at Marshall Space Flight Center. The product latency is 1.7 days, from the satellite overpass to the HLS data availability at LP DAAC. For more information see LP DAAC's [HLS Overview](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/).", "instrument": null, "keywords": "global,hls,hls2-l30,imagery,landsat,landsat-8,satellite,sentinel", "license": "not-provided", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "Landsat 8", "processingLevel": null, "title": "Harmonized Landsat Sentinel-2 (HLS) Version 2.0, Landsat Portion"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. Access: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. Reliability: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. Usage: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. Nighttime Lights: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "missionStartDate": "2012-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HREA: High Resolution Electricity Access"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Biodiversity Intactness"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Esri 10-Meter Land Cover (10-class)"}, "io-lulc-9-class": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V1"}, "io-lulc-annual-v02": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201cBare\u201d and any of the vegetative classes \u201cTrees,\u201d \u201cCrops,\u201d \u201cFlooded Vegetation,\u201d and \u201cRangeland\u201d. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-annual-v02,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V2"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "missionStartDate": "1984-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "JRC Global Surface Water"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HydroForecast - Kwando & Upper Zambezi Rivers"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "missionStartDate": "1972-07-25T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "title": "Landsat Collection 2 Level-1"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "missionStartDate": "2020-04-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MoBI: Map of Biodiversity Importance"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (500m)"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (250m)"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover Daily"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover 8-day"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity Daily"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity 8-Day"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (500m)"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (250m)"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire Daily"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire 8-Day"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 8-Day"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 4-Day"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2001-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Evapotranspiration Yearly Gap-Filled"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Primary Production Yearly Gap-Filled"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Burned Area Monthly"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\nStage 1: Semantic Segmentation\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\nStage 2: Polygonization\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are not spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "missionStartDate": "2014-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Microsoft Building Footprints"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "missionStartDate": "1984-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MTBS: Monitoring Trends in Burn Severity"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- model is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- scenario is one of \"historical\", \"ssp245\" or \"ssp585\".\n- variable is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- year depends on the value of scenario. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some experimental reference files as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "missionStartDate": "2000-02-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NASADEM HGT v001"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "missionStartDate": "1975-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "C-CAP Regional Land Cover and Change"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR NetCDFs"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - Optimum Interpolation CDR"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR NetCDFs"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n\| Abbreviation \| Description \|\n\| ------------ \| ---------------------------------------- \|\n\| prcp \| Precipitation over the time period \|\n\| tavg \| Mean temperature over the time period \|\n\| tmax \| Maximum temperature over the time period \|\n\| tmin \| Minimum temperature over the time period \|\n\nAnd an \"aggregation\":\n\n\| Abbreviation \| Description \|\n\| ------------ \| ------------------------------------------------------------------------------ \|\n\| max \| Maximum of the variable over the time period \|\n\| min \| Minimum of the variable over the time period \|\n\| std \| Standard deviation of the value over the time period \|\n\| flag \| An count of the number of inputs (months, years, etc.) to calculate the normal \|\n\| norm \| The normal for the variable over the time period \|\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (NetCDF)"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Tabular Climate Normals"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 1 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 1"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 2 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 2"}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 24-Hour Pass 2 sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 24-Hour Pass 2"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains Monthly data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nNote: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.\n", "instrument": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "missionStartDate": "1895-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Land Cover of Canada"}, "planet-nicfi-analytic": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Analytic)"}, "planet-nicfi-visual": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Visual)"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31Z", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "missionStartDate": "2016-04-25T11:33:47.368562Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land (Full Resolution)"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "missionStartDate": "2017-11-01T00:07:01.738487Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Water (Full Resolution)"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2020-08-08T23:11:15.617203Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Fire Radiative Power"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2016-04-19T01:35:17.188500Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Temperature"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2017-10-31T23:59:57.451604Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Sea Surface Temperature"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2016-03-01T14:07:51.632846Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Radar Altimetry"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2017-01-28T00:59:14.149496Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Ocean Radar Altimetry"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2020-04-16T19:36:28.012367Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Global Aerosol"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-22T16:51:00.001276Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Reflectance and Aerosol"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-27T11:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-04T23:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-08T08:09:40.491227Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "missionStartDate": "2018-04-30T00:18:50Z", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "title": "Sentinel-5P Level-2"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1958-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TerraClimate"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "missionStartDate": "2021-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "US Census"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USDA Cropland Data Layers (CDLs)"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP CONUS Collection 1.3"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "missionStartDate": "2000-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP Hawaii Collection 1.0"}}, "providers_config": {"3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "3dep-seamless": {"productType": "3dep-seamless"}, "alos-dem": {"productType": "alos-dem"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "aster-l1t": {"productType": "aster-l1t"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "chloris-biomass": {"productType": "chloris-biomass"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "conus404": {"productType": "conus404"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "deltares-floods": {"productType": "deltares-floods"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "drcog-lulc": {"productType": "drcog-lulc"}, "eclipse": {"productType": "eclipse"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "era5-pds": {"productType": "era5-pds"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}, "fia": {"productType": "fia"}, "fws-nwi": {"productType": "fws-nwi"}, "gap": {"productType": "gap"}, "gbif": {"productType": "gbif"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "goes-cmi": {"productType": "goes-cmi"}, "goes-glm": {"productType": "goes-glm"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gridmet": {"productType": "gridmet"}, "hgb": {"productType": "hgb"}, "hls2-l30": {"productType": "hls2-l30"}, "hrea": {"productType": "hrea"}, "io-biodiversity": {"productType": "io-biodiversity"}, "io-lulc": {"productType": "io-lulc"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-lulc-annual-v02": {"productType": "io-lulc-annual-v02"}, "jrc-gsw": {"productType": "jrc-gsw"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "mobi": {"productType": "mobi"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "ms-buildings": {"productType": "ms-buildings"}, "mtbs": {"productType": "mtbs"}, "naip": {"productType": "naip"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "nasadem": {"productType": "nasadem"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "terraclimate": {"productType": "terraclimate"}, "us-census": {"productType": "us-census"}, "usda-cdl": {"productType": "usda-cdl"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}}}, "usgs_satapi_aws": {"product_types_config": {"landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1972-07-25T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-1 Product"}, "landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product"}}, "providers_config": {"landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}}}, "wekeo_cmems": {"product_types_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\n\nThe operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00003", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1d-m-202105,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"abstract": "'''Short description:'''\n\nThe operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1d-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1m-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-pt1h-i-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-pt6h-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"abstract": "'''Short Description:'''\n\nThe Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\n'''DOI (product) :''' \n\nhttps://doi.org/10.48670/moi-00004", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-ice-002-011:cmems-mod-arc-phy-anfc-nextsim-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\n\nThe TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00006", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1d-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1y-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1d-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"abstract": "'''Short description:'''\n\nThe current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00007", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1y-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe Arctic Sea Ice Reanalysis system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, from Bering Strait to the North Atlantic. neXtSIM is forced by reanalyzed surface atmosphere forcings (ERA5) from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4b, the ARC MFC MYP system (002_003). neXtSIM assimilates satellite sea ice concentrations from Passive Microwave satellite sensors, and sea ice thickness from CS2SMOS in winter from October 2010 onwards. The output variables are sea ice concentrations (total, young ice, and multi-year ice), sea ice thickness, sea ice velocity, snow depth on sea ice, sea ice type, sea ice age, sea ice ridge volume fraction and sea ice albedo, provided at daily and monthly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00336", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1977-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1977-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Reanalysis"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"abstract": "'''Short description:'''\n\nThis Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, dissolved iron, dissolved cdom, hydrogen sulfide, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann et al, 2021) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the Baltic Sea physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-7-10days-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-ssh-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-ssh-anfc-detided-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1d-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1m-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :'''\n\nhttps://doi.org/10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1y-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1d-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1m-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1y-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"abstract": "'''This product has been archived''' \n\n'''Short description :'''\n\nThis Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-2km-climatology-p1m-m-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Hindcast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Hindcast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-aflux-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Hindcast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"abstract": "\"Short description:''' \n\nBLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.2-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, zooplankton and phytoplankton carbon biomass, oxygen-demand-units, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\n'''DOI (product) :''' \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-pt1h-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"abstract": "'''Short description''': \n\nThe BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 3 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"abstract": "'''Short description''': \n\nThe wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\n'''Citation''': \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-analysisforecast-wav-007-003:cmems-mod-blk-wav-anfc-2.5km-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"abstract": "'''Short description:''' \n\nThe biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\n'''DOI (product)''':\nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"abstract": "'''Short description''': \n\nThe BLKSEA_MULTIYEAR_PHY_007_004 product provides ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamic core is based on the NEMOv4.0 general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/40\u00ba and 121 vertical levels. NEMO is forced by atmospheric fluxes computed from a bulk formulation applied to ECMWF ERA5 atmospheric fields at the resolution of 1/4\u00ba in space and 1-h in time. A heat flux correction through sea surface temperature (SST) relaxation is employed using the ESA-CCI SST-L4 product. This version has an open lateral boundary, a new model characteristic that allows a better inflow/outflow representation across the Bosphorus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly (SLA) along-track observations from Copernicus and available in situ vertical profiles of temperature and salinity from both SeaDataNet and Copernicus datasets. Upgrades on data assimilation include an improved background error covariance matrix and an observation-based mean dynamic topography for the SLA assimilation.\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-aflux-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"abstract": "'''Short description''': \n\nThe wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.\n\n'''Citation''': \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). \n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-climatology-pt1m-m-202311,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-myint-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThe Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID[https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-028.pdf]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00015", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1d-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1m-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"abstract": "'''Short description'''\n\nThe Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-pt6h-i-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-pt6h-i-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-pt6h-i-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-climatology-uncertainty-p1m-m-202311,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1d-m-202411,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1m-m-202411,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1d-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1m-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-pt1h-m-202406,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-sl-pt1h-i-202411,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-uv-pt1h-i-202211,forecast,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"abstract": "'''Short description:'''\n \nThe operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-analysisforecast-wav-001-027:cmems-mod-glo-wav-anfc-0.083deg-pt3h-i-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"abstract": "'''Short description'''\n\nThe biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4 ocean physics produced at Mercator-Ocean and ERA-Interim atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00019", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"abstract": "'''Short description:'''\nThe Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [https://www.cls.fr CLS] on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n'''Forcings sources:'''\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n'''Vertical coverage:'''\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00020", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-fphy-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-climatology-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"abstract": "'''Short description:'''\n \nThe GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00021", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\nYou can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution : monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-climatology-p1m-m-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-pt3h-i-202411,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"abstract": "'''Short description:'''\n\nGLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-myint-0.2deg-pt3h-i-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "\"''Short description:'''\n\nThe IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 1/36\u00ba of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-analysisforecast-wav-005-005:cmems-mod-ibi-wav-anfc-0.027deg-pt1h-i-202411,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1y-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-myint-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-myint-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-climatology-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1d-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1m-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, zooplankton and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\nFor all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00028", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1y-m-202211,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-myint-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-myint-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1y-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-2d-pt1h-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-climatology-p1m-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1d-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1m-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Besides, daily means of air-sea fluxes are provided. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. For all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00029", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1y-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-hflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-hflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-mflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-mflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wcur-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wcur-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-wflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-myint-0.083deg-pt1h-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-aflux-0.027deg-p1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThe IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1980 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product and with the same horizontal resolution (1/36\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Besides, air-sea fluxes are provided. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-climatology-p1m-m-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-pt1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-30", "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"abstract": "'''Short description:''''\nGlobal Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-my-013-052:cmems-obs-ins-glo-phy-temp-sal-my-cora-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1960-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"abstract": "'''Short description:'''\nFor the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-nrt-013-002:cmems-obs-ins-glo-phy-temp-sal-nrt-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Real time in-situ observations objective analysis"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202411": {"abstract": "'''Short Description'''\n\nThe biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\nSalon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997\u20131022. DOI: https://doi.org/10.5194/os-15-997-2019\n\n''DOI (Product)'': \nhttps://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "'''Short Description'''\n\nThe physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations. \n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\"\n\n''DOI (Product)'':\nhttps://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"abstract": "'''Short description:'''\n \nMEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product)''': https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-wav-006-017:cmems-mod-med-wav-anfc-4.2km-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"abstract": "'''Short Description'''\nThe Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n''DOI (Interim dataset)'':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-pft-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-plankton-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc_my_4.2km-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc_my_4.2km-climatology_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-my-4.2km-climatology-p1m-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-cur-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mld-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-sal-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-ssh-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-tem-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-my-4.2km-climatology-p1m-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"abstract": "'''Short description:'''\n\nThe Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\n\nMEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. \n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 \n\n''DOI (Interim dataset)'': \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I \n \n''DOI (climatological dataset)'': \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM \n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-my-4.2km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-myint-4.2km-pt1h-i-202112,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:med-hcmr-wav-rean-h-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"abstract": "'''Short description:'''\n\nThis product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00046", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-climatology-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-p7d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"abstract": "'''Short description:'''\n\nThis product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-my-irr-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Surface ocean carbon fields"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-nrt-irr-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1985-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Surface ocean carbon fields"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThis product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\n'''DOI (product) :''' \nhttps://doi.org/10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-asc-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-des-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n\n'''DOI (product) :''' \nhttps://doi.org/10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l4-my-015-015:cmems-obs-mob-glo-phy-sss-my-multi-oi-p1w-202406,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-05-31", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"abstract": "'''Short description:'''\n\nThis product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\n'''Product citation''': \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"abstract": "'''Short description:'''\n\nYou can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 5 datasets: \n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1D-m, which delivers near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1M-m, which delivers near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy_my_0.125deg_P1D-m, which delivers multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy_my_0.125deg_P1M-m, which delivers multi-year reprocessed (REP) monthly data\n* cmems_obs-mob_glo_phy_mynrt_0.125deg-climatology-uncertainty_P1M-m, which delivers monthly static uncertainty data\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00052", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"abstract": "'''Short description''':\n\nYou can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\n'''DOI (product) :''' \nhttps://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:multiobs-glo-phy-w-3d-rep-015-007:cmems-obs-mob-glo-phy-cur-my-0.25deg-p7d-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 10-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-analysisforecast-wav-004-014:cmems-mod-nws-wav-anfc-0.027deg-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf CMEMS-NWS-QUID-004-011]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf CMEMS-NWS-PUM-004-009_011] for details.\n\n'''Associated products:'''\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009].\nAn analysis-forecast product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011 NWSHELF_MULTIYEAR_BGC_004_011].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-diato-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-dino-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-nano-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-pico-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-myint-7km-2d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026 GLOBAL_REANALYSIS_PHY_001_026] and at the Baltic boundary by the [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011 BALTICSEA_REANALYSIS_PHY_003_011]. The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf CMEMS-NWS-QUID-004-009]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf CMEMS-NWS-PUM-004-009_011] for further details.\n\n'''Associated products:'''\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011]. An analysis-forecast product is available from [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 NWSHELF_ANALYSISFORECAST_PHY_LR_004_011].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sss-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sst-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"abstract": "'''Short description:'''\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013 NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ECMWF ERA-5] Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf CMEMS-NWS-PUM-004-015] for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf CMEMS-NWS-QUID-004-015].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''Associated products:'''\n\n[https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014 NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-reanalysis-wav-004-015:meto-nws-wav-ran-202007,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-plankton-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 m''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-reflectance-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-transp-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products , and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''')\n\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 meters''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l4-my-009-124:cmems-obs-oc-arc-bgc-plankton-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-optics-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-multi-1km-p1d-202411,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-1km-p1d-202411,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-transp-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-optics-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-multi-1km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-1km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-transp-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-pp-my-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-pp-nrt-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l3-nrt-009-202:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l4-nrt-009-208:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-optics-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-optics-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-plankton-nrt-l3-olci-300m-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-multi-1km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': monthly \n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-nrt-009-132:cmems-obs-oc-bal-bgc-plankton-nrt-l4-olci-300m-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l3-nrt-009-206:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l4-nrt-009-212:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_BLK_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''', '''\"\"pp\"\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L4_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' and '''\"\"pp\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-optics-my-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-300m-p1d-202211,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\"\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily''', '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-optics-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-multi-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-300m-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-4km-p1d-202411,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-300m-p1d-202211,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-optics-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\"\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-climatology-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-pp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL''').\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-108:c3s-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-optics-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-multi-4km-p1m-202411,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-pp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l3-nrt-009-204:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l4-nrt-009-210:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l3-nrt-009-205:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l4-nrt-009-211:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"multi'''\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* '''''pp''''' with the Integrated Primary Production (PP)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"gap-free\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L4_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' and '''\"\"pp\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' (4 km for '''\"\"pp\"\"''') and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\ncmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\ncmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l3-nrt-009-203:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\ncmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\ncmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\nnetCDF-4, CF-1.7\nINSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l4-nrt-009-209:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p2d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"abstract": "'''Short description:''' \n\nAntarctic sea ice displacement during winter from medium resolution sensors since 2002\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p2d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"abstract": "'''Short description:''' \n\nArctic Sea and Ice surface temperature\n'''Detailed description:'' 'Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l3-my-011-021:cmems-obs-si-arc-phy-my-l3s-dmioi-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"abstract": "'''Short description:''' \nArctic Sea and Ice surface temperature\n\n'''Detailed description:'''\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l4-my-011-016:cmems-obs-si-arc-phy-my-l4-dmioi-p1d-m-202105,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-12-31", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "'''Short description:''' \n\nArctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer :\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p30d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p6d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-007:dmi-arc-seaice-berg-mosaic-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS_201907": {"abstract": "'''Short description:''' \n\nThe iceberg product contains 4 datasets (IW and EW modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.\n\nThe iceberg product also contains two additional datasets of individual iceberg positions in Greenland-Newfoundland-Labrador Waters. These datasets are in shapefile format to allow the best representation of the icebergs (the 1st dataset contains the iceberg point observations, the 2nd dataset contains the polygonized satellite coverage). These are also derived by applying a Constant False Alarm Rate (CFAR) algorithm on Sentinel-1 SAR imagery.\nDespite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00129", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-007:dmi-arc-seaice-berg-mosaic-iw-l4-nrt-obs-201907,level-4,marine-resources,marine-safety,near-real-time,not-applicable,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"abstract": "'''Short description:'''\n\nArctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-008:dmi-arc-seaice-temp-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-phy-sit-my-l4-1km-p1d-m-202211,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1980-11-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"abstract": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-seaice-conc-my-1km-202112,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1980-11-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-conc-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"abstract": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-thick-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"abstract": "'''Short description:'''\n\nArctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-phy-climate-l3-my-011-013:c3s-obs-si-glo-phy-my-nh-l3-p1m-202411,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-04-30", "missionStartDate": "1995-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"abstract": "'''Short description:'''\n\nArctic sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and Aprilt to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-phy-l4-nrt-011-014:esa-obs-si-arc-phy-sit-nrt-l4-multi-p1d-m-202411,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-04-11", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Ice Thickness derived from merging of L-Band radiometry and radar altimeter derived sea ice thickness"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"abstract": "'''Short description:''' \n\nFor the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-nh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-sh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_north_d_202007": {"abstract": "'''Short description:'''\n\nDTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00135", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-006:cmems-sat-si-glo-drift-nrt-north-d-202007,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-006:cmems-sat-si-glo-drift-nrt-south-d-202007,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"abstract": "'''Short description:''' \nThe CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-nh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-sh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-nrt-008-060:cmems-obs-sl-eur-phy-ssh-nrt-allsat-l4-duacs-0.0625deg-p1d-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-nrt-008-060:cmems-obs-sl-eur-phy-ssh-nrt-allsat-l4-duacs-0.125deg-p1d-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"abstract": "'''Short description:''' \n\nDUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1d-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1m-m-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-nrt-008-046:cmems-obs-sl-glo-phy-ssh-nrt-allsat-l4-duacs-0.125deg-p1d-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-nrt-008-046:cmems-obs-sl-glo-phy-ssh-nrt-allsat-l4-duacs-0.25deg-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-my-010-038:cmems-obs-sst-atl-phy-my-l3s-p1d-m-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-gir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pmw-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-nrt-l3s-p1d-m-202211,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:'''\n\nFor the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-nrt-observations-010-025:ifremer-atl-sst-l4-nrt-obs-full-time-serie-201904,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2018-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"abstract": "'''Short description:''' \n\nFor the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-rep-observations-010-026:cmems-ifremer-atl-sst-l4-rep-obs-full-time-serie-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"abstract": "'''Short description:''' \nFor the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-l3s-my-010-040:cmems-obs-sst-bal-phy-my-l3s-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"abstract": "'''Short description:'''\nFor the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-subskin-l4-nrt-010-034:cmems-obs-sst-bal-phy-subskin-nrt-l4-pt1h-m-202211,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-05-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l3s-nrt-observations-010-032:dmi-baltic-sst-l3s-nrt-obs-full-time-serie-201904,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "EO:MO:DAT:SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_B:DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE": {"abstract": "'''Short description:'''\n\nFor the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00155", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l4-nrt-observations-010-007-b:dmi-baltic-sst-l4-nrt-obs-full-time-serie,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2018-12-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea- Sea Surface Temperature Analysis L4"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"abstract": "'''Short description:''' \nFor the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l4-rep-observations-010-016:dmi-bal-sst-l4-rep-observations-010-016-202012,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-l3s-my-010-041:cmems-obs-sst-bs-phy-my-l3s-p1d-m-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "'''Short description:'''\n\nFor the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-subskin-l4-nrt-010-035:cmems-obs-sst-blk-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-a-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-b-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-b,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-d,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-a-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-c-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-rep-observations-010-022:cmems-sst-bs-sst-l4-rep-observations-010-022-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l3s-my-010-039:cmems-obs-sst-glo-phy-my-l3s-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_PHY_L4_MY_010_044:cmems_obs-sst_glo_phy_my_l4_P1D-m_202411": {"abstract": "'''Short description:'''\n\nFor the global ocean. The IFREMER/ODYSSEA Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.10deg. x 0.10deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00345", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sst-glo-phy-l4-my-010-044:cmems-obs-sst-glo-phy-my-l4-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean ODYSSEA L4 Sea Surface Temperature"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l4-nrt-010-043:cmems-obs-sst-glo-phy-nrt-l4-p1d-m-202303,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"abstract": "'''Short description:'''\n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:ifremer-glob-sst-l3-nrt-obs-full-time-serie-202211,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-gir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pmw-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of : Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-nrt-observations-010-001:metoffice-glo-sst-l4-nrt-obs-sst-v2,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"abstract": "'''Short description :'''\n\nThe OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00168", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,canary-current-system,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-011:metoffice-glo-sst-l4-rep-obs-sst-202003,global-ocean,level-4,marine-resources,marine-safety,modelling-data,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,south-brazilian-shelf,south-mid-atlantic-ridge,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-05-31", "missionStartDate": "1981-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "'''Short description:''' \nThe ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:c3s-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:esacci-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,eo:mo:dat:sst-med-phy-l3s-my-010-042:cmems-obs-sst-med-phy-my-l3s-p1d-m-202411,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "''' Short description: ''' \n\nFor the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services How to cite]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-phy-subskin-l4-nrt-010-036:cmems-obs-sst-med-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-a-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-b-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-b,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-d,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-a-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. Since November 2024, the L4 MED UHR processing chain makes use of an improved background field as initial guess for the Optimal Interpolation of this product. The improvement is obtained in terms of the effective spatial resolution via the application of a convolutional neural network (CNN). These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-c-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"abstract": "'''Short description:''' \n \nThe Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-rep-observations-010-021:cmems-sst-med-sst-l4-rep-observations-010-021-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"abstract": "'''Short description:'''\n\nNear-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-spc-l4-nrt-014-004:cmems-obs-wave-glo-phy-spc-nrt-multi-l4-1deg-pt3h-202112,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nMulti-Year gridded multi-mission merged satellite significant wave height based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2\u00b0 horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5\u00b0 horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-0.5deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nNear-Real-Time gridded multi-mission merged satellite significant wave height, based on CMEMS level-3 SWH datasets. Onyl valid data are included. It merges multiple along-track SWH data (Sentinel-6A,\u00a0 Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C) and produces daily gridded data at a 2\u00b0 horizontal resolution. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00338", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-arc-phy-hr-l3-my-012-105:cmems-obs-wind-arc-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-arc-phy-hr-l3-my-012-105:cmems-obs-wind-arc-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00339", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-atl-phy-hr-l3-my-012-106:cmems-obs-wind-atl-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-atl-phy-hr-l3-my-012-106:cmems-obs-wind-atl-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00340", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-bal-phy-hr-l3-my-012-107:cmems-obs-wind-bal-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-bal-phy-hr-l3-my-012-107:cmems-obs-wind-bal-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00341", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-blk-phy-hr-l3-my-012-108:cmems-obs-wind-blk-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-blk-phy-hr-l3-my-012-108:cmems-obs-wind-blk-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments. The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-climate-l4-my-012-003:cmems-obs-wind-glo-phy-my-l4-p1m-202411,global-ocean,iberian-biscay-irish-seas,level-4,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1994-07-16", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n0.5 degrees grid for the 50 km scatterometer L2 inputs,\n0.25 degrees grid based on 25 km scatterometer swath observations,\nand 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\nThe ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\nThe OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \n*The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.125deg-pt1h-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.25deg-pt1h-202406,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-nrt-012-004:cmems-obs-wind-glo-phy-nrt-l4-0.125deg-pt1h-202207,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nFor the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00342", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-med-phy-hr-l3-my-012-109:cmems-obs-wind-med-phy-my-l3-s1a-sar-asc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411", "instrument": null, "keywords": "eastward-wind,eo:mo:dat:wind-med-phy-hr-l3-my-012-109:cmems-obs-wind-med-phy-my-l3-s1a-sar-desc-0.01deg-p1d-i-202411,level-3,mediterranean-sea,near-real-time,northward-wind,not-applicable,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-speed,wind-to-direction", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-06-27", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}}, "providers_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT1H-i_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_PT6H-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_myint_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_myint_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-hflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-mflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wcur_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint-wflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_myint_0.083deg_PT1H-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc_my_4.2km-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc_my_4.2km-climatology_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"collection": "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS_201907": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007:DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS_201907"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_north_d_202007": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_north_d_202007"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006:cmems_sat-si_glo_drift_nrt_south_d_202007"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_B:DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE": {"collection": "EO:MO:DAT:SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_B:DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"collection": "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"collection": "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_PHY_L4_MY_010_044:cmems_obs-sst_glo_phy_my_l4_P1D-m_202411": {"collection": "EO:MO:DAT:SST_GLO_PHY_L4_MY_010_044:cmems_obs-sst_glo_phy_my_l4_P1D-m_202411"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"collection": "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"collection": "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ARC_PHY_HR_L3_MY_012_105:cmems_obs-wind_arc_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_ATL_PHY_HR_L3_MY_012_106:cmems_obs-wind_atl_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BAL_PHY_HR_L3_MY_012_107:cmems_obs-wind_bal_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_BLK_PHY_HR_L3_MY_012_108:cmems_obs-wind_blk_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"collection": "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-asc-0.01deg_P1D-i_202411"}, "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411": {"collection": "EO:MO:DAT:WIND_MED_PHY_HR_L3_MY_012_109:cmems_obs-wind_med_phy_my_l3-s1a-sar-desc-0.01deg_P1D-i_202411"}}}}	deprecated usgs login endpoint to login-token endpoint The current `login` endpoint, which is used for authentication within the API, will be deprecated in February of 2025. Users are encouraged to create one or more M2M Application Tokens and to transition to using the `login-token` endpoint in lieu of the current `login` endpoint prior to February 2025. This will allow for continued data access. The USGS has created a document to aid users in the creation and use of an Application Token. More details are available here: https://www.usgs.gov/media/files/m2m-application-token-documentation	CS-SI/eodag	diff --git a/tests/units/test_apis_plugins.py b/tests/units/test_apis_plugins.py index 0d6224ab..3cb0756b 100644 --- a/tests/units/test_apis_plugins.py +++ b/tests/units/test_apis_plugins.py @@ -485,21 +485,28 @@ class TestApisPluginUsgsApi(BaseApisPluginTest): USGSError("USGS error"), None, ] - with mock.patch("os.remove", autospec=True) as mock_os_remove: + with ( + mock.patch("os.remove", autospec=True) as mock_os_remove, + mock.patch("os.path.isfile", autospec=True) as mock_isfile, + ): self.api_plugin.authenticate() self.assertEqual(mock_api_login.call_count, 2) self.assertEqual(mock_api_logout.call_count, 0) + mock_isfile.assert_called_once_with(USGS_TMPFILE) mock_os_remove.assert_called_once_with(USGS_TMPFILE) mock_api_login.reset_mock() mock_api_logout.reset_mock() # with invalid credentials / USGSError mock_api_login.side_effect = USGSError() - with mock.patch("os.remove", autospec=True) as mock_os_remove: - with self.assertRaises(AuthenticationError): - self.api_plugin.authenticate() - self.assertEqual(mock_api_login.call_count, 2) - mock_api_logout.assert_not_called() + with ( + mock.patch("os.remove", autospec=True), + mock.patch("os.path.isfile", autospec=True), + self.assertRaises(AuthenticationError), + ): + self.api_plugin.authenticate() + self.assertEqual(mock_api_login.call_count, 2) + mock_api_logout.assert_not_called() @mock.patch("usgs.api.login", autospec=True) @mock.patch("usgs.api.logout", autospec=True) @@ -635,7 +642,6 @@ class TestApisPluginUsgsApi(BaseApisPluginTest): @responses.activate def run(): - product = EOProduct( "peps", dict(	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": -1, "issue_text_score": 0, "test_score": -1 }, "num_modified_files": 7 }	3.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt", "requirements-tutorials.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.27 boto3-stubs==1.37.27 botocore==1.37.27 botocore-stubs==1.37.27 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@643cb3c0e6228ebf83ba01bf38a80b05162a7ab4#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy==1.15.0 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.24 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.24 mypy-boto3-sqs==1.37.0 mypy-extensions==1.0.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.2 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.1 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 PyJWT==2.10.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.1.0 pytest-html==4.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tox-uv==1.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.25.6 types-cachetools==5.5.0.20240820 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-python-dateutil==2.9.0.20241206 types-PyYAML==6.0.12.20250402 types-requests==2.31.0.6 types-s3transfer==0.11.4 types-setuptools==78.1.0.20250329 types-tqdm==4.67.0.20250401 types-urllib3==1.26.25.14 typing-inspection==0.4.0 typing_extensions==4.13.1 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uv==0.6.12 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.30.0 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.27 - boto3-stubs==1.37.27 - botocore==1.37.27 - botocore-stubs==1.37.27 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==3.1.0b3.dev19+g643cb3c0 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy==1.15.0 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.24 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.24 - mypy-boto3-sqs==1.37.0 - mypy-extensions==1.0.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.2 - pydantic-core==2.33.1 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyjwt==2.10.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.1.0 - pytest-html==4.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tox-uv==1.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.25.6 - types-cachetools==5.5.0.20240820 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-python-dateutil==2.9.0.20241206 - types-pyyaml==6.0.12.20250402 - types-requests==2.31.0.6 - types-s3transfer==0.11.4 - types-setuptools==78.1.0.20250329 - types-tqdm==4.67.0.20250401 - types-urllib3==1.26.25.14 - typing-extensions==4.13.1 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uv==0.6.12 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.30.0 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_authenticate" ]	[]	[ "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download_all", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_dates_missing", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_custom_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_without_producttype", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_download", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_query", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_query_by_id" ]	[]	Apache License 2.0	null
CS-SI__eodag-190	af9afc8f4e21f6af129c10a1763fb560ac0dfbe2	2021-03-19 17:54:55	49b15ccd7aa5b83f19fb98b8b4a0dae279c2c18c	sbrunato: For `astraea_eod`, STAC API pagination only needs `item["links"][]["rel"] == "next"`. `page` parameter is only optional, see https://github.com/radiantearth/stac-api-spec/tree/master/item-search#paging. For STAC providers, we should this `next` entry by default instead of `page`+`limit`. This could be done by adding a new parameter in `providers.yml` / `stac_provider.yml`: ```yml pagination: next_page_url_key_path: '$.links[?(@.rel="next")].href' ``` And use `jsonpath_ng.ext.parse` instead of `jsonpath_ng.parse`, see https://github.com/h2non/jsonpath-ng/issues/8 maximlt: @sbrunato I have implemented a mechanism to retrieve the next page when a provider doesn't implement "page". In `search_iter_page` a search plugin is introspected to check whether it has a `pagination.next_page_url_key_path` key. If it does, it is assumed that it doesn't implement "page", and a kwarg `set_next_page_url` is passed down to `.query`. This is handled within `QueryStringSearch.do_search` where the response (JSON only right now) is parsed to get the "next" URL. It replaces the configuration key `pagination.next_page_url_tpl`. So the next call to `.query` will use that URL and will update it to get the next page. I have added comments since this logic is quite special. Maybe we'll come up with a better way in the future. maximlt: @sbrunato I've made the changes as we agreed: * `search_iter_page` doesn't propagate a boolean to trigger to "next" search through the plugin infrastructure down to `do_search`. Instead `do_search` takes care of setting an attribute `next_page_url` if the pagination config has the key `next_page_url_key_path`. * `search_iter_page` uses that URL after the first iteration if available. It uses it by replacing `search_plugin.config.pagination["next_page_url_tpl"]` * `search_plugin.config.pagination["next_page_url_tpl"]` and `search_plugin.next_page_url` are both reset after each iteration. The generator holds the `next_page_url` value in its internal state. This has the advantage that the search plugin is in a clean state after `next(dag.search_iter_page(**search_criteria))`. I thought it was quite important that `search_plugin.next_page_url` was reset every time, I didn't want a URL from a previous search to mess up with a new search (different criteria).	diff --git a/eodag/api/core.py b/eodag/api/core.py index 4655c3e3..6b84203c 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -59,6 +59,10 @@ logger = logging.getLogger("eodag.core") # pagination defaults DEFAULT_PAGE = 1 DEFAULT_ITEMS_PER_PAGE = 20 +# Default maximum number of items per page requested by search_all. 50 instead of +# 20 (DEFAULT_ITEMS_PER_PAGE) to increase it to the known and currentminimum +# value (mundi) +DEFAULT_MAX_ITEMS_PER_PAGE = 50 class EODataAccessGateway(object): @@ -243,7 +247,9 @@ class EODataAccessGateway(object): :type provider: str """ if provider not in self.available_providers(): - raise UnsupportedProvider("This provider is not recognised by eodag") + raise UnsupportedProvider( + f"This provider is not recognised by eodag: {provider}" + ) preferred_provider, max_priority = self.get_preferred_provider() if preferred_provider != provider: new_priority = max_priority + 1 @@ -391,22 +397,6 @@ class EODataAccessGateway(object): def guess_product_type(self, kwargs): """Find the eodag product type code that best matches a set of search params - >>> from eodag import EODataAccessGateway - >>> dag = EODataAccessGateway() - >>> dag.guess_product_type( - ... instrument="MSI", - ... platform="SENTINEL2", - ... platformSerialIdentifier="S2A", - ... ) # doctest: +NORMALIZE_WHITESPACE - ['S2_MSI_L1C', 'S2_MSI_L2A', 'S2_MSI_L2A_MAJA', 'S2_MSI_L2B_MAJA_SNOW', - 'S2_MSI_L2B_MAJA_WATER', 'S2_MSI_L3A_WASP'] - >>> import eodag.utils.exceptions - >>> try: - ... dag.guess_product_type() - ... raise AssertionError(u"NoMatchingProductType exception not raised") - ... except eodag.utils.exceptions.NoMatchingProductType: - ... pass - :param kwargs: A set of search parameters as keywords arguments :return: The best match for the given parameters :rtype: str @@ -455,7 +445,7 @@ class EODataAccessGateway(object): end=None, geom=None, locations=None, - kwargs + kwargs, ): """Look for products matching criteria on known providers. @@ -557,6 +547,295 @@ class EODataAccessGateway(object): return a list as a result of their processing. This requirement is enforced here. """ + search_kwargs = self._prepare_search( + start=start, end=end, geom=geom, locations=locations, kwargs + ) + if search_kwargs.get("id"): + provider = search_kwargs.get("provider") + return self._search_by_id(search_kwargs["id"], provider) + search_plugin = search_kwargs.pop("search_plugin") + search_kwargs.update( + page=page, + items_per_page=items_per_page, + ) + return self._do_search( + search_plugin, count=True, raise_errors=raise_errors, search_kwargs + ) + + def search_iter_page( + self, + items_per_page=DEFAULT_ITEMS_PER_PAGE, + start=None, + end=None, + geom=None, + locations=None, + kwargs, + ): + """Iterate over the pages of a products search. + + :param items_per_page: The number of results requested per page (default: 20) + :type items_per_page: int + :param start: Start sensing UTC time in iso format + :type start: str + :param end: End sensing UTC time in iso format + :type end: str + :param geom: Search area that can be defined in different ways: + + * with a Shapely geometry object: + ``class:`shapely.geometry.base.BaseGeometry``` + * with a bounding box (dict with keys: "lonmin", "latmin", "lonmax", "latmax"): + ``dict.fromkeys(["lonmin", "latmin", "lonmax", "latmax"])`` + * with a bounding box as list of float: + ``[lonmin, latmin, lonmax, latmax]`` + * with a WKT str + + :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] + :param locations: Location filtering by name using locations configuration + ``{"<location_name>"="<attr_regex>"}``. For example, ``{"country"="PA."}`` will use + the geometry of the features having the property ISO3 starting with + 'PA' such as Panama and Pakistan in the shapefile configured with + name=country and attr=ISO3 + :type locations: dict + :param dict kwargs: some other criteria that will be used to do the search, + using paramaters compatibles with the provider + :returns: An iterator that yields page per page a collection of EO products + matching the criteria + :rtype: Iterator[:class:`~eodag.api.search_result.SearchResult`] + + .. versionadded:: + 2.2.0 + """ + search_kwargs = self._prepare_search( + start=start, end=end, geom=geom, locations=locations, kwargs + ) + search_plugin = search_kwargs.pop("search_plugin") + iteration = 1 + # Store the search plugin config pagination.next_page_url_tpl to reset it later + # since it might be modified if the next_page_url mechanism is used by the + # plugin. + pagination_config = getattr(search_plugin.config, "pagination", {}) + prev_next_page_url_tpl = pagination_config.get("next_page_url_tpl") + # Page has to be set to a value even if use_next is True, this is required + # internally by the search plugin (see collect_search_urls) + search_kwargs.update( + page=1, + items_per_page=items_per_page, + ) + prev_product = None + next_page_url = None + while True: + if iteration > 1 and next_page_url: + pagination_config["next_page_url_tpl"] = next_page_url + logger.debug("Iterate over pages: search page %s", iteration) + try: + products, _ = self._do_search( + search_plugin, count=False, raise_errors=True, search_kwargs + ) + finally: + # we don't want that next(search_iter_page(...)) modifies the plugin + # indefinitely. So we reset after each request, but before the generator + # yields, the attr next_page_url (to None) and + # config.pagination["next_page_url_tpl"] (to its original value). + next_page_url = getattr(search_plugin, "next_page_url", None) + if next_page_url: + search_plugin.next_page_url = None + if prev_next_page_url_tpl: + search_plugin.config.pagination[ + "next_page_url_tpl" + ] = prev_next_page_url_tpl + if len(products) > 0: + # The first products between two iterations are compared. If they + # are actually the same product, it means the iteration failed at + # progressing for some reason. This is implemented as a workaround + # to some search plugins/providers not handling pagination. + product = products[0] + if ( + prev_product + and product.properties["id"] == prev_product.properties["id"] + and product.provider == prev_product.provider + ): + logger.warning( + "Iterate over pages: stop iterating since the next page " + "appears to have the same products as in the previous one. " + "This provider may not implement pagination.", + ) + last_page_with_products = iteration - 1 + break + yield products + prev_product = product + # Prevent a last search if the current one returned less than the + # maximum number of items asked for. + if len(products) < items_per_page: + last_page_with_products = iteration + break + else: + last_page_with_products = iteration - 1 + break + iteration += 1 + search_kwargs["page"] = iteration + logger.debug( + "Iterate over pages: last products found on page %s", + last_page_with_products, + ) + + def search_all( + self, + items_per_page=None, + start=None, + end=None, + geom=None, + locations=None, + *kwargs, + ): + """Search and return all the products matching the search criteria. + + It iterates over the pages of a search query and collects all the returned + products into a single :class:`~eodag.api.search_result.SearchResult` instance. + + :param items_per_page: (optional) The number of results requested internally per + page. The maximum number of items than can be requested + at once to a provider has been configured in EODAG for + some of them. If items_per_page is None and this number + is available for the searched provider, it is used to + limit the number of requests made. This should also + reduce the time required to collect all the products + matching the search criteria. If this number is not + available, a default value of 50 is used instead. + items_per_page can also be set to any arbitrary value. + :type items_per_page: int + :param start: Start sensing UTC time in iso format + :type start: str + :param end: End sensing UTC time in iso format + :type end: str + :param geom: Search area that can be defined in different ways: + + with a Shapely geometry object: + ``class:`shapely.geometry.base.BaseGeometry``` + * with a bounding box (dict with keys: "lonmin", "latmin", "lonmax", "latmax"): + ``dict.fromkeys(["lonmin", "latmin", "lonmax", "latmax"])`` + * with a bounding box as list of float: + ``[lonmin, latmin, lonmax, latmax]`` + * with a WKT str + + :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] + :param locations: Location filtering by name using locations configuration + ``{"<location_name>"="<attr_regex>"}``. For example, ``{"country"="PA."}`` will use + the geometry of the features having the property ISO3 starting with + 'PA' such as Panama and Pakistan in the shapefile configured with + name=country and attr=ISO3 + :type locations: dict + :param dict kwargs: some other criteria that will be used to do the search, + using paramaters compatibles with the provider + :returns: An iterator that yields page per page a collection of EO products + matching the criteria + :rtype: Iterator[:class:`~eodag.api.search_result.SearchResult`] + + .. versionadded:: + 2.2.0 + """ + # Prepare the search just to get the search plugin and the maximized value + # of items_per_page if defined for the provider used. + search_kwargs = self._prepare_search( + start=start, end=end, geom=geom, locations=locations, kwargs + ) + search_plugin = search_kwargs["search_plugin"] + if items_per_page is None: + items_per_page = search_plugin.config.pagination.get( + "max_items_per_page", DEFAULT_MAX_ITEMS_PER_PAGE + ) + logger.debug( + "Searching for all the products with provider %s and a maximum of %s " + "items per page.", + search_plugin.provider, + items_per_page, + ) + all_results = SearchResult([]) + for page_results in self.search_iter_page( + items_per_page=items_per_page, + start=start, + end=end, + geom=geom, + locations=locations, + kwargs, + ): + all_results.data.extend(page_results.data) + logger.info( + "Found %s result(s) on provider '%s'", + len(all_results), + search_plugin.provider, + ) + return all_results + + def _search_by_id(self, uid, provider=None): + """Internal method that enables searching a product by its id. + + Keeps requesting providers until a result matching the id is supplied. The + search plugins should be developed in the way that enable them to handle the + support of a search by id by the providers. The providers are requested one by + one, in the order defined by their priorities. Be aware that because of that, + the search can be slow, if the priority order is such that the provider that + contains the requested product has the lowest priority. However, you can always + speed up a little the search by passing the name of the provider on which to + perform the search, if this information is available + + :param uid: The uid of the EO product + :type uid: str + :param provider: (optional) The provider on which to search the product. + This may be useful for performance reasons when the user + knows this product is available on the given provider + :type provider: str + :returns: A search result with one EO product or None at all, and the number + of EO products retrieved (0 or 1) + :rtype: tuple(:class:`~eodag.api.search_result.SearchResult`, int) + + .. versionadded:: 1.0 + """ + for plugin in self._plugins_manager.get_search_plugins(provider=provider): + logger.info( + "Searching product with id '%s' on provider: %s", uid, plugin.provider + ) + logger.debug("Using plugin class for search: %s", plugin.__class__.__name__) + auth = self._plugins_manager.get_auth_plugin(plugin.provider) + results, _ = self._do_search(plugin, auth=auth, id=uid) + if len(results) == 1: + return results, 1 + return SearchResult([]), 0 + + def _prepare_search( + self, start=None, end=None, geom=None, locations=None, *kwargs + ): + """Internal method to prepare the search kwargs and get the search + and auth plugins. + + Product query: + By id (plus optional 'provider') + * By search params: + * productType query: + * By product type (e.g. 'S2_MSI_L1C') + * By params (e.g. 'platform'), see guess_product_type + * dates: 'start' and/or 'end' + * geometry: 'geom' or 'bbox' or 'box' + * search locations + * TODO: better expose cloudCover + * other search params are passed to Searchplugin.query() + + :param start: Start sensing UTC time in iso format + :type start: str + :param end: End sensing UTC time in iso format + :type end: str + :param geom: Search area that can be defined in different ways (see search) + :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] + :param locations: Location filtering by name using locations configuration + :type locations: dict + :param dict kwargs: Some other criteria + * id and/or a provider for a search by + * search criteria to guess the product type + * other criteria compatible with the provider + :returns: The prepared kwargs to make a query. + :rtype: dict + + .. versionadded:: 2.2 + """ product_type = kwargs.get("productType", None) if product_type is None: try: @@ -570,14 +849,18 @@ class EODataAccessGateway(object): "No product type could be guessed with provided arguments" ) else: - provider = kwargs.get("provider", None) - return self._search_by_id(kwargs["id"], provider=provider) + return kwargs kwargs["productType"] = product_type if start is not None: kwargs["startTimeFromAscendingNode"] = start if end is not None: kwargs["completionTimeFromAscendingNode"] = end + if "box" in kwargs or "bbox" in kwargs: + logger.warning( + "'box' or 'bbox' parameters are only supported for backwards " + " compatibility reasons. Usage of 'geom' is recommended." + ) if geom is not None: kwargs["geometry"] = geom box = kwargs.pop("box", None) @@ -593,95 +876,73 @@ class EODataAccessGateway(object): kwargs.pop(arg, None) del kwargs["locations"] - plugin = next( + search_plugin = next( self._plugins_manager.get_search_plugins(product_type=product_type) ) logger.info( - "Searching product type '%s' on provider: %s", product_type, plugin.provider + "Searching product type '%s' on provider: %s", + product_type, + search_plugin.provider, ) - # add product_types_config to plugin config + # Add product_types_config to plugin config. This dict contains product + # type metadata that will also be stored in each product's properties. try: - plugin.config.product_type_config = dict( + search_plugin.config.product_type_config = dict( [ p - for p in self.list_product_types(plugin.provider) + for p in self.list_product_types(search_plugin.provider) if p["ID"] == product_type ][0], - {"productType": product_type} + {"productType": product_type}, ) + # If the product isn't in the catalog, it's a generic product type. except IndexError: - plugin.config.product_type_config = dict( + search_plugin.config.product_type_config = dict( [ p - for p in self.list_product_types(plugin.provider) + for p in self.list_product_types(search_plugin.provider) if p["ID"] == GENERIC_PRODUCT_TYPE ][0], - {"productType": product_type} + {"productType": product_type}, ) - plugin.config.product_type_config.pop("ID", None) + # Remove the ID since this is equal to productType. + search_plugin.config.product_type_config.pop("ID", None) - logger.debug("Using plugin class for search: %s", plugin.__class__.__name__) - auth = self._plugins_manager.get_auth_plugin(plugin.provider) - return self._do_search( - plugin, - auth=auth, - page=page, - items_per_page=items_per_page, - raise_errors=raise_errors, - kwargs + logger.debug( + "Using plugin class for search: %s", search_plugin.__class__.__name__ ) + auth_plugin = self._plugins_manager.get_auth_plugin(search_plugin.provider) - def _search_by_id(self, uid, provider=None): - """Internal method that enables searching a product by its id. - - Keeps requesting providers until a result matching the id is supplied. The - search plugins should be developed in the way that enable them to handle the - support of a search by id by the providers. The providers are requested one by - one, in the order defined by their priorities. Be aware that because of that, - the search can be slow, if the priority order is such that the provider that - contains the requested product has the lowest priority. However, you can always - speed up a little the search by passing the name of the provider on which to - perform the search, if this information is available + return dict(search_plugin=search_plugin, auth=auth_plugin, kwargs) - :param uid: The uid of the EO product - :type uid: str - :param provider: (optional) The provider on which to search the product. - This may be useful for performance reasons when the user - knows this product is available on the given provider - :type provider: str - :returns: A search result with one EO product or None at all, and the number - of EO products retrieved (0 or 1) - :rtype: tuple(:class:`~eodag.api.search_result.SearchResult`, int) - - .. versionadded:: 1.0 - """ - for plugin in self._plugins_manager.get_search_plugins(provider=provider): - logger.info( - "Searching product with id '%s' on provider: %s", uid, plugin.provider - ) - logger.debug("Using plugin class for search: %s", plugin.__class__.__name__) - auth = self._plugins_manager.get_auth_plugin(plugin.provider) - results, _ = self._do_search(plugin, auth=auth, id=uid) - if len(results) == 1: - return results, 1 - return SearchResult([]), 0 - - def _do_search(self, search_plugin, kwargs): + def _do_search(self, search_plugin, count=True, raise_errors=False, kwargs): """Internal method that performs a search on a given provider. + :param search_plugin: A search plugin + :type search_plugin: eodag.plugins.base.Search + :param count: Whether to run a query with a count request or not (default: True) + :type count: bool + :param raise_errors: When an error occurs when searching, if this is set to + True, the error is raised (default: False) + :type raise_errors: bool + :param dict kwargs: some other criteria that will be used to do the search + :returns: A collection of EO products matching the criteria and the total + number of results found if count is True else None + :rtype: tuple(:class:`~eodag.api.search_result.SearchResult`, int or None) + .. versionadded:: 1.0 """ results = SearchResult([]) total_results = 0 try: - res, nb_res = search_plugin.query(kwargs) + res, nb_res = search_plugin.query(count=count, kwargs) # Only do the pagination computations when it makes sense. For example, # for a search by id, we can reasonably guess that the provider will return # At most 1 product, so we don't need such a thing as pagination page = kwargs.get("page") items_per_page = kwargs.get("items_per_page") - if page and items_per_page: + if page and items_per_page and count: # Take into account the fact that a provider may not return the count of # products (in that case, fallback to using the length of the results it # returned and the page requested. As an example, check the result of @@ -739,31 +1000,34 @@ class EODataAccessGateway(object): ) results.extend(res) - total_results += nb_res - logger.info( - "Found %s result(s) on provider '%s'", nb_res, search_plugin.provider - ) - # Hitting for instance - # https://theia.cnes.fr/atdistrib/resto2/api/collections/SENTINEL2/ - # search.json?startDate=2019-03-01&completionDate=2019-06-15 - # &processingLevel=LEVEL2A&maxRecords=1&page=1 - # returns a number (properties.totalResults) that is the number of - # products in the collection (here SENTINEL2) instead of the estimated - # total number of products matching the search criteria (start/end date). - # Remove this warning when this is fixed upstream by THEIA. - if search_plugin.provider == "theia": - logger.warning( - "Results found on provider 'theia' is the total number of products " - "available in the searched collection (e.g. SENTINEL2) instead of " - "the total number of products matching the search criteria" + total_results = None if nb_res is None else total_results + nb_res + if count: + logger.info( + "Found %s result(s) on provider '%s'", + nb_res, + search_plugin.provider, ) + # Hitting for instance + # https://theia.cnes.fr/atdistrib/resto2/api/collections/SENTINEL2/ + # search.json?startDate=2019-03-01&completionDate=2019-06-15 + # &processingLevel=LEVEL2A&maxRecords=1&page=1 + # returns a number (properties.totalResults) that is the number of + # products in the collection (here SENTINEL2) instead of the estimated + # total number of products matching the search criteria (start/end date). + # Remove this warning when this is fixed upstream by THEIA. + if search_plugin.provider == "theia": + logger.warning( + "Results found on provider 'theia' is the total number of products " + "available in the searched collection (e.g. SENTINEL2) instead of " + "the total number of products matching the search criteria" + ) except Exception: logger.info( "No result from provider '%s' due to an error during search. Raise " "verbosity of log messages for details", search_plugin.provider, ) - if kwargs.get("raise_errors"): + if raise_errors: # Raise the error, letting the application wrapping eodag know that # something went bad. This way it will be able to decide what to do next raise @@ -827,7 +1091,7 @@ class EODataAccessGateway(object): progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, - kwargs + kwargs, ): """Download all products resulting from a search. @@ -865,7 +1129,7 @@ class EODataAccessGateway(object): progress_callback=progress_callback, wait=wait, timeout=timeout, - kwargs + kwargs, ) # close progress_bar when finished if hasattr(progress_callback, "pb") and hasattr( @@ -931,7 +1195,7 @@ class EODataAccessGateway(object): provider=None, productType=None, timeout=HTTP_REQ_TIMEOUT, - kwargs + kwargs, ): """Loads STAC items from a geojson file / STAC catalog or collection, and convert to SearchResult. @@ -1000,7 +1264,7 @@ class EODataAccessGateway(object): progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, - kwargs + kwargs, ): """Download a single product. diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index 074ed5cb..b238440a 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -92,6 +92,9 @@ class QueryStringSearch(Search): - count_endpoint: (optional) The endpoint for counting the number of items satisfying a request + next_page_url_key_path: (optional) A JSONPATH expression used to retrieve + the URL of the next page in the response of the current page. + - free_text_search_operations: (optional) A tree structure of the form:: <search-param>: # e.g: $search @@ -153,9 +156,19 @@ class QueryStringSearch(Search): self.search_urls = [] self.query_params = dict() self.query_string = "" + self.next_page_url = None def query(self, items_per_page=None, page=None, count=True, kwargs): - """Perform a search on an OpenSearch-like interface""" + """Perform a search on an OpenSearch-like interface + + :param page: The page number to retur (default: 1) + :type page: int + :param items_per_page: The number of results that must appear in one single + page + :type items_per_page: int + :param count: To trigger a count request (default: True) + :type count: bool + """ product_type = kwargs.get("productType", None) if product_type == GENERIC_PRODUCT_TYPE: logger.warning( @@ -417,6 +430,9 @@ class QueryStringSearch(Search): except RequestError: raise StopIteration else: + next_page_url_key_path = self.config.pagination.get( + "next_page_url_key_path" + ) if self.config.result_type == "xml": root_node = etree.fromstring(response.content) namespaces = {k or "ns": v for k, v in root_node.nsmap.items()} @@ -426,8 +442,24 @@ class QueryStringSearch(Search): self.config.results_entry, namespaces=namespaces ) ] + if next_page_url_key_path: + raise NotImplementedError( + "Setting the next page url from an XML response has not " + "been implemented yet" + ) else: - result = response.json().get(self.config.results_entry, []) + resp_as_json = response.json() + if next_page_url_key_path: + path_parsed = parse(next_page_url_key_path) + try: + self.next_page_url = path_parsed.find(resp_as_json)[0].value + logger.debug( + "Next page URL collected and set for the next search", + ) + except IndexError: + logger.debug("Next page URL could not be collected") + + result = resp_as_json.get(self.config.results_entry, []) if getattr(self.config, "merge_responses", False): results = ( [dict(r, *result[i]) for i, r in enumerate(results)] diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index c034296e..9906327a 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -504,6 +504,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&page={page}' total_items_nb_key_path: '$.properties.totalResults' + # 2019/03/19: Returns a 400 error code if greater than 500. + max_items_per_page: 500 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9_]+$' @@ -676,6 +678,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&page={page}' total_items_nb_key_path: '$.properties.totalResults' + # 2019/03/19: 500 is the max, no error if greater + max_items_per_page: 500 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9_]+$' @@ -866,6 +870,9 @@ pagination: next_page_url_tpl: '{url}?{search}&size={items_per_page}&from={skip}' total_items_nb_key_path: '$.totalnb' + # 2019/03/19: It's not possible to get more than 10_000 products + # Returns a 400 Bad Request if more products are requested. + max_items_per_page: 10_000 results_entry: 'hits' discover_metadata: auto_discovery: true @@ -953,6 +960,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&page={page}' total_items_nb_key_path: '$.properties.totalResults' + # 2019/03/19: 2000 is the max, 400 error if greater + max_items_per_page: 2_000 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9]+$' @@ -1106,6 +1115,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&startIndex={skip_base_1}' total_items_nb_key_path: '//os:totalResults/text()' + # 2019/03/19: 50 is the max, no error if greater + max_items_per_page: 50 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9]+$' @@ -1328,6 +1339,8 @@ pagination: count_endpoint: 'https://catalogue.onda-dias.eu/dias-catalogue/Products/$count' next_page_url_tpl: '{url}?{search}&$top={items_per_page}&$skip={skip}' + # 2019/03/19: 2000 is the max, if greater 200 response but contains an error message + max_items_per_page: 2_000 results_entry: 'value' literal_search_params: $format: json @@ -1463,6 +1476,13 @@ search: !plugin type: StacSearch api_endpoint: https://eod-catalog-svc-prod.astraea.earth/search + pagination: + # 2019/03/19: The docs (https://eod-catalog-svc-prod.astraea.earth/api.html#operation/getSearchSTAC) + # say the max is 10_000. In practice 1_000 products are returned if more are asked (even greater + # than 10_000), without any error. + # This provider doesn't implement any pagination, let's just try to get the maximum number of + # products available at once then, so we stick to 10_000. + max_items_per_page: 10_000 products: S2_MSI_L1C: productType: sentinel2_l1c @@ -1496,6 +1516,10 @@ api_endpoint: https://landsatlook.usgs.gov/sat-api/collections/{collection}/items pagination: total_items_nb_key_path: '$.meta.found' + # 2019/03/19: no more than 10_000 (if greater, returns a 500 error code) + # but in practive if an Internal Server Error is returned for more than + # about 500 products. + max_items_per_page: 500 metadata_mapping: productType: '$.collection' completionTimeFromAscendingNode: diff --git a/eodag/resources/stac_provider.yml b/eodag/resources/stac_provider.yml index e3c8848c..b6f2ed19 100644 --- a/eodag/resources/stac_provider.yml +++ b/eodag/resources/stac_provider.yml @@ -20,8 +20,7 @@ search: pagination: next_page_url_tpl: '{url}?{search}&limit={items_per_page}&page={page}' total_items_nb_key_path: '$.context.matched' - pagination: - next_page_url_key_path: '$.links[?(@.rel="next")].href' + next_page_url_key_path: '$.links[?(@.rel="next")].href' discover_metadata: auto_discovery: true metadata_pattern: '^[a-zA-Z0-9_:-]+$'	Search for all the products matching some criteria with a given provider We have had a lot of discussions on a private GitLab instance before we moved to GitHub about how to search/download all the products matching some criteria. The problems we observed with the current API were: * The method `search` returns a tuple of `(SearchResult, int)`. The first item contains some products, in most cases 20 if the user hasn't defined `items_per_page` in the search (20 being the default value) or less than 20 if less products match the criteria. The second item is the total number of products matching the criteria, it is obtained after sending a count request (or equivalent) to the provider. This number may in some cases just be an estimate, usually lower than the correct value (this is true for peps at the time of writing). So what happens is that the user actually gets the results from the first page only, but it's not so easy to understand (https://github.com/CS-SI/eodag/issues/149) * We have the method `download_all` that does what it says, it downloads all the products contained in the `SearchResult` it takes as an argument. However, some users would expect that it just downloads all the products matching the criteria that were used to search them in the first place. * There is no clear way or example about how to search (and download) for all the products matching some criteria. The current way would be to execute a `search` by incrementing the page value given as a parameter. The issue with this approach is that it would each time send a count request (whether it's a proper request or not depends on the underlying search plugin implementation, but in most cases it's currently the case), while this doesn't seem necessary. Or by increasing `items_per_page` to a very high value, which may be too high (providers have their own limit) and result in potentially an obscure error. We have discussed different approaches: 1. Add some superpower to `download_all` so that it could download more than the products it would be given. The idea was to store in the `SearchResult` all the information required to run the same search, and as a consequence to run the search on the next page. The next products found would then have been downloaded. And so on until exhaustion of the results. It would have been used as follows: `products, _ = dag.search(*criteria); paths = dag.download_all(products, exhaust=True)` 2. Add a `search_all` method that would collect all the products matching some criteria. 2.1. By adding to `SearchResult` the possibility to trigger a search for the next page (same idea as with `download_all`) 2.2. By adding a `search_iter_page` method to `EODataAccessGateway` that would iterate over the pages of a search, and using that method internally to implement `search_all`. The approach 2.2.* was chosen to be implemented (which doesn't necessarily mean the other approaches can't or won't be implemented if they prove to be required): * Just adding more power to `download_all` felt like it was not sufficient. Since some providers do not return the exact count of products available in a `search`, it would have been impossible for these providers to know beforehand how many products `download_all` would download. This feels a a little inappropriate. * `search_all` happens to mirror `download_all` quite well, the usage of `products = dag.search_all(*criteria); paths = dag.download_all(products)` feels quite natural Exposing a method `search_iter_page` will help us to document and explain that (most) searches are paginated. This is not obvious to every user while this is important to understand how eodag works.	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index bd969304..83d9354e 100644 --- a/tests/context.py +++ b/tests/context.py @@ -25,7 +25,7 @@ import sys sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))) from eodag import EODataAccessGateway, api, config -from eodag.api.core import DEFAULT_ITEMS_PER_PAGE +from eodag.api.core import DEFAULT_ITEMS_PER_PAGE, DEFAULT_MAX_ITEMS_PER_PAGE from eodag.api.product import EOProduct from eodag.api.product.drivers import DRIVERS from eodag.api.product.drivers.base import NoDriver @@ -49,6 +49,8 @@ from eodag.utils.exceptions import ( AuthenticationError, DownloadError, MisconfiguredError, + NoMatchingProductType, + PluginImplementationError, UnsupportedDatasetAddressScheme, UnsupportedProvider, ValidationError, diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 08c2521a..451996a2 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -140,17 +140,17 @@ class EndToEndBase(unittest.TestCase): - Return one product to be downloaded """ search_criteria = { + "productType": product_type, "startTimeFromAscendingNode": start, "completionTimeFromAscendingNode": end, "geom": geom, } + if items_per_page: + search_criteria["items_per_page"] = items_per_page + if page: + search_criteria["page"] = page self.eodag.set_preferred_provider(provider) - results, nb_results = self.eodag.search( - productType=product_type, - page=page, - items_per_page=items_per_page, - search_criteria - ) + results, nb_results = self.eodag.search(search_criteria) if offline: results = [ prod @@ -165,6 +165,38 @@ class EndToEndBase(unittest.TestCase): else: return results + def execute_search_all( + self, + provider, + product_type, + start, + end, + geom, + items_per_page=None, + check_products=True, + ): + """Search all the products on provider: + + - First set the preferred provider as the one given in parameter + - Then do the search_all + - Then ensure that at least the first result originates from the provider + - Return all the products + """ + search_criteria = { + "startTimeFromAscendingNode": start, + "completionTimeFromAscendingNode": end, + "geom": geom, + } + self.eodag.set_preferred_provider(provider) + results = self.eodag.search_all( + productType=product_type, items_per_page=items_per_page, search_criteria + ) + if check_products: + self.assertGreater(len(results), 0) + one_product = results[0] + self.assertEqual(one_product.provider, provider) + return results + # @unittest.skip("skip auto run") class TestEODagEndToEnd(EndToEndBase): @@ -357,6 +389,32 @@ class TestEODagEndToEnd(EndToEndBase): ) self.assertGreaterEqual(os.stat(quicklook_file_path).st_size, 2 5) + def test__search_by_id_sobloo(self): + # A single test with sobloo to check that _search_by_id returns + # correctly the exact product looked for. + uid = "S2A_MSIL1C_20200810T030551_N0209_R075_T53WPU_20200810T050611" + provider = "sobloo" + + products, _ = self.eodag._search_by_id(uid=uid, provider=provider) + product = products[0] + + self.assertEqual(product.properties["id"], uid) + + def test_end_to_end_search_all_mundi_default(self): + # 23/03/2021: Got 16 products for this search + results = self.execute_search_all(MUNDI_SEARCH_ARGS) + self.assertGreater(len(results), 10) + + def test_end_to_end_search_all_mundi_iterate(self): + # 23/03/2021: Got 16 products for this search + results = self.execute_search_all(MUNDI_SEARCH_ARGS, items_per_page=10) + self.assertGreater(len(results), 10) + + def test_end_to_end_search_all_astraea_eod_iterate(self): + # 23/03/2021: Got 39 products for this search + results = self.execute_search_all(ASTRAE_EOD_SEARCH_ARGS, items_per_page=10) + self.assertGreater(len(results), 10) + class TestEODagEndToEndWrongCredentials(EndToEndBase): """Make real case tests with wrong credentials. This assumes the existence of a diff --git a/tests/units/test_core.py b/tests/units/test_core.py index df78fce7..221f70bc 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -17,17 +17,24 @@ # limitations under the License. import glob +import json import os import shutil import unittest +from copy import deepcopy from shapely import wkt -from shapely.geometry import LineString, MultiPolygon +from shapely.geometry import LineString, MultiPolygon, Polygon from eodag.utils import GENERIC_PRODUCT_TYPE from tests import TEST_RESOURCES_PATH from tests.context import ( + DEFAULT_MAX_ITEMS_PER_PAGE, EODataAccessGateway, + EOProduct, + NoMatchingProductType, + PluginImplementationError, + SearchResult, UnsupportedProvider, get_geometry_from_various, makedirs, @@ -143,25 +150,10 @@ class TestCore(unittest.TestCase): "earth_search", ] - def setUp(self): - super(TestCore, self).setUp() - self.dag = EODataAccessGateway() - - def tearDown(self): - super(TestCore, self).tearDown() - for old in glob.glob1(self.dag.conf_dir, ".old") + glob.glob1( - self.dag.conf_dir, "..old" - ): - old_path = os.path.join(self.dag.conf_dir, old) - if os.path.exists(old_path): - try: - os.remove(old_path) - except OSError: - shutil.rmtree(old_path) - if os.getenv("EODAG_CFG_FILE") is not None: - os.environ.pop("EODAG_CFG_FILE") - if os.getenv("EODAG_LOCS_CFG_FILE") is not None: - os.environ.pop("EODAG_LOCS_CFG_FILE") + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() + cls.conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") def test_supported_providers_in_unit_test(self): """Every provider must be referenced in the core unittest SUPPORTED_PROVIDERS class attribute""" # noqa @@ -216,25 +208,75 @@ class TestCore(unittest.TestCase): self.assertIn("sensorType", structure) self.assertIn(structure["ID"], self.SUPPORTED_PRODUCT_TYPES) - def test_core_object_creates_config_standard_location(self): - """The core object must create a user config file in standard user config location on instantiation""" # noqa - self.execution_involving_conf_dir(inspect="eodag.yml") - - def test_core_object_creates_index_if_not_exist(self): - """The core object must create an index in user config directory""" - self.execution_involving_conf_dir(inspect=".index") - @mock.patch("eodag.api.core.open_dir", autospec=True) @mock.patch("eodag.api.core.exists_in", autospec=True, return_value=True) def test_core_object_open_index_if_exists(self, exists_in_mock, open_dir_mock): """The core object must use the existing index dir if any""" - conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") - index_dir = os.path.join(conf_dir, ".index") + index_dir = os.path.join(self.conf_dir, ".index") if not os.path.exists(index_dir): makedirs(index_dir) EODataAccessGateway() open_dir_mock.assert_called_with(index_dir) + def test_core_object_set_default_locations_config(self): + """The core object must set the default locations config on instantiation""" # noqa + default_shpfile = os.path.join( + self.conf_dir, "shp", "ne_110m_admin_0_map_units.shp" + ) + self.assertIsInstance(self.dag.locations_config, list) + self.assertEqual( + self.dag.locations_config, + [dict(attr="ADM0_A3_US", name="country", path=default_shpfile)], + ) + + def test_core_object_locations_file_not_found(self): + """The core object must set the locations to an empty list when the file is not found""" # noqa + dag = EODataAccessGateway(locations_conf_path="no_locations.yml") + self.assertEqual(dag.locations_config, []) + + def test_rebuild_index(self): + """Change eodag version and check that whoosh index is rebuilt""" + index_dir = os.path.join(self.dag.conf_dir, ".index") + index_dir_mtime = os.path.getmtime(index_dir) + + self.assertNotEqual(self.dag.get_version(), "fake-version") + + with mock.patch( + "eodag.api.core.EODataAccessGateway.get_version", + autospec=True, + return_value="fake-version", + ): + self.assertEqual(self.dag.get_version(), "fake-version") + + self.dag.build_index() + + # check that index_dir has beeh re-created + self.assertNotEqual(os.path.getmtime(index_dir), index_dir_mtime) + + +class TestCoreConfWithEnvVar(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() + + @classmethod + def tearDownClass(cls): + if os.getenv("EODAG_CFG_FILE") is not None: + os.environ.pop("EODAG_CFG_FILE") + if os.getenv("EODAG_LOCS_CFG_FILE") is not None: + os.environ.pop("EODAG_LOCS_CFG_FILE") + + def test_core_object_prioritize_locations_file_in_envvar(self): + """The core object must use the locations file pointed to by the EODAG_LOCS_CFG_FILE env var""" # noqa + os.environ["EODAG_LOCS_CFG_FILE"] = os.path.join( + TEST_RESOURCES_PATH, "file_locations_override.yml" + ) + dag = EODataAccessGateway() + self.assertEqual( + dag.locations_config, + [dict(attr="dummyattr", name="dummyname", path="dummypath.shp")], + ) + def test_core_object_prioritize_config_file_in_envvar(self): """The core object must use the config file pointed to by the EODAG_CFG_FILE env var""" # noqa os.environ["EODAG_CFG_FILE"] = os.path.join( @@ -246,6 +288,24 @@ class TestCore(unittest.TestCase): # peps outputs prefix is set to /data self.assertEqual(dag.providers_config["peps"].download.outputs_prefix, "/data") + +class TestCoreInvolvingConfDir(unittest.TestCase): + def setUp(self): + super(TestCoreInvolvingConfDir, self).setUp() + self.dag = EODataAccessGateway() + + def tearDown(self): + super(TestCoreInvolvingConfDir, self).tearDown() + for old in glob.glob1(self.dag.conf_dir, ".old") + glob.glob1( + self.dag.conf_dir, "..old" + ): + old_path = os.path.join(self.dag.conf_dir, old) + if os.path.exists(old_path): + try: + os.remove(old_path) + except OSError: + shutil.rmtree(old_path) + def execution_involving_conf_dir(self, inspect=None): """Check that the path(s) inspected (str, list) are created after the instantation of EODataAccessGateway. If they were already there, rename them (.old), instantiate, @@ -273,36 +333,23 @@ class TestCore(unittest.TestCase): os.unlink(current) shutil.move(old, current) + def test_core_object_creates_config_standard_location(self): + """The core object must create a user config file in standard user config location on instantiation""" # noqa + self.execution_involving_conf_dir(inspect="eodag.yml") + + def test_core_object_creates_index_if_not_exist(self): + """The core object must create an index in user config directory""" + self.execution_involving_conf_dir(inspect=".index") + def test_core_object_creates_locations_standard_location(self): """The core object must create a locations config file and a shp dir in standard user config location on instantiation""" # noqa self.execution_involving_conf_dir(inspect=["locations.yml", "shp"]) - def test_core_object_set_default_locations_config(self): - """The core object must set the default locations config on instantiation""" # noqa - dag = EODataAccessGateway() - conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") - default_shpfile = os.path.join(conf_dir, "shp", "ne_110m_admin_0_map_units.shp") - self.assertIsInstance(dag.locations_config, list) - self.assertEqual( - dag.locations_config, - [dict(attr="ADM0_A3_US", name="country", path=default_shpfile)], - ) - - def test_core_object_locations_file_not_found(self): - """The core object must set the locations to an empty list when the file is not found""" # noqa - dag = EODataAccessGateway(locations_conf_path="no_locations.yml") - self.assertEqual(dag.locations_config, []) - def test_core_object_prioritize_locations_file_in_envvar(self): - """The core object must use the locations file pointed to by the EODAG_LOCS_CFG_FILE env var""" # noqa - os.environ["EODAG_LOCS_CFG_FILE"] = os.path.join( - TEST_RESOURCES_PATH, "file_locations_override.yml" - ) - dag = EODataAccessGateway() - self.assertEqual( - dag.locations_config, - [dict(attr="dummyattr", name="dummyname", path="dummypath.shp")], - ) +class TestCoreGeometry(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() def test_get_geometry_from_various_no_locations(self): """The search geometry can be set from a dict, list, tuple, WKT string or shapely geom""" @@ -415,9 +462,8 @@ class TestCore(unittest.TestCase): locations_config, locations=dict(country="FRA"), geometry=geometry ) self.assertIsInstance(geom_combined, MultiPolygon) - self.assertEquals( - len(geom_combined), 4 - ) # France + Guyana + Corsica + somewhere over Poland + # France + Guyana + Corsica + somewhere over Poland + self.assertEquals(len(geom_combined), 4) geometry = { "lonmin": 0, "latmin": 50, @@ -428,26 +474,591 @@ class TestCore(unittest.TestCase): locations_config, locations=dict(country="FRA"), geometry=geometry ) self.assertIsInstance(geom_combined, MultiPolygon) - self.assertEquals( - len(geom_combined), 3 - ) # The bounding box overlaps with France inland + # The bounding box overlaps with France inland + self.assertEquals(len(geom_combined), 3) - def test_rebuild_index(self): - """Change eodag version and check that whoosh index is rebuilt""" - index_dir = os.path.join(self.dag.conf_dir, ".index") - index_dir_mtime = os.path.getmtime(index_dir) +class TestCoreSearch(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() + # Get a SearchResult obj with 2 S2_MSI_L1C peps products + search_results_file = os.path.join( + TEST_RESOURCES_PATH, "eodag_search_result_peps.geojson" + ) + with open(search_results_file, encoding="utf-8") as f: + search_results_geojson = json.load(f) + cls.search_results = SearchResult.from_geojson(search_results_geojson) + cls.search_results_size = len(cls.search_results) + # Change the id of these products, to emulate different products + search_results_data_2 = deepcopy(cls.search_results.data) + search_results_data_2[0].properties["id"] = "a" + search_results_data_2[1].properties["id"] = "b" + cls.search_results_2 = SearchResult(search_results_data_2) + cls.search_results_size_2 = len(cls.search_results_2) - self.assertNotEqual(self.dag.get_version(), "fake-version") + def test_guess_product_type_with_kwargs(self): + """guess_product_type must return the products matching the given kwargs""" + kwargs = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + actual = self.dag.guess_product_type(kwargs) + expected = [ + "S2_MSI_L1C", + "S2_MSI_L2A", + "S2_MSI_L2A_MAJA", + "S2_MSI_L2B_MAJA_SNOW", + "S2_MSI_L2B_MAJA_WATER", + "S2_MSI_L3A_WASP", + ] + self.assertEqual(actual, expected) - with mock.patch( - "eodag.api.core.EODataAccessGateway.get_version", - autospec=True, - return_value="fake-version", - ): - self.assertEqual(self.dag.get_version(), "fake-version") + def test_guess_product_type_without_kwargs(self): + """guess_product_type must raise an exception when no kwargs are provided""" + with self.assertRaises(NoMatchingProductType): + self.dag.guess_product_type() - self.dag.build_index() + def test__prepare_search_no_parameters(self): + """_prepare_search must create some kwargs even when no parameter has been provided""" # noqa + prepared_search = self.dag._prepare_search() + expected = { + "geometry": None, + "productType": None, + } + self.assertDictContainsSubset(expected, prepared_search) + expected = set(["geometry", "productType", "auth", "search_plugin"]) + self.assertSetEqual(expected, set(prepared_search)) - # check that index_dir has beeh re-created - self.assertNotEqual(os.path.getmtime(index_dir), index_dir_mtime) + def test__prepare_search_dates(self): + """_prepare_search must handle start & end dates""" + base = { + "start": "2020-01-01", + "end": "2020-02-01", + } + prepared_search = self.dag._prepare_search(base) + expected = { + "startTimeFromAscendingNode": base["start"], + "completionTimeFromAscendingNode": base["end"], + } + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_geom(self): + """_prepare_search must handle geom, box and bbox""" + # The default way to provide a geom is through the 'geom' argument. + base = {"geom": (0, 50, 2, 52)} + # "geom": "POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))" + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + # 'box' and 'bbox' are supported for backwards compatibility, + # The priority is geom > bbox > box + base = {"box": (0, 50, 2, 52)} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + base = {"bbox": (0, 50, 2, 52)} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + base = { + "geom": "POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))", + "bbox": (0, 50, 2, 52), + "box": (0, 50, 1, 51), + } + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + self.assertNotIn("bbox", prepared_search) + self.assertNotIn("bbox", prepared_search) + self.assertIsInstance(prepared_search["geometry"], Polygon) + + def test__prepare_search_locations(self): + """_prepare_search must handle a location search""" + # When locations where introduced they could be passed + # as regular kwargs. The new and recommended way to provide + # them is through the 'locations' parameter. + base = {"locations": {"country": "FRA"}} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + + # TODO: Remove this when support for locations kwarg is dropped. + base = {"country": "FRA"} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + self.assertNotIn("country", prepared_search) + + def test__prepare_search_product_type_provided(self): + """_prepare_search must handle when a product type is given""" + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + expected = {"productType": base["productType"]} + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_product_type_guess_it(self): + """_prepare_search must guess a product type when required to""" + # Uses guess_product_type to find the product matching + # the best the given params. + base = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + prepared_search = self.dag._prepare_search(base) + expected = {"productType": "S2_MSI_L1C", base} + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_with_id(self): + """_prepare_search must handle a search by id""" + base = {"id": "dummy-id", "provider": "sobloo"} + prepared_search = self.dag._prepare_search(base) + expected = base + self.assertDictEqual(expected, prepared_search) + + def test__prepare_search_preserve_additional_kwargs(self): + """_prepare_search must preserve additional kwargs""" + base = { + "productType": "S2_MSI_L1C", + "cloudCover": 10, + } + prepared_search = self.dag._prepare_search(base) + expected = base + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_search_plugin_has_known_product_properties(self): + """_prepare_search must attach the product properties to the search plugin""" + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("peps") + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + # Just check that the title has been set correctly. There are more (e.g. + # abstract, platform, etc.) but this is sufficient to check that the + # product_type_config dict has been created and populated. + self.assertEqual( + prepared_search["search_plugin"].config.product_type_config["title"], + "SENTINEL2 Level-1C", + ) + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + def test__prepare_search_search_plugin_has_generic_product_properties(self): + """_prepare_search must be able to attach the generic product properties to the search plugin""" # noqa + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("peps") + base = {"productType": "product_unknown_to_eodag"} + prepared_search = self.dag._prepare_search(base) + # product_type_config is still created if the product is not known to eodag + # however it contains no data. + self.assertIsNone( + prepared_search["search_plugin"].config.product_type_config["title"], + ) + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + def test__prepare_search_peps_plugins_product_available(self): + """_prepare_search must return the search and auth plugins when productType is defined""" # noqa + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("peps") + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + self.assertEqual(prepared_search["search_plugin"].provider, "peps") + self.assertEqual(prepared_search["auth"].provider, "peps") + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + def test__prepare_search_no_plugins_when_search_by_id(self): + """_prepare_search must not return the search and auth plugins for a search by id""" # noqa + base = {"id": "some_id", "provider": "some_provider"} + prepared_search = self.dag._prepare_search(base) + self.assertNotIn("search_plugin", prepared_search) + self.assertNotIn("auth", prepared_search) + + def test__prepare_search_peps_plugins_product_not_available(self): + """_prepare_search can use another set of search and auth plugins than the ones of the preferred provider""" # noqa + # Document a special behaviour whereby the search and auth plugins don't + # correspond to the preferred one. This occurs whenever the searched product + # isn't available for the preferred provider but is made available by another + # one. In that case peps provides it and happens to be the first one on the list + # of providers that make it available. + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("theia") + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + self.assertEqual(prepared_search["search_plugin"].provider, "peps") + self.assertEqual(prepared_search["auth"].provider, "peps") + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_counts_by_default(self, search_plugin): + """__do_search must create a count query by default""" + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data, # a list must be returned by .query + self.search_results_size, + ) + sr, estimate = self.dag._do_search(search_plugin=search_plugin) + self.assertIsInstance(sr, SearchResult) + self.assertEqual(len(sr), self.search_results_size) + self.assertEqual(estimate, self.search_results_size) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_without_count(self, search_plugin): + """__do_search must be able to create a query without a count""" + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data, + None, # .query must return None if count is False + ) + sr, estimate = self.dag._do_search(search_plugin=search_plugin, count=False) + self.assertIsNone(estimate) + self.assertEqual(len(sr), self.search_results_size) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_paginated_handle_no_count_returned(self, search_plugin): + """__do_search must provide a best estimate when a provider doesn't return a count""" # noqa + search_plugin.provider = "peps" + # If the provider doesn't return a count, .query returns 0 + search_plugin.query.return_value = (self.search_results.data, 0) + page = 4 + sr, estimate = self.dag._do_search( + search_plugin=search_plugin, + page=page, + items_per_page=2, + ) + self.assertEqual(len(sr), self.search_results_size) + # The count guess is: page number_of_products_returned + self.assertEqual(estimate, page * self.search_results_size) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_paginated_handle_fuzzy_count(self, search_plugin): + """__do_search must provide a best estimate when a provider returns a fuzzy count""" # noqa + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data * 4, # 8 products returned + 22, # fuzzy number, less than the real total count + ) + page = 4 + items_per_page = 10 + sr, estimate = self.dag._do_search( + search_plugin=search_plugin, + page=page, + items_per_page=items_per_page, + ) + # At page 4 with 10 items_per_page we should have a count of at least 30 + # products available. However the provider returned 22. We know it's wrong. + # So we update the count with our current knowledge: 30 + 8 + # Note that this estimate could still be largely inferior to the real total + # count. + expected_estimate = items_per_page * (page - 1) + len(sr) + self.assertEqual(len(sr), 8) + self.assertEqual(estimate, expected_estimate) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_paginated_handle_null_count(self, search_plugin): + """__do_search must provide a best estimate when a provider returns a null count""" # noqa + # TODO: check the underlying implementation, it doesn't make so much sense since + # this case is already covered with nb_res = len(res) * page. This one uses + # nb_res = items_per_page * (page - 1) whick actually makes more sense. Choose + # one of them. + search_plugin.provider = "peps" + search_plugin.query.return_value = ([], 0) + page = 4 + items_per_page = 10 + sr, estimate = self.dag._do_search( + search_plugin=search_plugin, + page=page, + items_per_page=items_per_page, + ) + expected_estimate = items_per_page * (page - 1) + self.assertEqual(len(sr), 0) + self.assertEqual(estimate, expected_estimate) + + def test__do_search_does_not_raise_by_default(self): + """__do_search must not raise any error by default""" + # provider attribute required internally by __do_search for logging purposes. + class DummySearchPlugin: + provider = "peps" + + sr, estimate = self.dag._do_search(search_plugin=DummySearchPlugin()) + self.assertIsInstance(sr, SearchResult) + self.assertEqual(len(sr), 0) + self.assertEqual(estimate, 0) + + def test__do_search_can_raise_errors(self): + """__do_search must not raise any error by default""" + + class DummySearchPlugin: + provider = "peps" + + # AttributeError raised when .query is tried to be accessed on the dummy plugin. + with self.assertRaises(AttributeError): + self.dag._do_search(search_plugin=DummySearchPlugin(), raise_errors=True) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_query_products_must_be_a_list(self, search_plugin): + """__do_search expects that each search plugin returns a list of products.""" + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results, + self.search_results_size, + ) + with self.assertRaises(PluginImplementationError): + self.dag._do_search(search_plugin=search_plugin, raise_errors=True) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_register_downloader_if_search_intersection(self, search_plugin): + """__do_search must register each product's downloader if search_intersection is not None""" # noqa + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data, + self.search_results_size, + ) + sr, _ = self.dag._do_search(search_plugin=search_plugin) + for product in sr: + self.assertIsNotNone(product.downloader) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_doest_not_register_downloader_if_no_search_intersection( + self, search_plugin + ): + """__do_search must not register downloaders if search_intersection is None""" + + class DummyProduct: + seach_intersecion = None + + search_plugin.provider = "peps" + search_plugin.query.return_value = ([DummyProduct(), DummyProduct()], 2) + sr, _ = self.dag._do_search(search_plugin=search_plugin) + for product in sr: + self.assertIsNone(product.downloader) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_returns_iterator(self, search_plugin, prepare_seach): + """search_iter_page must return an iterator""" + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + (self.search_results_2.data, None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page(items_per_page=2) + first_result_page = next(page_iterator) + self.assertIsInstance(first_result_page, SearchResult) + self.assertEqual(len(first_result_page), self.search_results_size) + second_result_page = next(page_iterator) + self.assertIsInstance(second_result_page, SearchResult) + self.assertEqual(len(second_result_page), self.search_results_size_2) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_exhaust_get_all_pages_and_quit_early( + self, search_plugin, prepare_seach + ): + """search_iter_page must stop as soon as less than items_per_page products were retrieved""" # noqa + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + ([self.search_results_2.data[0]], None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page(items_per_page=2) + all_page_results = list(page_iterator) + self.assertEqual(len(all_page_results), 2) + self.assertIsInstance(all_page_results[0], SearchResult) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_exhaust_get_all_pages_no_products_last_page( + self, search_plugin, prepare_seach + ): + """search_iter_page must stop if the page doesn't return any product""" + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + (self.search_results_2.data, None), + ([], None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page(items_per_page=2) + all_page_results = list(page_iterator) + self.assertEqual(len(all_page_results), 2) + self.assertIsInstance(all_page_results[0], SearchResult) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_does_not_handle_query_errors( + self, search_plugin, prepare_seach + ): + """search_iter_page must propagate errors""" + search_plugin.provider = "peps" + search_plugin.query.side_effect = AttributeError() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page() + with self.assertRaises(AttributeError): + next(page_iterator) + + @mock.patch( + "eodag.plugins.search.qssearch.QueryStringSearch._request", autospec=True + ) + @mock.patch( + "eodag.plugins.search.qssearch.QueryStringSearch.normalize_results", + autospec=True, + ) + def test_search_iter_page_must_reset_next_attrs_if_next_mechanism( + self, normalize_results, _request + ): + """search_iter_page must reset the search plugin if the next mechanism is used""" + # More specifically: next_page_url must be None and + # config.pagination["next_page_url_tpl"] must be equal to its original value. + _request.return_value.json.return_value = { + "features": [], + "links": [{"rel": "next", "href": "url/to/next/page"}], + } + + p1 = EOProduct("dummy", dict(geometry="POINT (0 0)", id="1")) + p1.search_intersection = None + p2 = EOProduct("dummy", dict(geometry="POINT (0 0)", id="2")) + p2.search_intersection = None + normalize_results.side_effect = [[p1], [p2]] + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + pagination: + next_page_url_tpl: 'dummy_next_page_url_tpl' + next_page_url_key_path: '$.links[?(@.rel="next")].href' + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + + page_iterator = dag.search_iter_page(productType="S2_MSI_L1C") + next(page_iterator) + search_plugin = next( + dag._plugins_manager.get_search_plugins(product_type="S2_MSI_L1C") + ) + self.assertIsNone(search_plugin.next_page_url) + self.assertEqual( + search_plugin.config.pagination["next_page_url_tpl"], + "dummy_next_page_url_tpl", + ) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_all_must_collect_them_all(self, search_plugin, prepare_seach): + """search_all must return all the products available""" # noqa + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + ([self.search_results_2.data[0]], None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + + # Infinite generator her because passing directly the dict to + # prepare_search.return_value (or side_effect) didn't work. One function + # would do a dict.pop("search_plugin") that would remove the item from the + # mocked return value. Later calls would then break + def yield_search_plugin(): + while True: + yield {"search_plugin": search_plugin} + + prepare_seach.side_effect = yield_search_plugin() + all_results = self.dag.search_all(items_per_page=2) + self.assertIsInstance(all_results, SearchResult) + self.assertEqual(len(all_results), 3) + + @mock.patch("eodag.api.core.EODataAccessGateway.search_iter_page", autospec=True) + def test_search_all_use_max_items_per_page(self, mocked_search_iter_page): + """search_all must use the configured parameter max_items_per_page if available""" # noqa + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + pagination: + max_items_per_page: 2 + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + mocked_search_iter_page.return_value = (self.search_results for _ in range(1)) + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + dag.search_all(productType="S2_MSI_L1C") + self.assertEqual(mocked_search_iter_page.call_args[1]["items_per_page"], 2) + + @mock.patch("eodag.api.core.EODataAccessGateway.search_iter_page", autospec=True) + def test_search_all_use_default_value(self, mocked_search_iter_page): + """search_all must use the DEFAULT_MAX_ITEMS_PER_PAGE if the provider's one wasn't configured""" # noqa + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + mocked_search_iter_page.return_value = (self.search_results for _ in range(1)) + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + dag.search_all(productType="S2_MSI_L1C") + self.assertEqual( + mocked_search_iter_page.call_args[1]["items_per_page"], + DEFAULT_MAX_ITEMS_PER_PAGE, + ) + + @mock.patch("eodag.api.core.EODataAccessGateway.search_iter_page", autospec=True) + def test_search_all_user_items_per_page(self, mocked_search_iter_page): + """search_all must use the value of items_per_page provided by the user""" + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + mocked_search_iter_page.return_value = (self.search_results for _ in range(1)) + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + dag.search_all(productType="S2_MSI_L1C", items_per_page=7) + self.assertEqual(mocked_search_iter_page.call_args[1]["items_per_page"], 7)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 4 }	2.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "nose", "coverage", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@af9afc8f4e21f6af129c10a1763fb560ac0dfbe2#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-195	11a6c14378b0f02b8708b38ce7bc68a984beb4ce	2021-03-22 06:07:45	49b15ccd7aa5b83f19fb98b8b4a0dae279c2c18c		diff --git a/docs/_static/custom.css b/docs/_static/custom.css new file mode 100644 index 00000000..6a54a42e --- /dev/null +++ b/docs/_static/custom.css @@ -0,0 +1,4 @@ +div.sphinxsidebar { + max-height: 100%; + overflow-y: auto; +} diff --git a/docs/conf.py b/docs/conf.py index 5357ea66..3c6fddcb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -111,7 +111,7 @@ html_theme_options = { "logo": "eodag_logo.png", "logo_name": False, "show_related": True, - "fixed_sidebar": False, + "fixed_sidebar": True, "show_powered_by": False, "github_user": "CS-SI", "github_repo": "eodag", diff --git a/eodag/api/core.py b/eodag/api/core.py index 15ada6d6..3bb9b089 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -100,6 +100,8 @@ class EODataAccessGateway(object): override_config_from_env(self.providers_config) self._plugins_manager = PluginManager(self.providers_config) + # use updated providers_config + self.providers_config = self._plugins_manager.providers_config # Build a search index for product types self._product_types_index = None diff --git a/eodag/config.py b/eodag/config.py index 2a9e45d7..2334ce75 100644 --- a/eodag/config.py +++ b/eodag/config.py @@ -335,6 +335,38 @@ def override_config_from_mapping(config, mapping): logger.warning("%s skipped: %s", provider, e) +def merge_configs(config, other_config): + """Override a configuration with the values of another configuration + + :param dict config: An eodag providers configuration dictionary + :param dict other_config: An eodag providers configuration dictionary + """ + # configs union with other_config values as default + other_config = dict(config, other_config) + + for provider, new_conf in other_config.items(): + old_conf = config.get(provider, None) + + if old_conf: + # update non-objects values + new_conf = dict(old_conf.__dict__, new_conf.__dict__) + + for conf_k, conf_v in new_conf.items(): + old_conf_v = getattr(old_conf, conf_k, None) + + if isinstance(conf_v, PluginConfig) and isinstance( + old_conf_v, PluginConfig + ): + old_conf_v.update(conf_v.__dict__) + new_conf[conf_k] = old_conf_v + elif isinstance(old_conf_v, PluginConfig): + new_conf[conf_k] = old_conf_v + + setattr(config[provider], conf_k, new_conf[conf_k]) + else: + config[provider] = new_conf + + def load_yml_config(yml_path): """Build a conf dictionnary from given yml absolute path diff --git a/eodag/plugins/manager.py b/eodag/plugins/manager.py index 8abf723b..42b359e7 100644 --- a/eodag/plugins/manager.py +++ b/eodag/plugins/manager.py @@ -21,7 +21,7 @@ from pathlib import Path import pkg_resources -from eodag.config import load_config +from eodag.config import load_config, merge_configs from eodag.plugins.apis.base import Api from eodag.plugins.authentication.base import Authentication from eodag.plugins.base import EODAGPluginMount @@ -79,17 +79,21 @@ class PluginManager(object): "Check that the plugin module (%s) is importable", entry_point.module_name, ) - if entry_point.dist.project_name != "eodag": + if entry_point.dist.key != "eodag": # use plugin providers if any plugin_providers_config_path = [ x - for x in Path(entry_point.dist.location).rglob("providers.yml") - if all(i not in str(x) for i in ["build", ".tox"]) + for x in Path( + entry_point.dist.location, + pkg_resources.to_filename(entry_point.dist.key), + ).rglob("providers.yml") ] if plugin_providers_config_path: - self.providers_config.update( - load_config(plugin_providers_config_path[0]) + plugin_providers_config = load_config( + plugin_providers_config_path[0] ) + merge_configs(plugin_providers_config, self.providers_config) + self.providers_config = plugin_providers_config self.product_type_to_provider_config_map = {} for provider_config in self.providers_config.values():	End-to-end test fails for sobloo ``` FAIL: test_end_to_end_search_download_airbus (test_end_to_end.TestEODagEndToEnd) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/maxime/TRAVAIL/06_EODAG/01_eodag/eodag/tests/test_end_to_end.py", line 278, in test_end_to_end_search_download_airbus self.execute_download(product, expected_filename) File "/home/maxime/TRAVAIL/06_EODAG/01_eodag/eodag/tests/test_end_to_end.py", line 253, in execute_download self.assertIn( AssertionError: 'S2A_MSIL1C_20200510T105031_N0209_R051_T31TDH_20200510T112219.zip' not found in ['.downloaded'] ``` The reason is that the product found appears to be OFFLINE.	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index c0f3a8b0..bd969304 100644 --- a/tests/context.py +++ b/tests/context.py @@ -31,7 +31,7 @@ from eodag.api.product.drivers import DRIVERS from eodag.api.product.drivers.base import NoDriver from eodag.api.search_result import SearchResult from eodag.cli import download, eodag, list_pt, search_crunch -from eodag.config import load_default_config +from eodag.config import load_default_config, merge_configs from eodag.plugins.authentication.base import Authentication from eodag.plugins.crunch.filter_date import FilterDate from eodag.plugins.crunch.filter_latest_tpl_name import FilterLatestByName diff --git a/tests/resources/fake_ext_plugin/eodag_fakeplugin/__init__.py b/tests/resources/fake_ext_plugin/eodag_fakeplugin/__init__.py new file mode 100644 index 00000000..ec2fd1e3 --- /dev/null +++ b/tests/resources/fake_ext_plugin/eodag_fakeplugin/__init__.py @@ -0,0 +1,28 @@ +# -- coding: utf-8 -- +# Copyright 2021, CS GROUP - France, http://www.c-s.fr +# +# This file is part of EODAG project +# https://www.github.com/CS-SI/EODAG +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Filter Cython warnings +# Apparently (see +# https://stackoverflow.com/questions/40845304/runtimewarning-numpy-dtype-size-changed-may-indicate-binary-incompatibility # noqa +# and https://github.com/numpy/numpy/issues/11788) this is caused by Cython and affect pre-built Python packages that +# depends on numpy and ship with a pre-built version of numpy that is older than 1.15.1 (where the warning is silenced +# exactly as below) +"""EODAG fake_ext_plugin.""" + + +class FakePluginAPI: + pass diff --git a/tests/resources/fake_ext_plugin/providers.yml b/tests/resources/fake_ext_plugin/eodag_fakeplugin/providers.yml similarity index 100% rename from tests/resources/fake_ext_plugin/providers.yml rename to tests/resources/fake_ext_plugin/eodag_fakeplugin/providers.yml diff --git a/tests/test_config.py b/tests/test_config.py index 2d01ad25..dac5a227 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -23,7 +23,7 @@ from io import StringIO import yaml.parser -from tests.context import ValidationError, config +from tests.context import ValidationError, config, merge_configs class TestProviderConfig(unittest.TestCase): @@ -136,6 +136,48 @@ class TestProviderConfig(unittest.TestCase): self.assertTrue(hasattr(provider_config.api, "newParam")) self.assertEqual(provider_config.api.newParam, "val") + def test_provider_config_merge(self): + """Merge 2 providers configs""" + config_stream1 = StringIO( + """!provider + name: provider1 + provider_param: val + provider_param2: val2 + api: !plugin + type: MyPluginClass + plugin_param1: value1 + pluginParam2: value2 + """ + ) + config_stream2 = StringIO( + """!provider + name: provider1 + provider_param: val1 + provider_param3: val3 + api: !plugin + type: MyPluginClass + pluginParam2: value3 + """ + ) + provider_config1 = yaml.load(config_stream1, Loader=yaml.Loader) + provider_config2 = yaml.load(config_stream2, Loader=yaml.Loader) + + providers_config = { + "provider1": provider_config1, + "provider2": provider_config1, + } + + merge_configs( + providers_config, + {"provider1": provider_config2, "provider3": provider_config1}, + ) + self.assertEqual(len(providers_config), 3) + self.assertEqual(providers_config["provider1"].provider_param, "val1") + self.assertEqual(providers_config["provider1"].provider_param2, "val2") + self.assertEqual(providers_config["provider1"].provider_param3, "val3") + self.assertEqual(providers_config["provider1"].api.plugin_param1, "value1") + self.assertEqual(providers_config["provider1"].api.pluginParam2, "value3") + class TestPluginConfig(unittest.TestCase): def test_plugin_config_valid(self): diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 7f387df8..08c2521a 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -38,8 +38,8 @@ THEIA_SEARCH_ARGS = [ SOBLOO_SEARCH_ARGS = [ "sobloo", "S2_MSI_L1C", - "2020-05-01", - "2020-06-01", + "2021-01-01", + "2021-02-01", [0.2563590566012408, 43.19555008715042, 2.379835675499976, 43.907759172380565], ] PEPS_BEFORE_20161205_SEARCH_ARGS = [ @@ -272,14 +272,14 @@ class TestEODagEndToEnd(EndToEndBase): expected_filename = "{}.tar.bz".format(product.properties["title"]) self.execute_download(product, expected_filename) - def test_end_to_end_search_download_airbus(self): + def test_end_to_end_search_download_sobloo(self): product = self.execute_search(SOBLOO_SEARCH_ARGS) expected_filename = "{}.zip".format(product.properties["title"]) self.execute_download(product, expected_filename) - def test_end_to_end_search_download_airbus_noresult(self): + def test_end_to_end_search_download_sobloo_noresult(self): """Requesting a page on sobloo with no results must return an empty SearchResult""" - # As of 2021-02-23 this search at page 1 returns 68 products, so at page 2 there + # As of 2021-03-19 this search at page 1 returns 66 products, so at page 2 there # are no products available and sobloo returns a response without products (`hits`). product = self.execute_search( SOBLOO_SEARCH_ARGS, page=2, items_per_page=100, check_product=False	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 4 }	2.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "nose", "coverage", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@11a6c14378b0f02b8708b38ce7bc68a984beb4ce#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_config.py::TestProviderConfig::test_provider_config_merge", "tests/test_config.py::TestProviderConfig::test_provider_config_name", "tests/test_config.py::TestProviderConfig::test_provider_config_update", "tests/test_config.py::TestProviderConfig::test_provider_config_valid", "tests/test_config.py::TestPluginConfig::test_plugin_config_update", "tests/test_config.py::TestPluginConfig::test_plugin_config_valid", "tests/test_config.py::TestConfigFunctions::test_load_default_config", "tests/test_config.py::TestConfigFunctions::test_override_config_from_env", "tests/test_config.py::TestConfigFunctions::test_override_config_from_file", "tests/test_config.py::TestConfigFunctions::test_override_config_from_str", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-200	af9afc8f4e21f6af129c10a1763fb560ac0dfbe2	2021-03-24 12:48:40	49b15ccd7aa5b83f19fb98b8b4a0dae279c2c18c		diff --git a/CHANGES.rst b/CHANGES.rst index 59bcdfc1..9998e581 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,11 @@ Release history --------------- +2.2.0 (2021-xx-xx) +++++++++++++++++++ + +- More explicit signature for ``setup_logging``, fixes `GH-197 <https://github.com/CS-SI/eodag/issues/197>`_ + 2.1.1 (2021-03-18) ++++++++++++++++++ diff --git a/eodag/api/core.py b/eodag/api/core.py index 4655c3e3..31a214cd 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -59,6 +59,10 @@ logger = logging.getLogger("eodag.core") # pagination defaults DEFAULT_PAGE = 1 DEFAULT_ITEMS_PER_PAGE = 20 +# Default maximum number of items per page requested by search_all. 50 instead of +# 20 (DEFAULT_ITEMS_PER_PAGE) to increase it to the known and currentminimum +# value (mundi) +DEFAULT_MAX_ITEMS_PER_PAGE = 50 class EODataAccessGateway(object): @@ -243,7 +247,9 @@ class EODataAccessGateway(object): :type provider: str """ if provider not in self.available_providers(): - raise UnsupportedProvider("This provider is not recognised by eodag") + raise UnsupportedProvider( + f"This provider is not recognised by eodag: {provider}" + ) preferred_provider, max_priority = self.get_preferred_provider() if preferred_provider != provider: new_priority = max_priority + 1 @@ -391,22 +397,6 @@ class EODataAccessGateway(object): def guess_product_type(self, kwargs): """Find the eodag product type code that best matches a set of search params - >>> from eodag import EODataAccessGateway - >>> dag = EODataAccessGateway() - >>> dag.guess_product_type( - ... instrument="MSI", - ... platform="SENTINEL2", - ... platformSerialIdentifier="S2A", - ... ) # doctest: +NORMALIZE_WHITESPACE - ['S2_MSI_L1C', 'S2_MSI_L2A', 'S2_MSI_L2A_MAJA', 'S2_MSI_L2B_MAJA_SNOW', - 'S2_MSI_L2B_MAJA_WATER', 'S2_MSI_L3A_WASP'] - >>> import eodag.utils.exceptions - >>> try: - ... dag.guess_product_type() - ... raise AssertionError(u"NoMatchingProductType exception not raised") - ... except eodag.utils.exceptions.NoMatchingProductType: - ... pass - :param kwargs: A set of search parameters as keywords arguments :return: The best match for the given parameters :rtype: str @@ -438,7 +428,7 @@ class EODataAccessGateway(object): kwargs[search_key] ) if results is None: - results = searcher.search(query) + results = searcher.search(query, limit=None) else: results.upgrade_and_extend(searcher.search(query)) guesses = [r["ID"] for r in results or []] @@ -455,7 +445,7 @@ class EODataAccessGateway(object): end=None, geom=None, locations=None, - kwargs + kwargs, ): """Look for products matching criteria on known providers. @@ -557,6 +547,295 @@ class EODataAccessGateway(object): return a list as a result of their processing. This requirement is enforced here. """ + search_kwargs = self._prepare_search( + start=start, end=end, geom=geom, locations=locations, kwargs + ) + if search_kwargs.get("id"): + provider = search_kwargs.get("provider") + return self._search_by_id(search_kwargs["id"], provider) + search_plugin = search_kwargs.pop("search_plugin") + search_kwargs.update( + page=page, + items_per_page=items_per_page, + ) + return self._do_search( + search_plugin, count=True, raise_errors=raise_errors, search_kwargs + ) + + def search_iter_page( + self, + items_per_page=DEFAULT_ITEMS_PER_PAGE, + start=None, + end=None, + geom=None, + locations=None, + kwargs, + ): + """Iterate over the pages of a products search. + + :param items_per_page: The number of results requested per page (default: 20) + :type items_per_page: int + :param start: Start sensing UTC time in iso format + :type start: str + :param end: End sensing UTC time in iso format + :type end: str + :param geom: Search area that can be defined in different ways: + + * with a Shapely geometry object: + ``class:`shapely.geometry.base.BaseGeometry``` + * with a bounding box (dict with keys: "lonmin", "latmin", "lonmax", "latmax"): + ``dict.fromkeys(["lonmin", "latmin", "lonmax", "latmax"])`` + * with a bounding box as list of float: + ``[lonmin, latmin, lonmax, latmax]`` + * with a WKT str + + :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] + :param locations: Location filtering by name using locations configuration + ``{"<location_name>"="<attr_regex>"}``. For example, ``{"country"="PA."}`` will use + the geometry of the features having the property ISO3 starting with + 'PA' such as Panama and Pakistan in the shapefile configured with + name=country and attr=ISO3 + :type locations: dict + :param dict kwargs: some other criteria that will be used to do the search, + using paramaters compatibles with the provider + :returns: An iterator that yields page per page a collection of EO products + matching the criteria + :rtype: Iterator[:class:`~eodag.api.search_result.SearchResult`] + + .. versionadded:: + 2.2.0 + """ + search_kwargs = self._prepare_search( + start=start, end=end, geom=geom, locations=locations, kwargs + ) + search_plugin = search_kwargs.pop("search_plugin") + iteration = 1 + # Store the search plugin config pagination.next_page_url_tpl to reset it later + # since it might be modified if the next_page_url mechanism is used by the + # plugin. + pagination_config = getattr(search_plugin.config, "pagination", {}) + prev_next_page_url_tpl = pagination_config.get("next_page_url_tpl") + # Page has to be set to a value even if use_next is True, this is required + # internally by the search plugin (see collect_search_urls) + search_kwargs.update( + page=1, + items_per_page=items_per_page, + ) + prev_product = None + next_page_url = None + while True: + if iteration > 1 and next_page_url: + pagination_config["next_page_url_tpl"] = next_page_url + logger.debug("Iterate over pages: search page %s", iteration) + try: + products, _ = self._do_search( + search_plugin, count=False, raise_errors=True, search_kwargs + ) + finally: + # we don't want that next(search_iter_page(...)) modifies the plugin + # indefinitely. So we reset after each request, but before the generator + # yields, the attr next_page_url (to None) and + # config.pagination["next_page_url_tpl"] (to its original value). + next_page_url = getattr(search_plugin, "next_page_url", None) + if next_page_url: + search_plugin.next_page_url = None + if prev_next_page_url_tpl: + search_plugin.config.pagination[ + "next_page_url_tpl" + ] = prev_next_page_url_tpl + if len(products) > 0: + # The first products between two iterations are compared. If they + # are actually the same product, it means the iteration failed at + # progressing for some reason. This is implemented as a workaround + # to some search plugins/providers not handling pagination. + product = products[0] + if ( + prev_product + and product.properties["id"] == prev_product.properties["id"] + and product.provider == prev_product.provider + ): + logger.warning( + "Iterate over pages: stop iterating since the next page " + "appears to have the same products as in the previous one. " + "This provider may not implement pagination.", + ) + last_page_with_products = iteration - 1 + break + yield products + prev_product = product + # Prevent a last search if the current one returned less than the + # maximum number of items asked for. + if len(products) < items_per_page: + last_page_with_products = iteration + break + else: + last_page_with_products = iteration - 1 + break + iteration += 1 + search_kwargs["page"] = iteration + logger.debug( + "Iterate over pages: last products found on page %s", + last_page_with_products, + ) + + def search_all( + self, + items_per_page=None, + start=None, + end=None, + geom=None, + locations=None, + *kwargs, + ): + """Search and return all the products matching the search criteria. + + It iterates over the pages of a search query and collects all the returned + products into a single :class:`~eodag.api.search_result.SearchResult` instance. + + :param items_per_page: (optional) The number of results requested internally per + page. The maximum number of items than can be requested + at once to a provider has been configured in EODAG for + some of them. If items_per_page is None and this number + is available for the searched provider, it is used to + limit the number of requests made. This should also + reduce the time required to collect all the products + matching the search criteria. If this number is not + available, a default value of 50 is used instead. + items_per_page can also be set to any arbitrary value. + :type items_per_page: int + :param start: Start sensing UTC time in iso format + :type start: str + :param end: End sensing UTC time in iso format + :type end: str + :param geom: Search area that can be defined in different ways: + + with a Shapely geometry object: + ``class:`shapely.geometry.base.BaseGeometry``` + * with a bounding box (dict with keys: "lonmin", "latmin", "lonmax", "latmax"): + ``dict.fromkeys(["lonmin", "latmin", "lonmax", "latmax"])`` + * with a bounding box as list of float: + ``[lonmin, latmin, lonmax, latmax]`` + * with a WKT str + + :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] + :param locations: Location filtering by name using locations configuration + ``{"<location_name>"="<attr_regex>"}``. For example, ``{"country"="PA."}`` will use + the geometry of the features having the property ISO3 starting with + 'PA' such as Panama and Pakistan in the shapefile configured with + name=country and attr=ISO3 + :type locations: dict + :param dict kwargs: some other criteria that will be used to do the search, + using paramaters compatibles with the provider + :returns: An iterator that yields page per page a collection of EO products + matching the criteria + :rtype: Iterator[:class:`~eodag.api.search_result.SearchResult`] + + .. versionadded:: + 2.2.0 + """ + # Prepare the search just to get the search plugin and the maximized value + # of items_per_page if defined for the provider used. + search_kwargs = self._prepare_search( + start=start, end=end, geom=geom, locations=locations, kwargs + ) + search_plugin = search_kwargs["search_plugin"] + if items_per_page is None: + items_per_page = search_plugin.config.pagination.get( + "max_items_per_page", DEFAULT_MAX_ITEMS_PER_PAGE + ) + logger.debug( + "Searching for all the products with provider %s and a maximum of %s " + "items per page.", + search_plugin.provider, + items_per_page, + ) + all_results = SearchResult([]) + for page_results in self.search_iter_page( + items_per_page=items_per_page, + start=start, + end=end, + geom=geom, + locations=locations, + kwargs, + ): + all_results.data.extend(page_results.data) + logger.info( + "Found %s result(s) on provider '%s'", + len(all_results), + search_plugin.provider, + ) + return all_results + + def _search_by_id(self, uid, provider=None): + """Internal method that enables searching a product by its id. + + Keeps requesting providers until a result matching the id is supplied. The + search plugins should be developed in the way that enable them to handle the + support of a search by id by the providers. The providers are requested one by + one, in the order defined by their priorities. Be aware that because of that, + the search can be slow, if the priority order is such that the provider that + contains the requested product has the lowest priority. However, you can always + speed up a little the search by passing the name of the provider on which to + perform the search, if this information is available + + :param uid: The uid of the EO product + :type uid: str + :param provider: (optional) The provider on which to search the product. + This may be useful for performance reasons when the user + knows this product is available on the given provider + :type provider: str + :returns: A search result with one EO product or None at all, and the number + of EO products retrieved (0 or 1) + :rtype: tuple(:class:`~eodag.api.search_result.SearchResult`, int) + + .. versionadded:: 1.0 + """ + for plugin in self._plugins_manager.get_search_plugins(provider=provider): + logger.info( + "Searching product with id '%s' on provider: %s", uid, plugin.provider + ) + logger.debug("Using plugin class for search: %s", plugin.__class__.__name__) + auth = self._plugins_manager.get_auth_plugin(plugin.provider) + results, _ = self._do_search(plugin, auth=auth, id=uid) + if len(results) == 1: + return results, 1 + return SearchResult([]), 0 + + def _prepare_search( + self, start=None, end=None, geom=None, locations=None, *kwargs + ): + """Internal method to prepare the search kwargs and get the search + and auth plugins. + + Product query: + By id (plus optional 'provider') + * By search params: + * productType query: + * By product type (e.g. 'S2_MSI_L1C') + * By params (e.g. 'platform'), see guess_product_type + * dates: 'start' and/or 'end' + * geometry: 'geom' or 'bbox' or 'box' + * search locations + * TODO: better expose cloudCover + * other search params are passed to Searchplugin.query() + + :param start: Start sensing UTC time in iso format + :type start: str + :param end: End sensing UTC time in iso format + :type end: str + :param geom: Search area that can be defined in different ways (see search) + :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] + :param locations: Location filtering by name using locations configuration + :type locations: dict + :param dict kwargs: Some other criteria + * id and/or a provider for a search by + * search criteria to guess the product type + * other criteria compatible with the provider + :returns: The prepared kwargs to make a query. + :rtype: dict + + .. versionadded:: 2.2 + """ product_type = kwargs.get("productType", None) if product_type is None: try: @@ -570,14 +849,18 @@ class EODataAccessGateway(object): "No product type could be guessed with provided arguments" ) else: - provider = kwargs.get("provider", None) - return self._search_by_id(kwargs["id"], provider=provider) + return kwargs kwargs["productType"] = product_type if start is not None: kwargs["startTimeFromAscendingNode"] = start if end is not None: kwargs["completionTimeFromAscendingNode"] = end + if "box" in kwargs or "bbox" in kwargs: + logger.warning( + "'box' or 'bbox' parameters are only supported for backwards " + " compatibility reasons. Usage of 'geom' is recommended." + ) if geom is not None: kwargs["geometry"] = geom box = kwargs.pop("box", None) @@ -593,95 +876,73 @@ class EODataAccessGateway(object): kwargs.pop(arg, None) del kwargs["locations"] - plugin = next( + search_plugin = next( self._plugins_manager.get_search_plugins(product_type=product_type) ) logger.info( - "Searching product type '%s' on provider: %s", product_type, plugin.provider + "Searching product type '%s' on provider: %s", + product_type, + search_plugin.provider, ) - # add product_types_config to plugin config + # Add product_types_config to plugin config. This dict contains product + # type metadata that will also be stored in each product's properties. try: - plugin.config.product_type_config = dict( + search_plugin.config.product_type_config = dict( [ p - for p in self.list_product_types(plugin.provider) + for p in self.list_product_types(search_plugin.provider) if p["ID"] == product_type ][0], - {"productType": product_type} + {"productType": product_type}, ) + # If the product isn't in the catalog, it's a generic product type. except IndexError: - plugin.config.product_type_config = dict( + search_plugin.config.product_type_config = dict( [ p - for p in self.list_product_types(plugin.provider) + for p in self.list_product_types(search_plugin.provider) if p["ID"] == GENERIC_PRODUCT_TYPE ][0], - {"productType": product_type} + {"productType": product_type}, ) - plugin.config.product_type_config.pop("ID", None) + # Remove the ID since this is equal to productType. + search_plugin.config.product_type_config.pop("ID", None) - logger.debug("Using plugin class for search: %s", plugin.__class__.__name__) - auth = self._plugins_manager.get_auth_plugin(plugin.provider) - return self._do_search( - plugin, - auth=auth, - page=page, - items_per_page=items_per_page, - raise_errors=raise_errors, - kwargs + logger.debug( + "Using plugin class for search: %s", search_plugin.__class__.__name__ ) + auth_plugin = self._plugins_manager.get_auth_plugin(search_plugin.provider) - def _search_by_id(self, uid, provider=None): - """Internal method that enables searching a product by its id. - - Keeps requesting providers until a result matching the id is supplied. The - search plugins should be developed in the way that enable them to handle the - support of a search by id by the providers. The providers are requested one by - one, in the order defined by their priorities. Be aware that because of that, - the search can be slow, if the priority order is such that the provider that - contains the requested product has the lowest priority. However, you can always - speed up a little the search by passing the name of the provider on which to - perform the search, if this information is available + return dict(search_plugin=search_plugin, auth=auth_plugin, kwargs) - :param uid: The uid of the EO product - :type uid: str - :param provider: (optional) The provider on which to search the product. - This may be useful for performance reasons when the user - knows this product is available on the given provider - :type provider: str - :returns: A search result with one EO product or None at all, and the number - of EO products retrieved (0 or 1) - :rtype: tuple(:class:`~eodag.api.search_result.SearchResult`, int) - - .. versionadded:: 1.0 - """ - for plugin in self._plugins_manager.get_search_plugins(provider=provider): - logger.info( - "Searching product with id '%s' on provider: %s", uid, plugin.provider - ) - logger.debug("Using plugin class for search: %s", plugin.__class__.__name__) - auth = self._plugins_manager.get_auth_plugin(plugin.provider) - results, _ = self._do_search(plugin, auth=auth, id=uid) - if len(results) == 1: - return results, 1 - return SearchResult([]), 0 - - def _do_search(self, search_plugin, kwargs): + def _do_search(self, search_plugin, count=True, raise_errors=False, kwargs): """Internal method that performs a search on a given provider. + :param search_plugin: A search plugin + :type search_plugin: eodag.plugins.base.Search + :param count: Whether to run a query with a count request or not (default: True) + :type count: bool + :param raise_errors: When an error occurs when searching, if this is set to + True, the error is raised (default: False) + :type raise_errors: bool + :param dict kwargs: some other criteria that will be used to do the search + :returns: A collection of EO products matching the criteria and the total + number of results found if count is True else None + :rtype: tuple(:class:`~eodag.api.search_result.SearchResult`, int or None) + .. versionadded:: 1.0 """ results = SearchResult([]) total_results = 0 try: - res, nb_res = search_plugin.query(kwargs) + res, nb_res = search_plugin.query(count=count, kwargs) # Only do the pagination computations when it makes sense. For example, # for a search by id, we can reasonably guess that the provider will return # At most 1 product, so we don't need such a thing as pagination page = kwargs.get("page") items_per_page = kwargs.get("items_per_page") - if page and items_per_page: + if page and items_per_page and count: # Take into account the fact that a provider may not return the count of # products (in that case, fallback to using the length of the results it # returned and the page requested. As an example, check the result of @@ -739,31 +1000,34 @@ class EODataAccessGateway(object): ) results.extend(res) - total_results += nb_res - logger.info( - "Found %s result(s) on provider '%s'", nb_res, search_plugin.provider - ) - # Hitting for instance - # https://theia.cnes.fr/atdistrib/resto2/api/collections/SENTINEL2/ - # search.json?startDate=2019-03-01&completionDate=2019-06-15 - # &processingLevel=LEVEL2A&maxRecords=1&page=1 - # returns a number (properties.totalResults) that is the number of - # products in the collection (here SENTINEL2) instead of the estimated - # total number of products matching the search criteria (start/end date). - # Remove this warning when this is fixed upstream by THEIA. - if search_plugin.provider == "theia": - logger.warning( - "Results found on provider 'theia' is the total number of products " - "available in the searched collection (e.g. SENTINEL2) instead of " - "the total number of products matching the search criteria" + total_results = None if nb_res is None else total_results + nb_res + if count: + logger.info( + "Found %s result(s) on provider '%s'", + nb_res, + search_plugin.provider, ) + # Hitting for instance + # https://theia.cnes.fr/atdistrib/resto2/api/collections/SENTINEL2/ + # search.json?startDate=2019-03-01&completionDate=2019-06-15 + # &processingLevel=LEVEL2A&maxRecords=1&page=1 + # returns a number (properties.totalResults) that is the number of + # products in the collection (here SENTINEL2) instead of the estimated + # total number of products matching the search criteria (start/end date). + # Remove this warning when this is fixed upstream by THEIA. + if search_plugin.provider == "theia": + logger.warning( + "Results found on provider 'theia' is the total number of products " + "available in the searched collection (e.g. SENTINEL2) instead of " + "the total number of products matching the search criteria" + ) except Exception: logger.info( "No result from provider '%s' due to an error during search. Raise " "verbosity of log messages for details", search_plugin.provider, ) - if kwargs.get("raise_errors"): + if raise_errors: # Raise the error, letting the application wrapping eodag know that # something went bad. This way it will be able to decide what to do next raise @@ -827,7 +1091,7 @@ class EODataAccessGateway(object): progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, - kwargs + kwargs, ): """Download all products resulting from a search. @@ -865,7 +1129,7 @@ class EODataAccessGateway(object): progress_callback=progress_callback, wait=wait, timeout=timeout, - kwargs + kwargs, ) # close progress_bar when finished if hasattr(progress_callback, "pb") and hasattr( @@ -931,7 +1195,7 @@ class EODataAccessGateway(object): provider=None, productType=None, timeout=HTTP_REQ_TIMEOUT, - kwargs + kwargs, ): """Loads STAC items from a geojson file / STAC catalog or collection, and convert to SearchResult. @@ -1000,7 +1264,7 @@ class EODataAccessGateway(object): progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, - kwargs + kwargs, ): """Download a single product. diff --git a/eodag/cli.py b/eodag/cli.py index b25bfa6b..b4a9f7ef 100755 --- a/eodag/cli.py +++ b/eodag/cli.py @@ -264,8 +264,7 @@ def search_crunch(ctx, kwargs): click.echo(search_crunch.get_help(ctx)) sys.exit(-1) - kwargs["verbose"] = ctx.obj["verbosity"] - setup_logging(kwargs) + setup_logging(verbose=ctx.obj["verbosity"]) if kwargs["box"] != (None,) * 4: rect = kwargs.pop("box") @@ -457,8 +456,7 @@ def download(ctx, kwargs): click.echo("Nothing to do (no search results file provided)") click.echo(download.get_help(ctx)) sys.exit(1) - kwargs["verbose"] = ctx.obj["verbosity"] - setup_logging(kwargs) + setup_logging(verbose=ctx.obj["verbosity"]) conf_file = kwargs.pop("conf") if conf_file: conf_file = click.format_filename(conf_file) diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index 074ed5cb..b238440a 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -92,6 +92,9 @@ class QueryStringSearch(Search): - count_endpoint: (optional) The endpoint for counting the number of items satisfying a request + next_page_url_key_path: (optional) A JSONPATH expression used to retrieve + the URL of the next page in the response of the current page. + - free_text_search_operations: (optional) A tree structure of the form:: <search-param>: # e.g: $search @@ -153,9 +156,19 @@ class QueryStringSearch(Search): self.search_urls = [] self.query_params = dict() self.query_string = "" + self.next_page_url = None def query(self, items_per_page=None, page=None, count=True, kwargs): - """Perform a search on an OpenSearch-like interface""" + """Perform a search on an OpenSearch-like interface + + :param page: The page number to retur (default: 1) + :type page: int + :param items_per_page: The number of results that must appear in one single + page + :type items_per_page: int + :param count: To trigger a count request (default: True) + :type count: bool + """ product_type = kwargs.get("productType", None) if product_type == GENERIC_PRODUCT_TYPE: logger.warning( @@ -417,6 +430,9 @@ class QueryStringSearch(Search): except RequestError: raise StopIteration else: + next_page_url_key_path = self.config.pagination.get( + "next_page_url_key_path" + ) if self.config.result_type == "xml": root_node = etree.fromstring(response.content) namespaces = {k or "ns": v for k, v in root_node.nsmap.items()} @@ -426,8 +442,24 @@ class QueryStringSearch(Search): self.config.results_entry, namespaces=namespaces ) ] + if next_page_url_key_path: + raise NotImplementedError( + "Setting the next page url from an XML response has not " + "been implemented yet" + ) else: - result = response.json().get(self.config.results_entry, []) + resp_as_json = response.json() + if next_page_url_key_path: + path_parsed = parse(next_page_url_key_path) + try: + self.next_page_url = path_parsed.find(resp_as_json)[0].value + logger.debug( + "Next page URL collected and set for the next search", + ) + except IndexError: + logger.debug("Next page URL could not be collected") + + result = resp_as_json.get(self.config.results_entry, []) if getattr(self.config, "merge_responses", False): results = ( [dict(r, result[i]) for i, r in enumerate(results)] diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index c034296e..9906327a 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -504,6 +504,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&page={page}' total_items_nb_key_path: '$.properties.totalResults' + # 2019/03/19: Returns a 400 error code if greater than 500. + max_items_per_page: 500 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9_]+$' @@ -676,6 +678,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&page={page}' total_items_nb_key_path: '$.properties.totalResults' + # 2019/03/19: 500 is the max, no error if greater + max_items_per_page: 500 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9_]+$' @@ -866,6 +870,9 @@ pagination: next_page_url_tpl: '{url}?{search}&size={items_per_page}&from={skip}' total_items_nb_key_path: '$.totalnb' + # 2019/03/19: It's not possible to get more than 10_000 products + # Returns a 400 Bad Request if more products are requested. + max_items_per_page: 10_000 results_entry: 'hits' discover_metadata: auto_discovery: true @@ -953,6 +960,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&page={page}' total_items_nb_key_path: '$.properties.totalResults' + # 2019/03/19: 2000 is the max, 400 error if greater + max_items_per_page: 2_000 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9]+$' @@ -1106,6 +1115,8 @@ pagination: next_page_url_tpl: '{url}?{search}&maxRecords={items_per_page}&startIndex={skip_base_1}' total_items_nb_key_path: '//os:totalResults/text()' + # 2019/03/19: 50 is the max, no error if greater + max_items_per_page: 50 discover_metadata: auto_discovery: true metadata_pattern: '^(?!collection)[a-zA-Z0-9]+$' @@ -1328,6 +1339,8 @@ pagination: count_endpoint: 'https://catalogue.onda-dias.eu/dias-catalogue/Products/$count' next_page_url_tpl: '{url}?{search}&$top={items_per_page}&$skip={skip}' + # 2019/03/19: 2000 is the max, if greater 200 response but contains an error message + max_items_per_page: 2_000 results_entry: 'value' literal_search_params: $format: json @@ -1463,6 +1476,13 @@ search: !plugin type: StacSearch api_endpoint: https://eod-catalog-svc-prod.astraea.earth/search + pagination: + # 2019/03/19: The docs (https://eod-catalog-svc-prod.astraea.earth/api.html#operation/getSearchSTAC) + # say the max is 10_000. In practice 1_000 products are returned if more are asked (even greater + # than 10_000), without any error. + # This provider doesn't implement any pagination, let's just try to get the maximum number of + # products available at once then, so we stick to 10_000. + max_items_per_page: 10_000 products: S2_MSI_L1C: productType: sentinel2_l1c @@ -1496,6 +1516,10 @@ api_endpoint: https://landsatlook.usgs.gov/sat-api/collections/{collection}/items pagination: total_items_nb_key_path: '$.meta.found' + # 2019/03/19: no more than 10_000 (if greater, returns a 500 error code) + # but in practive if an Internal Server Error is returned for more than + # about 500 products. + max_items_per_page: 500 metadata_mapping: productType: '$.collection' completionTimeFromAscendingNode: diff --git a/eodag/resources/stac_provider.yml b/eodag/resources/stac_provider.yml index e3c8848c..b6f2ed19 100644 --- a/eodag/resources/stac_provider.yml +++ b/eodag/resources/stac_provider.yml @@ -20,8 +20,7 @@ search: pagination: next_page_url_tpl: '{url}?{search}&limit={items_per_page}&page={page}' total_items_nb_key_path: '$.context.matched' - pagination: - next_page_url_key_path: '$.links[?(@.rel="next")].href' + next_page_url_key_path: '$.links[?(@.rel="next")].href' discover_metadata: auto_discovery: true metadata_pattern: '^[a-zA-Z0-9_:-]+$' diff --git a/eodag/utils/logging.py b/eodag/utils/logging.py index 2a6ed5aa..12e629ea 100644 --- a/eodag/utils/logging.py +++ b/eodag/utils/logging.py @@ -19,15 +19,17 @@ import logging.config -def setup_logging(kwargs): +def setup_logging(verbose): """Define logging level - :param kwargs: Accepted Keyword arguments: - verbose=0-3 logging level (None to max) - :type kwargs: dict + :param verbose: Accepted values: + + 0: no logging + * 1 or 2: INFO level + * 3: DEBUG level + :type verbose: int """ - verbosity = kwargs.pop("verbose") - if verbosity == 0: + if verbose == 0: logging.config.dictConfig( { "version": 1, @@ -40,7 +42,7 @@ def setup_logging(kwargs): }, } ) - elif verbosity <= 2: + elif verbose <= 2: logging.config.dictConfig( { "version": 1, @@ -71,7 +73,7 @@ def setup_logging(kwargs): }, } ) - elif verbosity == 3: + elif verbose == 3: logging.config.dictConfig( { "version": 1, @@ -102,3 +104,5 @@ def setup_logging(**kwargs): }, } ) + else: + raise ValueError("'verbose' must be one of: 0, 1, 2, 3")	Improve the signature of setup_logging Which is currently ```python def setup_logging(**kwargs): ``` while: ```python def setup_logging(verbose=None): ``` would be more explicit. To make it exactly backwards compatible we'd need to raise a KeyError if called with `setup_logging()`. I don't expect anyone to rely on this behavior, so I'd suggest we change it to: ```python def setup_logging(verbose): ```	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index bd969304..83d9354e 100644 --- a/tests/context.py +++ b/tests/context.py @@ -25,7 +25,7 @@ import sys sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))) from eodag import EODataAccessGateway, api, config -from eodag.api.core import DEFAULT_ITEMS_PER_PAGE +from eodag.api.core import DEFAULT_ITEMS_PER_PAGE, DEFAULT_MAX_ITEMS_PER_PAGE from eodag.api.product import EOProduct from eodag.api.product.drivers import DRIVERS from eodag.api.product.drivers.base import NoDriver @@ -49,6 +49,8 @@ from eodag.utils.exceptions import ( AuthenticationError, DownloadError, MisconfiguredError, + NoMatchingProductType, + PluginImplementationError, UnsupportedDatasetAddressScheme, UnsupportedProvider, ValidationError, diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 08c2521a..451996a2 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -140,17 +140,17 @@ class EndToEndBase(unittest.TestCase): - Return one product to be downloaded """ search_criteria = { + "productType": product_type, "startTimeFromAscendingNode": start, "completionTimeFromAscendingNode": end, "geom": geom, } + if items_per_page: + search_criteria["items_per_page"] = items_per_page + if page: + search_criteria["page"] = page self.eodag.set_preferred_provider(provider) - results, nb_results = self.eodag.search( - productType=product_type, - page=page, - items_per_page=items_per_page, - search_criteria - ) + results, nb_results = self.eodag.search(search_criteria) if offline: results = [ prod @@ -165,6 +165,38 @@ class EndToEndBase(unittest.TestCase): else: return results + def execute_search_all( + self, + provider, + product_type, + start, + end, + geom, + items_per_page=None, + check_products=True, + ): + """Search all the products on provider: + + - First set the preferred provider as the one given in parameter + - Then do the search_all + - Then ensure that at least the first result originates from the provider + - Return all the products + """ + search_criteria = { + "startTimeFromAscendingNode": start, + "completionTimeFromAscendingNode": end, + "geom": geom, + } + self.eodag.set_preferred_provider(provider) + results = self.eodag.search_all( + productType=product_type, items_per_page=items_per_page, search_criteria + ) + if check_products: + self.assertGreater(len(results), 0) + one_product = results[0] + self.assertEqual(one_product.provider, provider) + return results + # @unittest.skip("skip auto run") class TestEODagEndToEnd(EndToEndBase): @@ -357,6 +389,32 @@ class TestEODagEndToEnd(EndToEndBase): ) self.assertGreaterEqual(os.stat(quicklook_file_path).st_size, 2 5) + def test__search_by_id_sobloo(self): + # A single test with sobloo to check that _search_by_id returns + # correctly the exact product looked for. + uid = "S2A_MSIL1C_20200810T030551_N0209_R075_T53WPU_20200810T050611" + provider = "sobloo" + + products, _ = self.eodag._search_by_id(uid=uid, provider=provider) + product = products[0] + + self.assertEqual(product.properties["id"], uid) + + def test_end_to_end_search_all_mundi_default(self): + # 23/03/2021: Got 16 products for this search + results = self.execute_search_all(MUNDI_SEARCH_ARGS) + self.assertGreater(len(results), 10) + + def test_end_to_end_search_all_mundi_iterate(self): + # 23/03/2021: Got 16 products for this search + results = self.execute_search_all(MUNDI_SEARCH_ARGS, items_per_page=10) + self.assertGreater(len(results), 10) + + def test_end_to_end_search_all_astraea_eod_iterate(self): + # 23/03/2021: Got 39 products for this search + results = self.execute_search_all(ASTRAE_EOD_SEARCH_ARGS, items_per_page=10) + self.assertGreater(len(results), 10) + class TestEODagEndToEndWrongCredentials(EndToEndBase): """Make real case tests with wrong credentials. This assumes the existence of a diff --git a/tests/units/test_core.py b/tests/units/test_core.py index df78fce7..4bde0171 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -17,17 +17,24 @@ # limitations under the License. import glob +import json import os import shutil import unittest +from copy import deepcopy from shapely import wkt -from shapely.geometry import LineString, MultiPolygon +from shapely.geometry import LineString, MultiPolygon, Polygon from eodag.utils import GENERIC_PRODUCT_TYPE from tests import TEST_RESOURCES_PATH from tests.context import ( + DEFAULT_MAX_ITEMS_PER_PAGE, EODataAccessGateway, + EOProduct, + NoMatchingProductType, + PluginImplementationError, + SearchResult, UnsupportedProvider, get_geometry_from_various, makedirs, @@ -143,25 +150,10 @@ class TestCore(unittest.TestCase): "earth_search", ] - def setUp(self): - super(TestCore, self).setUp() - self.dag = EODataAccessGateway() - - def tearDown(self): - super(TestCore, self).tearDown() - for old in glob.glob1(self.dag.conf_dir, ".old") + glob.glob1( - self.dag.conf_dir, "..old" - ): - old_path = os.path.join(self.dag.conf_dir, old) - if os.path.exists(old_path): - try: - os.remove(old_path) - except OSError: - shutil.rmtree(old_path) - if os.getenv("EODAG_CFG_FILE") is not None: - os.environ.pop("EODAG_CFG_FILE") - if os.getenv("EODAG_LOCS_CFG_FILE") is not None: - os.environ.pop("EODAG_LOCS_CFG_FILE") + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() + cls.conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") def test_supported_providers_in_unit_test(self): """Every provider must be referenced in the core unittest SUPPORTED_PROVIDERS class attribute""" # noqa @@ -216,25 +208,75 @@ class TestCore(unittest.TestCase): self.assertIn("sensorType", structure) self.assertIn(structure["ID"], self.SUPPORTED_PRODUCT_TYPES) - def test_core_object_creates_config_standard_location(self): - """The core object must create a user config file in standard user config location on instantiation""" # noqa - self.execution_involving_conf_dir(inspect="eodag.yml") - - def test_core_object_creates_index_if_not_exist(self): - """The core object must create an index in user config directory""" - self.execution_involving_conf_dir(inspect=".index") - @mock.patch("eodag.api.core.open_dir", autospec=True) @mock.patch("eodag.api.core.exists_in", autospec=True, return_value=True) def test_core_object_open_index_if_exists(self, exists_in_mock, open_dir_mock): """The core object must use the existing index dir if any""" - conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") - index_dir = os.path.join(conf_dir, ".index") + index_dir = os.path.join(self.conf_dir, ".index") if not os.path.exists(index_dir): makedirs(index_dir) EODataAccessGateway() open_dir_mock.assert_called_with(index_dir) + def test_core_object_set_default_locations_config(self): + """The core object must set the default locations config on instantiation""" # noqa + default_shpfile = os.path.join( + self.conf_dir, "shp", "ne_110m_admin_0_map_units.shp" + ) + self.assertIsInstance(self.dag.locations_config, list) + self.assertEqual( + self.dag.locations_config, + [dict(attr="ADM0_A3_US", name="country", path=default_shpfile)], + ) + + def test_core_object_locations_file_not_found(self): + """The core object must set the locations to an empty list when the file is not found""" # noqa + dag = EODataAccessGateway(locations_conf_path="no_locations.yml") + self.assertEqual(dag.locations_config, []) + + def test_rebuild_index(self): + """Change eodag version and check that whoosh index is rebuilt""" + index_dir = os.path.join(self.dag.conf_dir, ".index") + index_dir_mtime = os.path.getmtime(index_dir) + + self.assertNotEqual(self.dag.get_version(), "fake-version") + + with mock.patch( + "eodag.api.core.EODataAccessGateway.get_version", + autospec=True, + return_value="fake-version", + ): + self.assertEqual(self.dag.get_version(), "fake-version") + + self.dag.build_index() + + # check that index_dir has beeh re-created + self.assertNotEqual(os.path.getmtime(index_dir), index_dir_mtime) + + +class TestCoreConfWithEnvVar(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() + + @classmethod + def tearDownClass(cls): + if os.getenv("EODAG_CFG_FILE") is not None: + os.environ.pop("EODAG_CFG_FILE") + if os.getenv("EODAG_LOCS_CFG_FILE") is not None: + os.environ.pop("EODAG_LOCS_CFG_FILE") + + def test_core_object_prioritize_locations_file_in_envvar(self): + """The core object must use the locations file pointed to by the EODAG_LOCS_CFG_FILE env var""" # noqa + os.environ["EODAG_LOCS_CFG_FILE"] = os.path.join( + TEST_RESOURCES_PATH, "file_locations_override.yml" + ) + dag = EODataAccessGateway() + self.assertEqual( + dag.locations_config, + [dict(attr="dummyattr", name="dummyname", path="dummypath.shp")], + ) + def test_core_object_prioritize_config_file_in_envvar(self): """The core object must use the config file pointed to by the EODAG_CFG_FILE env var""" # noqa os.environ["EODAG_CFG_FILE"] = os.path.join( @@ -246,6 +288,24 @@ class TestCore(unittest.TestCase): # peps outputs prefix is set to /data self.assertEqual(dag.providers_config["peps"].download.outputs_prefix, "/data") + +class TestCoreInvolvingConfDir(unittest.TestCase): + def setUp(self): + super(TestCoreInvolvingConfDir, self).setUp() + self.dag = EODataAccessGateway() + + def tearDown(self): + super(TestCoreInvolvingConfDir, self).tearDown() + for old in glob.glob1(self.dag.conf_dir, ".old") + glob.glob1( + self.dag.conf_dir, "..old" + ): + old_path = os.path.join(self.dag.conf_dir, old) + if os.path.exists(old_path): + try: + os.remove(old_path) + except OSError: + shutil.rmtree(old_path) + def execution_involving_conf_dir(self, inspect=None): """Check that the path(s) inspected (str, list) are created after the instantation of EODataAccessGateway. If they were already there, rename them (.old), instantiate, @@ -273,36 +333,23 @@ class TestCore(unittest.TestCase): os.unlink(current) shutil.move(old, current) + def test_core_object_creates_config_standard_location(self): + """The core object must create a user config file in standard user config location on instantiation""" # noqa + self.execution_involving_conf_dir(inspect="eodag.yml") + + def test_core_object_creates_index_if_not_exist(self): + """The core object must create an index in user config directory""" + self.execution_involving_conf_dir(inspect=".index") + def test_core_object_creates_locations_standard_location(self): """The core object must create a locations config file and a shp dir in standard user config location on instantiation""" # noqa self.execution_involving_conf_dir(inspect=["locations.yml", "shp"]) - def test_core_object_set_default_locations_config(self): - """The core object must set the default locations config on instantiation""" # noqa - dag = EODataAccessGateway() - conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") - default_shpfile = os.path.join(conf_dir, "shp", "ne_110m_admin_0_map_units.shp") - self.assertIsInstance(dag.locations_config, list) - self.assertEqual( - dag.locations_config, - [dict(attr="ADM0_A3_US", name="country", path=default_shpfile)], - ) - def test_core_object_locations_file_not_found(self): - """The core object must set the locations to an empty list when the file is not found""" # noqa - dag = EODataAccessGateway(locations_conf_path="no_locations.yml") - self.assertEqual(dag.locations_config, []) - - def test_core_object_prioritize_locations_file_in_envvar(self): - """The core object must use the locations file pointed to by the EODAG_LOCS_CFG_FILE env var""" # noqa - os.environ["EODAG_LOCS_CFG_FILE"] = os.path.join( - TEST_RESOURCES_PATH, "file_locations_override.yml" - ) - dag = EODataAccessGateway() - self.assertEqual( - dag.locations_config, - [dict(attr="dummyattr", name="dummyname", path="dummypath.shp")], - ) +class TestCoreGeometry(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() def test_get_geometry_from_various_no_locations(self): """The search geometry can be set from a dict, list, tuple, WKT string or shapely geom""" @@ -415,9 +462,8 @@ class TestCore(unittest.TestCase): locations_config, locations=dict(country="FRA"), geometry=geometry ) self.assertIsInstance(geom_combined, MultiPolygon) - self.assertEquals( - len(geom_combined), 4 - ) # France + Guyana + Corsica + somewhere over Poland + # France + Guyana + Corsica + somewhere over Poland + self.assertEquals(len(geom_combined), 4) geometry = { "lonmin": 0, "latmin": 50, @@ -428,26 +474,604 @@ class TestCore(unittest.TestCase): locations_config, locations=dict(country="FRA"), geometry=geometry ) self.assertIsInstance(geom_combined, MultiPolygon) - self.assertEquals( - len(geom_combined), 3 - ) # The bounding box overlaps with France inland + # The bounding box overlaps with France inland + self.assertEquals(len(geom_combined), 3) - def test_rebuild_index(self): - """Change eodag version and check that whoosh index is rebuilt""" - index_dir = os.path.join(self.dag.conf_dir, ".index") - index_dir_mtime = os.path.getmtime(index_dir) +class TestCoreSearch(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.dag = EODataAccessGateway() + # Get a SearchResult obj with 2 S2_MSI_L1C peps products + search_results_file = os.path.join( + TEST_RESOURCES_PATH, "eodag_search_result_peps.geojson" + ) + with open(search_results_file, encoding="utf-8") as f: + search_results_geojson = json.load(f) + cls.search_results = SearchResult.from_geojson(search_results_geojson) + cls.search_results_size = len(cls.search_results) + # Change the id of these products, to emulate different products + search_results_data_2 = deepcopy(cls.search_results.data) + search_results_data_2[0].properties["id"] = "a" + search_results_data_2[1].properties["id"] = "b" + cls.search_results_2 = SearchResult(search_results_data_2) + cls.search_results_size_2 = len(cls.search_results_2) - self.assertNotEqual(self.dag.get_version(), "fake-version") + def test_guess_product_type_with_kwargs(self): + """guess_product_type must return the products matching the given kwargs""" + kwargs = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + actual = self.dag.guess_product_type(kwargs) + expected = [ + "S2_MSI_L1C", + "S2_MSI_L2A", + "S2_MSI_L2A_MAJA", + "S2_MSI_L2B_MAJA_SNOW", + "S2_MSI_L2B_MAJA_WATER", + "S2_MSI_L3A_WASP", + ] + self.assertEqual(actual, expected) - with mock.patch( - "eodag.api.core.EODataAccessGateway.get_version", - autospec=True, - return_value="fake-version", - ): - self.assertEqual(self.dag.get_version(), "fake-version") + def test_guess_product_type_without_kwargs(self): + """guess_product_type must raise an exception when no kwargs are provided""" + with self.assertRaises(NoMatchingProductType): + self.dag.guess_product_type() - self.dag.build_index() + def test_guess_product_type_has_no_limit(self): + """guess_product_type must run a whoosh search without any limit""" + # Filter that should give more than 10 products referenced in the catalog. + opt_prods = [ + p for p in self.dag.list_product_types() if p["sensorType"] == "OPTICAL" + ] + if len(opt_prods) <= 10: + self.skipTest("This test requires that more than 10 products are 'OPTICAL'") + guesses = self.dag.guess_product_type( + sensorType="OPTICAL", + ) + self.assertGreater(len(guesses), 10) - # check that index_dir has beeh re-created - self.assertNotEqual(os.path.getmtime(index_dir), index_dir_mtime) + def test__prepare_search_no_parameters(self): + """_prepare_search must create some kwargs even when no parameter has been provided""" # noqa + prepared_search = self.dag._prepare_search() + expected = { + "geometry": None, + "productType": None, + } + self.assertDictContainsSubset(expected, prepared_search) + expected = set(["geometry", "productType", "auth", "search_plugin"]) + self.assertSetEqual(expected, set(prepared_search)) + + def test__prepare_search_dates(self): + """_prepare_search must handle start & end dates""" + base = { + "start": "2020-01-01", + "end": "2020-02-01", + } + prepared_search = self.dag._prepare_search(base) + expected = { + "startTimeFromAscendingNode": base["start"], + "completionTimeFromAscendingNode": base["end"], + } + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_geom(self): + """_prepare_search must handle geom, box and bbox""" + # The default way to provide a geom is through the 'geom' argument. + base = {"geom": (0, 50, 2, 52)} + # "geom": "POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))" + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + # 'box' and 'bbox' are supported for backwards compatibility, + # The priority is geom > bbox > box + base = {"box": (0, 50, 2, 52)} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + base = {"bbox": (0, 50, 2, 52)} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + base = { + "geom": "POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))", + "bbox": (0, 50, 2, 52), + "box": (0, 50, 1, 51), + } + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + self.assertNotIn("bbox", prepared_search) + self.assertNotIn("bbox", prepared_search) + self.assertIsInstance(prepared_search["geometry"], Polygon) + + def test__prepare_search_locations(self): + """_prepare_search must handle a location search""" + # When locations where introduced they could be passed + # as regular kwargs. The new and recommended way to provide + # them is through the 'locations' parameter. + base = {"locations": {"country": "FRA"}} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + + # TODO: Remove this when support for locations kwarg is dropped. + base = {"country": "FRA"} + prepared_search = self.dag._prepare_search(base) + self.assertIn("geometry", prepared_search) + self.assertNotIn("country", prepared_search) + + def test__prepare_search_product_type_provided(self): + """_prepare_search must handle when a product type is given""" + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + expected = {"productType": base["productType"]} + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_product_type_guess_it(self): + """_prepare_search must guess a product type when required to""" + # Uses guess_product_type to find the product matching + # the best the given params. + base = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + prepared_search = self.dag._prepare_search(base) + expected = {"productType": "S2_MSI_L1C", base} + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_with_id(self): + """_prepare_search must handle a search by id""" + base = {"id": "dummy-id", "provider": "sobloo"} + prepared_search = self.dag._prepare_search(base) + expected = base + self.assertDictEqual(expected, prepared_search) + + def test__prepare_search_preserve_additional_kwargs(self): + """_prepare_search must preserve additional kwargs""" + base = { + "productType": "S2_MSI_L1C", + "cloudCover": 10, + } + prepared_search = self.dag._prepare_search(base) + expected = base + self.assertDictContainsSubset(expected, prepared_search) + + def test__prepare_search_search_plugin_has_known_product_properties(self): + """_prepare_search must attach the product properties to the search plugin""" + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("peps") + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + # Just check that the title has been set correctly. There are more (e.g. + # abstract, platform, etc.) but this is sufficient to check that the + # product_type_config dict has been created and populated. + self.assertEqual( + prepared_search["search_plugin"].config.product_type_config["title"], + "SENTINEL2 Level-1C", + ) + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + def test__prepare_search_search_plugin_has_generic_product_properties(self): + """_prepare_search must be able to attach the generic product properties to the search plugin""" # noqa + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("peps") + base = {"productType": "product_unknown_to_eodag"} + prepared_search = self.dag._prepare_search(base) + # product_type_config is still created if the product is not known to eodag + # however it contains no data. + self.assertIsNone( + prepared_search["search_plugin"].config.product_type_config["title"], + ) + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + def test__prepare_search_peps_plugins_product_available(self): + """_prepare_search must return the search and auth plugins when productType is defined""" # noqa + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("peps") + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + self.assertEqual(prepared_search["search_plugin"].provider, "peps") + self.assertEqual(prepared_search["auth"].provider, "peps") + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + def test__prepare_search_no_plugins_when_search_by_id(self): + """_prepare_search must not return the search and auth plugins for a search by id""" # noqa + base = {"id": "some_id", "provider": "some_provider"} + prepared_search = self.dag._prepare_search(base) + self.assertNotIn("search_plugin", prepared_search) + self.assertNotIn("auth", prepared_search) + + def test__prepare_search_peps_plugins_product_not_available(self): + """_prepare_search can use another set of search and auth plugins than the ones of the preferred provider""" # noqa + # Document a special behaviour whereby the search and auth plugins don't + # correspond to the preferred one. This occurs whenever the searched product + # isn't available for the preferred provider but is made available by another + # one. In that case peps provides it and happens to be the first one on the list + # of providers that make it available. + prev_fav_provider = self.dag.get_preferred_provider()[0] + try: + self.dag.set_preferred_provider("theia") + base = {"productType": "S2_MSI_L1C"} + prepared_search = self.dag._prepare_search(base) + self.assertEqual(prepared_search["search_plugin"].provider, "peps") + self.assertEqual(prepared_search["auth"].provider, "peps") + finally: + self.dag.set_preferred_provider(prev_fav_provider) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_counts_by_default(self, search_plugin): + """__do_search must create a count query by default""" + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data, # a list must be returned by .query + self.search_results_size, + ) + sr, estimate = self.dag._do_search(search_plugin=search_plugin) + self.assertIsInstance(sr, SearchResult) + self.assertEqual(len(sr), self.search_results_size) + self.assertEqual(estimate, self.search_results_size) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_without_count(self, search_plugin): + """__do_search must be able to create a query without a count""" + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data, + None, # .query must return None if count is False + ) + sr, estimate = self.dag._do_search(search_plugin=search_plugin, count=False) + self.assertIsNone(estimate) + self.assertEqual(len(sr), self.search_results_size) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_paginated_handle_no_count_returned(self, search_plugin): + """__do_search must provide a best estimate when a provider doesn't return a count""" # noqa + search_plugin.provider = "peps" + # If the provider doesn't return a count, .query returns 0 + search_plugin.query.return_value = (self.search_results.data, 0) + page = 4 + sr, estimate = self.dag._do_search( + search_plugin=search_plugin, + page=page, + items_per_page=2, + ) + self.assertEqual(len(sr), self.search_results_size) + # The count guess is: page number_of_products_returned + self.assertEqual(estimate, page * self.search_results_size) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_paginated_handle_fuzzy_count(self, search_plugin): + """__do_search must provide a best estimate when a provider returns a fuzzy count""" # noqa + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data * 4, # 8 products returned + 22, # fuzzy number, less than the real total count + ) + page = 4 + items_per_page = 10 + sr, estimate = self.dag._do_search( + search_plugin=search_plugin, + page=page, + items_per_page=items_per_page, + ) + # At page 4 with 10 items_per_page we should have a count of at least 30 + # products available. However the provider returned 22. We know it's wrong. + # So we update the count with our current knowledge: 30 + 8 + # Note that this estimate could still be largely inferior to the real total + # count. + expected_estimate = items_per_page * (page - 1) + len(sr) + self.assertEqual(len(sr), 8) + self.assertEqual(estimate, expected_estimate) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_paginated_handle_null_count(self, search_plugin): + """__do_search must provide a best estimate when a provider returns a null count""" # noqa + # TODO: check the underlying implementation, it doesn't make so much sense since + # this case is already covered with nb_res = len(res) * page. This one uses + # nb_res = items_per_page * (page - 1) whick actually makes more sense. Choose + # one of them. + search_plugin.provider = "peps" + search_plugin.query.return_value = ([], 0) + page = 4 + items_per_page = 10 + sr, estimate = self.dag._do_search( + search_plugin=search_plugin, + page=page, + items_per_page=items_per_page, + ) + expected_estimate = items_per_page * (page - 1) + self.assertEqual(len(sr), 0) + self.assertEqual(estimate, expected_estimate) + + def test__do_search_does_not_raise_by_default(self): + """__do_search must not raise any error by default""" + # provider attribute required internally by __do_search for logging purposes. + class DummySearchPlugin: + provider = "peps" + + sr, estimate = self.dag._do_search(search_plugin=DummySearchPlugin()) + self.assertIsInstance(sr, SearchResult) + self.assertEqual(len(sr), 0) + self.assertEqual(estimate, 0) + + def test__do_search_can_raise_errors(self): + """__do_search must not raise any error by default""" + + class DummySearchPlugin: + provider = "peps" + + # AttributeError raised when .query is tried to be accessed on the dummy plugin. + with self.assertRaises(AttributeError): + self.dag._do_search(search_plugin=DummySearchPlugin(), raise_errors=True) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_query_products_must_be_a_list(self, search_plugin): + """__do_search expects that each search plugin returns a list of products.""" + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results, + self.search_results_size, + ) + with self.assertRaises(PluginImplementationError): + self.dag._do_search(search_plugin=search_plugin, raise_errors=True) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_register_downloader_if_search_intersection(self, search_plugin): + """__do_search must register each product's downloader if search_intersection is not None""" # noqa + search_plugin.provider = "peps" + search_plugin.query.return_value = ( + self.search_results.data, + self.search_results_size, + ) + sr, _ = self.dag._do_search(search_plugin=search_plugin) + for product in sr: + self.assertIsNotNone(product.downloader) + + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test__do_search_doest_not_register_downloader_if_no_search_intersection( + self, search_plugin + ): + """__do_search must not register downloaders if search_intersection is None""" + + class DummyProduct: + seach_intersecion = None + + search_plugin.provider = "peps" + search_plugin.query.return_value = ([DummyProduct(), DummyProduct()], 2) + sr, _ = self.dag._do_search(search_plugin=search_plugin) + for product in sr: + self.assertIsNone(product.downloader) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_returns_iterator(self, search_plugin, prepare_seach): + """search_iter_page must return an iterator""" + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + (self.search_results_2.data, None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page(items_per_page=2) + first_result_page = next(page_iterator) + self.assertIsInstance(first_result_page, SearchResult) + self.assertEqual(len(first_result_page), self.search_results_size) + second_result_page = next(page_iterator) + self.assertIsInstance(second_result_page, SearchResult) + self.assertEqual(len(second_result_page), self.search_results_size_2) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_exhaust_get_all_pages_and_quit_early( + self, search_plugin, prepare_seach + ): + """search_iter_page must stop as soon as less than items_per_page products were retrieved""" # noqa + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + ([self.search_results_2.data[0]], None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page(items_per_page=2) + all_page_results = list(page_iterator) + self.assertEqual(len(all_page_results), 2) + self.assertIsInstance(all_page_results[0], SearchResult) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_exhaust_get_all_pages_no_products_last_page( + self, search_plugin, prepare_seach + ): + """search_iter_page must stop if the page doesn't return any product""" + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + (self.search_results_2.data, None), + ([], None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page(items_per_page=2) + all_page_results = list(page_iterator) + self.assertEqual(len(all_page_results), 2) + self.assertIsInstance(all_page_results[0], SearchResult) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_iter_page_does_not_handle_query_errors( + self, search_plugin, prepare_seach + ): + """search_iter_page must propagate errors""" + search_plugin.provider = "peps" + search_plugin.query.side_effect = AttributeError() + prepare_seach.return_value = dict(search_plugin=search_plugin) + page_iterator = self.dag.search_iter_page() + with self.assertRaises(AttributeError): + next(page_iterator) + + @mock.patch( + "eodag.plugins.search.qssearch.QueryStringSearch._request", autospec=True + ) + @mock.patch( + "eodag.plugins.search.qssearch.QueryStringSearch.normalize_results", + autospec=True, + ) + def test_search_iter_page_must_reset_next_attrs_if_next_mechanism( + self, normalize_results, _request + ): + """search_iter_page must reset the search plugin if the next mechanism is used""" + # More specifically: next_page_url must be None and + # config.pagination["next_page_url_tpl"] must be equal to its original value. + _request.return_value.json.return_value = { + "features": [], + "links": [{"rel": "next", "href": "url/to/next/page"}], + } + + p1 = EOProduct("dummy", dict(geometry="POINT (0 0)", id="1")) + p1.search_intersection = None + p2 = EOProduct("dummy", dict(geometry="POINT (0 0)", id="2")) + p2.search_intersection = None + normalize_results.side_effect = [[p1], [p2]] + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + pagination: + next_page_url_tpl: 'dummy_next_page_url_tpl' + next_page_url_key_path: '$.links[?(@.rel="next")].href' + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + + page_iterator = dag.search_iter_page(productType="S2_MSI_L1C") + next(page_iterator) + search_plugin = next( + dag._plugins_manager.get_search_plugins(product_type="S2_MSI_L1C") + ) + self.assertIsNone(search_plugin.next_page_url) + self.assertEqual( + search_plugin.config.pagination["next_page_url_tpl"], + "dummy_next_page_url_tpl", + ) + + @mock.patch("eodag.api.core.EODataAccessGateway._prepare_search", autospec=True) + @mock.patch("eodag.plugins.search.qssearch.QueryStringSearch", autospec=True) + def test_search_all_must_collect_them_all(self, search_plugin, prepare_seach): + """search_all must return all the products available""" # noqa + search_plugin.provider = "peps" + search_plugin.query.side_effect = [ + (self.search_results.data, None), + ([self.search_results_2.data[0]], None), + ] + + class DummyConfig: + pagination = {} + + search_plugin.config = DummyConfig() + + # Infinite generator her because passing directly the dict to + # prepare_search.return_value (or side_effect) didn't work. One function + # would do a dict.pop("search_plugin") that would remove the item from the + # mocked return value. Later calls would then break + def yield_search_plugin(): + while True: + yield {"search_plugin": search_plugin} + + prepare_seach.side_effect = yield_search_plugin() + all_results = self.dag.search_all(items_per_page=2) + self.assertIsInstance(all_results, SearchResult) + self.assertEqual(len(all_results), 3) + + @mock.patch("eodag.api.core.EODataAccessGateway.search_iter_page", autospec=True) + def test_search_all_use_max_items_per_page(self, mocked_search_iter_page): + """search_all must use the configured parameter max_items_per_page if available""" # noqa + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + pagination: + max_items_per_page: 2 + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + mocked_search_iter_page.return_value = (self.search_results for _ in range(1)) + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + dag.search_all(productType="S2_MSI_L1C") + self.assertEqual(mocked_search_iter_page.call_args[1]["items_per_page"], 2) + + @mock.patch("eodag.api.core.EODataAccessGateway.search_iter_page", autospec=True) + def test_search_all_use_default_value(self, mocked_search_iter_page): + """search_all must use the DEFAULT_MAX_ITEMS_PER_PAGE if the provider's one wasn't configured""" # noqa + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + mocked_search_iter_page.return_value = (self.search_results for _ in range(1)) + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + dag.search_all(productType="S2_MSI_L1C") + self.assertEqual( + mocked_search_iter_page.call_args[1]["items_per_page"], + DEFAULT_MAX_ITEMS_PER_PAGE, + ) + + @mock.patch("eodag.api.core.EODataAccessGateway.search_iter_page", autospec=True) + def test_search_all_user_items_per_page(self, mocked_search_iter_page): + """search_all must use the value of items_per_page provided by the user""" + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + mocked_search_iter_page.return_value = (self.search_results for _ in range(1)) + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + dag.search_all(productType="S2_MSI_L1C", items_per_page=7) + self.assertEqual(mocked_search_iter_page.call_args[1]["items_per_page"], 7)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 7 }	2.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "sphinx" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	alabaster==0.7.16 attrs==25.3.0 babel==2.17.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@af9afc8f4e21f6af129c10a1763fb560ac0dfbe2#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 imagesize==1.4.1 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 snowballstemmer==2.2.0 Sphinx==7.4.7 sphinxcontrib-applehelp==2.0.0 sphinxcontrib-devhelp==2.0.0 sphinxcontrib-htmlhelp==2.1.0 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==2.0.0 sphinxcontrib-serializinghtml==2.0.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.30.0 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - alabaster==0.7.16 - attrs==25.3.0 - babel==2.17.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - imagesize==1.4.1 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - snowballstemmer==2.2.0 - sphinx==7.4.7 - sphinxcontrib-applehelp==2.0.0 - sphinxcontrib-devhelp==2.0.0 - sphinxcontrib-htmlhelp==2.1.0 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==2.0.0 - sphinxcontrib-serializinghtml==2.0.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.30.0 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-201	ab1c1054e802d7a25980c331eb19a1c48f1ffc53	2021-03-24 15:17:18	49b15ccd7aa5b83f19fb98b8b4a0dae279c2c18c		diff --git a/eodag/api/core.py b/eodag/api/core.py index 6b84203c..31a214cd 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -428,7 +428,7 @@ class EODataAccessGateway(object): kwargs[search_key] ) if results is None: - results = searcher.search(query) + results = searcher.search(query, limit=None) else: results.upgrade_and_extend(searcher.search(query)) guesses = [r["ID"] for r in results or []]	guess_product_type doesn't return all the matching products The method `Searcher.search()` of whoosh [accepts a `limit` parameter](https://whoosh.readthedocs.io/en/latest/searching.html#the-searcher-object) that can be set to `None` to get all the results. By default `limit` is set to 10. I think we should set `limit=None` to make sure that `guess_product_type` returns the best matching products. This limiting parameter is provided by whoosh for performance reasons, I don't think performance is a problem for us here. While I'm at it, should `guess_product_type` be actually part of the public API of eodag? We don't use it in any of the notebooks. It's also difficult to use without looking directly at the source code to find which search keys it supports (e.g. `platformSerialIdentifier`).	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 221f70bc..4bde0171 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -520,6 +520,19 @@ class TestCoreSearch(unittest.TestCase): with self.assertRaises(NoMatchingProductType): self.dag.guess_product_type() + def test_guess_product_type_has_no_limit(self): + """guess_product_type must run a whoosh search without any limit""" + # Filter that should give more than 10 products referenced in the catalog. + opt_prods = [ + p for p in self.dag.list_product_types() if p["sensorType"] == "OPTICAL" + ] + if len(opt_prods) <= 10: + self.skipTest("This test requires that more than 10 products are 'OPTICAL'") + guesses = self.dag.guess_product_type( + sensorType="OPTICAL", + ) + self.assertGreater(len(guesses), 10) + def test__prepare_search_no_parameters(self): """_prepare_search must create some kwargs even when no parameter has been provided""" # noqa prepared_search = self.dag._prepare_search()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 1 }	2.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@ab1c1054e802d7a25980c331eb19a1c48f1ffc53#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit" ]	[ "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex" ]	[ "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator" ]	[]	Apache License 2.0	null
CS-SI__eodag-204	ab1c1054e802d7a25980c331eb19a1c48f1ffc53	2021-03-24 17:30:07	49b15ccd7aa5b83f19fb98b8b4a0dae279c2c18c		diff --git a/CHANGES.rst b/CHANGES.rst index 59bcdfc1..9998e581 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,11 @@ Release history --------------- +2.2.0 (2021-xx-xx) +++++++++++++++++++ + +- More explicit signature for ``setup_logging``, fixes `GH-197 <https://github.com/CS-SI/eodag/issues/197>`_ + 2.1.1 (2021-03-18) ++++++++++++++++++ diff --git a/README.rst b/README.rst index bf279ae5..6404fd97 100644 --- a/README.rst +++ b/README.rst @@ -178,22 +178,23 @@ Then you can start playing with it: --start 2018-01-01 \ --end 2018-01-31 \ --cloudCover 20 \ - --productType S2_MSI_L1C - --cruncher FilterLatestIntersect \ + --productType S2_MSI_L1C \ + --all \ --storage my_search.geojson -The request above search for product types `S2_MSI_L1C` and will crunch the result using cruncher `FilterLatestIntersect` -and storing the overall result to `my_search.geojson`. +The request above searches for `S2_MSI_L1C` product types in a given bounding box, in January 2018. The command fetches internally all +the products that match these criteria. Without `--all`, it would only fetch the products found on the first result page. +It finally saves the results in a GeoJSON file. You can pass arguments to a cruncher on the command line by doing this (example with using `FilterOverlap` cruncher which takes `minimum_overlap` as argument):: - eodag search -f my_conf.yml -b 1 43 2 44 -s 2018-01-01 -e 2018-01-31 -p S2_MSI_L1C \ + eodag search -f my_conf.yml -b 1 43 2 44 -s 2018-01-01 -e 2018-01-31 -p S2_MSI_L1C --all \ --cruncher FilterOverlap \ --cruncher-args FilterOverlap minimum_overlap 10 The request above means : "Give me all the products of type `S2_MSI_L1C`, use `FilterOverlap` to keep only those products -that are contained in the bbox I gave you, or whom spatial extent overlaps at least 10% (`minimum_overlap`) of the surface +that are contained in the bbox I gave you, or whose spatial extent overlaps at least 10% (`minimum_overlap`) of the surface of this bbox" * To download the result of a previous call to `search`:: diff --git a/docs/use.rst b/docs/use.rst index ef129fd0..42f0cb6a 100644 --- a/docs/use.rst +++ b/docs/use.rst @@ -172,23 +172,24 @@ Then you can start playing with it: --box 1 43 2 44 \ --start 2018-01-01 --end 2018-01-31 \ --productType S2_MSI_L1C \ - --cruncher FilterLatestIntersect \ + --all \ --storage my_search.geojson -The request above search for product types `S2_MSI_L1C` and will crunch the result using cruncher `FilterLatestIntersect` -and storing the overall result to `my_search.geojson`. +The request above searches for `S2_MSI_L1C` product types in a given bounding box, in January 2018. The command fetches internally all +the products that match these criteria. Without `--all`, it would only fetch the products found on the first result page. +It finally saves the results in a GeoJSON file. You can pass arguments to a cruncher on the command line by doing this (example with using `FilterOverlap` cruncher which takes `minimum_overlap` as argument): .. code-block:: console - eodag search -f my_conf.yml -b 1 43 2 44 -s 2018-01-01 -e 2018-01-31 -p S2_MSI_L1C \ + eodag search -f my_conf.yml -b 1 43 2 44 -s 2018-01-01 -e 2018-01-31 -p S2_MSI_L1C --all \ --cruncher FilterOverlap \ --cruncher-args FilterOverlap minimum_overlap 10 The request above means : "Give me all the products of type `S2_MSI_L1C`, use `FilterOverlap` to keep only those products -that are contained in the bbox I gave you, or whom spatial extent overlaps at least 10% (`minimum_overlap`) of the surface +that are contained in the bbox I gave you, or whose spatial extent overlaps at least 10% (`minimum_overlap`) of the surface of this bbox" You can use `eaodag search` with custom parameters. Custom parameters will be used as is in the query string search sent diff --git a/eodag/api/core.py b/eodag/api/core.py index 6b84203c..31a214cd 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -428,7 +428,7 @@ class EODataAccessGateway(object): kwargs[search_key] ) if results is None: - results = searcher.search(query) + results = searcher.search(query, limit=None) else: results.upgrade_and_extend(searcher.search(query)) guesses = [r["ID"] for r in results or []] diff --git a/eodag/cli.py b/eodag/cli.py index b25bfa6b..9832e1c7 100755 --- a/eodag/cli.py +++ b/eodag/cli.py @@ -210,11 +210,12 @@ def version(): @click.option( "--items", type=int, - default=DEFAULT_ITEMS_PER_PAGE, - show_default=True, + # default=DEFAULT_ITEMS_PER_PAGE, + show_default=False, help="The number of items to return. Eodag is bound to whatever limitation the " "providers have on the number of results they return. This option allows " - "to control how many items eodag should request", + "to control how many items eodag should request " + f"[default: {DEFAULT_ITEMS_PER_PAGE}]", ) @click.option( "--page", @@ -223,6 +224,15 @@ def version(): show_default=True, help="Retrieve the given page", ) [email protected]( + "--all", + is_flag=True, + help="Retrieve ALL the products that match the search criteria. It collects " + "products by iterating over the results pages until no more products are available." + "At each iteration, the maximum number of items searched is either 'items' if set, " + "or a maximum value defined internally for the requested provider, or a default " + "maximum value equals to 50.", +) @click.option( "--locations", type=str, @@ -264,8 +274,7 @@ def search_crunch(ctx, kwargs): click.echo(search_crunch.get_help(ctx)) sys.exit(-1) - kwargs["verbose"] = ctx.obj["verbosity"] - setup_logging(kwargs) + setup_logging(verbose=ctx.obj["verbosity"]) if kwargs["box"] != (None,) * 4: rect = kwargs.pop("box") @@ -333,10 +342,21 @@ def search_crunch(ctx, kwargs): ) # Search - results, total = gateway.search( - page=page, items_per_page=items_per_page, criteria - ) - click.echo("Found a total number of {} products".format(total)) + get_all_products = kwargs.pop("all") + if get_all_products: + # search_all needs items_per_page to be None if the user lets eodag determines + # what value it should take. + items_per_page = None if items_per_page is None else items_per_page + results = gateway.search_all(items_per_page=items_per_page, criteria) + else: + # search should better take a value that is not None + items_per_page = ( + DEFAULT_ITEMS_PER_PAGE if items_per_page is None else items_per_page + ) + results, total = gateway.search( + page=page, items_per_page=items_per_page, criteria + ) + click.echo("Found a total number of {} products".format(total)) click.echo("Returned {} products".format(len(results))) # Crunch ! @@ -384,7 +404,7 @@ def list_pt(ctx, kwargs): platformSerialIdentifier=kwargs.get("platformserialidentifier"), processingLevel=kwargs.get("processinglevel"), sensorType=kwargs.get("sensortype"), - kwargs + kwargs, ) except NoMatchingProductType: if any( @@ -457,8 +477,7 @@ def download(ctx, kwargs): click.echo("Nothing to do (no search results file provided)") click.echo(download.get_help(ctx)) sys.exit(1) - kwargs["verbose"] = ctx.obj["verbosity"] - setup_logging(kwargs) + setup_logging(verbose=ctx.obj["verbosity"]) conf_file = kwargs.pop("conf") if conf_file: conf_file = click.format_filename(conf_file) diff --git a/eodag/utils/logging.py b/eodag/utils/logging.py index 2a6ed5aa..12e629ea 100644 --- a/eodag/utils/logging.py +++ b/eodag/utils/logging.py @@ -19,15 +19,17 @@ import logging.config -def setup_logging(kwargs): +def setup_logging(verbose): """Define logging level - :param kwargs: Accepted Keyword arguments: - verbose=0-3 logging level (None to max) - :type kwargs: dict + :param verbose: Accepted values: + + * 0: no logging + * 1 or 2: INFO level + * 3: DEBUG level + :type verbose: int """ - verbosity = kwargs.pop("verbose") - if verbosity == 0: + if verbose == 0: logging.config.dictConfig( { "version": 1, @@ -40,7 +42,7 @@ def setup_logging(kwargs): }, } ) - elif verbosity <= 2: + elif verbose <= 2: logging.config.dictConfig( { "version": 1, @@ -71,7 +73,7 @@ def setup_logging(kwargs): }, } ) - elif verbosity == 3: + elif verbose == 3: logging.config.dictConfig( { "version": 1, @@ -102,3 +104,5 @@ def setup_logging(**kwargs): }, } ) + else: + raise ValueError("'verbose' must be one of: 0, 1, 2, 3")	Add to the search command the ability to search for all the products matching some criteria The API will soon get a `search_all` method that will allow to collect at once all the products from a provider that match a set of criteria. The CLI should also benefit from this enhancement. I suggest that we add a `--all` option to the `search` command. Internally instead of calling `search` it would call `search_all`.	CS-SI/eodag	diff --git a/tests/test_cli.py b/tests/test_cli.py index c3c7d742..94e07cc3 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -29,6 +29,7 @@ from pkg_resources import resource_filename from eodag.utils import GENERIC_PRODUCT_TYPE from tests import TEST_RESOURCES_PATH from tests.context import ( + DEFAULT_ITEMS_PER_PAGE, AuthenticationError, MisconfiguredError, download, @@ -153,7 +154,7 @@ class TestEodagCli(unittest.TestCase): ) api_obj = dag.return_value api_obj.search.assert_called_once_with( - items_per_page=20, + items_per_page=DEFAULT_ITEMS_PER_PAGE, page=1, startTimeFromAscendingNode=None, completionTimeFromAscendingNode=None, @@ -198,7 +199,7 @@ class TestEodagCli(unittest.TestCase): ) api_obj = dag.return_value api_obj.search.assert_called_once_with( - items_per_page=20, + items_per_page=DEFAULT_ITEMS_PER_PAGE, page=1, startTimeFromAscendingNode=None, completionTimeFromAscendingNode=None, @@ -295,7 +296,7 @@ class TestEodagCli(unittest.TestCase): user_conf_file_path=conf_file, locations_conf_path=None ) api_obj.search.assert_called_once_with( - items_per_page=20, page=1, criteria + items_per_page=DEFAULT_ITEMS_PER_PAGE, page=1, criteria ) api_obj.crunch.assert_called_once_with( search_results, search_criteria=criteria, {cruncher: {}} @@ -328,6 +329,41 @@ class TestEodagCli(unittest.TestCase): {cruncher: {"minimum_overlap": 10}} ) + @mock.patch("eodag.cli.EODataAccessGateway", autospec=True) + def test_eodag_search_all(self, dag): + """Calling eodag search with --bbox argument valid""" + with self.user_conf() as conf_file: + product_type = "whatever" + self.runner.invoke( + eodag, + [ + "search", + "--conf", + conf_file, + "-p", + product_type, + "-g", + "POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))", + "--all", + ], + ) + api_obj = dag.return_value + api_obj.search_all.assert_called_once_with( + items_per_page=None, + startTimeFromAscendingNode=None, + completionTimeFromAscendingNode=None, + cloudCover=None, + geometry="POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))", + instrument=None, + platform=None, + platformSerialIdentifier=None, + processingLevel=None, + sensorType=None, + productType=product_type, + id=None, + locations=None, + ) + def test_eodag_list_product_type_ok(self): """Calling eodag list without provider return all supported product types""" all_supported_product_types = [ diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 221f70bc..4bde0171 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -520,6 +520,19 @@ class TestCoreSearch(unittest.TestCase): with self.assertRaises(NoMatchingProductType): self.dag.guess_product_type() + def test_guess_product_type_has_no_limit(self): + """guess_product_type must run a whoosh search without any limit""" + # Filter that should give more than 10 products referenced in the catalog. + opt_prods = [ + p for p in self.dag.list_product_types() if p["sensorType"] == "OPTICAL" + ] + if len(opt_prods) <= 10: + self.skipTest("This test requires that more than 10 products are 'OPTICAL'") + guesses = self.dag.guess_product_type( + sensorType="OPTICAL", + ) + self.assertGreater(len(guesses), 10) + def test__prepare_search_no_parameters(self): """_prepare_search must create some kwargs even when no parameter has been provided""" # noqa prepared_search = self.dag._prepare_search()	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 6 }	2.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@ab1c1054e802d7a25980c331eb19a1c48f1ffc53#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_cli.py::TestEodagCli::test_eodag_search_all", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit" ]	[ "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_cruncher", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex" ]	[ "tests/test_cli.py::TestCore::test_core_object_locations_file_not_found", "tests/test_cli.py::TestCore::test_core_object_open_index_if_exists", "tests/test_cli.py::TestCore::test_core_object_set_default_locations_config", "tests/test_cli.py::TestCore::test_list_product_types_for_provider_ok", "tests/test_cli.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/test_cli.py::TestCore::test_list_product_types_ok", "tests/test_cli.py::TestCore::test_rebuild_index", "tests/test_cli.py::TestCore::test_supported_product_types_in_unit_test", "tests/test_cli.py::TestCore::test_supported_providers_in_unit_test", "tests/test_cli.py::TestEodagCli::test_eodag_download_missingcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_download_no_search_results_arg", "tests/test_cli.py::TestEodagCli::test_eodag_download_ok", "tests/test_cli.py::TestEodagCli::test_eodag_download_wrongcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_ok", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ko", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ok", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_geom_mutually_exclusive", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_storage_arg", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_conf_file_inexistent", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_max_cloud_out_of_range", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_args", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_producttype_arg", "tests/test_cli.py::TestEodagCli::test_eodag_with_only_verbose_opt", "tests/test_cli.py::TestEodagCli::test_eodag_without_args", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator" ]	[]	Apache License 2.0	null
CS-SI__eodag-228	fec965f791a74dbf658e0833e1507b7ac950d09d	2021-04-12 16:05:31	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 84fc9ea9..2f0b132d 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -163,6 +163,12 @@ class AwsDownload(Download): def download(self, product, auth=None, progress_callback=None, kwargs): """Download method for AWS S3 API. + The product can be downloaded as it is, or as SAFE-formatted product. + SAFE-build is configured for a given provider and product type. + If the product title is configured to be updated during download and + SAFE-formatted, its destination path will be: + `{outputs_prefix}/{title}/{updated_title}.SAFE` + :param product: The EO product to download :type product: :class:`~eodag.api.product.EOProduct` :param auth: (optional) The configuration of a plugin of type Authentication @@ -176,6 +182,18 @@ class AwsDownload(Download): :return: The absolute path to the downloaded product in the local filesystem :rtype: str """ + # prepare download & create dirs (before updating metadata) + product_local_path, record_filename = self._prepare_download(product, kwargs) + if not product_local_path or not record_filename: + return product_local_path + product_local_path = product_local_path.replace(".zip", "") + # remove existing incomplete file + if os.path.isfile(product_local_path): + os.remove(product_local_path) + # create product dest dir + if not os.path.isdir(product_local_path): + os.makedirs(product_local_path) + product_conf = getattr(self.config, "products", {}).get( product.product_type, {} ) @@ -228,18 +246,6 @@ class AwsDownload(Download): ) ) - # prepare download & create dirs - product_local_path, record_filename = self._prepare_download(product, *kwargs) - if not product_local_path or not record_filename: - return product_local_path - product_local_path = product_local_path.replace(".zip", "") - # remove existing incomplete file - if os.path.isfile(product_local_path): - os.remove(product_local_path) - # create product dest dir - if not os.path.isdir(product_local_path): - os.makedirs(product_local_path) - # progress bar init if progress_callback is None: progress_callback = get_progress_callback() diff --git a/eodag/resources/product_types.yml b/eodag/resources/product_types.yml index 004a1d3a..6a9a313b 100644 --- a/eodag/resources/product_types.yml +++ b/eodag/resources/product_types.yml @@ -360,6 +360,8 @@ S1_SAR_OCN: derived from internally generated Level-1 GRD images of SM, IW or EW modes. The RVL is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. + SAFE formatted product, see + https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification instrument: SAR platform: SENTINEL1 platformSerialIdentifier: S1A,S1B @@ -381,6 +383,31 @@ S1_SAR_GRD: Medium Resolution (MR). The resolution is dependent upon the amount of multi-looking performed. Level-1 GRD products are available in MR and HR for IW and EW modes, MR for WV mode and MR, HR and FR for SM mode. + SAFE formatted product, see + https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification + instrument: SAR + platform: SENTINEL1 + platformSerialIdentifier: S1A,S1B + processingLevel: L1 + sensorType: RADAR + license: proprietary + title: SENTINEL1 Level-1 Ground Range Detected + missionStartDate: "2014-04-03T00:00:00Z" + +S1_SAR_GRD_JP2: + abstract: \| + Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and + projected to ground range using an Earth ellipsoid model. Phase information is lost. The resulting product has + approximately square spatial resolution pixels and square pixel spacing with reduced speckle at the cost of worse + spatial resolution. + GRD products can be in one of three resolutions: + Full Resolution (FR), + High Resolution (HR), + Medium Resolution (MR). + The resolution is dependent upon the amount of multi-looking performed. Level-1 GRD products are available in MR + and HR for IW and EW modes, MR for WV mode and MR, HR and FR for SM mode. + Product without SAFE formatting, see + https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification instrument: SAR platform: SENTINEL1 platformSerialIdentifier: S1A,S1B @@ -396,6 +423,8 @@ S1_SAR_SLC: data from the satellite and provided in zero-Doppler slant-range geometry. The products include a single look in each dimension using the full transmit signal bandwidth and consist of complex samples preserving the phase information. + SAFE formatted product, see + https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification instrument: SAR platform: SENTINEL1 platformSerialIdentifier: S1A,S1B @@ -410,6 +439,8 @@ S1_SAR_RAW: The SAR Level-0 products consist of the sequence of Flexible Dynamic Block Adaptive Quantization (FDBAQ) compressed unfocused SAR raw data. For the data to be usable, it will need to be decompressed and processed using a SAR processor. + SAFE formatted product, see + https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification instrument: SAR platform: SENTINEL1 platformSerialIdentifier: S1A,S1B @@ -429,6 +460,7 @@ S2_MSI_L1C: meters depending on the native resolution of the different spectral bands. In Level-1C products, pixel coordinates refer to the upper left corner of the pixel. Level-1C products will additionally include Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). + SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats instrument: MSI platform: SENTINEL2 platformSerialIdentifier: S2A,S2B @@ -499,10 +531,59 @@ S2_MSI_L1C: full_width_half_max: 0.242 gsd: 20 +S2_MSI_L1C_JP2: + abstract: \| + The Level-1C product is composed of 100x100 km2 tiles (ortho-images in UTM/WGS84 projection). It results from + using a Digital Elevation Model (DEM) to project the image in cartographic geometry. Per-pixel radiometric + measurements are provided in Top Of Atmosphere (TOA) reflectances along with the parameters to transform them + into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 + meters depending on the native resolution of the different spectral bands. In Level-1C products, pixel + coordinates refer to the upper left corner of the pixel. Level-1C products will additionally include Cloud Masks + and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). + Product without SAFE formatting, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats + instrument: MSI + platform: SENTINEL2 + platformSerialIdentifier: S2A,S2B + processingLevel: L1 + sensorType: OPTICAL + license: proprietary + missionStartDate: "2015-06-23T00:00:00Z" + title: SENTINEL2 Level-1C + + S2_MSI_L2A: abstract: \| The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection). + SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats + instrument: MSI + platform: SENTINEL2 + platformSerialIdentifier: S2A,S2B + processingLevel: L2 + sensorType: OPTICAL + license: proprietary + title: SENTINEL2 Level-2A + missionStartDate: "2015-06-23T00:00:00Z" + +S2_MSI_L2A_JP2: + abstract: \| + The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C + products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection). + Product without SAFE formatting, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats + instrument: MSI + platform: SENTINEL2 + platformSerialIdentifier: S2A,S2B + processingLevel: L2 + sensorType: OPTICAL + license: proprietary + title: SENTINEL2 Level-2A + missionStartDate: "2015-06-23T00:00:00Z" + +S2_MSI_L2A_COG: + abstract: \| + The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C + products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection). + Product containing Cloud Optimized GeoTIFF images, without SAFE formatting. instrument: MSI platform: SENTINEL2 platformSerialIdentifier: S2A,S2B diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 83f702d7..6b3bd5f6 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1672,3 +1672,35 @@ - tilePath auth: !plugin type: AwsAuth + +--- +!provider + name: earth_search_cog + priority: 0 + roles: + - host + description: Earth Search + url: https://www.element84.com/earth-search/ + search: !plugin + type: StacSearch + api_endpoint: https://earth-search.aws.element84.com/v0/search + metadata_mapping: + id: + - 'ids=["{id}"]' + - '$.id' + productType: + - 'collections=["{productType}"]' + - '$.collection' + geometry: + - 'bbox=[{geometry.bounds#csv_list}]' + - '$.geometry' + platformSerialIdentifier: '$.id.`split(_, 0, -1)`' + polarizationMode: '$.id.`sub(/.{14}([A-Z]{2})./, \\1)`' + products: + S2_MSI_L2A_COG: + productType: sentinel-s2-l2a-cogs + GENERIC_PRODUCT_TYPE: + productType: '{productType}' + download: !plugin + type: HTTPDownload + base_uri: 'https://sentinel-cogs.s3.us-west-2.amazonaws.com' diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index 2edfc6b3..a3fdf94e 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -127,3 +127,8 @@ earth_search: aws_profile: download: outputs_prefix: +earth_search_cog: + priority: # Lower value means lower priority (Default: 0) + search: # Search parameters configuration + download: + outputs_prefix:	Sentinel SAFE / non-SAFE product types distinction All providers do not provide Sentinel products in SAFE format, which means for example that searching `S2_MSI_L1C` products on `peps` or `astraea_eod` can return products packaged differently, and differently formatted ids. We should add more product types to be able to distinguish: - Sentinel products in SAFE / jp2 format (S2_MSI_L1C peps) - Sentinel products in non-SAFE / jp2 format (S2_MSI_L1C astraea_eod) - Sentinel products in cog format (sentinel-s2-l2a-cogs earth_search) See https://github.com/CS-SI/eodag/issues/136#issuecomment-808082569, #171	CS-SI/eodag	diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 294d0d5e..be2dee90 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -78,6 +78,13 @@ EARTH_SEARCH_SEARCH_ARGS = [ "2020-01-15", [0.2563590566012408, 43.19555008715042, 2.379835675499976, 43.907759172380565], ] +EARTH_SEARCH_COG_SEARCH_ARGS = [ + "earth_search_cog", + "S2_MSI_L2A_COG", + "2020-01-01", + "2020-01-15", + [0.2563590566012408, 43.19555008715042, 2.379835675499976, 43.907759172380565], +] USGS_SATAPI_AWS_SEARCH_ARGS = [ "usgs_satapi_aws", "LANDSAT_C2L1", @@ -360,13 +367,18 @@ class TestEODagEndToEnd(EndToEndBase): def test_end_to_end_search_download_astraea_eod(self): product = self.execute_search(ASTRAE_EOD_SEARCH_ARGS) expected_filename = "{}".format(product.properties["title"]) - self.execute_download(product, expected_filename) + self.execute_download(product, expected_filename, wait_sec=15) def test_end_to_end_search_download_earth_search(self): product = self.execute_search(EARTH_SEARCH_SEARCH_ARGS) expected_filename = "{}".format(product.properties["title"]) self.execute_download(product, expected_filename) + def test_end_to_end_search_download_earth_search_cog(self): + product = self.execute_search(EARTH_SEARCH_COG_SEARCH_ARGS) + expected_filename = "{}".format(product.properties["title"]) + self.execute_download(product, expected_filename, wait_sec=20) + def test_end_to_end_search_download_usgs_satapi_aws(self): product = self.execute_search(USGS_SATAPI_AWS_SEARCH_ARGS) expected_filename = "{}".format(product.properties["title"]) diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 2cc06b56..cb6022e4 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -77,6 +77,7 @@ class TestCore(unittest.TestCase): "astraea_eod", "earth_search", ], + "S2_MSI_L2A_COG": ["earth_search_cog"], "S2_MSI_L2A_MAJA": ["theia"], "S2_MSI_L2B_MAJA_SNOW": ["theia"], "S2_MSI_L2B_MAJA_WATER": ["theia"], @@ -135,6 +136,7 @@ class TestCore(unittest.TestCase): "astraea_eod", "usgs_satapi_aws", "earth_search", + "earth_search_cog", ], } SUPPORTED_PROVIDERS = [ @@ -150,6 +152,7 @@ class TestCore(unittest.TestCase): "astraea_eod", "usgs_satapi_aws", "earth_search", + "earth_search_cog", ] @classmethod @@ -510,6 +513,7 @@ class TestCoreSearch(unittest.TestCase): expected = [ "S2_MSI_L1C", "S2_MSI_L2A", + "S2_MSI_L2A_COG", "S2_MSI_L2A_MAJA", "S2_MSI_L2B_MAJA_SNOW", "S2_MSI_L2B_MAJA_WATER",	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_issue_reference", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 0 }, "num_modified_files": 4 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@fec965f791a74dbf658e0833e1507b7ac950d09d#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator" ]	[]	Apache License 2.0	null
CS-SI__eodag-244	0632e4147f26119bea43e0672f6d06e75d216a20	2021-04-20 13:08:43	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/api/core.py b/eodag/api/core.py index 6efceb2a..1e91f5d9 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -47,6 +47,7 @@ from eodag.utils import ( _deprecated, get_geometry_from_various, makedirs, + uri_to_path, ) from eodag.utils.exceptions import ( NoMatchingProductType, @@ -1281,8 +1282,10 @@ class EODataAccessGateway(object): This is an alias to the method of the same name on :class:`~eodag.api.product.EOProduct`, but it performs some additional checks like verifying that a downloader and authenticator are registered - for the product before trying to download it. If the metadata mapping for - `downloadLink` is set to something that can be interpreted as a link on a + for the product before trying to download it. + + If the metadata mapping for `downloadLink` is set to something that can be + interpreted as a link on a local filesystem, the download is skipped (by now, only a link starting with `file://` is supported). Therefore, any user that knows how to extract product location from product metadata on a provider can override the @@ -1316,10 +1319,15 @@ class EODataAccessGateway(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ - if product.location.startswith("file"): + if product.location.startswith("file://"): logger.info("Local product detected. Download skipped") - return product.location + return uri_to_path(product.location) if product.downloader is None: auth = product.downloader_auth if auth is None: diff --git a/eodag/api/product/_product.py b/eodag/api/product/_product.py index a0d98184..a40e74bd 100644 --- a/eodag/api/product/_product.py +++ b/eodag/api/product/_product.py @@ -240,6 +240,11 @@ class EOProduct(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ if progress_callback is None: progress_callback = ProgressCallback() @@ -254,7 +259,7 @@ class EOProduct(object): if self.downloader_auth is not None else self.downloader_auth ) - fs_location = self.downloader.download( + fs_path = self.downloader.download( self, auth=auth, progress_callback=progress_callback, @@ -262,13 +267,8 @@ class EOProduct(object): timeout=timeout, *kwargs ) - if fs_location is None: - raise DownloadError( - "Invalid file location returned by download process: '{}'".format( - fs_location - ) - ) - self.location = "file://{}".format(fs_location) + if fs_path is None: + raise DownloadError("Missing file location returned by download process") logger.debug( "Product location updated from '%s' to '%s'", self.remote_location, @@ -279,7 +279,7 @@ class EOProduct(object): "'remote_location' property: %s", self.remote_location, ) - return self.location + return fs_path def get_quicklook(self, filename=None, base_dir=None, progress_callback=None): """Download the quick look image of a given EOProduct from its provider if it diff --git a/eodag/plugins/apis/base.py b/eodag/plugins/apis/base.py index 6a775973..b6d385ef 100644 --- a/eodag/plugins/apis/base.py +++ b/eodag/plugins/apis/base.py @@ -18,12 +18,40 @@ import logging from eodag.plugins.base import PluginTopic +from eodag.plugins.download.base import DEFAULT_DOWNLOAD_TIMEOUT, DEFAULT_DOWNLOAD_WAIT logger = logging.getLogger("eodag.plugins.apis.base") class Api(PluginTopic): - """Plugins API Base plugin""" + """Plugins API Base plugin + + An Api plugin has three download methods that it must implement: + - ``query``: search for products + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + The download methods must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record* file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + """ def query(self, args, count=True, kwargs): """Implementation of how the products must be searched goes here. @@ -32,14 +60,77 @@ class Api(PluginTopic): which will be processed by a Download plugin (2) and the total number of products matching the search criteria. If ``count`` is False, the second element returned must be ``None``. - .. versionchanged:: - 2.1 + .. versionchanged:: 2.1 - A new optional boolean parameter ``count`` which defaults to ``True``, it - allows to trigger or not a count query. + A new optional boolean parameter ``count`` which defaults to ``True``, it + allows to trigger or not a count query. """ raise NotImplementedError("A Api plugin must implement a method named query") - def download(self, args, kwargs): - """Implementation of how the products must be downloaded.""" - raise NotImplementedError("A Api plugin must implement a method named download") + def download( + self, + product, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str + """ + raise NotImplementedError( + "An Api plugin must implement a method named download" + ) + + def download_all( + self, + products, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + """ + Base download_all method. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list + """ + raise NotImplementedError( + "A Api plugin must implement a method named download_all" + ) diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 37ce4443..768778e5 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -37,7 +37,12 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import GENERIC_PRODUCT_TYPE, format_dict_items, get_progress_callback +from eodag.utils import ( + GENERIC_PRODUCT_TYPE, + format_dict_items, + get_progress_callback, + path_to_uri, +) from eodag.utils.exceptions import AuthenticationError, NotAvailableError logger = logging.getLogger("eodag.plugins.apis.usgs") @@ -164,6 +169,8 @@ class UsgsApi(Api, Download): product, outputs_extension=".tar.gz", kwargs ) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -264,8 +271,11 @@ class UsgsApi(Api, Download): ) new_fs_path = fs_path[: fs_path.index(".tar.gz")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product_path = self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product.location = path_to_uri(product_path) + return product_path def download_all( self, diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 2f0b132d..687985c6 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -34,7 +34,7 @@ from eodag.api.product.metadata_mapping import ( properties_from_xml, ) from eodag.plugins.download.base import Download -from eodag.utils import get_progress_callback, urlparse +from eodag.utils import get_progress_callback, path_to_uri, urlparse from eodag.utils.exceptions import AuthenticationError, DownloadError logger = logging.getLogger("eodag.plugins.download.aws") @@ -185,6 +185,8 @@ class AwsDownload(Download): # prepare download & create dirs (before updating metadata) product_local_path, record_filename = self._prepare_download(product, kwargs) if not product_local_path or not record_filename: + if product_local_path: + product.location = path_to_uri(product_local_path) return product_local_path product_local_path = product_local_path.replace(".zip", "") # remove existing incomplete file @@ -395,6 +397,7 @@ class AwsDownload(Download): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path def get_authenticated_objects(self, bucket_name, prefix, auth_dict): diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index 8d6dbd3e..93001e9a 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -25,7 +25,7 @@ from datetime import datetime, timedelta from time import sleep from eodag.plugins.base import PluginTopic -from eodag.utils import get_progress_callback, sanitize +from eodag.utils import get_progress_callback, sanitize, uri_to_path from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -43,6 +43,31 @@ DEFAULT_DOWNLOAD_TIMEOUT = 20 class Download(PluginTopic): """Base Download Plugin. + A Download plugin has two download methods that it must implement: + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + They must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record* file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + :param provider: An eodag providers configuration dictionary :type provider: dict :param config: Path to the user configuration file @@ -62,8 +87,26 @@ class Download(PluginTopic): timeout=DEFAULT_DOWNLOAD_TIMEOUT, kwargs, ): - """ - Base download method. Not available, should be defined for each plugin + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str """ raise NotImplementedError( "A Download plugin must implement a method named download" @@ -78,8 +121,7 @@ class Download(PluginTopic): :rtype: tuple """ if product.location != product.remote_location: - scheme_prefix_len = len("file://") - fs_path = product.location[scheme_prefix_len:] + fs_path = uri_to_path(product.location) # The fs path of a product is either a file (if 'extract' config is False) or a directory if os.path.isfile(fs_path) or os.path.isdir(fs_path): logger.info( @@ -249,8 +291,28 @@ class Download(PluginTopic): kwargs, ): """ - A sequential download_all implementation - using download method for every products + Base download_all method. + + This specific implementation uses the ``download`` method implemented by + the plugin to sequentially attempt to download products. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list """ paths = [] # initiate retry loop diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 11662a18..f54fff86 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -32,7 +32,7 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import get_progress_callback +from eodag.utils import get_progress_callback, path_to_uri from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -70,6 +70,8 @@ class HTTPDownload(Download): """ fs_path, record_filename = self._prepare_download(product, kwargs) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -80,7 +82,7 @@ class HTTPDownload(Download): # download assets if exist instead of remote_location try: - return self._download_assets( + fs_path = self._download_assets( product, fs_path.replace(".zip", ""), record_filename, @@ -88,6 +90,8 @@ class HTTPDownload(Download): progress_callback, kwargs ) + product.location = path_to_uri(fs_path) + return fs_path except NotAvailableError: pass @@ -218,8 +222,11 @@ class HTTPDownload(Download): ) new_fs_path = fs_path[: fs_path.index(".zip")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, kwargs) + product_path = self._finalize(fs_path, kwargs) + product.location = path_to_uri(product_path) + return product_path except NotAvailableError as e: if not getattr(self.config, "order_enabled", False): diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index 71876ea4..2f5628b9 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -29,7 +29,7 @@ from requests import HTTPError from eodag.api.product.metadata_mapping import OFFLINE_STATUS from eodag.plugins.download.aws import AwsDownload from eodag.plugins.download.http import HTTPDownload -from eodag.utils import get_progress_callback, urljoin +from eodag.utils import get_progress_callback, path_to_uri, urljoin from eodag.utils.exceptions import ( AuthenticationError, DownloadError, @@ -158,6 +158,7 @@ class S3RestDownload(AwsDownload): url_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() record_filename = os.path.join(download_records_dir, url_hash) if os.path.isfile(record_filename) and os.path.exists(product_local_path): + product.location = path_to_uri(product_local_path) return product_local_path # Remove the record file if product_local_path is absent (e.g. it was deleted while record wasn't) elif os.path.isfile(record_filename): @@ -215,4 +216,5 @@ class S3RestDownload(AwsDownload): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index f48e3eb0..2c6f7e9d 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -463,7 +463,7 @@ def download_stac_item_by_id(catalogs, item_id, provider=None): product_path = eodag_api.download(product) - return product_path.replace("file://", "") + return product_path def get_stac_catalogs(url, root="/", catalogs=[], provider=None): diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 70798110..444b9e71 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -35,6 +35,7 @@ import warnings from collections import defaultdict from datetime import datetime, timezone from itertools import repeat, starmap +from pathlib import Path # All modules using these should import them from utils package from urllib.parse import ( # noqa; noqa @@ -43,8 +44,10 @@ from urllib.parse import ( # noqa; noqa urlencode, urljoin, urlparse, + urlsplit, urlunparse, ) +from urllib.request import url2pathname import click import shapefile @@ -203,6 +206,24 @@ def strip_accents(s): ) +def uri_to_path(uri): + """ + Convert a file URI (e.g. 'file:///tmp') to a local path (e.g. '/tmp') + """ + if not uri.startswith("file"): + raise ValueError("A file URI must be provided (e.g. 'file:///tmp'") + _, _, path, _, _ = urlsplit(uri) + # On Windows urlsplit returns the path starting with a slash ('/C:/User) + path = url2pathname(path) + # url2pathname removes it + return path + + +def path_to_uri(path): + """Convert a local absolute path to a file URI""" + return Path(path).as_uri() + + def mutate_dict_in_place(func, mapping): """Apply func to values of mapping.	Inconsistent filepaths returned by the download methods `download_all` returns for instance: ```python ['/home/maxime/TRAVAIL/06_EODAG/01_eodag/eodag/docs/notebooks/api_user_guide/eodag_workspace_download/S2A_MSIL1C_20201229T110451_N0209_R094_T31TCL_20201229T131620/S2A_MSIL1C_20201229T110451_N0209_R094_T31TCL_20201229T131620.SAFE', '/home/maxime/TRAVAIL/06_EODAG/01_eodag/eodag/docs/notebooks/api_user_guide/eodag_workspace_download/S2A_MSIL1C_20201226T105451_N0209_R051_T31TCK_20201226T130209/S2A_MSIL1C_20201226T105451_N0209_R051_T31TCK_20201226T130209.SAFE'] ``` while `download` returns: ``` 'file:///home/maxime/TRAVAIL/06_EODAG/01_eodag/eodag/docs/notebooks/api_user_guide/eodag_workspace_download/S2A_MSIL1C_20201116T105331_N0209_R051_T31TCL_20201116T130215/S2A_MSIL1C_20201116T105331_N0209_R051_T31TCL_20201116T130215.SAFE' ``` I think all the download methods should return a consistent local path. It'd say they should all prepend `file://` since it's documented in `download`. It seems it's been there for a while.	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index 96807bc8..df74276a 100644 --- a/tests/context.py +++ b/tests/context.py @@ -44,7 +44,13 @@ from eodag.plugins.manager import PluginManager from eodag.plugins.search.base import Search from eodag.rest import server as eodag_http_server from eodag.rest.utils import eodag_api, get_date -from eodag.utils import get_geometry_from_various, get_timestamp, makedirs +from eodag.utils import ( + get_geometry_from_various, + get_timestamp, + makedirs, + path_to_uri, + uri_to_path, +) from eodag.utils.exceptions import ( AddressNotFound, AuthenticationError, diff --git a/tests/test_cli.py b/tests/test_cli.py index 94e07cc3..a4171dbb 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -427,7 +427,7 @@ class TestEodagCli(unittest.TestCase): TEST_RESOURCES_PATH, "eodag_search_result.geojson" ) config_path = os.path.join(TEST_RESOURCES_PATH, "file_config_override.yml") - dag.return_value.download_all.return_value = ["file:///fake_path"] + dag.return_value.download_all.return_value = ["/fake_path"] result = self.runner.invoke( eodag, ["download", "--search-results", search_results_path, "-f", config_path], @@ -435,7 +435,7 @@ class TestEodagCli(unittest.TestCase): dag.assert_called_once_with(user_conf_file_path=config_path) dag.return_value.deserialize.assert_called_once_with(search_results_path) self.assertEqual(dag.return_value.download_all.call_count, 1) - self.assertEqual("Downloaded file:///fake_path\n", result.output) + self.assertEqual("Downloaded /fake_path\n", result.output) # Testing the case when no downloaded path is returned dag.return_value.download_all.return_value = [None] diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index be2dee90..ca0a145c 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -18,15 +18,22 @@ import datetime import glob +import hashlib import multiprocessing import os import shutil +import time import unittest from pathlib import Path from eodag.api.product.metadata_mapping import ONLINE_STATUS from tests import TEST_RESOURCES_PATH, TESTS_DOWNLOAD_PATH -from tests.context import AuthenticationError, EODataAccessGateway +from tests.context import ( + AuthenticationError, + EODataAccessGateway, + SearchResult, + uri_to_path, +) THEIA_SEARCH_ARGS = [ "theia", @@ -148,8 +155,8 @@ class EndToEndBase(unittest.TestCase): """ search_criteria = { "productType": product_type, - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } if items_per_page: @@ -190,8 +197,8 @@ class EndToEndBase(unittest.TestCase): - Return all the products """ search_criteria = { - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } self.eodag.set_preferred_provider(provider) @@ -428,6 +435,196 @@ class TestEODagEndToEnd(EndToEndBase): self.assertGreater(len(results), 10) +class TestEODagEndToEndComplete(unittest.TestCase): + """Make real and complete test cases that search for products, download them and + extract them. There should be just a tiny number of these tests which can be quite + long to run. + + There must be a user conf file in the test resources folder named user_conf.yml + """ + + @classmethod + def setUpClass(cls): + + # use tests/resources/user_conf.yml if exists else default file ~/.config/eodag/eodag.yml + tests_user_conf = os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + if not os.path.isfile(tests_user_conf): + unittest.SkipTest("Missing user conf file with credentials") + cls.eodag = EODataAccessGateway( + user_conf_file_path=os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + ) + + # create TESTS_DOWNLOAD_PATH is not exists + if not os.path.exists(TESTS_DOWNLOAD_PATH): + os.makedirs(TESTS_DOWNLOAD_PATH) + + for provider, conf in cls.eodag.providers_config.items(): + # Change download directory to TESTS_DOWNLOAD_PATH for tests + if hasattr(conf, "download") and hasattr(conf.download, "outputs_prefix"): + conf.download.outputs_prefix = TESTS_DOWNLOAD_PATH + elif hasattr(conf, "api") and hasattr(conf.api, "outputs_prefix"): + conf.api.outputs_prefix = TESTS_DOWNLOAD_PATH + else: + # no outputs_prefix found for provider + pass + # Force all providers implementing RestoSearch and defining how to retrieve + # products by specifying the + # location scheme to use https, enabling actual downloading of the product + if ( + getattr(getattr(conf, "search", {}), "product_location_scheme", "https") + == "file" + ): + conf.search.product_location_scheme = "https" + + def tearDown(self): + """Clear the test directory""" + for p in Path(TESTS_DOWNLOAD_PATH).glob("*/"): + try: + os.remove(p) + except OSError: + shutil.rmtree(p) + + def test_end_to_end_complete_peps(self): + """Complete end-to-end test with PEPS for download and download_all""" + # Search for products that are ONLINE and as small as possible + today = datetime.date.today() + month_span = datetime.timedelta(weeks=4) + search_results, _ = self.eodag.search( + productType="S2_MSI_L1C", + start=(today - month_span).isoformat(), + end=today.isoformat(), + geom={"lonmin": 1, "latmin": 42, "lonmax": 5, "latmax": 46}, + items_per_page=100, + ) + prods_sorted_by_size = SearchResult( + sorted(search_results, key=lambda p: p.properties["resourceSize"]) + ) + prods_online = [ + p for p in prods_sorted_by_size if p.properties["storageStatus"] == "ONLINE" + ] + if len(prods_online) < 2: + unittest.skip( + "Not enough ONLINE products found, update the search criteria." + ) + + # Retrieve one product to work with + product = prods_online[0] + + prev_remote_location = product.remote_location + prev_location = product.location + # The expected product's archive filename is based on the product's title + expected_product_name = f"{product.properties['title']}.zip" + + # Download the product, but DON'T extract it + archive_file_path = self.eodag.download(product, extract=False) + + # The archive must have been downloaded + self.assertTrue(os.path.isfile(archive_file_path)) + # Its name must be the "{product_title}.zip" + self.assertIn( + expected_product_name, os.listdir(product.downloader.config.outputs_prefix) + ) + # Its size should be >= 5 KB + archive_size = os.stat(archive_file_path).st_size + self.assertGreaterEqual(archive_size, 5 * 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # That points to the downloaded archive + self.assertEqual(uri_to_path(product.location), archive_file_path) + # A .downloaded folder must have been created + record_dir = os.path.join(TESTS_DOWNLOAD_PATH, ".downloaded") + self.assertTrue(os.path.isdir(record_dir)) + # It must contain a file per product downloade, whose name is + # the MD5 hash of the product's remote location + expected_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() + record_file = os.path.join(record_dir, expected_hash) + self.assertTrue(os.path.isfile(record_file)) + # Its content must be the product's remote location + record_content = Path(record_file).read_text() + self.assertEqual(record_content, product.remote_location) + + # The product should not be downloaded again if the download method + # is executed again + previous_archive_file_path = archive_file_path + previous_location = product.location + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The paths should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(product.location, previous_location) + + # If we emulate that the product has just been found, it should not + # be downloaded again since the record file is still present. + product.location = product.remote_location + # Pretty much the same checks as with the previous step + previous_archive_file_path = archive_file_path + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The returned path should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(uri_to_path(product.location), archive_file_path) + + # Remove the archive + os.remove(archive_file_path) + + # Now, the archive is removed but its associated record file + # still exists. Downloading the product again should really + # download it, if its location points to the remote location. + # The product should be automatically extracted. + product.location = product.remote_location + product_dir_path = self.eodag.download(product) + + # Its size should be >= 5 KB + downloaded_size = sum( + f.stat().st_size for f in Path(product_dir_path).glob("/") if f.is_file() + ) + self.assertGreaterEqual(downloaded_size, 5 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # The location must point to a SAFE directory + self.assertTrue(product.location.endswith("SAFE")) + # The path must point to a SAFE directory + self.assertTrue(os.path.isdir(product_dir_path)) + self.assertTrue(product_dir_path.endswith("SAFE")) + + # Remove the archive and extracted product and reset the product's location + os.remove(archive_file_path) + shutil.rmtree(Path(product_dir_path).parent) + product.location = product.remote_location + + # Now let's check download_all + products = prods_sorted_by_size[:2] + # Pass a copy because download_all empties the list + archive_paths = self.eodag.download_all(products[:], extract=False) + + # The returned paths must point to the downloaded archives + # Each product's location must be a URI path to the archive + for product, archive_path in zip(products, archive_paths): + self.assertTrue(os.path.isfile(archive_path)) + self.assertEqual(uri_to_path(product.location), archive_path) + + # Downloading the product again should not download them, since + # they are all already there. + prev_archive_paths = archive_paths + start_time = time.time() + archive_paths = self.eodag.download_all(products[:], extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + self.assertEqual(archive_paths, prev_archive_paths) + + # @unittest.skip("skip auto run") class TestEODagEndToEndWrongCredentials(EndToEndBase): """Make real case tests with wrong credentials. This assumes the existence of a @@ -462,7 +659,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) def test_end_to_end_good_apikey_wrong_credentials_aws_eos(self): @@ -489,7 +686,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) self.assertGreater(len(results), 0) one_product = results[0] @@ -525,5 +722,5 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], USGS_SEARCH_ARGS[1:]) - ) + ), ) diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index f43aaa84..9222648a 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -295,7 +295,6 @@ class TestEOProduct(EODagTestCase): self.requests_http_get.assert_called_with( self.download_url, stream=True, auth=None, params={} ) - product_file_path = product_file_path[len("file://") :] download_records_dir = pathlib.Path(product_file_path).parent / ".downloaded" # A .downloaded folder should be created, including a text file that # lists the downloaded product by their url @@ -332,7 +331,7 @@ class TestEOProduct(EODagTestCase): # Download product_dir_path = product.download() - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) # The returned path must be a directory. self.assertTrue(product_dir_path.is_dir()) # Check that the extracted dir has at least one file, there are more @@ -379,7 +378,7 @@ class TestEOProduct(EODagTestCase): self.download_url, stream=True, auth=None, params={"fakeparam": "dummy"} ) # Check that "outputs_prefix" is respected. - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) self.assertEqual(product_dir_path.parent.name, output_dir_name) # We've asked to extract the product so there should be a directory. self.assertTrue(product_dir_path.is_dir()) diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 58732712..ef865dff 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -16,10 +16,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import sys import unittest from datetime import datetime -from tests.context import get_timestamp +from tests.context import get_timestamp, path_to_uri, uri_to_path class TestUtils(unittest.TestCase): @@ -33,3 +34,21 @@ class TestUtils(unittest.TestCase): expected_dt = datetime.strptime(requested_date, date_format) actual_utc_dt = datetime.utcfromtimestamp(ts_in_secs) self.assertEqual(actual_utc_dt, expected_dt) + + def test_uri_to_path(self): + if sys.platform == "win32": + expected_path = r"C:\tmp\file.txt" + tested_uri = r"file:///C:/tmp/file.txt" + else: + expected_path = "/tmp/file.txt" + tested_uri = "file:///tmp/file.txt" + actual_path = uri_to_path(tested_uri) + self.assertEqual(actual_path, expected_path) + with self.assertRaises(ValueError): + uri_to_path("not_a_uri") + + def test_path_to_uri(self): + if sys.platform == "win32": + self.assertEqual(path_to_uri(r"C:\tmp\file.txt"), "file:///C:/tmp/file.txt") + else: + self.assertEqual(path_to_uri("/tmp/file.txt"), "file:///tmp/file.txt")	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 10 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt", "requirements-dev.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@0632e4147f26119bea43e0672f6d06e75d216a20#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_cli.py::TestCore::test_core_object_locations_file_not_found", "tests/test_cli.py::TestCore::test_core_object_open_index_if_exists", "tests/test_cli.py::TestCore::test_core_object_set_default_locations_config", "tests/test_cli.py::TestCore::test_list_product_types_for_provider_ok", "tests/test_cli.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/test_cli.py::TestCore::test_list_product_types_ok", "tests/test_cli.py::TestCore::test_rebuild_index", "tests/test_cli.py::TestCore::test_supported_product_types_in_unit_test", "tests/test_cli.py::TestCore::test_supported_providers_in_unit_test", "tests/test_cli.py::TestEodagCli::test_eodag_download_missingcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_download_no_search_results_arg", "tests/test_cli.py::TestEodagCli::test_eodag_download_ok", "tests/test_cli.py::TestEodagCli::test_eodag_download_wrongcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_ok", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ko", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ok", "tests/test_cli.py::TestEodagCli::test_eodag_search_all", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_geom_mutually_exclusive", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_storage_arg", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_conf_file_inexistent", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_max_cloud_out_of_range", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_args", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_producttype_arg", "tests/test_cli.py::TestEodagCli::test_eodag_with_only_verbose_opt", "tests/test_cli.py::TestEodagCli::test_eodag_without_args", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[ "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_cruncher", "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-248	0632e4147f26119bea43e0672f6d06e75d216a20	2021-04-21 08:52:52	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/api/core.py b/eodag/api/core.py index 6efceb2a..556d03c9 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -47,6 +47,7 @@ from eodag.utils import ( _deprecated, get_geometry_from_various, makedirs, + uri_to_path, ) from eodag.utils.exceptions import ( NoMatchingProductType, @@ -434,7 +435,7 @@ class EODataAccessGateway(object): if results is None: results = searcher.search(query, limit=None) else: - results.upgrade_and_extend(searcher.search(query)) + results.upgrade_and_extend(searcher.search(query, limit=None)) guesses = [r["ID"] for r in results or []] if guesses: return guesses @@ -844,6 +845,19 @@ class EODataAccessGateway(object): if product_type is None: try: guesses = self.guess_product_type(kwargs) + + # guess_product_type raises a NoMatchingProductType error if no product + # is found. Here, the supported search params are removed from the + # kwargs if present, not to propagate them to the query itself. + for param in ( + "instrument", + "platform", + "platformSerialIdentifier", + "processingLevel", + "sensorType", + ): + kwargs.pop(param, None) + # By now, only use the best bet product_type = guesses[0] except NoMatchingProductType: @@ -1281,8 +1295,10 @@ class EODataAccessGateway(object): This is an alias to the method of the same name on :class:`~eodag.api.product.EOProduct`, but it performs some additional checks like verifying that a downloader and authenticator are registered - for the product before trying to download it. If the metadata mapping for - `downloadLink` is set to something that can be interpreted as a link on a + for the product before trying to download it. + + If the metadata mapping for `downloadLink` is set to something that can be + interpreted as a link on a local filesystem, the download is skipped (by now, only a link starting with `file://` is supported). Therefore, any user that knows how to extract product location from product metadata on a provider can override the @@ -1316,10 +1332,15 @@ class EODataAccessGateway(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ - if product.location.startswith("file"): + if product.location.startswith("file://"): logger.info("Local product detected. Download skipped") - return product.location + return uri_to_path(product.location) if product.downloader is None: auth = product.downloader_auth if auth is None: diff --git a/eodag/api/product/_product.py b/eodag/api/product/_product.py index a0d98184..a40e74bd 100644 --- a/eodag/api/product/_product.py +++ b/eodag/api/product/_product.py @@ -240,6 +240,11 @@ class EOProduct(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ if progress_callback is None: progress_callback = ProgressCallback() @@ -254,7 +259,7 @@ class EOProduct(object): if self.downloader_auth is not None else self.downloader_auth ) - fs_location = self.downloader.download( + fs_path = self.downloader.download( self, auth=auth, progress_callback=progress_callback, @@ -262,13 +267,8 @@ class EOProduct(object): timeout=timeout, kwargs ) - if fs_location is None: - raise DownloadError( - "Invalid file location returned by download process: '{}'".format( - fs_location - ) - ) - self.location = "file://{}".format(fs_location) + if fs_path is None: + raise DownloadError("Missing file location returned by download process") logger.debug( "Product location updated from '%s' to '%s'", self.remote_location, @@ -279,7 +279,7 @@ class EOProduct(object): "'remote_location' property: %s", self.remote_location, ) - return self.location + return fs_path def get_quicklook(self, filename=None, base_dir=None, progress_callback=None): """Download the quick look image of a given EOProduct from its provider if it diff --git a/eodag/plugins/apis/base.py b/eodag/plugins/apis/base.py index 6a775973..b6d385ef 100644 --- a/eodag/plugins/apis/base.py +++ b/eodag/plugins/apis/base.py @@ -18,12 +18,40 @@ import logging from eodag.plugins.base import PluginTopic +from eodag.plugins.download.base import DEFAULT_DOWNLOAD_TIMEOUT, DEFAULT_DOWNLOAD_WAIT logger = logging.getLogger("eodag.plugins.apis.base") class Api(PluginTopic): - """Plugins API Base plugin""" + """Plugins API Base plugin + + An Api plugin has three download methods that it must implement: + - ``query``: search for products + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + The download methods must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + """ def query(self, args, count=True, kwargs): """Implementation of how the products must be searched goes here. @@ -32,14 +60,77 @@ class Api(PluginTopic): which will be processed by a Download plugin (2) and the total number of products matching the search criteria. If ``count`` is False, the second element returned must be ``None``. - .. versionchanged:: - 2.1 + .. versionchanged:: 2.1 - A new optional boolean parameter ``count`` which defaults to ``True``, it - allows to trigger or not a count query. + A new optional boolean parameter ``count`` which defaults to ``True``, it + allows to trigger or not a count query. """ raise NotImplementedError("A Api plugin must implement a method named query") - def download(self, args, kwargs): - """Implementation of how the products must be downloaded.""" - raise NotImplementedError("A Api plugin must implement a method named download") + def download( + self, + product, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str + """ + raise NotImplementedError( + "An Api plugin must implement a method named download" + ) + + def download_all( + self, + products, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + """ + Base download_all method. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list + """ + raise NotImplementedError( + "A Api plugin must implement a method named download_all" + ) diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 37ce4443..768778e5 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -37,7 +37,12 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import GENERIC_PRODUCT_TYPE, format_dict_items, get_progress_callback +from eodag.utils import ( + GENERIC_PRODUCT_TYPE, + format_dict_items, + get_progress_callback, + path_to_uri, +) from eodag.utils.exceptions import AuthenticationError, NotAvailableError logger = logging.getLogger("eodag.plugins.apis.usgs") @@ -164,6 +169,8 @@ class UsgsApi(Api, Download): product, outputs_extension=".tar.gz", kwargs ) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -264,8 +271,11 @@ class UsgsApi(Api, Download): ) new_fs_path = fs_path[: fs_path.index(".tar.gz")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product_path = self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product.location = path_to_uri(product_path) + return product_path def download_all( self, diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 2f0b132d..687985c6 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -34,7 +34,7 @@ from eodag.api.product.metadata_mapping import ( properties_from_xml, ) from eodag.plugins.download.base import Download -from eodag.utils import get_progress_callback, urlparse +from eodag.utils import get_progress_callback, path_to_uri, urlparse from eodag.utils.exceptions import AuthenticationError, DownloadError logger = logging.getLogger("eodag.plugins.download.aws") @@ -185,6 +185,8 @@ class AwsDownload(Download): # prepare download & create dirs (before updating metadata) product_local_path, record_filename = self._prepare_download(product, kwargs) if not product_local_path or not record_filename: + if product_local_path: + product.location = path_to_uri(product_local_path) return product_local_path product_local_path = product_local_path.replace(".zip", "") # remove existing incomplete file @@ -395,6 +397,7 @@ class AwsDownload(Download): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path def get_authenticated_objects(self, bucket_name, prefix, auth_dict): diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index 8d6dbd3e..93001e9a 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -25,7 +25,7 @@ from datetime import datetime, timedelta from time import sleep from eodag.plugins.base import PluginTopic -from eodag.utils import get_progress_callback, sanitize +from eodag.utils import get_progress_callback, sanitize, uri_to_path from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -43,6 +43,31 @@ DEFAULT_DOWNLOAD_TIMEOUT = 20 class Download(PluginTopic): """Base Download Plugin. + A Download plugin has two download methods that it must implement: + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + They must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record* file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + :param provider: An eodag providers configuration dictionary :type provider: dict :param config: Path to the user configuration file @@ -62,8 +87,26 @@ class Download(PluginTopic): timeout=DEFAULT_DOWNLOAD_TIMEOUT, kwargs, ): - """ - Base download method. Not available, should be defined for each plugin + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str """ raise NotImplementedError( "A Download plugin must implement a method named download" @@ -78,8 +121,7 @@ class Download(PluginTopic): :rtype: tuple """ if product.location != product.remote_location: - scheme_prefix_len = len("file://") - fs_path = product.location[scheme_prefix_len:] + fs_path = uri_to_path(product.location) # The fs path of a product is either a file (if 'extract' config is False) or a directory if os.path.isfile(fs_path) or os.path.isdir(fs_path): logger.info( @@ -249,8 +291,28 @@ class Download(PluginTopic): kwargs, ): """ - A sequential download_all implementation - using download method for every products + Base download_all method. + + This specific implementation uses the ``download`` method implemented by + the plugin to sequentially attempt to download products. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list """ paths = [] # initiate retry loop diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 11662a18..f54fff86 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -32,7 +32,7 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import get_progress_callback +from eodag.utils import get_progress_callback, path_to_uri from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -70,6 +70,8 @@ class HTTPDownload(Download): """ fs_path, record_filename = self._prepare_download(product, kwargs) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -80,7 +82,7 @@ class HTTPDownload(Download): # download assets if exist instead of remote_location try: - return self._download_assets( + fs_path = self._download_assets( product, fs_path.replace(".zip", ""), record_filename, @@ -88,6 +90,8 @@ class HTTPDownload(Download): progress_callback, kwargs ) + product.location = path_to_uri(fs_path) + return fs_path except NotAvailableError: pass @@ -218,8 +222,11 @@ class HTTPDownload(Download): ) new_fs_path = fs_path[: fs_path.index(".zip")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, kwargs) + product_path = self._finalize(fs_path, kwargs) + product.location = path_to_uri(product_path) + return product_path except NotAvailableError as e: if not getattr(self.config, "order_enabled", False): diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index 71876ea4..2f5628b9 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -29,7 +29,7 @@ from requests import HTTPError from eodag.api.product.metadata_mapping import OFFLINE_STATUS from eodag.plugins.download.aws import AwsDownload from eodag.plugins.download.http import HTTPDownload -from eodag.utils import get_progress_callback, urljoin +from eodag.utils import get_progress_callback, path_to_uri, urljoin from eodag.utils.exceptions import ( AuthenticationError, DownloadError, @@ -158,6 +158,7 @@ class S3RestDownload(AwsDownload): url_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() record_filename = os.path.join(download_records_dir, url_hash) if os.path.isfile(record_filename) and os.path.exists(product_local_path): + product.location = path_to_uri(product_local_path) return product_local_path # Remove the record file if product_local_path is absent (e.g. it was deleted while record wasn't) elif os.path.isfile(record_filename): @@ -215,4 +216,5 @@ class S3RestDownload(AwsDownload): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index f48e3eb0..2c6f7e9d 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -463,7 +463,7 @@ def download_stac_item_by_id(catalogs, item_id, provider=None): product_path = eodag_api.download(product) - return product_path.replace("file://", "") + return product_path def get_stac_catalogs(url, root="/", catalogs=[], provider=None): diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 70798110..0c21649b 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -35,6 +35,7 @@ import warnings from collections import defaultdict from datetime import datetime, timezone from itertools import repeat, starmap +from pathlib import Path # All modules using these should import them from utils package from urllib.parse import ( # noqa; noqa @@ -43,8 +44,10 @@ from urllib.parse import ( # noqa; noqa urlencode, urljoin, urlparse, + urlsplit, urlunparse, ) +from urllib.request import url2pathname import click import shapefile @@ -203,6 +206,24 @@ def strip_accents(s): ) +def uri_to_path(uri): + """ + Convert a file URI (e.g. 'file:///tmp') to a local path (e.g. '/tmp') + """ + if not uri.startswith("file"): + raise ValueError("A file URI must be provided (e.g. 'file:///tmp'") + _, _, path, _, _ = urlsplit(uri) + # On Windows urlsplit returns the path starting with a slash ('/C:/User) + path = url2pathname(path) + # url2pathname removes it + return path + + +def path_to_uri(path): + """Convert a local absolute path to a file URI""" + return Path(path).as_uri() + + def mutate_dict_in_place(func, mapping): """Apply func to values of mapping. @@ -853,13 +874,13 @@ def _deprecated(reason="", version=None): @functools.wraps(callable) def wrapper(args, kwargs): - warnings.simplefilter("always", DeprecationWarning) - warnings.warn( - f"Call to deprecated {ctype} {cname} {reason_}{version_}", - category=DeprecationWarning, - stacklevel=2, - ) - warnings.simplefilter("default", DeprecationWarning) + with warnings.catch_warnings(): + warnings.simplefilter("always", DeprecationWarning) + warnings.warn( + f"Call to deprecated {ctype} {cname} {reason_}{version_}", + category=DeprecationWarning, + stacklevel=2, + ) return callable(args, **kwargs) return wrapper	Kwargs passed to guess a product type are propagated A search made with the following criteria guesses that it should use the product type `S2_MSI_L1C`: ```python from eodag import EODataAccessGateway dag = EODataAccessGateway() dag.set_preferred_provider("peps") search_criteria = { 'start': '2021-03-01', 'end': '2021-03-31', 'geom': {'lonmin': 1, 'latmin': 43, 'lonmax': 2, 'latmax': 44}, 'instrument': 'MSI', 'platform': 'SENTINEL2', 'platformSerialIdentifier': 'S2A', 'processingLevel': 'L1', 'sensorType': 'OPTICAL' } dag.search(**search_criteria) ``` The URL sent is this one: https://peps.cnes.fr/resto/api/collections/S2ST/search.json?instrument=MSI&platform=S2A&processingLevel=L1&sensorType=OPTICAL&startDate=2021-03-01&completionDate=2021-03-31&geometry=POLYGON ((1.0000 43.0000, 1.0000 44.0000, 2.0000 44.0000, 2.0000 43.0000, 1.0000 43.0000))&productType=S2MSI1C&maxRecords=20&page=1 As it can be seen the URL has the "hint" kwargs (e.g. `instrument`) as query parameters. I don't know whether they should be preserved or not. The problem here actually is that the search returns 0 results while it should return some in this area and time period. By removing `processingLevel=L1` the request returns some results, but not the same number as if the search was made before hints and just with `productType="S2_MSI_L1C"`.	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index 96807bc8..df74276a 100644 --- a/tests/context.py +++ b/tests/context.py @@ -44,7 +44,13 @@ from eodag.plugins.manager import PluginManager from eodag.plugins.search.base import Search from eodag.rest import server as eodag_http_server from eodag.rest.utils import eodag_api, get_date -from eodag.utils import get_geometry_from_various, get_timestamp, makedirs +from eodag.utils import ( + get_geometry_from_various, + get_timestamp, + makedirs, + path_to_uri, + uri_to_path, +) from eodag.utils.exceptions import ( AddressNotFound, AuthenticationError, diff --git a/tests/test_cli.py b/tests/test_cli.py index 94e07cc3..a4171dbb 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -427,7 +427,7 @@ class TestEodagCli(unittest.TestCase): TEST_RESOURCES_PATH, "eodag_search_result.geojson" ) config_path = os.path.join(TEST_RESOURCES_PATH, "file_config_override.yml") - dag.return_value.download_all.return_value = ["file:///fake_path"] + dag.return_value.download_all.return_value = ["/fake_path"] result = self.runner.invoke( eodag, ["download", "--search-results", search_results_path, "-f", config_path], @@ -435,7 +435,7 @@ class TestEodagCli(unittest.TestCase): dag.assert_called_once_with(user_conf_file_path=config_path) dag.return_value.deserialize.assert_called_once_with(search_results_path) self.assertEqual(dag.return_value.download_all.call_count, 1) - self.assertEqual("Downloaded file:///fake_path\n", result.output) + self.assertEqual("Downloaded /fake_path\n", result.output) # Testing the case when no downloaded path is returned dag.return_value.download_all.return_value = [None] diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index be2dee90..ca0a145c 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -18,15 +18,22 @@ import datetime import glob +import hashlib import multiprocessing import os import shutil +import time import unittest from pathlib import Path from eodag.api.product.metadata_mapping import ONLINE_STATUS from tests import TEST_RESOURCES_PATH, TESTS_DOWNLOAD_PATH -from tests.context import AuthenticationError, EODataAccessGateway +from tests.context import ( + AuthenticationError, + EODataAccessGateway, + SearchResult, + uri_to_path, +) THEIA_SEARCH_ARGS = [ "theia", @@ -148,8 +155,8 @@ class EndToEndBase(unittest.TestCase): """ search_criteria = { "productType": product_type, - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } if items_per_page: @@ -190,8 +197,8 @@ class EndToEndBase(unittest.TestCase): - Return all the products """ search_criteria = { - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } self.eodag.set_preferred_provider(provider) @@ -428,6 +435,196 @@ class TestEODagEndToEnd(EndToEndBase): self.assertGreater(len(results), 10) +class TestEODagEndToEndComplete(unittest.TestCase): + """Make real and complete test cases that search for products, download them and + extract them. There should be just a tiny number of these tests which can be quite + long to run. + + There must be a user conf file in the test resources folder named user_conf.yml + """ + + @classmethod + def setUpClass(cls): + + # use tests/resources/user_conf.yml if exists else default file ~/.config/eodag/eodag.yml + tests_user_conf = os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + if not os.path.isfile(tests_user_conf): + unittest.SkipTest("Missing user conf file with credentials") + cls.eodag = EODataAccessGateway( + user_conf_file_path=os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + ) + + # create TESTS_DOWNLOAD_PATH is not exists + if not os.path.exists(TESTS_DOWNLOAD_PATH): + os.makedirs(TESTS_DOWNLOAD_PATH) + + for provider, conf in cls.eodag.providers_config.items(): + # Change download directory to TESTS_DOWNLOAD_PATH for tests + if hasattr(conf, "download") and hasattr(conf.download, "outputs_prefix"): + conf.download.outputs_prefix = TESTS_DOWNLOAD_PATH + elif hasattr(conf, "api") and hasattr(conf.api, "outputs_prefix"): + conf.api.outputs_prefix = TESTS_DOWNLOAD_PATH + else: + # no outputs_prefix found for provider + pass + # Force all providers implementing RestoSearch and defining how to retrieve + # products by specifying the + # location scheme to use https, enabling actual downloading of the product + if ( + getattr(getattr(conf, "search", {}), "product_location_scheme", "https") + == "file" + ): + conf.search.product_location_scheme = "https" + + def tearDown(self): + """Clear the test directory""" + for p in Path(TESTS_DOWNLOAD_PATH).glob("*/"): + try: + os.remove(p) + except OSError: + shutil.rmtree(p) + + def test_end_to_end_complete_peps(self): + """Complete end-to-end test with PEPS for download and download_all""" + # Search for products that are ONLINE and as small as possible + today = datetime.date.today() + month_span = datetime.timedelta(weeks=4) + search_results, _ = self.eodag.search( + productType="S2_MSI_L1C", + start=(today - month_span).isoformat(), + end=today.isoformat(), + geom={"lonmin": 1, "latmin": 42, "lonmax": 5, "latmax": 46}, + items_per_page=100, + ) + prods_sorted_by_size = SearchResult( + sorted(search_results, key=lambda p: p.properties["resourceSize"]) + ) + prods_online = [ + p for p in prods_sorted_by_size if p.properties["storageStatus"] == "ONLINE" + ] + if len(prods_online) < 2: + unittest.skip( + "Not enough ONLINE products found, update the search criteria." + ) + + # Retrieve one product to work with + product = prods_online[0] + + prev_remote_location = product.remote_location + prev_location = product.location + # The expected product's archive filename is based on the product's title + expected_product_name = f"{product.properties['title']}.zip" + + # Download the product, but DON'T extract it + archive_file_path = self.eodag.download(product, extract=False) + + # The archive must have been downloaded + self.assertTrue(os.path.isfile(archive_file_path)) + # Its name must be the "{product_title}.zip" + self.assertIn( + expected_product_name, os.listdir(product.downloader.config.outputs_prefix) + ) + # Its size should be >= 5 KB + archive_size = os.stat(archive_file_path).st_size + self.assertGreaterEqual(archive_size, 5 * 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # That points to the downloaded archive + self.assertEqual(uri_to_path(product.location), archive_file_path) + # A .downloaded folder must have been created + record_dir = os.path.join(TESTS_DOWNLOAD_PATH, ".downloaded") + self.assertTrue(os.path.isdir(record_dir)) + # It must contain a file per product downloade, whose name is + # the MD5 hash of the product's remote location + expected_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() + record_file = os.path.join(record_dir, expected_hash) + self.assertTrue(os.path.isfile(record_file)) + # Its content must be the product's remote location + record_content = Path(record_file).read_text() + self.assertEqual(record_content, product.remote_location) + + # The product should not be downloaded again if the download method + # is executed again + previous_archive_file_path = archive_file_path + previous_location = product.location + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The paths should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(product.location, previous_location) + + # If we emulate that the product has just been found, it should not + # be downloaded again since the record file is still present. + product.location = product.remote_location + # Pretty much the same checks as with the previous step + previous_archive_file_path = archive_file_path + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The returned path should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(uri_to_path(product.location), archive_file_path) + + # Remove the archive + os.remove(archive_file_path) + + # Now, the archive is removed but its associated record file + # still exists. Downloading the product again should really + # download it, if its location points to the remote location. + # The product should be automatically extracted. + product.location = product.remote_location + product_dir_path = self.eodag.download(product) + + # Its size should be >= 5 KB + downloaded_size = sum( + f.stat().st_size for f in Path(product_dir_path).glob("/") if f.is_file() + ) + self.assertGreaterEqual(downloaded_size, 5 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # The location must point to a SAFE directory + self.assertTrue(product.location.endswith("SAFE")) + # The path must point to a SAFE directory + self.assertTrue(os.path.isdir(product_dir_path)) + self.assertTrue(product_dir_path.endswith("SAFE")) + + # Remove the archive and extracted product and reset the product's location + os.remove(archive_file_path) + shutil.rmtree(Path(product_dir_path).parent) + product.location = product.remote_location + + # Now let's check download_all + products = prods_sorted_by_size[:2] + # Pass a copy because download_all empties the list + archive_paths = self.eodag.download_all(products[:], extract=False) + + # The returned paths must point to the downloaded archives + # Each product's location must be a URI path to the archive + for product, archive_path in zip(products, archive_paths): + self.assertTrue(os.path.isfile(archive_path)) + self.assertEqual(uri_to_path(product.location), archive_path) + + # Downloading the product again should not download them, since + # they are all already there. + prev_archive_paths = archive_paths + start_time = time.time() + archive_paths = self.eodag.download_all(products[:], extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + self.assertEqual(archive_paths, prev_archive_paths) + + # @unittest.skip("skip auto run") class TestEODagEndToEndWrongCredentials(EndToEndBase): """Make real case tests with wrong credentials. This assumes the existence of a @@ -462,7 +659,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) def test_end_to_end_good_apikey_wrong_credentials_aws_eos(self): @@ -489,7 +686,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) self.assertGreater(len(results), 0) one_product = results[0] @@ -525,5 +722,5 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], USGS_SEARCH_ARGS[1:]) - ) + ), ) diff --git a/tests/units/test_core.py b/tests/units/test_core.py index cb6022e4..69cf9a3e 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -367,7 +367,7 @@ class TestCoreGeometry(unittest.TestCase): "lonmax": 2, "latmax": 52, } - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Bad dict with a missing key del geometry["lonmin"] self.assertRaises( @@ -378,10 +378,10 @@ class TestCoreGeometry(unittest.TestCase): ) # Tuple geometry = (0, 50, 2, 52) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List geometry = list(geometry) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List without 4 items geometry.pop() self.assertRaises( @@ -392,7 +392,7 @@ class TestCoreGeometry(unittest.TestCase): ) # WKT geometry = ref_geom_as_wkt - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Some other shapely geom geometry = LineString([[0, 0], [1, 1]]) self.assertIsInstance( @@ -409,7 +409,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="FRA") ) self.assertIsInstance(geom_france, MultiPolygon) - self.assertEquals(len(geom_france), 3) # France + Guyana + Corsica + self.assertEqual(len(geom_france), 3) # France + Guyana + Corsica def test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs( self, @@ -444,7 +444,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="PA[A-Z]") ) self.assertIsInstance(geom_regex_pa, MultiPolygon) - self.assertEquals(len(geom_regex_pa), 2) + self.assertEqual(len(geom_regex_pa), 2) def test_get_geometry_from_various_locations_no_match_raises_error(self): """If the location search doesn't match any of the feature attribute a ValueError must be raised""" @@ -468,7 +468,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # France + Guyana + Corsica + somewhere over Poland - self.assertEquals(len(geom_combined), 4) + self.assertEqual(len(geom_combined), 4) geometry = { "lonmin": 0, "latmin": 50, @@ -480,7 +480,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # The bounding box overlaps with France inland - self.assertEquals(len(geom_combined), 3) + self.assertEqual(len(geom_combined), 3) class TestCoreSearch(unittest.TestCase): @@ -546,7 +546,6 @@ class TestCoreSearch(unittest.TestCase): "geometry": None, "productType": None, } - self.assertDictContainsSubset(expected, prepared_search) expected = set(["geometry", "productType", "auth", "search_plugin"]) self.assertSetEqual(expected, set(prepared_search)) @@ -557,11 +556,10 @@ class TestCoreSearch(unittest.TestCase): "end": "2020-02-01", } prepared_search = self.dag._prepare_search(base) - expected = { - "startTimeFromAscendingNode": base["start"], - "completionTimeFromAscendingNode": base["end"], - } - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["startTimeFromAscendingNode"], base["start"]) + self.assertEqual( + prepared_search["completionTimeFromAscendingNode"], base["end"] + ) def test__prepare_search_geom(self): """_prepare_search must handle geom, box and bbox""" @@ -608,8 +606,7 @@ class TestCoreSearch(unittest.TestCase): """_prepare_search must handle when a product type is given""" base = {"productType": "S2_MSI_L1C"} prepared_search = self.dag._prepare_search(base) - expected = {"productType": base["productType"]} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) def test__prepare_search_product_type_guess_it(self): """_prepare_search must guess a product type when required to""" @@ -621,8 +618,19 @@ class TestCoreSearch(unittest.TestCase): platformSerialIdentifier="S2A", ) prepared_search = self.dag._prepare_search(base) - expected = {"productType": "S2_MSI_L1C", base} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], "S2_MSI_L1C") + + def test__prepare_search_remove_guess_kwargs(self): + """_prepare_search must remove the guess kwargs""" + # Uses guess_product_type to find the product matching + # the best the given params. + base = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + prepared_search = self.dag._prepare_search(base) + self.assertEqual(len(base.keys() & prepared_search.keys()), 0) def test__prepare_search_with_id(self): """_prepare_search must handle a search by id""" @@ -638,8 +646,8 @@ class TestCoreSearch(unittest.TestCase): "cloudCover": 10, } prepared_search = self.dag._prepare_search(base) - expected = base - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) + self.assertEqual(prepared_search["cloudCover"], base["cloudCover"]) def test__prepare_search_search_plugin_has_known_product_properties(self): """_prepare_search must attach the product properties to the search plugin""" diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index f43aaa84..0faf7704 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -90,23 +90,18 @@ class TestEOProduct(EODagTestCase): self.provider, self.eoproduct_props, productType=self.product_type ) geo_interface = geojson.loads(geojson.dumps(product)) - self.assertDictContainsSubset( - { - "type": "Feature", - "geometry": self._tuples_to_lists(geometry.mapping(self.geometry)), - }, - geo_interface, + self.assertEqual(geo_interface["type"], "Feature") + self.assertEqual( + geo_interface["geometry"], + self._tuples_to_lists(geometry.mapping(self.geometry)), ) - self.assertDictContainsSubset( - { - "eodag_provider": self.provider, - "eodag_search_intersection": self._tuples_to_lists( - geometry.mapping(product.search_intersection) - ), - "eodag_product_type": self.product_type, - }, - geo_interface["properties"], + properties = geo_interface["properties"] + self.assertEqual(properties["eodag_provider"], self.provider) + self.assertEqual( + properties["eodag_search_intersection"], + self._tuples_to_lists(geometry.mapping(product.search_intersection)), ) + self.assertEqual(properties["eodag_product_type"], self.product_type) def test_eoproduct_from_geointerface(self): """EOProduct must be build-able from its geo-interface""" @@ -295,7 +290,6 @@ class TestEOProduct(EODagTestCase): self.requests_http_get.assert_called_with( self.download_url, stream=True, auth=None, params={} ) - product_file_path = product_file_path[len("file://") :] download_records_dir = pathlib.Path(product_file_path).parent / ".downloaded" # A .downloaded folder should be created, including a text file that # lists the downloaded product by their url @@ -332,7 +326,7 @@ class TestEOProduct(EODagTestCase): # Download product_dir_path = product.download() - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) # The returned path must be a directory. self.assertTrue(product_dir_path.is_dir()) # Check that the extracted dir has at least one file, there are more @@ -379,7 +373,7 @@ class TestEOProduct(EODagTestCase): self.download_url, stream=True, auth=None, params={"fakeparam": "dummy"} ) # Check that "outputs_prefix" is respected. - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) self.assertEqual(product_dir_path.parent.name, output_dir_name) # We've asked to extract the product so there should be a directory. self.assertTrue(product_dir_path.is_dir()) diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 28369fee..09dd221d 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -106,8 +106,8 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): info_message=mock.ANY, exception_message=mock.ANY, ) - self.assertEquals(estimate, self.sobloo_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.sobloo_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -144,7 +144,7 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -196,8 +196,8 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.awseos_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.awseos_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -232,7 +232,7 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -294,6 +294,6 @@ class TestSearchPluginODataV4Search(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.onda_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.onda_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) diff --git a/tests/units/test_search_result.py b/tests/units/test_search_result.py index f1697117..d4e52303 100644 --- a/tests/units/test_search_result.py +++ b/tests/units/test_search_result.py @@ -32,9 +32,8 @@ class TestSearchResult(unittest.TestCase): def test_search_result_geo_interface(self): """SearchResult must provide a FeatureCollection geo-interface""" geo_interface = geojson.loads(geojson.dumps(self.search_result)) - self.assertDictContainsSubset( - {"type": "FeatureCollection", "features": []}, geo_interface - ) + self.assertEqual(geo_interface["type"], "FeatureCollection") + self.assertEqual(geo_interface["features"], []) def test_search_result_is_list_like(self): """SearchResult must provide a list interface""" diff --git a/tests/units/test_stac_reader.py b/tests/units/test_stac_reader.py index 8960ee81..f242fb38 100644 --- a/tests/units/test_stac_reader.py +++ b/tests/units/test_stac_reader.py @@ -47,10 +47,8 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.child_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.child_cat_len) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - items[0], - ) + self.assertEqual(items[0]["type"], "Feature") + self.assertEqual(items[0]["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_root_not_recursive(self): """fetch_stac_items from root must provide an empty list when no recursive""" @@ -62,23 +60,20 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.root_cat, recursive=True) self.assertEqual(len(items), self.root_cat_len) for item in items: - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, item - ) + self.assertEqual(item["type"], "Feature") + self.assertEqual(item["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_item(self): """fetch_stac_items from an item must return it""" item = fetch_stac_items(self.item) self.assertIsInstance(item, list) self.assertEqual(len(item), 1) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - item[0], - ) + self.assertEqual(item[0]["type"], "Feature") + self.assertEqual(item[0]["collection"], "S2_MSI_L1C") def test_stact_reader_fetch_singlefile_catalog(self): """fetch_stact_items must return all the items from a single file catalog""" items = fetch_stac_items(self.singlefile_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.singlefile_cat_len) - self.assertDictContainsSubset({"type": "Feature"}, items[0]) + self.assertEqual(items[0]["type"], "Feature") diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 58732712..ef865dff 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -16,10 +16,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import sys import unittest from datetime import datetime -from tests.context import get_timestamp +from tests.context import get_timestamp, path_to_uri, uri_to_path class TestUtils(unittest.TestCase): @@ -33,3 +34,21 @@ class TestUtils(unittest.TestCase): expected_dt = datetime.strptime(requested_date, date_format) actual_utc_dt = datetime.utcfromtimestamp(ts_in_secs) self.assertEqual(actual_utc_dt, expected_dt) + + def test_uri_to_path(self): + if sys.platform == "win32": + expected_path = r"C:\tmp\file.txt" + tested_uri = r"file:///C:/tmp/file.txt" + else: + expected_path = "/tmp/file.txt" + tested_uri = "file:///tmp/file.txt" + actual_path = uri_to_path(tested_uri) + self.assertEqual(actual_path, expected_path) + with self.assertRaises(ValueError): + uri_to_path("not_a_uri") + + def test_path_to_uri(self): + if sys.platform == "win32": + self.assertEqual(path_to_uri(r"C:\tmp\file.txt"), "file:///C:/tmp/file.txt") + else: + self.assertEqual(path_to_uri("/tmp/file.txt"), "file:///tmp/file.txt")	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 10 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist", "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@0632e4147f26119bea43e0672f6d06e75d216a20#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_cli.py::TestCore::test_core_object_locations_file_not_found", "tests/test_cli.py::TestCore::test_core_object_open_index_if_exists", "tests/test_cli.py::TestCore::test_core_object_set_default_locations_config", "tests/test_cli.py::TestCore::test_list_product_types_for_provider_ok", "tests/test_cli.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/test_cli.py::TestCore::test_list_product_types_ok", "tests/test_cli.py::TestCore::test_rebuild_index", "tests/test_cli.py::TestCore::test_supported_product_types_in_unit_test", "tests/test_cli.py::TestCore::test_supported_providers_in_unit_test", "tests/test_cli.py::TestEodagCli::test_eodag_download_missingcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_download_no_search_results_arg", "tests/test_cli.py::TestEodagCli::test_eodag_download_ok", "tests/test_cli.py::TestEodagCli::test_eodag_download_wrongcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_ok", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ko", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ok", "tests/test_cli.py::TestEodagCli::test_eodag_search_all", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_geom_mutually_exclusive", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_storage_arg", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_conf_file_inexistent", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_max_cloud_out_of_range", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_args", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_producttype_arg", "tests/test_cli.py::TestEodagCli::test_eodag_with_only_verbose_opt", "tests/test_cli.py::TestEodagCli::test_eodag_without_args", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda", "tests/units/test_search_result.py::TestSearchResult::test_search_result_from_feature_collection", "tests/units/test_search_result.py::TestSearchResult::test_search_result_geo_interface", "tests/units/test_search_result.py::TestSearchResult::test_search_result_is_list_like", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[ "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_cruncher", "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_child", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_item", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_not_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stact_reader_fetch_singlefile_catalog" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-249	84aac9405cd346a366e7499d043e1e6959a66f30	2021-04-21 09:39:18	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index bfcb8ac1..46632f7d 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -199,7 +199,7 @@ class Download(PluginTopic): """ extract = kwargs.pop("extract", None) extract = ( - extract if extract is not None else getattr(self.config, "extract", False) + extract if extract is not None else getattr(self.config, "extract", True) ) outputs_extension = kwargs.pop("outputs_extension", ".zip") diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index a3fdf94e..a3b92f90 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -19,8 +19,8 @@ peps: priority: # Lower value means lower priority (Default: 1) search: # Search parameters configuration download: - extract: # whether to extract the downloaded products (true or false). - outputs_prefix: # where to store downloaded products. + extract: # whether to extract the downloaded products, only applies to archived products (true or false, Default: true). + outputs_prefix: # where to store downloaded products, as an absolute file path (Default: local temporary directory) dl_url_params: # additional parameters to pass over to the download url as an url parameter auth: credentials:	Default behaviour for extracting products I've just used `eodag-sentinelsat` and was surprised to see I had to set the `extract` parameter to a value (True or False). Before that, I never set this value and it seems to me that products were always extracted. Currently in providers.yml: * usgs: true * aws_eos: (irrelevant as AWS download) * theia: true * peps: true * sobloo: true * creodias: true * mundi: true * (wekeo): broken now * onda: true * astraea_eod: (irrelevant as AWS download) * usgs_satapi_aws: (irrelevant as AWS download) * earth_search: (irrelevant as AWS download) * earth_search_cog: (irrelevant as COG) The logic in `plugins.download.base.Download._finalize` is to set it to False if `extract` is not provided (either dynamically through kwargs or via configuration): https://github.com/CS-SI/eodag/blob/0632e4147f26119bea43e0672f6d06e75d216a20/eodag/plugins/download/base.py#L151-L161 It is called by `HttpDownload.download` and `UsgsApi.download` (and by `Download._prepare_download` to finalize extracting a product if it couldn't be achieved previously for some reason). The template indicates: ``` extract: # whether to extract the downloaded products (true or false). ``` Should we: * Specify that the default behavior is to automatically extract products? * Update `_finalize` accordingly? * Indicate to plugin developers that they should extract automatically by default? * Update eodag-sentinelsat accordingly?	CS-SI/eodag	diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index 0faf7704..29da9d2c 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -283,29 +283,36 @@ class TestEOProduct(EODagTestCase): downloader = HTTPDownload(provider=self.provider, config=dl_config) product.register_downloader(downloader, None) - # Download - product_file_path = product.download() - - # Check that the mocked request was properly called. - self.requests_http_get.assert_called_with( - self.download_url, stream=True, auth=None, params={} - ) - download_records_dir = pathlib.Path(product_file_path).parent / ".downloaded" - # A .downloaded folder should be created, including a text file that - # lists the downloaded product by their url - self.assertTrue(download_records_dir.is_dir()) - files_in_records_dir = list(download_records_dir.iterdir()) - self.assertEqual(len(files_in_records_dir), 1) - records_file = files_in_records_dir[0] - actual_download_url = records_file.read_text() - self.assertEqual(actual_download_url, self.download_url) - # Check that the downloaded product is a zipfile - self.assertTrue(zipfile.is_zipfile(product_file_path)) - - # Teardown - os.remove(product_file_path) - os.remove(str(records_file)) - os.rmdir(str(download_records_dir)) + try: + # Download + product_dir_path = product.download() + + # Check that the mocked request was properly called. + self.requests_http_get.assert_called_with( + self.download_url, stream=True, auth=None, params={} + ) + download_records_dir = pathlib.Path(product_dir_path).parent / ".downloaded" + # A .downloaded folder should be created, including a text file that + # lists the downloaded product by their url + self.assertTrue(download_records_dir.is_dir()) + files_in_records_dir = list(download_records_dir.iterdir()) + self.assertEqual(len(files_in_records_dir), 1) + records_file = files_in_records_dir[0] + actual_download_url = records_file.read_text() + self.assertEqual(actual_download_url, self.download_url) + # Since extraction is True by default, check that the returned path is the + # product's directory. + self.assertTrue(os.path.isdir(product_dir_path)) + # Check that the ZIP file is still there + product_dir_path = pathlib.Path(product_dir_path) + product_zip = product_dir_path.parent / (product_dir_path.name + ".zip") + self.assertTrue(zipfile.is_zipfile(product_zip)) + finally: + # Teardown + os.remove(product_zip) + shutil.rmtree(product_dir_path) + os.remove(str(records_file)) + os.rmdir(str(download_records_dir)) def test_eoproduct_download_http_extract(self): """eoproduct.download over must be able to extract a product""" @@ -324,24 +331,25 @@ class TestEOProduct(EODagTestCase): downloader = HTTPDownload(provider=self.provider, config=dl_config) product.register_downloader(downloader, None) - # Download - product_dir_path = product.download() - product_dir_path = pathlib.Path(product_dir_path) - # The returned path must be a directory. - self.assertTrue(product_dir_path.is_dir()) - # Check that the extracted dir has at least one file, there are more - # but that should be enough. - self.assertGreaterEqual(len(list(product_dir_path.glob("*/"))), 1) - # The zip file should is around - product_zip_file = product_dir_path.with_suffix(".SAFE.zip") - self.assertTrue(product_zip_file.is_file) - - download_records_dir = pathlib.Path(product_dir_path).parent / ".downloaded" - - # Teardown - shutil.rmtree(str(product_dir_path)) - os.remove(str(product_zip_file)) - shutil.rmtree(str(download_records_dir)) + try: + # Download + product_dir_path = product.download() + product_dir_path = pathlib.Path(product_dir_path) + # The returned path must be a directory. + self.assertTrue(product_dir_path.is_dir()) + # Check that the extracted dir has at least one file, there are more + # but that should be enough. + self.assertGreaterEqual(len(list(product_dir_path.glob("*/"))), 1) + # The zip file should is around + product_zip_file = product_dir_path.with_suffix(".SAFE.zip") + self.assertTrue(product_zip_file.is_file) + + download_records_dir = pathlib.Path(product_dir_path).parent / ".downloaded" + finally: + # Teardown + shutil.rmtree(str(product_dir_path)) + os.remove(str(product_zip_file)) + shutil.rmtree(str(download_records_dir)) def test_eoproduct_download_http_dynamic_options(self): """eoproduct.download must accept the download options to be set automatically""" @@ -358,34 +366,35 @@ class TestEOProduct(EODagTestCase): output_dir_name = "_testeodag" output_dir = pathlib.Path(tempfile.gettempdir()) / output_dir_name - if output_dir.is_dir(): + try: + if output_dir.is_dir(): + shutil.rmtree(str(output_dir)) + output_dir.mkdir() + + # Download + product_dir_path = product.download( + outputs_prefix=str(output_dir), + extract=True, + dl_url_params={"fakeparam": "dummy"}, + ) + # Check that dl_url_params are properly passed to the GET request + self.requests_http_get.assert_called_with( + self.download_url, stream=True, auth=None, params={"fakeparam": "dummy"} + ) + # Check that "outputs_prefix" is respected. + product_dir_path = pathlib.Path(product_dir_path) + self.assertEqual(product_dir_path.parent.name, output_dir_name) + # We've asked to extract the product so there should be a directory. + self.assertTrue(product_dir_path.is_dir()) + # Check that the extracted dir has at least one file, there are more + # but that should be enough. + self.assertGreaterEqual(len(list(product_dir_path.glob("*/"))), 1) + # The downloaded zip file is still around + product_zip_file = product_dir_path.with_suffix(".SAFE.zip") + self.assertTrue(product_zip_file.is_file) + finally: + # Teardown (all the created files are within outputs_prefix) shutil.rmtree(str(output_dir)) - output_dir.mkdir() - - # Download - product_dir_path = product.download( - outputs_prefix=str(output_dir), - extract=True, - dl_url_params={"fakeparam": "dummy"}, - ) - # Check that dl_url_params are properly passed to the GET request - self.requests_http_get.assert_called_with( - self.download_url, stream=True, auth=None, params={"fakeparam": "dummy"} - ) - # Check that "outputs_prefix" is respected. - product_dir_path = pathlib.Path(product_dir_path) - self.assertEqual(product_dir_path.parent.name, output_dir_name) - # We've asked to extract the product so there should be a directory. - self.assertTrue(product_dir_path.is_dir()) - # Check that the extracted dir has at least one file, there are more - # but that should be enough. - self.assertGreaterEqual(len(list(product_dir_path.glob("*/"))), 1) - # The downloaded zip file is still around - product_zip_file = product_dir_path.with_suffix(".SAFE.zip") - self.assertTrue(product_zip_file.is_file) - - # Teardown (all the created files are within outputs_prefix) - shutil.rmtree(str(output_dir)) def _download_response_archive(self): class Response(object):	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 2 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@84aac9405cd346a366e7499d043e1e6959a66f30#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default" ]	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none" ]	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-249
CS-SI__eodag-253	0632e4147f26119bea43e0672f6d06e75d216a20	2021-04-21 15:44:30	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/api/core.py b/eodag/api/core.py index 6efceb2a..556d03c9 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -47,6 +47,7 @@ from eodag.utils import ( _deprecated, get_geometry_from_various, makedirs, + uri_to_path, ) from eodag.utils.exceptions import ( NoMatchingProductType, @@ -434,7 +435,7 @@ class EODataAccessGateway(object): if results is None: results = searcher.search(query, limit=None) else: - results.upgrade_and_extend(searcher.search(query)) + results.upgrade_and_extend(searcher.search(query, limit=None)) guesses = [r["ID"] for r in results or []] if guesses: return guesses @@ -844,6 +845,19 @@ class EODataAccessGateway(object): if product_type is None: try: guesses = self.guess_product_type(kwargs) + + # guess_product_type raises a NoMatchingProductType error if no product + # is found. Here, the supported search params are removed from the + # kwargs if present, not to propagate them to the query itself. + for param in ( + "instrument", + "platform", + "platformSerialIdentifier", + "processingLevel", + "sensorType", + ): + kwargs.pop(param, None) + # By now, only use the best bet product_type = guesses[0] except NoMatchingProductType: @@ -1281,8 +1295,10 @@ class EODataAccessGateway(object): This is an alias to the method of the same name on :class:`~eodag.api.product.EOProduct`, but it performs some additional checks like verifying that a downloader and authenticator are registered - for the product before trying to download it. If the metadata mapping for - `downloadLink` is set to something that can be interpreted as a link on a + for the product before trying to download it. + + If the metadata mapping for `downloadLink` is set to something that can be + interpreted as a link on a local filesystem, the download is skipped (by now, only a link starting with `file://` is supported). Therefore, any user that knows how to extract product location from product metadata on a provider can override the @@ -1316,10 +1332,15 @@ class EODataAccessGateway(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ - if product.location.startswith("file"): + if product.location.startswith("file://"): logger.info("Local product detected. Download skipped") - return product.location + return uri_to_path(product.location) if product.downloader is None: auth = product.downloader_auth if auth is None: diff --git a/eodag/api/product/_product.py b/eodag/api/product/_product.py index a0d98184..a40e74bd 100644 --- a/eodag/api/product/_product.py +++ b/eodag/api/product/_product.py @@ -240,6 +240,11 @@ class EOProduct(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ if progress_callback is None: progress_callback = ProgressCallback() @@ -254,7 +259,7 @@ class EOProduct(object): if self.downloader_auth is not None else self.downloader_auth ) - fs_location = self.downloader.download( + fs_path = self.downloader.download( self, auth=auth, progress_callback=progress_callback, @@ -262,13 +267,8 @@ class EOProduct(object): timeout=timeout, kwargs ) - if fs_location is None: - raise DownloadError( - "Invalid file location returned by download process: '{}'".format( - fs_location - ) - ) - self.location = "file://{}".format(fs_location) + if fs_path is None: + raise DownloadError("Missing file location returned by download process") logger.debug( "Product location updated from '%s' to '%s'", self.remote_location, @@ -279,7 +279,7 @@ class EOProduct(object): "'remote_location' property: %s", self.remote_location, ) - return self.location + return fs_path def get_quicklook(self, filename=None, base_dir=None, progress_callback=None): """Download the quick look image of a given EOProduct from its provider if it diff --git a/eodag/plugins/apis/base.py b/eodag/plugins/apis/base.py index 6a775973..b6d385ef 100644 --- a/eodag/plugins/apis/base.py +++ b/eodag/plugins/apis/base.py @@ -18,12 +18,40 @@ import logging from eodag.plugins.base import PluginTopic +from eodag.plugins.download.base import DEFAULT_DOWNLOAD_TIMEOUT, DEFAULT_DOWNLOAD_WAIT logger = logging.getLogger("eodag.plugins.apis.base") class Api(PluginTopic): - """Plugins API Base plugin""" + """Plugins API Base plugin + + An Api plugin has three download methods that it must implement: + - ``query``: search for products + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + The download methods must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + """ def query(self, args, count=True, kwargs): """Implementation of how the products must be searched goes here. @@ -32,14 +60,77 @@ class Api(PluginTopic): which will be processed by a Download plugin (2) and the total number of products matching the search criteria. If ``count`` is False, the second element returned must be ``None``. - .. versionchanged:: - 2.1 + .. versionchanged:: 2.1 - A new optional boolean parameter ``count`` which defaults to ``True``, it - allows to trigger or not a count query. + A new optional boolean parameter ``count`` which defaults to ``True``, it + allows to trigger or not a count query. """ raise NotImplementedError("A Api plugin must implement a method named query") - def download(self, args, kwargs): - """Implementation of how the products must be downloaded.""" - raise NotImplementedError("A Api plugin must implement a method named download") + def download( + self, + product, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str + """ + raise NotImplementedError( + "An Api plugin must implement a method named download" + ) + + def download_all( + self, + products, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + """ + Base download_all method. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list + """ + raise NotImplementedError( + "A Api plugin must implement a method named download_all" + ) diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 37ce4443..768778e5 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -37,7 +37,12 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import GENERIC_PRODUCT_TYPE, format_dict_items, get_progress_callback +from eodag.utils import ( + GENERIC_PRODUCT_TYPE, + format_dict_items, + get_progress_callback, + path_to_uri, +) from eodag.utils.exceptions import AuthenticationError, NotAvailableError logger = logging.getLogger("eodag.plugins.apis.usgs") @@ -164,6 +169,8 @@ class UsgsApi(Api, Download): product, outputs_extension=".tar.gz", kwargs ) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -264,8 +271,11 @@ class UsgsApi(Api, Download): ) new_fs_path = fs_path[: fs_path.index(".tar.gz")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product_path = self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product.location = path_to_uri(product_path) + return product_path def download_all( self, diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 2f0b132d..687985c6 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -34,7 +34,7 @@ from eodag.api.product.metadata_mapping import ( properties_from_xml, ) from eodag.plugins.download.base import Download -from eodag.utils import get_progress_callback, urlparse +from eodag.utils import get_progress_callback, path_to_uri, urlparse from eodag.utils.exceptions import AuthenticationError, DownloadError logger = logging.getLogger("eodag.plugins.download.aws") @@ -185,6 +185,8 @@ class AwsDownload(Download): # prepare download & create dirs (before updating metadata) product_local_path, record_filename = self._prepare_download(product, kwargs) if not product_local_path or not record_filename: + if product_local_path: + product.location = path_to_uri(product_local_path) return product_local_path product_local_path = product_local_path.replace(".zip", "") # remove existing incomplete file @@ -395,6 +397,7 @@ class AwsDownload(Download): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path def get_authenticated_objects(self, bucket_name, prefix, auth_dict): diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index 8d6dbd3e..bfcb8ac1 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -25,7 +25,7 @@ from datetime import datetime, timedelta from time import sleep from eodag.plugins.base import PluginTopic -from eodag.utils import get_progress_callback, sanitize +from eodag.utils import get_progress_callback, sanitize, uri_to_path from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -43,6 +43,31 @@ DEFAULT_DOWNLOAD_TIMEOUT = 20 class Download(PluginTopic): """Base Download Plugin. + A Download plugin has two download methods that it must implement: + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + They must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record* file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + :param provider: An eodag providers configuration dictionary :type provider: dict :param config: Path to the user configuration file @@ -62,8 +87,26 @@ class Download(PluginTopic): timeout=DEFAULT_DOWNLOAD_TIMEOUT, kwargs, ): - """ - Base download method. Not available, should be defined for each plugin + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str """ raise NotImplementedError( "A Download plugin must implement a method named download" @@ -78,8 +121,7 @@ class Download(PluginTopic): :rtype: tuple """ if product.location != product.remote_location: - scheme_prefix_len = len("file://") - fs_path = product.location[scheme_prefix_len:] + fs_path = uri_to_path(product.location) # The fs path of a product is either a file (if 'extract' config is False) or a directory if os.path.isfile(fs_path) or os.path.isdir(fs_path): logger.info( @@ -249,9 +291,32 @@ class Download(PluginTopic): kwargs, ): """ - A sequential download_all implementation - using download method for every products + Base download_all method. + + This specific implementation uses the ``download`` method implemented by + the plugin to sequentially attempt to download products. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list """ + # Products are going to be removed one by one from this sequence once + # downloaded. + products = products[:] paths = [] # initiate retry loop start_time = datetime.now() diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 11662a18..f54fff86 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -32,7 +32,7 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import get_progress_callback +from eodag.utils import get_progress_callback, path_to_uri from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -70,6 +70,8 @@ class HTTPDownload(Download): """ fs_path, record_filename = self._prepare_download(product, kwargs) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -80,7 +82,7 @@ class HTTPDownload(Download): # download assets if exist instead of remote_location try: - return self._download_assets( + fs_path = self._download_assets( product, fs_path.replace(".zip", ""), record_filename, @@ -88,6 +90,8 @@ class HTTPDownload(Download): progress_callback, kwargs ) + product.location = path_to_uri(fs_path) + return fs_path except NotAvailableError: pass @@ -218,8 +222,11 @@ class HTTPDownload(Download): ) new_fs_path = fs_path[: fs_path.index(".zip")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, kwargs) + product_path = self._finalize(fs_path, kwargs) + product.location = path_to_uri(product_path) + return product_path except NotAvailableError as e: if not getattr(self.config, "order_enabled", False): diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index 71876ea4..2f5628b9 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -29,7 +29,7 @@ from requests import HTTPError from eodag.api.product.metadata_mapping import OFFLINE_STATUS from eodag.plugins.download.aws import AwsDownload from eodag.plugins.download.http import HTTPDownload -from eodag.utils import get_progress_callback, urljoin +from eodag.utils import get_progress_callback, path_to_uri, urljoin from eodag.utils.exceptions import ( AuthenticationError, DownloadError, @@ -158,6 +158,7 @@ class S3RestDownload(AwsDownload): url_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() record_filename = os.path.join(download_records_dir, url_hash) if os.path.isfile(record_filename) and os.path.exists(product_local_path): + product.location = path_to_uri(product_local_path) return product_local_path # Remove the record file if product_local_path is absent (e.g. it was deleted while record wasn't) elif os.path.isfile(record_filename): @@ -215,4 +216,5 @@ class S3RestDownload(AwsDownload): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index f48e3eb0..2c6f7e9d 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -463,7 +463,7 @@ def download_stac_item_by_id(catalogs, item_id, provider=None): product_path = eodag_api.download(product) - return product_path.replace("file://", "") + return product_path def get_stac_catalogs(url, root="/", catalogs=[], provider=None): diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 70798110..0c21649b 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -35,6 +35,7 @@ import warnings from collections import defaultdict from datetime import datetime, timezone from itertools import repeat, starmap +from pathlib import Path # All modules using these should import them from utils package from urllib.parse import ( # noqa; noqa @@ -43,8 +44,10 @@ from urllib.parse import ( # noqa; noqa urlencode, urljoin, urlparse, + urlsplit, urlunparse, ) +from urllib.request import url2pathname import click import shapefile @@ -203,6 +206,24 @@ def strip_accents(s): ) +def uri_to_path(uri): + """ + Convert a file URI (e.g. 'file:///tmp') to a local path (e.g. '/tmp') + """ + if not uri.startswith("file"): + raise ValueError("A file URI must be provided (e.g. 'file:///tmp'") + _, _, path, _, _ = urlsplit(uri) + # On Windows urlsplit returns the path starting with a slash ('/C:/User) + path = url2pathname(path) + # url2pathname removes it + return path + + +def path_to_uri(path): + """Convert a local absolute path to a file URI""" + return Path(path).as_uri() + + def mutate_dict_in_place(func, mapping): """Apply func to values of mapping. @@ -853,13 +874,13 @@ def _deprecated(reason="", version=None): @functools.wraps(callable) def wrapper(args, kwargs): - warnings.simplefilter("always", DeprecationWarning) - warnings.warn( - f"Call to deprecated {ctype} {cname} {reason_}{version_}", - category=DeprecationWarning, - stacklevel=2, - ) - warnings.simplefilter("default", DeprecationWarning) + with warnings.catch_warnings(): + warnings.simplefilter("always", DeprecationWarning) + warnings.warn( + f"Call to deprecated {ctype} {cname} {reason_}{version_}", + category=DeprecationWarning, + stacklevel=2, + ) return callable(args, **kwargs) return wrapper	download_all removes the products contained by the passed SearchResult The items of a `SearchResult` (it could also just be a list of `EOProduct`) passed to `download_all` can get cleared by the method. I observed this behaviour with downloading products from peps. If it is the intended behaviour, it should be well documented because it is quite surprising. If not, the problem comes certainly from this line (PEPS uses a HTTPPlugin which itself uses the base implementation of `search_all`): https://github.com/CS-SI/eodag/blob/fec965f791a74dbf658e0833e1507b7ac950d09d/eodag/plugins/download/base.py#L308 Run this snippet to reproduce the bug: ```python from eodag import EODataAccessGateway from eodag.api.search_result import SearchResult dag = EODataAccessGateway('path/to/peps_config.yml') # Adapt the path dag.set_preferred_provider("peps") search_criteria = dict( productType='S2_MSI_L1C', start='2021-03-01', end='2021-03-31', geom={"lonmin": 1, "latmin": 42, "lonmax": 5, "latmax": 46}, ) search_results = dag.search_all(**search_criteria) print(f'Got {len(search_results)} products.') # Download the smallest files sorted_prods_per_size = SearchResult( sorted(search_results, key=lambda p: p.properties["resourceSize"]) ) prods_to_download = sorted_prods_per_size[:2] # Just to make sure not to order products assert all([p.properties["storageStatus"] == "ONLINE" for p in prods_to_download]) paths = dag.download_all(prods_to_download) if not prods_to_download: print("Oh oh I'm empty now") ``` If this is a bug, we should check the behaviour of the other download plugins.	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index 96807bc8..df74276a 100644 --- a/tests/context.py +++ b/tests/context.py @@ -44,7 +44,13 @@ from eodag.plugins.manager import PluginManager from eodag.plugins.search.base import Search from eodag.rest import server as eodag_http_server from eodag.rest.utils import eodag_api, get_date -from eodag.utils import get_geometry_from_various, get_timestamp, makedirs +from eodag.utils import ( + get_geometry_from_various, + get_timestamp, + makedirs, + path_to_uri, + uri_to_path, +) from eodag.utils.exceptions import ( AddressNotFound, AuthenticationError, diff --git a/tests/test_cli.py b/tests/test_cli.py index 94e07cc3..a4171dbb 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -427,7 +427,7 @@ class TestEodagCli(unittest.TestCase): TEST_RESOURCES_PATH, "eodag_search_result.geojson" ) config_path = os.path.join(TEST_RESOURCES_PATH, "file_config_override.yml") - dag.return_value.download_all.return_value = ["file:///fake_path"] + dag.return_value.download_all.return_value = ["/fake_path"] result = self.runner.invoke( eodag, ["download", "--search-results", search_results_path, "-f", config_path], @@ -435,7 +435,7 @@ class TestEodagCli(unittest.TestCase): dag.assert_called_once_with(user_conf_file_path=config_path) dag.return_value.deserialize.assert_called_once_with(search_results_path) self.assertEqual(dag.return_value.download_all.call_count, 1) - self.assertEqual("Downloaded file:///fake_path\n", result.output) + self.assertEqual("Downloaded /fake_path\n", result.output) # Testing the case when no downloaded path is returned dag.return_value.download_all.return_value = [None] diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index be2dee90..ca0a145c 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -18,15 +18,22 @@ import datetime import glob +import hashlib import multiprocessing import os import shutil +import time import unittest from pathlib import Path from eodag.api.product.metadata_mapping import ONLINE_STATUS from tests import TEST_RESOURCES_PATH, TESTS_DOWNLOAD_PATH -from tests.context import AuthenticationError, EODataAccessGateway +from tests.context import ( + AuthenticationError, + EODataAccessGateway, + SearchResult, + uri_to_path, +) THEIA_SEARCH_ARGS = [ "theia", @@ -148,8 +155,8 @@ class EndToEndBase(unittest.TestCase): """ search_criteria = { "productType": product_type, - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } if items_per_page: @@ -190,8 +197,8 @@ class EndToEndBase(unittest.TestCase): - Return all the products """ search_criteria = { - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } self.eodag.set_preferred_provider(provider) @@ -428,6 +435,196 @@ class TestEODagEndToEnd(EndToEndBase): self.assertGreater(len(results), 10) +class TestEODagEndToEndComplete(unittest.TestCase): + """Make real and complete test cases that search for products, download them and + extract them. There should be just a tiny number of these tests which can be quite + long to run. + + There must be a user conf file in the test resources folder named user_conf.yml + """ + + @classmethod + def setUpClass(cls): + + # use tests/resources/user_conf.yml if exists else default file ~/.config/eodag/eodag.yml + tests_user_conf = os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + if not os.path.isfile(tests_user_conf): + unittest.SkipTest("Missing user conf file with credentials") + cls.eodag = EODataAccessGateway( + user_conf_file_path=os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + ) + + # create TESTS_DOWNLOAD_PATH is not exists + if not os.path.exists(TESTS_DOWNLOAD_PATH): + os.makedirs(TESTS_DOWNLOAD_PATH) + + for provider, conf in cls.eodag.providers_config.items(): + # Change download directory to TESTS_DOWNLOAD_PATH for tests + if hasattr(conf, "download") and hasattr(conf.download, "outputs_prefix"): + conf.download.outputs_prefix = TESTS_DOWNLOAD_PATH + elif hasattr(conf, "api") and hasattr(conf.api, "outputs_prefix"): + conf.api.outputs_prefix = TESTS_DOWNLOAD_PATH + else: + # no outputs_prefix found for provider + pass + # Force all providers implementing RestoSearch and defining how to retrieve + # products by specifying the + # location scheme to use https, enabling actual downloading of the product + if ( + getattr(getattr(conf, "search", {}), "product_location_scheme", "https") + == "file" + ): + conf.search.product_location_scheme = "https" + + def tearDown(self): + """Clear the test directory""" + for p in Path(TESTS_DOWNLOAD_PATH).glob("*/"): + try: + os.remove(p) + except OSError: + shutil.rmtree(p) + + def test_end_to_end_complete_peps(self): + """Complete end-to-end test with PEPS for download and download_all""" + # Search for products that are ONLINE and as small as possible + today = datetime.date.today() + month_span = datetime.timedelta(weeks=4) + search_results, _ = self.eodag.search( + productType="S2_MSI_L1C", + start=(today - month_span).isoformat(), + end=today.isoformat(), + geom={"lonmin": 1, "latmin": 42, "lonmax": 5, "latmax": 46}, + items_per_page=100, + ) + prods_sorted_by_size = SearchResult( + sorted(search_results, key=lambda p: p.properties["resourceSize"]) + ) + prods_online = [ + p for p in prods_sorted_by_size if p.properties["storageStatus"] == "ONLINE" + ] + if len(prods_online) < 2: + unittest.skip( + "Not enough ONLINE products found, update the search criteria." + ) + + # Retrieve one product to work with + product = prods_online[0] + + prev_remote_location = product.remote_location + prev_location = product.location + # The expected product's archive filename is based on the product's title + expected_product_name = f"{product.properties['title']}.zip" + + # Download the product, but DON'T extract it + archive_file_path = self.eodag.download(product, extract=False) + + # The archive must have been downloaded + self.assertTrue(os.path.isfile(archive_file_path)) + # Its name must be the "{product_title}.zip" + self.assertIn( + expected_product_name, os.listdir(product.downloader.config.outputs_prefix) + ) + # Its size should be >= 5 KB + archive_size = os.stat(archive_file_path).st_size + self.assertGreaterEqual(archive_size, 5 * 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # That points to the downloaded archive + self.assertEqual(uri_to_path(product.location), archive_file_path) + # A .downloaded folder must have been created + record_dir = os.path.join(TESTS_DOWNLOAD_PATH, ".downloaded") + self.assertTrue(os.path.isdir(record_dir)) + # It must contain a file per product downloade, whose name is + # the MD5 hash of the product's remote location + expected_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() + record_file = os.path.join(record_dir, expected_hash) + self.assertTrue(os.path.isfile(record_file)) + # Its content must be the product's remote location + record_content = Path(record_file).read_text() + self.assertEqual(record_content, product.remote_location) + + # The product should not be downloaded again if the download method + # is executed again + previous_archive_file_path = archive_file_path + previous_location = product.location + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The paths should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(product.location, previous_location) + + # If we emulate that the product has just been found, it should not + # be downloaded again since the record file is still present. + product.location = product.remote_location + # Pretty much the same checks as with the previous step + previous_archive_file_path = archive_file_path + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The returned path should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(uri_to_path(product.location), archive_file_path) + + # Remove the archive + os.remove(archive_file_path) + + # Now, the archive is removed but its associated record file + # still exists. Downloading the product again should really + # download it, if its location points to the remote location. + # The product should be automatically extracted. + product.location = product.remote_location + product_dir_path = self.eodag.download(product) + + # Its size should be >= 5 KB + downloaded_size = sum( + f.stat().st_size for f in Path(product_dir_path).glob("/") if f.is_file() + ) + self.assertGreaterEqual(downloaded_size, 5 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # The location must point to a SAFE directory + self.assertTrue(product.location.endswith("SAFE")) + # The path must point to a SAFE directory + self.assertTrue(os.path.isdir(product_dir_path)) + self.assertTrue(product_dir_path.endswith("SAFE")) + + # Remove the archive and extracted product and reset the product's location + os.remove(archive_file_path) + shutil.rmtree(Path(product_dir_path).parent) + product.location = product.remote_location + + # Now let's check download_all + products = prods_sorted_by_size[:2] + # Pass a copy because download_all empties the list + archive_paths = self.eodag.download_all(products[:], extract=False) + + # The returned paths must point to the downloaded archives + # Each product's location must be a URI path to the archive + for product, archive_path in zip(products, archive_paths): + self.assertTrue(os.path.isfile(archive_path)) + self.assertEqual(uri_to_path(product.location), archive_path) + + # Downloading the product again should not download them, since + # they are all already there. + prev_archive_paths = archive_paths + start_time = time.time() + archive_paths = self.eodag.download_all(products[:], extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + self.assertEqual(archive_paths, prev_archive_paths) + + # @unittest.skip("skip auto run") class TestEODagEndToEndWrongCredentials(EndToEndBase): """Make real case tests with wrong credentials. This assumes the existence of a @@ -462,7 +659,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) def test_end_to_end_good_apikey_wrong_credentials_aws_eos(self): @@ -489,7 +686,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) self.assertGreater(len(results), 0) one_product = results[0] @@ -525,5 +722,5 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], USGS_SEARCH_ARGS[1:]) - ) + ), ) diff --git a/tests/units/test_core.py b/tests/units/test_core.py index cb6022e4..69cf9a3e 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -367,7 +367,7 @@ class TestCoreGeometry(unittest.TestCase): "lonmax": 2, "latmax": 52, } - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Bad dict with a missing key del geometry["lonmin"] self.assertRaises( @@ -378,10 +378,10 @@ class TestCoreGeometry(unittest.TestCase): ) # Tuple geometry = (0, 50, 2, 52) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List geometry = list(geometry) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List without 4 items geometry.pop() self.assertRaises( @@ -392,7 +392,7 @@ class TestCoreGeometry(unittest.TestCase): ) # WKT geometry = ref_geom_as_wkt - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Some other shapely geom geometry = LineString([[0, 0], [1, 1]]) self.assertIsInstance( @@ -409,7 +409,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="FRA") ) self.assertIsInstance(geom_france, MultiPolygon) - self.assertEquals(len(geom_france), 3) # France + Guyana + Corsica + self.assertEqual(len(geom_france), 3) # France + Guyana + Corsica def test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs( self, @@ -444,7 +444,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="PA[A-Z]") ) self.assertIsInstance(geom_regex_pa, MultiPolygon) - self.assertEquals(len(geom_regex_pa), 2) + self.assertEqual(len(geom_regex_pa), 2) def test_get_geometry_from_various_locations_no_match_raises_error(self): """If the location search doesn't match any of the feature attribute a ValueError must be raised""" @@ -468,7 +468,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # France + Guyana + Corsica + somewhere over Poland - self.assertEquals(len(geom_combined), 4) + self.assertEqual(len(geom_combined), 4) geometry = { "lonmin": 0, "latmin": 50, @@ -480,7 +480,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # The bounding box overlaps with France inland - self.assertEquals(len(geom_combined), 3) + self.assertEqual(len(geom_combined), 3) class TestCoreSearch(unittest.TestCase): @@ -546,7 +546,6 @@ class TestCoreSearch(unittest.TestCase): "geometry": None, "productType": None, } - self.assertDictContainsSubset(expected, prepared_search) expected = set(["geometry", "productType", "auth", "search_plugin"]) self.assertSetEqual(expected, set(prepared_search)) @@ -557,11 +556,10 @@ class TestCoreSearch(unittest.TestCase): "end": "2020-02-01", } prepared_search = self.dag._prepare_search(base) - expected = { - "startTimeFromAscendingNode": base["start"], - "completionTimeFromAscendingNode": base["end"], - } - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["startTimeFromAscendingNode"], base["start"]) + self.assertEqual( + prepared_search["completionTimeFromAscendingNode"], base["end"] + ) def test__prepare_search_geom(self): """_prepare_search must handle geom, box and bbox""" @@ -608,8 +606,7 @@ class TestCoreSearch(unittest.TestCase): """_prepare_search must handle when a product type is given""" base = {"productType": "S2_MSI_L1C"} prepared_search = self.dag._prepare_search(base) - expected = {"productType": base["productType"]} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) def test__prepare_search_product_type_guess_it(self): """_prepare_search must guess a product type when required to""" @@ -621,8 +618,19 @@ class TestCoreSearch(unittest.TestCase): platformSerialIdentifier="S2A", ) prepared_search = self.dag._prepare_search(base) - expected = {"productType": "S2_MSI_L1C", base} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], "S2_MSI_L1C") + + def test__prepare_search_remove_guess_kwargs(self): + """_prepare_search must remove the guess kwargs""" + # Uses guess_product_type to find the product matching + # the best the given params. + base = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + prepared_search = self.dag._prepare_search(base) + self.assertEqual(len(base.keys() & prepared_search.keys()), 0) def test__prepare_search_with_id(self): """_prepare_search must handle a search by id""" @@ -638,8 +646,8 @@ class TestCoreSearch(unittest.TestCase): "cloudCover": 10, } prepared_search = self.dag._prepare_search(base) - expected = base - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) + self.assertEqual(prepared_search["cloudCover"], base["cloudCover"]) def test__prepare_search_search_plugin_has_known_product_properties(self): """_prepare_search must attach the product properties to the search plugin""" diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index f43aaa84..0faf7704 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -90,23 +90,18 @@ class TestEOProduct(EODagTestCase): self.provider, self.eoproduct_props, productType=self.product_type ) geo_interface = geojson.loads(geojson.dumps(product)) - self.assertDictContainsSubset( - { - "type": "Feature", - "geometry": self._tuples_to_lists(geometry.mapping(self.geometry)), - }, - geo_interface, + self.assertEqual(geo_interface["type"], "Feature") + self.assertEqual( + geo_interface["geometry"], + self._tuples_to_lists(geometry.mapping(self.geometry)), ) - self.assertDictContainsSubset( - { - "eodag_provider": self.provider, - "eodag_search_intersection": self._tuples_to_lists( - geometry.mapping(product.search_intersection) - ), - "eodag_product_type": self.product_type, - }, - geo_interface["properties"], + properties = geo_interface["properties"] + self.assertEqual(properties["eodag_provider"], self.provider) + self.assertEqual( + properties["eodag_search_intersection"], + self._tuples_to_lists(geometry.mapping(product.search_intersection)), ) + self.assertEqual(properties["eodag_product_type"], self.product_type) def test_eoproduct_from_geointerface(self): """EOProduct must be build-able from its geo-interface""" @@ -295,7 +290,6 @@ class TestEOProduct(EODagTestCase): self.requests_http_get.assert_called_with( self.download_url, stream=True, auth=None, params={} ) - product_file_path = product_file_path[len("file://") :] download_records_dir = pathlib.Path(product_file_path).parent / ".downloaded" # A .downloaded folder should be created, including a text file that # lists the downloaded product by their url @@ -332,7 +326,7 @@ class TestEOProduct(EODagTestCase): # Download product_dir_path = product.download() - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) # The returned path must be a directory. self.assertTrue(product_dir_path.is_dir()) # Check that the extracted dir has at least one file, there are more @@ -379,7 +373,7 @@ class TestEOProduct(EODagTestCase): self.download_url, stream=True, auth=None, params={"fakeparam": "dummy"} ) # Check that "outputs_prefix" is respected. - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) self.assertEqual(product_dir_path.parent.name, output_dir_name) # We've asked to extract the product so there should be a directory. self.assertTrue(product_dir_path.is_dir()) diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 28369fee..09dd221d 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -106,8 +106,8 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): info_message=mock.ANY, exception_message=mock.ANY, ) - self.assertEquals(estimate, self.sobloo_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.sobloo_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -144,7 +144,7 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -196,8 +196,8 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.awseos_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.awseos_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -232,7 +232,7 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -294,6 +294,6 @@ class TestSearchPluginODataV4Search(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.onda_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.onda_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) diff --git a/tests/units/test_search_result.py b/tests/units/test_search_result.py index f1697117..d4e52303 100644 --- a/tests/units/test_search_result.py +++ b/tests/units/test_search_result.py @@ -32,9 +32,8 @@ class TestSearchResult(unittest.TestCase): def test_search_result_geo_interface(self): """SearchResult must provide a FeatureCollection geo-interface""" geo_interface = geojson.loads(geojson.dumps(self.search_result)) - self.assertDictContainsSubset( - {"type": "FeatureCollection", "features": []}, geo_interface - ) + self.assertEqual(geo_interface["type"], "FeatureCollection") + self.assertEqual(geo_interface["features"], []) def test_search_result_is_list_like(self): """SearchResult must provide a list interface""" diff --git a/tests/units/test_stac_reader.py b/tests/units/test_stac_reader.py index 8960ee81..f242fb38 100644 --- a/tests/units/test_stac_reader.py +++ b/tests/units/test_stac_reader.py @@ -47,10 +47,8 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.child_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.child_cat_len) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - items[0], - ) + self.assertEqual(items[0]["type"], "Feature") + self.assertEqual(items[0]["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_root_not_recursive(self): """fetch_stac_items from root must provide an empty list when no recursive""" @@ -62,23 +60,20 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.root_cat, recursive=True) self.assertEqual(len(items), self.root_cat_len) for item in items: - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, item - ) + self.assertEqual(item["type"], "Feature") + self.assertEqual(item["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_item(self): """fetch_stac_items from an item must return it""" item = fetch_stac_items(self.item) self.assertIsInstance(item, list) self.assertEqual(len(item), 1) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - item[0], - ) + self.assertEqual(item[0]["type"], "Feature") + self.assertEqual(item[0]["collection"], "S2_MSI_L1C") def test_stact_reader_fetch_singlefile_catalog(self): """fetch_stact_items must return all the items from a single file catalog""" items = fetch_stac_items(self.singlefile_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.singlefile_cat_len) - self.assertDictContainsSubset({"type": "Feature"}, items[0]) + self.assertEqual(items[0]["type"], "Feature") diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 58732712..ef865dff 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -16,10 +16,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import sys import unittest from datetime import datetime -from tests.context import get_timestamp +from tests.context import get_timestamp, path_to_uri, uri_to_path class TestUtils(unittest.TestCase): @@ -33,3 +34,21 @@ class TestUtils(unittest.TestCase): expected_dt = datetime.strptime(requested_date, date_format) actual_utc_dt = datetime.utcfromtimestamp(ts_in_secs) self.assertEqual(actual_utc_dt, expected_dt) + + def test_uri_to_path(self): + if sys.platform == "win32": + expected_path = r"C:\tmp\file.txt" + tested_uri = r"file:///C:/tmp/file.txt" + else: + expected_path = "/tmp/file.txt" + tested_uri = "file:///tmp/file.txt" + actual_path = uri_to_path(tested_uri) + self.assertEqual(actual_path, expected_path) + with self.assertRaises(ValueError): + uri_to_path("not_a_uri") + + def test_path_to_uri(self): + if sys.platform == "win32": + self.assertEqual(path_to_uri(r"C:\tmp\file.txt"), "file:///C:/tmp/file.txt") + else: + self.assertEqual(path_to_uri("/tmp/file.txt"), "file:///tmp/file.txt")	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 10 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@0632e4147f26119bea43e0672f6d06e75d216a20#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_cli.py::TestCore::test_core_object_locations_file_not_found", "tests/test_cli.py::TestCore::test_core_object_open_index_if_exists", "tests/test_cli.py::TestCore::test_core_object_set_default_locations_config", "tests/test_cli.py::TestCore::test_list_product_types_for_provider_ok", "tests/test_cli.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/test_cli.py::TestCore::test_list_product_types_ok", "tests/test_cli.py::TestCore::test_rebuild_index", "tests/test_cli.py::TestCore::test_supported_product_types_in_unit_test", "tests/test_cli.py::TestCore::test_supported_providers_in_unit_test", "tests/test_cli.py::TestEodagCli::test_eodag_download_missingcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_download_no_search_results_arg", "tests/test_cli.py::TestEodagCli::test_eodag_download_ok", "tests/test_cli.py::TestEodagCli::test_eodag_download_wrongcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_ok", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ko", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ok", "tests/test_cli.py::TestEodagCli::test_eodag_search_all", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_geom_mutually_exclusive", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_storage_arg", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_conf_file_inexistent", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_max_cloud_out_of_range", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_args", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_producttype_arg", "tests/test_cli.py::TestEodagCli::test_eodag_with_only_verbose_opt", "tests/test_cli.py::TestEodagCli::test_eodag_without_args", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda", "tests/units/test_search_result.py::TestSearchResult::test_search_result_from_feature_collection", "tests/units/test_search_result.py::TestSearchResult::test_search_result_geo_interface", "tests/units/test_search_result.py::TestSearchResult::test_search_result_is_list_like", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[ "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_cruncher", "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_child", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_item", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_not_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stact_reader_fetch_singlefile_catalog" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-254	0632e4147f26119bea43e0672f6d06e75d216a20	2021-04-21 15:53:45	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/api/core.py b/eodag/api/core.py index 6efceb2a..556d03c9 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -47,6 +47,7 @@ from eodag.utils import ( _deprecated, get_geometry_from_various, makedirs, + uri_to_path, ) from eodag.utils.exceptions import ( NoMatchingProductType, @@ -434,7 +435,7 @@ class EODataAccessGateway(object): if results is None: results = searcher.search(query, limit=None) else: - results.upgrade_and_extend(searcher.search(query)) + results.upgrade_and_extend(searcher.search(query, limit=None)) guesses = [r["ID"] for r in results or []] if guesses: return guesses @@ -844,6 +845,19 @@ class EODataAccessGateway(object): if product_type is None: try: guesses = self.guess_product_type(kwargs) + + # guess_product_type raises a NoMatchingProductType error if no product + # is found. Here, the supported search params are removed from the + # kwargs if present, not to propagate them to the query itself. + for param in ( + "instrument", + "platform", + "platformSerialIdentifier", + "processingLevel", + "sensorType", + ): + kwargs.pop(param, None) + # By now, only use the best bet product_type = guesses[0] except NoMatchingProductType: @@ -1281,8 +1295,10 @@ class EODataAccessGateway(object): This is an alias to the method of the same name on :class:`~eodag.api.product.EOProduct`, but it performs some additional checks like verifying that a downloader and authenticator are registered - for the product before trying to download it. If the metadata mapping for - `downloadLink` is set to something that can be interpreted as a link on a + for the product before trying to download it. + + If the metadata mapping for `downloadLink` is set to something that can be + interpreted as a link on a local filesystem, the download is skipped (by now, only a link starting with `file://` is supported). Therefore, any user that knows how to extract product location from product metadata on a provider can override the @@ -1316,10 +1332,15 @@ class EODataAccessGateway(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ - if product.location.startswith("file"): + if product.location.startswith("file://"): logger.info("Local product detected. Download skipped") - return product.location + return uri_to_path(product.location) if product.downloader is None: auth = product.downloader_auth if auth is None: diff --git a/eodag/api/product/_product.py b/eodag/api/product/_product.py index a0d98184..8deaeb61 100644 --- a/eodag/api/product/_product.py +++ b/eodag/api/product/_product.py @@ -240,6 +240,11 @@ class EOProduct(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ if progress_callback is None: progress_callback = ProgressCallback() @@ -254,7 +259,7 @@ class EOProduct(object): if self.downloader_auth is not None else self.downloader_auth ) - fs_location = self.downloader.download( + fs_path = self.downloader.download( self, auth=auth, progress_callback=progress_callback, @@ -262,13 +267,8 @@ class EOProduct(object): timeout=timeout, kwargs ) - if fs_location is None: - raise DownloadError( - "Invalid file location returned by download process: '{}'".format( - fs_location - ) - ) - self.location = "file://{}".format(fs_location) + if fs_path is None: + raise DownloadError("Missing file location returned by download process") logger.debug( "Product location updated from '%s' to '%s'", self.remote_location, @@ -279,24 +279,23 @@ class EOProduct(object): "'remote_location' property: %s", self.remote_location, ) - return self.location + return fs_path def get_quicklook(self, filename=None, base_dir=None, progress_callback=None): - """Download the quick look image of a given EOProduct from its provider if it + """Download the quicklook image of a given EOProduct from its provider if it exists. - :param filename: (optional) the name to give to the downloaded quicklook. + :param filename: (optional) the name to give to the downloaded quicklook. If not + given, it defaults to the product's ID (without file extension). :type filename: str :param base_dir: (optional) the absolute path of the directory where to store - the quicklooks in the filesystem. If it is not given, it - defaults to the `quicklooks` directory under this EO product - downloader's ``outputs_prefix`` config param + the quicklooks in the filesystem. If not given, it defaults to the + `quicklooks` directory under this EO product downloader's ``outputs_prefix`` + config param (e.g. '/tmp/quicklooks/') :type base_dir: str - :param progress_callback: (optional) A method or a callable object - which takes a current size and a maximum - size as inputs and handle progress bar - creation and update to give the user a - feedback on the download progress + :param progress_callback: (optional) A method or a callable object which takes + a current size and a maximum size as inputs and handle progress bar creation + and update to give the user a feedback on the download progress :type progress_callback: :class:`~eodag.utils.ProgressCallback` or None :returns: The absolute path of the downloaded quicklook :rtype: str diff --git a/eodag/plugins/apis/base.py b/eodag/plugins/apis/base.py index 6a775973..b6d385ef 100644 --- a/eodag/plugins/apis/base.py +++ b/eodag/plugins/apis/base.py @@ -18,12 +18,40 @@ import logging from eodag.plugins.base import PluginTopic +from eodag.plugins.download.base import DEFAULT_DOWNLOAD_TIMEOUT, DEFAULT_DOWNLOAD_WAIT logger = logging.getLogger("eodag.plugins.apis.base") class Api(PluginTopic): - """Plugins API Base plugin""" + """Plugins API Base plugin + + An Api plugin has three download methods that it must implement: + - ``query``: search for products + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + The download methods must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + """ def query(self, args, count=True, kwargs): """Implementation of how the products must be searched goes here. @@ -32,14 +60,77 @@ class Api(PluginTopic): which will be processed by a Download plugin (2) and the total number of products matching the search criteria. If ``count`` is False, the second element returned must be ``None``. - .. versionchanged:: - 2.1 + .. versionchanged:: 2.1 - A new optional boolean parameter ``count`` which defaults to ``True``, it - allows to trigger or not a count query. + A new optional boolean parameter ``count`` which defaults to ``True``, it + allows to trigger or not a count query. """ raise NotImplementedError("A Api plugin must implement a method named query") - def download(self, args, kwargs): - """Implementation of how the products must be downloaded.""" - raise NotImplementedError("A Api plugin must implement a method named download") + def download( + self, + product, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str + """ + raise NotImplementedError( + "An Api plugin must implement a method named download" + ) + + def download_all( + self, + products, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + """ + Base download_all method. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list + """ + raise NotImplementedError( + "A Api plugin must implement a method named download_all" + ) diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 37ce4443..768778e5 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -37,7 +37,12 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import GENERIC_PRODUCT_TYPE, format_dict_items, get_progress_callback +from eodag.utils import ( + GENERIC_PRODUCT_TYPE, + format_dict_items, + get_progress_callback, + path_to_uri, +) from eodag.utils.exceptions import AuthenticationError, NotAvailableError logger = logging.getLogger("eodag.plugins.apis.usgs") @@ -164,6 +169,8 @@ class UsgsApi(Api, Download): product, outputs_extension=".tar.gz", kwargs ) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -264,8 +271,11 @@ class UsgsApi(Api, Download): ) new_fs_path = fs_path[: fs_path.index(".tar.gz")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product_path = self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product.location = path_to_uri(product_path) + return product_path def download_all( self, diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 2f0b132d..687985c6 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -34,7 +34,7 @@ from eodag.api.product.metadata_mapping import ( properties_from_xml, ) from eodag.plugins.download.base import Download -from eodag.utils import get_progress_callback, urlparse +from eodag.utils import get_progress_callback, path_to_uri, urlparse from eodag.utils.exceptions import AuthenticationError, DownloadError logger = logging.getLogger("eodag.plugins.download.aws") @@ -185,6 +185,8 @@ class AwsDownload(Download): # prepare download & create dirs (before updating metadata) product_local_path, record_filename = self._prepare_download(product, kwargs) if not product_local_path or not record_filename: + if product_local_path: + product.location = path_to_uri(product_local_path) return product_local_path product_local_path = product_local_path.replace(".zip", "") # remove existing incomplete file @@ -395,6 +397,7 @@ class AwsDownload(Download): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path def get_authenticated_objects(self, bucket_name, prefix, auth_dict): diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index 8d6dbd3e..bfcb8ac1 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -25,7 +25,7 @@ from datetime import datetime, timedelta from time import sleep from eodag.plugins.base import PluginTopic -from eodag.utils import get_progress_callback, sanitize +from eodag.utils import get_progress_callback, sanitize, uri_to_path from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -43,6 +43,31 @@ DEFAULT_DOWNLOAD_TIMEOUT = 20 class Download(PluginTopic): """Base Download Plugin. + A Download plugin has two download methods that it must implement: + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + They must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record* file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + :param provider: An eodag providers configuration dictionary :type provider: dict :param config: Path to the user configuration file @@ -62,8 +87,26 @@ class Download(PluginTopic): timeout=DEFAULT_DOWNLOAD_TIMEOUT, kwargs, ): - """ - Base download method. Not available, should be defined for each plugin + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str """ raise NotImplementedError( "A Download plugin must implement a method named download" @@ -78,8 +121,7 @@ class Download(PluginTopic): :rtype: tuple """ if product.location != product.remote_location: - scheme_prefix_len = len("file://") - fs_path = product.location[scheme_prefix_len:] + fs_path = uri_to_path(product.location) # The fs path of a product is either a file (if 'extract' config is False) or a directory if os.path.isfile(fs_path) or os.path.isdir(fs_path): logger.info( @@ -249,9 +291,32 @@ class Download(PluginTopic): kwargs, ): """ - A sequential download_all implementation - using download method for every products + Base download_all method. + + This specific implementation uses the ``download`` method implemented by + the plugin to sequentially attempt to download products. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list """ + # Products are going to be removed one by one from this sequence once + # downloaded. + products = products[:] paths = [] # initiate retry loop start_time = datetime.now() diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 11662a18..f54fff86 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -32,7 +32,7 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import get_progress_callback +from eodag.utils import get_progress_callback, path_to_uri from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -70,6 +70,8 @@ class HTTPDownload(Download): """ fs_path, record_filename = self._prepare_download(product, kwargs) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -80,7 +82,7 @@ class HTTPDownload(Download): # download assets if exist instead of remote_location try: - return self._download_assets( + fs_path = self._download_assets( product, fs_path.replace(".zip", ""), record_filename, @@ -88,6 +90,8 @@ class HTTPDownload(Download): progress_callback, kwargs ) + product.location = path_to_uri(fs_path) + return fs_path except NotAvailableError: pass @@ -218,8 +222,11 @@ class HTTPDownload(Download): ) new_fs_path = fs_path[: fs_path.index(".zip")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, kwargs) + product_path = self._finalize(fs_path, kwargs) + product.location = path_to_uri(product_path) + return product_path except NotAvailableError as e: if not getattr(self.config, "order_enabled", False): diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index 71876ea4..2f5628b9 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -29,7 +29,7 @@ from requests import HTTPError from eodag.api.product.metadata_mapping import OFFLINE_STATUS from eodag.plugins.download.aws import AwsDownload from eodag.plugins.download.http import HTTPDownload -from eodag.utils import get_progress_callback, urljoin +from eodag.utils import get_progress_callback, path_to_uri, urljoin from eodag.utils.exceptions import ( AuthenticationError, DownloadError, @@ -158,6 +158,7 @@ class S3RestDownload(AwsDownload): url_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() record_filename = os.path.join(download_records_dir, url_hash) if os.path.isfile(record_filename) and os.path.exists(product_local_path): + product.location = path_to_uri(product_local_path) return product_local_path # Remove the record file if product_local_path is absent (e.g. it was deleted while record wasn't) elif os.path.isfile(record_filename): @@ -215,4 +216,5 @@ class S3RestDownload(AwsDownload): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index f48e3eb0..2c6f7e9d 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -463,7 +463,7 @@ def download_stac_item_by_id(catalogs, item_id, provider=None): product_path = eodag_api.download(product) - return product_path.replace("file://", "") + return product_path def get_stac_catalogs(url, root="/", catalogs=[], provider=None): diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 70798110..0c21649b 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -35,6 +35,7 @@ import warnings from collections import defaultdict from datetime import datetime, timezone from itertools import repeat, starmap +from pathlib import Path # All modules using these should import them from utils package from urllib.parse import ( # noqa; noqa @@ -43,8 +44,10 @@ from urllib.parse import ( # noqa; noqa urlencode, urljoin, urlparse, + urlsplit, urlunparse, ) +from urllib.request import url2pathname import click import shapefile @@ -203,6 +206,24 @@ def strip_accents(s): ) +def uri_to_path(uri): + """ + Convert a file URI (e.g. 'file:///tmp') to a local path (e.g. '/tmp') + """ + if not uri.startswith("file"): + raise ValueError("A file URI must be provided (e.g. 'file:///tmp'") + _, _, path, _, _ = urlsplit(uri) + # On Windows urlsplit returns the path starting with a slash ('/C:/User) + path = url2pathname(path) + # url2pathname removes it + return path + + +def path_to_uri(path): + """Convert a local absolute path to a file URI""" + return Path(path).as_uri() + + def mutate_dict_in_place(func, mapping): """Apply func to values of mapping. @@ -853,13 +874,13 @@ def _deprecated(reason="", version=None): @functools.wraps(callable) def wrapper(args, kwargs): - warnings.simplefilter("always", DeprecationWarning) - warnings.warn( - f"Call to deprecated {ctype} {cname} {reason_}{version_}", - category=DeprecationWarning, - stacklevel=2, - ) - warnings.simplefilter("default", DeprecationWarning) + with warnings.catch_warnings(): + warnings.simplefilter("always", DeprecationWarning) + warnings.warn( + f"Call to deprecated {ctype} {cname} {reason_}{version_}", + category=DeprecationWarning, + stacklevel=2, + ) return callable(args, **kwargs) return wrapper	Error while downloading a quicklook in a directory where the product has already been downloaded and extracted It might be an edge case but I got caught by this one. ``` Traceback (most recent call last): File "/home/maxime/TRAVAIL/06_EODAG/01_eodag/eodag/snippet_quicklook_peps.py", line 20, in <module> prods[0].get_quicklook(base_dir="/tmp") File "/home/maxime/TRAVAIL/06_EODAG/01_eodag/eodag/eodag/api/product/_product.py", line 392, in get_quicklook with open(quicklook_file, "wb") as fhandle: IsADirectoryError: [Errno 21] Is a directory: '/tmp/S2B_MSIL1C_20200815T030549_N0209_R075_T55XEB_20200815T051416' ``` I first downloaded a product in a directory, say `work`. The product was automatically extracted to `work/productID`. I then tried to download the quicklook of that product with `get_quicklook` in the same directory `work`. If not provided, a quicklook get its filename from the product's ID: https://github.com/CS-SI/eodag/blob/fec965f791a74dbf658e0833e1507b7ac950d09d/eodag/api/product/_product.py#L344-L347 In that case then, `get_quicklook` tried to save the quicklook to `work/productID` which was an existing folder, hence the error reported above. I assume quicklooks get their filename from the product's ID because some providers don't provide an URL, instead they provide the encoded image content (onda, at least). Here I guess the simplest thing to do would be to check if the file path of the quicklook to download is a directory, and raise a nicer error in that case.	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index 96807bc8..df74276a 100644 --- a/tests/context.py +++ b/tests/context.py @@ -44,7 +44,13 @@ from eodag.plugins.manager import PluginManager from eodag.plugins.search.base import Search from eodag.rest import server as eodag_http_server from eodag.rest.utils import eodag_api, get_date -from eodag.utils import get_geometry_from_various, get_timestamp, makedirs +from eodag.utils import ( + get_geometry_from_various, + get_timestamp, + makedirs, + path_to_uri, + uri_to_path, +) from eodag.utils.exceptions import ( AddressNotFound, AuthenticationError, diff --git a/tests/test_cli.py b/tests/test_cli.py index 94e07cc3..a4171dbb 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -427,7 +427,7 @@ class TestEodagCli(unittest.TestCase): TEST_RESOURCES_PATH, "eodag_search_result.geojson" ) config_path = os.path.join(TEST_RESOURCES_PATH, "file_config_override.yml") - dag.return_value.download_all.return_value = ["file:///fake_path"] + dag.return_value.download_all.return_value = ["/fake_path"] result = self.runner.invoke( eodag, ["download", "--search-results", search_results_path, "-f", config_path], @@ -435,7 +435,7 @@ class TestEodagCli(unittest.TestCase): dag.assert_called_once_with(user_conf_file_path=config_path) dag.return_value.deserialize.assert_called_once_with(search_results_path) self.assertEqual(dag.return_value.download_all.call_count, 1) - self.assertEqual("Downloaded file:///fake_path\n", result.output) + self.assertEqual("Downloaded /fake_path\n", result.output) # Testing the case when no downloaded path is returned dag.return_value.download_all.return_value = [None] diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index be2dee90..ca0a145c 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -18,15 +18,22 @@ import datetime import glob +import hashlib import multiprocessing import os import shutil +import time import unittest from pathlib import Path from eodag.api.product.metadata_mapping import ONLINE_STATUS from tests import TEST_RESOURCES_PATH, TESTS_DOWNLOAD_PATH -from tests.context import AuthenticationError, EODataAccessGateway +from tests.context import ( + AuthenticationError, + EODataAccessGateway, + SearchResult, + uri_to_path, +) THEIA_SEARCH_ARGS = [ "theia", @@ -148,8 +155,8 @@ class EndToEndBase(unittest.TestCase): """ search_criteria = { "productType": product_type, - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } if items_per_page: @@ -190,8 +197,8 @@ class EndToEndBase(unittest.TestCase): - Return all the products """ search_criteria = { - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } self.eodag.set_preferred_provider(provider) @@ -428,6 +435,196 @@ class TestEODagEndToEnd(EndToEndBase): self.assertGreater(len(results), 10) +class TestEODagEndToEndComplete(unittest.TestCase): + """Make real and complete test cases that search for products, download them and + extract them. There should be just a tiny number of these tests which can be quite + long to run. + + There must be a user conf file in the test resources folder named user_conf.yml + """ + + @classmethod + def setUpClass(cls): + + # use tests/resources/user_conf.yml if exists else default file ~/.config/eodag/eodag.yml + tests_user_conf = os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + if not os.path.isfile(tests_user_conf): + unittest.SkipTest("Missing user conf file with credentials") + cls.eodag = EODataAccessGateway( + user_conf_file_path=os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + ) + + # create TESTS_DOWNLOAD_PATH is not exists + if not os.path.exists(TESTS_DOWNLOAD_PATH): + os.makedirs(TESTS_DOWNLOAD_PATH) + + for provider, conf in cls.eodag.providers_config.items(): + # Change download directory to TESTS_DOWNLOAD_PATH for tests + if hasattr(conf, "download") and hasattr(conf.download, "outputs_prefix"): + conf.download.outputs_prefix = TESTS_DOWNLOAD_PATH + elif hasattr(conf, "api") and hasattr(conf.api, "outputs_prefix"): + conf.api.outputs_prefix = TESTS_DOWNLOAD_PATH + else: + # no outputs_prefix found for provider + pass + # Force all providers implementing RestoSearch and defining how to retrieve + # products by specifying the + # location scheme to use https, enabling actual downloading of the product + if ( + getattr(getattr(conf, "search", {}), "product_location_scheme", "https") + == "file" + ): + conf.search.product_location_scheme = "https" + + def tearDown(self): + """Clear the test directory""" + for p in Path(TESTS_DOWNLOAD_PATH).glob("*/"): + try: + os.remove(p) + except OSError: + shutil.rmtree(p) + + def test_end_to_end_complete_peps(self): + """Complete end-to-end test with PEPS for download and download_all""" + # Search for products that are ONLINE and as small as possible + today = datetime.date.today() + month_span = datetime.timedelta(weeks=4) + search_results, _ = self.eodag.search( + productType="S2_MSI_L1C", + start=(today - month_span).isoformat(), + end=today.isoformat(), + geom={"lonmin": 1, "latmin": 42, "lonmax": 5, "latmax": 46}, + items_per_page=100, + ) + prods_sorted_by_size = SearchResult( + sorted(search_results, key=lambda p: p.properties["resourceSize"]) + ) + prods_online = [ + p for p in prods_sorted_by_size if p.properties["storageStatus"] == "ONLINE" + ] + if len(prods_online) < 2: + unittest.skip( + "Not enough ONLINE products found, update the search criteria." + ) + + # Retrieve one product to work with + product = prods_online[0] + + prev_remote_location = product.remote_location + prev_location = product.location + # The expected product's archive filename is based on the product's title + expected_product_name = f"{product.properties['title']}.zip" + + # Download the product, but DON'T extract it + archive_file_path = self.eodag.download(product, extract=False) + + # The archive must have been downloaded + self.assertTrue(os.path.isfile(archive_file_path)) + # Its name must be the "{product_title}.zip" + self.assertIn( + expected_product_name, os.listdir(product.downloader.config.outputs_prefix) + ) + # Its size should be >= 5 KB + archive_size = os.stat(archive_file_path).st_size + self.assertGreaterEqual(archive_size, 5 * 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # That points to the downloaded archive + self.assertEqual(uri_to_path(product.location), archive_file_path) + # A .downloaded folder must have been created + record_dir = os.path.join(TESTS_DOWNLOAD_PATH, ".downloaded") + self.assertTrue(os.path.isdir(record_dir)) + # It must contain a file per product downloade, whose name is + # the MD5 hash of the product's remote location + expected_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() + record_file = os.path.join(record_dir, expected_hash) + self.assertTrue(os.path.isfile(record_file)) + # Its content must be the product's remote location + record_content = Path(record_file).read_text() + self.assertEqual(record_content, product.remote_location) + + # The product should not be downloaded again if the download method + # is executed again + previous_archive_file_path = archive_file_path + previous_location = product.location + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The paths should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(product.location, previous_location) + + # If we emulate that the product has just been found, it should not + # be downloaded again since the record file is still present. + product.location = product.remote_location + # Pretty much the same checks as with the previous step + previous_archive_file_path = archive_file_path + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The returned path should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(uri_to_path(product.location), archive_file_path) + + # Remove the archive + os.remove(archive_file_path) + + # Now, the archive is removed but its associated record file + # still exists. Downloading the product again should really + # download it, if its location points to the remote location. + # The product should be automatically extracted. + product.location = product.remote_location + product_dir_path = self.eodag.download(product) + + # Its size should be >= 5 KB + downloaded_size = sum( + f.stat().st_size for f in Path(product_dir_path).glob("/") if f.is_file() + ) + self.assertGreaterEqual(downloaded_size, 5 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # The location must point to a SAFE directory + self.assertTrue(product.location.endswith("SAFE")) + # The path must point to a SAFE directory + self.assertTrue(os.path.isdir(product_dir_path)) + self.assertTrue(product_dir_path.endswith("SAFE")) + + # Remove the archive and extracted product and reset the product's location + os.remove(archive_file_path) + shutil.rmtree(Path(product_dir_path).parent) + product.location = product.remote_location + + # Now let's check download_all + products = prods_sorted_by_size[:2] + # Pass a copy because download_all empties the list + archive_paths = self.eodag.download_all(products[:], extract=False) + + # The returned paths must point to the downloaded archives + # Each product's location must be a URI path to the archive + for product, archive_path in zip(products, archive_paths): + self.assertTrue(os.path.isfile(archive_path)) + self.assertEqual(uri_to_path(product.location), archive_path) + + # Downloading the product again should not download them, since + # they are all already there. + prev_archive_paths = archive_paths + start_time = time.time() + archive_paths = self.eodag.download_all(products[:], extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + self.assertEqual(archive_paths, prev_archive_paths) + + # @unittest.skip("skip auto run") class TestEODagEndToEndWrongCredentials(EndToEndBase): """Make real case tests with wrong credentials. This assumes the existence of a @@ -462,7 +659,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) def test_end_to_end_good_apikey_wrong_credentials_aws_eos(self): @@ -489,7 +686,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) self.assertGreater(len(results), 0) one_product = results[0] @@ -525,5 +722,5 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], USGS_SEARCH_ARGS[1:]) - ) + ), ) diff --git a/tests/units/test_core.py b/tests/units/test_core.py index cb6022e4..69cf9a3e 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -367,7 +367,7 @@ class TestCoreGeometry(unittest.TestCase): "lonmax": 2, "latmax": 52, } - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Bad dict with a missing key del geometry["lonmin"] self.assertRaises( @@ -378,10 +378,10 @@ class TestCoreGeometry(unittest.TestCase): ) # Tuple geometry = (0, 50, 2, 52) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List geometry = list(geometry) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List without 4 items geometry.pop() self.assertRaises( @@ -392,7 +392,7 @@ class TestCoreGeometry(unittest.TestCase): ) # WKT geometry = ref_geom_as_wkt - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Some other shapely geom geometry = LineString([[0, 0], [1, 1]]) self.assertIsInstance( @@ -409,7 +409,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="FRA") ) self.assertIsInstance(geom_france, MultiPolygon) - self.assertEquals(len(geom_france), 3) # France + Guyana + Corsica + self.assertEqual(len(geom_france), 3) # France + Guyana + Corsica def test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs( self, @@ -444,7 +444,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="PA[A-Z]") ) self.assertIsInstance(geom_regex_pa, MultiPolygon) - self.assertEquals(len(geom_regex_pa), 2) + self.assertEqual(len(geom_regex_pa), 2) def test_get_geometry_from_various_locations_no_match_raises_error(self): """If the location search doesn't match any of the feature attribute a ValueError must be raised""" @@ -468,7 +468,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # France + Guyana + Corsica + somewhere over Poland - self.assertEquals(len(geom_combined), 4) + self.assertEqual(len(geom_combined), 4) geometry = { "lonmin": 0, "latmin": 50, @@ -480,7 +480,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # The bounding box overlaps with France inland - self.assertEquals(len(geom_combined), 3) + self.assertEqual(len(geom_combined), 3) class TestCoreSearch(unittest.TestCase): @@ -546,7 +546,6 @@ class TestCoreSearch(unittest.TestCase): "geometry": None, "productType": None, } - self.assertDictContainsSubset(expected, prepared_search) expected = set(["geometry", "productType", "auth", "search_plugin"]) self.assertSetEqual(expected, set(prepared_search)) @@ -557,11 +556,10 @@ class TestCoreSearch(unittest.TestCase): "end": "2020-02-01", } prepared_search = self.dag._prepare_search(base) - expected = { - "startTimeFromAscendingNode": base["start"], - "completionTimeFromAscendingNode": base["end"], - } - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["startTimeFromAscendingNode"], base["start"]) + self.assertEqual( + prepared_search["completionTimeFromAscendingNode"], base["end"] + ) def test__prepare_search_geom(self): """_prepare_search must handle geom, box and bbox""" @@ -608,8 +606,7 @@ class TestCoreSearch(unittest.TestCase): """_prepare_search must handle when a product type is given""" base = {"productType": "S2_MSI_L1C"} prepared_search = self.dag._prepare_search(base) - expected = {"productType": base["productType"]} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) def test__prepare_search_product_type_guess_it(self): """_prepare_search must guess a product type when required to""" @@ -621,8 +618,19 @@ class TestCoreSearch(unittest.TestCase): platformSerialIdentifier="S2A", ) prepared_search = self.dag._prepare_search(base) - expected = {"productType": "S2_MSI_L1C", base} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], "S2_MSI_L1C") + + def test__prepare_search_remove_guess_kwargs(self): + """_prepare_search must remove the guess kwargs""" + # Uses guess_product_type to find the product matching + # the best the given params. + base = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + prepared_search = self.dag._prepare_search(base) + self.assertEqual(len(base.keys() & prepared_search.keys()), 0) def test__prepare_search_with_id(self): """_prepare_search must handle a search by id""" @@ -638,8 +646,8 @@ class TestCoreSearch(unittest.TestCase): "cloudCover": 10, } prepared_search = self.dag._prepare_search(base) - expected = base - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) + self.assertEqual(prepared_search["cloudCover"], base["cloudCover"]) def test__prepare_search_search_plugin_has_known_product_properties(self): """_prepare_search must attach the product properties to the search plugin""" diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index f43aaa84..0faf7704 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -90,23 +90,18 @@ class TestEOProduct(EODagTestCase): self.provider, self.eoproduct_props, productType=self.product_type ) geo_interface = geojson.loads(geojson.dumps(product)) - self.assertDictContainsSubset( - { - "type": "Feature", - "geometry": self._tuples_to_lists(geometry.mapping(self.geometry)), - }, - geo_interface, + self.assertEqual(geo_interface["type"], "Feature") + self.assertEqual( + geo_interface["geometry"], + self._tuples_to_lists(geometry.mapping(self.geometry)), ) - self.assertDictContainsSubset( - { - "eodag_provider": self.provider, - "eodag_search_intersection": self._tuples_to_lists( - geometry.mapping(product.search_intersection) - ), - "eodag_product_type": self.product_type, - }, - geo_interface["properties"], + properties = geo_interface["properties"] + self.assertEqual(properties["eodag_provider"], self.provider) + self.assertEqual( + properties["eodag_search_intersection"], + self._tuples_to_lists(geometry.mapping(product.search_intersection)), ) + self.assertEqual(properties["eodag_product_type"], self.product_type) def test_eoproduct_from_geointerface(self): """EOProduct must be build-able from its geo-interface""" @@ -295,7 +290,6 @@ class TestEOProduct(EODagTestCase): self.requests_http_get.assert_called_with( self.download_url, stream=True, auth=None, params={} ) - product_file_path = product_file_path[len("file://") :] download_records_dir = pathlib.Path(product_file_path).parent / ".downloaded" # A .downloaded folder should be created, including a text file that # lists the downloaded product by their url @@ -332,7 +326,7 @@ class TestEOProduct(EODagTestCase): # Download product_dir_path = product.download() - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) # The returned path must be a directory. self.assertTrue(product_dir_path.is_dir()) # Check that the extracted dir has at least one file, there are more @@ -379,7 +373,7 @@ class TestEOProduct(EODagTestCase): self.download_url, stream=True, auth=None, params={"fakeparam": "dummy"} ) # Check that "outputs_prefix" is respected. - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) self.assertEqual(product_dir_path.parent.name, output_dir_name) # We've asked to extract the product so there should be a directory. self.assertTrue(product_dir_path.is_dir()) diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 28369fee..09dd221d 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -106,8 +106,8 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): info_message=mock.ANY, exception_message=mock.ANY, ) - self.assertEquals(estimate, self.sobloo_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.sobloo_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -144,7 +144,7 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -196,8 +196,8 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.awseos_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.awseos_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -232,7 +232,7 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -294,6 +294,6 @@ class TestSearchPluginODataV4Search(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.onda_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.onda_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) diff --git a/tests/units/test_search_result.py b/tests/units/test_search_result.py index f1697117..d4e52303 100644 --- a/tests/units/test_search_result.py +++ b/tests/units/test_search_result.py @@ -32,9 +32,8 @@ class TestSearchResult(unittest.TestCase): def test_search_result_geo_interface(self): """SearchResult must provide a FeatureCollection geo-interface""" geo_interface = geojson.loads(geojson.dumps(self.search_result)) - self.assertDictContainsSubset( - {"type": "FeatureCollection", "features": []}, geo_interface - ) + self.assertEqual(geo_interface["type"], "FeatureCollection") + self.assertEqual(geo_interface["features"], []) def test_search_result_is_list_like(self): """SearchResult must provide a list interface""" diff --git a/tests/units/test_stac_reader.py b/tests/units/test_stac_reader.py index 8960ee81..f242fb38 100644 --- a/tests/units/test_stac_reader.py +++ b/tests/units/test_stac_reader.py @@ -47,10 +47,8 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.child_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.child_cat_len) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - items[0], - ) + self.assertEqual(items[0]["type"], "Feature") + self.assertEqual(items[0]["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_root_not_recursive(self): """fetch_stac_items from root must provide an empty list when no recursive""" @@ -62,23 +60,20 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.root_cat, recursive=True) self.assertEqual(len(items), self.root_cat_len) for item in items: - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, item - ) + self.assertEqual(item["type"], "Feature") + self.assertEqual(item["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_item(self): """fetch_stac_items from an item must return it""" item = fetch_stac_items(self.item) self.assertIsInstance(item, list) self.assertEqual(len(item), 1) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - item[0], - ) + self.assertEqual(item[0]["type"], "Feature") + self.assertEqual(item[0]["collection"], "S2_MSI_L1C") def test_stact_reader_fetch_singlefile_catalog(self): """fetch_stact_items must return all the items from a single file catalog""" items = fetch_stac_items(self.singlefile_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.singlefile_cat_len) - self.assertDictContainsSubset({"type": "Feature"}, items[0]) + self.assertEqual(items[0]["type"], "Feature") diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 58732712..ef865dff 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -16,10 +16,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import sys import unittest from datetime import datetime -from tests.context import get_timestamp +from tests.context import get_timestamp, path_to_uri, uri_to_path class TestUtils(unittest.TestCase): @@ -33,3 +34,21 @@ class TestUtils(unittest.TestCase): expected_dt = datetime.strptime(requested_date, date_format) actual_utc_dt = datetime.utcfromtimestamp(ts_in_secs) self.assertEqual(actual_utc_dt, expected_dt) + + def test_uri_to_path(self): + if sys.platform == "win32": + expected_path = r"C:\tmp\file.txt" + tested_uri = r"file:///C:/tmp/file.txt" + else: + expected_path = "/tmp/file.txt" + tested_uri = "file:///tmp/file.txt" + actual_path = uri_to_path(tested_uri) + self.assertEqual(actual_path, expected_path) + with self.assertRaises(ValueError): + uri_to_path("not_a_uri") + + def test_path_to_uri(self): + if sys.platform == "win32": + self.assertEqual(path_to_uri(r"C:\tmp\file.txt"), "file:///C:/tmp/file.txt") + else: + self.assertEqual(path_to_uri("/tmp/file.txt"), "file:///tmp/file.txt")	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 3, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 10 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "nose", "nose-cov", "pandoc", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 cov-core==1.15.0 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@0632e4147f26119bea43e0672f6d06e75d216a20#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 nose-cov==1.6 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandoc==2.4 platformdirs==4.3.7 pluggy==1.5.0 plumbum==1.9.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - cov-core==1.15.0 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - nose-cov==1.6 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandoc==2.4 - platformdirs==4.3.7 - pluggy==1.5.0 - plumbum==1.9.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_cli.py::TestCore::test_core_object_locations_file_not_found", "tests/test_cli.py::TestCore::test_core_object_open_index_if_exists", "tests/test_cli.py::TestCore::test_core_object_set_default_locations_config", "tests/test_cli.py::TestCore::test_list_product_types_for_provider_ok", "tests/test_cli.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/test_cli.py::TestCore::test_list_product_types_ok", "tests/test_cli.py::TestCore::test_rebuild_index", "tests/test_cli.py::TestCore::test_supported_product_types_in_unit_test", "tests/test_cli.py::TestCore::test_supported_providers_in_unit_test", "tests/test_cli.py::TestEodagCli::test_eodag_download_missingcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_download_no_search_results_arg", "tests/test_cli.py::TestEodagCli::test_eodag_download_ok", "tests/test_cli.py::TestEodagCli::test_eodag_download_wrongcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_ok", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ko", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ok", "tests/test_cli.py::TestEodagCli::test_eodag_search_all", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_geom_mutually_exclusive", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_storage_arg", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_conf_file_inexistent", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_max_cloud_out_of_range", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_args", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_producttype_arg", "tests/test_cli.py::TestEodagCli::test_eodag_with_only_verbose_opt", "tests/test_cli.py::TestEodagCli::test_eodag_without_args", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda", "tests/units/test_search_result.py::TestSearchResult::test_search_result_from_feature_collection", "tests/units/test_search_result.py::TestSearchResult::test_search_result_geo_interface", "tests/units/test_search_result.py::TestSearchResult::test_search_result_is_list_like", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[ "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_cruncher", "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_child", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_item", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_not_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stact_reader_fetch_singlefile_catalog" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-257	d18287d37a7deb98457d7068402218983150c7f8	2021-04-21 19:45:33	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/api/core.py b/eodag/api/core.py index 25fe07bb..8cd4814c 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -487,9 +487,13 @@ class EODataAccessGateway(object): :param raise_errors: When an error occurs when searching, if this is set to True, the error is raised (default: False) :type raise_errors: bool - :param start: Start sensing UTC time in iso format + :param start: Start sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type start: str - :param end: End sensing UTC time in iso format + :param end: End sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type end: str :param geom: Search area that can be defined in different ways: @@ -600,9 +604,13 @@ class EODataAccessGateway(object): :param items_per_page: The number of results requested per page (default: 20) :type items_per_page: int - :param start: Start sensing UTC time in iso format + :param start: Start sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type start: str - :param end: End sensing UTC time in iso format + :param end: End sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type end: str :param geom: Search area that can be defined in different ways: @@ -728,9 +736,13 @@ class EODataAccessGateway(object): available, a default value of 50 is used instead. items_per_page can also be set to any arbitrary value. :type items_per_page: int - :param start: Start sensing UTC time in iso format + :param start: Start sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type start: str - :param end: End sensing UTC time in iso format + :param end: End sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type end: str :param geom: Search area that can be defined in different ways: @@ -844,9 +856,13 @@ class EODataAccessGateway(object): * TODO: better expose cloudCover * other search params are passed to Searchplugin.query() - :param start: Start sensing UTC time in iso format + :param start: Start sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type start: str - :param end: End sensing UTC time in iso format + :param end: End sensing time in ISO 8601 format (e.g. "1990-11-26", + "1990-11-26T14:30:10.153Z", "1990-11-26T14:30:10+02:00", ...). + If no time offset is given, the time is assumed to be given in UTC. :type end: str :param geom: Search area that can be defined in different ways (see search) :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] diff --git a/eodag/api/product/metadata_mapping.py b/eodag/api/product/metadata_mapping.py index b3f0acb7..d5b253e7 100644 --- a/eodag/api/product/metadata_mapping.py +++ b/eodag/api/product/metadata_mapping.py @@ -22,7 +22,8 @@ from datetime import datetime from string import Formatter import geojson -from dateutil.tz import tzutc +from dateutil.parser import isoparse +from dateutil.tz import UTC, tzutc from jsonpath_ng.ext import parse from lxml import etree from lxml.etree import XPathEvalError @@ -117,7 +118,7 @@ def format_metadata(search_param, args, kwargs): """Format a string of form {<field_name>#<conversion_function>} The currently understood converters are: - - ``utc_to_timestamp_milliseconds``: converts a utc date string to a timestamp in + - ``datetime_to_timestamp_milliseconds``: converts a utc date string to a timestamp in milliseconds - ``to_wkt``: converts a geometry to its well known text representation - ``to_iso_utc_datetime_from_milliseconds``: converts a utc timestamp in given @@ -183,13 +184,14 @@ def format_metadata(search_param, args, *kwargs): return super(MetadataFormatter, self).convert_field(value, conversion) @staticmethod - def convert_utc_to_timestamp_milliseconds(value): - if len(value) == 10: - return int( - 1e3 get_timestamp(value, date_format="%Y-%m-%d", as_utc=True) - ) - else: - return int(1e3 * get_timestamp(value, as_utc=True)) + def convert_datetime_to_timestamp_milliseconds(date_time): + """Convert a date_time (str) to a Unix timestamp in milliseconds + + "2021-04-21T18:27:19.123Z" => "1619029639123" + "2021-04-21" => "1618963200000" + "2021-04-21T00:00:00+02:00" => "1618956000000" + """ + return int(1e3 * get_timestamp(date_time)) @staticmethod def convert_to_wkt_convex_hull(value): @@ -254,6 +256,10 @@ def format_metadata(search_param, args, kwargs): @staticmethod def convert_to_iso_utc_datetime_from_milliseconds(timestamp): + """Convert a timestamp in milliseconds (int) to its ISO8601 UTC format + + 1619029639123 => "2021-04-21T18:27:19.123Z" + """ try: return ( datetime.fromtimestamp(timestamp / 1e3, tzutc()) @@ -264,22 +270,33 @@ def format_metadata(search_param, args, *kwargs): return timestamp @staticmethod - def convert_to_iso_utc_datetime(dt): - for idx, fmt in enumerate(("%Y-%m-%d", "%Y-%m-%dT%H:%M:%S")): - try: - return ( - datetime.strptime(dt, fmt) - .replace(tzinfo=tzutc()) - .isoformat(timespec="milliseconds") - .replace("+00:00", "Z") - ) - except ValueError: - if idx == 1: - raise + def convert_to_iso_utc_datetime(date_time): + """Convert a date_time (str) to its ISO 8601 representation in UTC + + "2021-04-21" => "2021-04-21T00:00:00.000Z" + "2021-04-21T00:00:00.000+02:00" => "2021-04-20T22:00:00.000Z" + """ + dt = isoparse(date_time) + if not dt.tzinfo: + dt = dt.replace(tzinfo=UTC) + elif dt.tzinfo is not UTC: + dt = dt.astimezone(UTC) + return dt.isoformat(timespec="milliseconds").replace("+00:00", "Z") @staticmethod def convert_to_iso_date(datetime_string): - return datetime_string[:10] + """Convert an ISO8601 datetime (str) to its ISO8601 date format + + "2021-04-21T18:27:19.123Z" => "2021-04-21" + "2021-04-21" => "2021-04-21" + "2021-04-21T00:00:00+06:00" => "2021-04-20" ! + """ + dt = isoparse(datetime_string) + if not dt.tzinfo: + dt = dt.replace(tzinfo=UTC) + elif dt.tzinfo is not UTC: + dt = dt.astimezone(UTC) + return dt.isoformat()[:10] @staticmethod def convert_remove_extension(string): diff --git a/eodag/cli.py b/eodag/cli.py index c3dc7315..408d6a24 100755 --- a/eodag/cli.py +++ b/eodag/cli.py @@ -156,13 +156,13 @@ def version(): "-s", "--start", type=click.DateTime(), - help="Start sensing UTC time of the product (in ISO8601 format: yyyy-MM-ddThh:mm:ss.SSSZ)", + help="Start sensing time in ISO8601 format (e.g. '1990-11-26', '1990-11-26T14:30:10'). UTC is assumed", ) @click.option( "-e", "--end", type=click.DateTime(), - help="End sensing UTC time of the product (in ISO8601 format: yyyy-MM-ddThh:mm:ss.SSSZ)", + help="End sensing time in ISO8601 format (e.g. '1990-11-26', '1990-11-26T14:30:10'). UTC is assumed", ) @click.option( "-c", diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 6b3bd5f6..4d0b2c44 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -913,10 +913,10 @@ # OpenSearch Parameters for Acquistion Parameters Search (Table 6) availabilityTime: '$.data.availabilityTime' startTimeFromAscendingNode: - - 'f=acquisition.beginViewingDate:gte:{startTimeFromAscendingNode#utc_to_timestamp_milliseconds}' + - 'f=acquisition.beginViewingDate:gte:{startTimeFromAscendingNode#datetime_to_timestamp_milliseconds}' - '{$.data.acquisition.beginViewingDate#to_iso_utc_datetime_from_milliseconds}' completionTimeFromAscendingNode: - - 'f=acquisition.endViewingDate:lte:{completionTimeFromAscendingNode#utc_to_timestamp_milliseconds}' + - 'f=acquisition.endViewingDate:lte:{completionTimeFromAscendingNode#datetime_to_timestamp_milliseconds}' - '{$.data.acquisition.endViewingDate#to_iso_utc_datetime_from_milliseconds}' polarizationChannels: '$.data.acquisition.polarization' sensorMode: '$.data.acquisition.sensorMode' diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 0c21649b..fe7745a3 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -33,7 +33,6 @@ import types import unicodedata import warnings from collections import defaultdict -from datetime import datetime, timezone from itertools import repeat, starmap from pathlib import Path @@ -52,6 +51,8 @@ from urllib.request import url2pathname import click import shapefile import shapely.wkt +from dateutil.parser import isoparse +from dateutil.tz import UTC from jsonpath_ng import jsonpath from jsonpath_ng.ext import parse from requests.auth import AuthBase @@ -331,28 +332,19 @@ def maybe_generator(obj): yield obj -def get_timestamp(date_time, date_format="%Y-%m-%dT%H:%M:%S", as_utc=False): - """Returns the given UTC date_time string formatted with date_format as timestamp +def get_timestamp(date_time): + """Return the Unix timestamp of an ISO8601 date/datetime in seconds. + + If the datetime has no offset, it is assumed to be an UTC datetime. :param date_time: the datetime string to return as timestamp :type date_time: str - :param date_format: (optional) the date format in which date_time is given, - defaults to '%Y-%m-%dT%H:%M:%S' - :type date_format: str - :param as_utc: (optional) if ``True``, consider the input ``date_time`` as UTC, - defaults to ``False`` - :type date_format: bool :returns: the timestamp corresponding to the date_time string in seconds :rtype: float - - .. versionchanged:: - 2.1.0 - - The optional parameter ``as_utc`` to consider the input date as UTC. """ - dt = datetime.strptime(date_time, date_format) - if as_utc: - dt = dt.replace(tzinfo=timezone.utc) + dt = isoparse(date_time) + if not dt.tzinfo: + dt = dt.replace(tzinfo=UTC) return dt.timestamp()	ISO8601 formatted time fails on some providers Time formatted using ISO8601 (`yyyy-MM-ddThh:mm:ss.SSSZ`) fails for some providers like `sobloo` or `astraea_eod`, when `yyyy-MM-ddThh:mm:ss` is ok. Fix is needed to make both work	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index df74276a..9dcb79e6 100644 --- a/tests/context.py +++ b/tests/context.py @@ -29,6 +29,7 @@ from eodag.api.core import DEFAULT_ITEMS_PER_PAGE, DEFAULT_MAX_ITEMS_PER_PAGE from eodag.api.product import EOProduct from eodag.api.product.drivers import DRIVERS from eodag.api.product.drivers.base import NoDriver +from eodag.api.product.metadata_mapping import format_metadata from eodag.api.search_result import SearchResult from eodag.cli import download, eodag, list_pt, search_crunch from eodag.config import load_default_config, merge_configs diff --git a/tests/units/test_metadata_mapping.py b/tests/units/test_metadata_mapping.py new file mode 100644 index 00000000..e510ed38 --- /dev/null +++ b/tests/units/test_metadata_mapping.py @@ -0,0 +1,88 @@ +# -- coding: utf-8 -- +# Copyright 2021, CS GROUP - France, http://www.c-s.fr +# +# This file is part of EODAG project +# https://www.github.com/CS-SI/EODAG +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import unittest + +from tests.context import format_metadata + + +class TestMetadataFormatter(unittest.TestCase): + def test_convert_datetime_to_timestamp_milliseconds(self): + to_format = "{fieldname#datetime_to_timestamp_milliseconds}" + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T18:27:19.123Z"), + "1619029639123", + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T18:27:19.123"), + "1619029639123", + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21"), "1618963200000" + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T00:00:00+02:00"), + "1618956000000", + ) + + def test_convert_to_iso_utc_datetime(self): + to_format = "{fieldname#to_iso_utc_datetime}" + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T18:27:19.123Z"), + "2021-04-21T18:27:19.123Z", + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T18:27:19.123"), + "2021-04-21T18:27:19.123Z", + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21"), + "2021-04-21T00:00:00.000Z", + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T00:00:00.000+02:00"), + "2021-04-20T22:00:00.000Z", + ) + + def test_convert_to_iso_date(self): + to_format = "{fieldname#to_iso_date}" + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T18:27:19.123Z"), + "2021-04-21", + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T18:27:19.123"), + "2021-04-21", + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21"), "2021-04-21" + ) + self.assertEqual( + format_metadata(to_format, fieldname="2021-04-21T00:00:00+06:00"), + "2021-04-20", + ) + + def test_convert_to_iso_utc_datetime_from_milliseconds(self): + to_format = "{fieldname#to_iso_utc_datetime_from_milliseconds}" + self.assertEqual( + format_metadata(to_format, fieldname=1619029639123), + "2021-04-21T18:27:19.123Z", + ) + self.assertEqual( + format_metadata(to_format, fieldname=1618963200000), + "2021-04-21T00:00:00.000Z", + ) diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index ef865dff..69902012 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -25,16 +25,28 @@ from tests.context import get_timestamp, path_to_uri, uri_to_path class TestUtils(unittest.TestCase): def test_utils_get_timestamp(self): - """get_timestamp must handle UTC dates""" - requested_date = "2020-08-08" - date_format = "%Y-%m-%d" - # If as_utc is False, get_timestamp takes into account the - # local timezone (because of datetime.timestamp) - ts_in_secs = get_timestamp(requested_date, date_format=date_format, as_utc=True) - expected_dt = datetime.strptime(requested_date, date_format) + """get_timestamp must return a UNIX timestamp""" + # Date to timestamp to date, this assumes the date is in UTC + requested_date = "2020-08-08" # Considered as 2020-08-08T00:00:00Z + ts_in_secs = get_timestamp(requested_date) + expected_dt = datetime.strptime(requested_date, "%Y-%m-%d") actual_utc_dt = datetime.utcfromtimestamp(ts_in_secs) self.assertEqual(actual_utc_dt, expected_dt) + # Handle UTC datetime + self.assertEqual(get_timestamp("2021-04-21T18:27:19.123Z"), 1619029639.123) + # If date/datetime not in UTC, it assumes it's in UTC + self.assertEqual( + get_timestamp("2021-04-21T18:27:19.123"), + get_timestamp("2021-04-21T18:27:19.123Z"), + ) + self.assertEqual( + get_timestamp("2021-04-21"), get_timestamp("2021-04-21T00:00:00.000Z") + ) + + # Non UTC datetime are also supported + self.assertEqual(get_timestamp("2021-04-21T00:00:00+02:00"), 1618956000) + def test_uri_to_path(self): if sys.platform == "win32": expected_path = r"C:\tmp\file.txt"	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 5 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y make pandoc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@d18287d37a7deb98457d7068402218983150c7f8#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.30.0 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.30.0 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_datetime_to_timestamp_milliseconds", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_date", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_utc_datetime", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[]	[ "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_utc_datetime_from_milliseconds", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_uri_to_path" ]	[]	Apache License 2.0	null
CS-SI__eodag-260	0632e4147f26119bea43e0672f6d06e75d216a20	2021-04-21 22:16:22	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/api/core.py b/eodag/api/core.py index 6efceb2a..92e4b6f5 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -47,6 +47,7 @@ from eodag.utils import ( _deprecated, get_geometry_from_various, makedirs, + uri_to_path, ) from eodag.utils.exceptions import ( NoMatchingProductType, @@ -434,7 +435,7 @@ class EODataAccessGateway(object): if results is None: results = searcher.search(query, limit=None) else: - results.upgrade_and_extend(searcher.search(query)) + results.upgrade_and_extend(searcher.search(query, limit=None)) guesses = [r["ID"] for r in results or []] if guesses: return guesses @@ -844,6 +845,19 @@ class EODataAccessGateway(object): if product_type is None: try: guesses = self.guess_product_type(kwargs) + + # guess_product_type raises a NoMatchingProductType error if no product + # is found. Here, the supported search params are removed from the + # kwargs if present, not to propagate them to the query itself. + for param in ( + "instrument", + "platform", + "platformSerialIdentifier", + "processingLevel", + "sensorType", + ): + kwargs.pop(param, None) + # By now, only use the best bet product_type = guesses[0] except NoMatchingProductType: @@ -883,11 +897,20 @@ class EODataAccessGateway(object): search_plugin = next( self._plugins_manager.get_search_plugins(product_type=product_type) ) - logger.info( - "Searching product type '%s' on provider: %s", - product_type, - search_plugin.provider, - ) + if search_plugin.provider != self.get_preferred_provider()[0]: + logger.warning( + "Product type '%s' is not available with provider '%s'. " + "Searching it on provider '%s' instead.", + product_type, + self.get_preferred_provider()[0], + search_plugin.provider, + ) + else: + logger.info( + "Searching product type '%s' on provider: %s", + product_type, + search_plugin.provider, + ) # Add product_types_config to plugin config. This dict contains product # type metadata that will also be stored in each product's properties. try: @@ -1281,8 +1304,10 @@ class EODataAccessGateway(object): This is an alias to the method of the same name on :class:`~eodag.api.product.EOProduct`, but it performs some additional checks like verifying that a downloader and authenticator are registered - for the product before trying to download it. If the metadata mapping for - `downloadLink` is set to something that can be interpreted as a link on a + for the product before trying to download it. + + If the metadata mapping for `downloadLink` is set to something that can be + interpreted as a link on a local filesystem, the download is skipped (by now, only a link starting with `file://` is supported). Therefore, any user that knows how to extract product location from product metadata on a provider can override the @@ -1316,10 +1341,15 @@ class EODataAccessGateway(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ - if product.location.startswith("file"): + if product.location.startswith("file://"): logger.info("Local product detected. Download skipped") - return product.location + return uri_to_path(product.location) if product.downloader is None: auth = product.downloader_auth if auth is None: diff --git a/eodag/api/product/_product.py b/eodag/api/product/_product.py index a0d98184..8deaeb61 100644 --- a/eodag/api/product/_product.py +++ b/eodag/api/product/_product.py @@ -240,6 +240,11 @@ class EOProduct(object): :rtype: str :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` + + .. versionchanged:: 2.3.0 + + Returns a file system path instead of a file URI ('/tmp' instead of + 'file:///tmp'). """ if progress_callback is None: progress_callback = ProgressCallback() @@ -254,7 +259,7 @@ class EOProduct(object): if self.downloader_auth is not None else self.downloader_auth ) - fs_location = self.downloader.download( + fs_path = self.downloader.download( self, auth=auth, progress_callback=progress_callback, @@ -262,13 +267,8 @@ class EOProduct(object): timeout=timeout, kwargs ) - if fs_location is None: - raise DownloadError( - "Invalid file location returned by download process: '{}'".format( - fs_location - ) - ) - self.location = "file://{}".format(fs_location) + if fs_path is None: + raise DownloadError("Missing file location returned by download process") logger.debug( "Product location updated from '%s' to '%s'", self.remote_location, @@ -279,24 +279,23 @@ class EOProduct(object): "'remote_location' property: %s", self.remote_location, ) - return self.location + return fs_path def get_quicklook(self, filename=None, base_dir=None, progress_callback=None): - """Download the quick look image of a given EOProduct from its provider if it + """Download the quicklook image of a given EOProduct from its provider if it exists. - :param filename: (optional) the name to give to the downloaded quicklook. + :param filename: (optional) the name to give to the downloaded quicklook. If not + given, it defaults to the product's ID (without file extension). :type filename: str :param base_dir: (optional) the absolute path of the directory where to store - the quicklooks in the filesystem. If it is not given, it - defaults to the `quicklooks` directory under this EO product - downloader's ``outputs_prefix`` config param + the quicklooks in the filesystem. If not given, it defaults to the + `quicklooks` directory under this EO product downloader's ``outputs_prefix`` + config param (e.g. '/tmp/quicklooks/') :type base_dir: str - :param progress_callback: (optional) A method or a callable object - which takes a current size and a maximum - size as inputs and handle progress bar - creation and update to give the user a - feedback on the download progress + :param progress_callback: (optional) A method or a callable object which takes + a current size and a maximum size as inputs and handle progress bar creation + and update to give the user a feedback on the download progress :type progress_callback: :class:`~eodag.utils.ProgressCallback` or None :returns: The absolute path of the downloaded quicklook :rtype: str diff --git a/eodag/plugins/apis/base.py b/eodag/plugins/apis/base.py index 6a775973..b6d385ef 100644 --- a/eodag/plugins/apis/base.py +++ b/eodag/plugins/apis/base.py @@ -18,12 +18,40 @@ import logging from eodag.plugins.base import PluginTopic +from eodag.plugins.download.base import DEFAULT_DOWNLOAD_TIMEOUT, DEFAULT_DOWNLOAD_WAIT logger = logging.getLogger("eodag.plugins.apis.base") class Api(PluginTopic): - """Plugins API Base plugin""" + """Plugins API Base plugin + + An Api plugin has three download methods that it must implement: + - ``query``: search for products + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + The download methods must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + """ def query(self, args, count=True, kwargs): """Implementation of how the products must be searched goes here. @@ -32,14 +60,77 @@ class Api(PluginTopic): which will be processed by a Download plugin (2) and the total number of products matching the search criteria. If ``count`` is False, the second element returned must be ``None``. - .. versionchanged:: - 2.1 + .. versionchanged:: 2.1 - A new optional boolean parameter ``count`` which defaults to ``True``, it - allows to trigger or not a count query. + A new optional boolean parameter ``count`` which defaults to ``True``, it + allows to trigger or not a count query. """ raise NotImplementedError("A Api plugin must implement a method named query") - def download(self, args, kwargs): - """Implementation of how the products must be downloaded.""" - raise NotImplementedError("A Api plugin must implement a method named download") + def download( + self, + product, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str + """ + raise NotImplementedError( + "An Api plugin must implement a method named download" + ) + + def download_all( + self, + products, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): + """ + Base download_all method. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list + """ + raise NotImplementedError( + "A Api plugin must implement a method named download_all" + ) diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 37ce4443..768778e5 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -37,7 +37,12 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import GENERIC_PRODUCT_TYPE, format_dict_items, get_progress_callback +from eodag.utils import ( + GENERIC_PRODUCT_TYPE, + format_dict_items, + get_progress_callback, + path_to_uri, +) from eodag.utils.exceptions import AuthenticationError, NotAvailableError logger = logging.getLogger("eodag.plugins.apis.usgs") @@ -164,6 +169,8 @@ class UsgsApi(Api, Download): product, outputs_extension=".tar.gz", kwargs ) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -264,8 +271,11 @@ class UsgsApi(Api, Download): ) new_fs_path = fs_path[: fs_path.index(".tar.gz")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product_path = self._finalize(fs_path, outputs_extension=".tar.gz", kwargs) + product.location = path_to_uri(product_path) + return product_path def download_all( self, diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 2f0b132d..687985c6 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -34,7 +34,7 @@ from eodag.api.product.metadata_mapping import ( properties_from_xml, ) from eodag.plugins.download.base import Download -from eodag.utils import get_progress_callback, urlparse +from eodag.utils import get_progress_callback, path_to_uri, urlparse from eodag.utils.exceptions import AuthenticationError, DownloadError logger = logging.getLogger("eodag.plugins.download.aws") @@ -185,6 +185,8 @@ class AwsDownload(Download): # prepare download & create dirs (before updating metadata) product_local_path, record_filename = self._prepare_download(product, kwargs) if not product_local_path or not record_filename: + if product_local_path: + product.location = path_to_uri(product_local_path) return product_local_path product_local_path = product_local_path.replace(".zip", "") # remove existing incomplete file @@ -395,6 +397,7 @@ class AwsDownload(Download): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path def get_authenticated_objects(self, bucket_name, prefix, auth_dict): diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index 8d6dbd3e..bfcb8ac1 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -25,7 +25,7 @@ from datetime import datetime, timedelta from time import sleep from eodag.plugins.base import PluginTopic -from eodag.utils import get_progress_callback, sanitize +from eodag.utils import get_progress_callback, sanitize, uri_to_path from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -43,6 +43,31 @@ DEFAULT_DOWNLOAD_TIMEOUT = 20 class Download(PluginTopic): """Base Download Plugin. + A Download plugin has two download methods that it must implement: + - ``download``: download a single ``EOProduct`` + - ``download_all``: download multiple products from a ``SearchResult`` + + They must: + - download data in the ``outputs_prefix`` folder defined in the plugin's + configuration or passed through kwargs + - extract products from their archive (if relevant) if ``extract`` is set to True + (True by default) + - save a product in an archive/directory (in ``outputs_prefix``) whose name must be + the product's ``title`` property + - update the product's ``location`` attribute once its data is downloaded (and + eventually after it's extracted) to the product's location given as a file URI + (e.g. 'file:///tmp/product_folder' on Linux or + 'file:///C:/Users/username/AppData/LOcal/Temp' on Windows) + - save a record* file in the directory ``outputs_prefix/.downloaded`` whose name + is built on the MD5 hash of the product's ``remote_location`` attribute + (``hashlib.md5(remote_location.encode("utf-8")).hexdigest()``) and whose content is + the product's ``remote_location`` attribute itself. + - not try to download a product whose ``location`` attribute already points to an + existing file/directory + - not try to download a product if its record file exists as long as the expected + product's file/directory. If the record file only is found, it must be deleted + (it certainly indicates that the download didn't complete) + :param provider: An eodag providers configuration dictionary :type provider: dict :param config: Path to the user configuration file @@ -62,8 +87,26 @@ class Download(PluginTopic): timeout=DEFAULT_DOWNLOAD_TIMEOUT, kwargs, ): - """ - Base download method. Not available, should be defined for each plugin + r""" + Base download method. Not available, it must be defined for each plugin. + + :param product: EO product to download + :type product: :class:`~eodag.api.product.EOProduct` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: The absolute path to the downloaded product in the local filesystem + (e.g. '/tmp/product.zip' on Linux or + 'C:\\Users\\username\\AppData\\Local\\Temp\\product.zip' on Windows) + :rtype: str """ raise NotImplementedError( "A Download plugin must implement a method named download" @@ -78,8 +121,7 @@ class Download(PluginTopic): :rtype: tuple """ if product.location != product.remote_location: - scheme_prefix_len = len("file://") - fs_path = product.location[scheme_prefix_len:] + fs_path = uri_to_path(product.location) # The fs path of a product is either a file (if 'extract' config is False) or a directory if os.path.isfile(fs_path) or os.path.isdir(fs_path): logger.info( @@ -249,9 +291,32 @@ class Download(PluginTopic): kwargs, ): """ - A sequential download_all implementation - using download method for every products + Base download_all method. + + This specific implementation uses the ``download`` method implemented by + the plugin to sequentially attempt to download products. + + :param products: Products to download + :type products: :class:`~eodag.api.search_result.SearchResult` + :param progress_callback: A progress callback + :type progress_callback: :class:`~eodag.utils.ProgressCallback`, optional + :param wait: If download fails, wait time in minutes between two download tries + :type wait: int, optional + :param timeout: If download fails, maximum time in minutes before stop retrying + to download + :type timeout: int, optional + :param dict kwargs: ``outputs_prefix` (``str``), `extract` (``bool``) and + ``dl_url_params`` (``dict``) can be provided as additional kwargs and will + override any other values defined in a configuration file or with + environment variables. + :returns: List of absolute paths to the downloaded products in the local + filesystem (e.g. ``['/tmp/product.zip']`` on Linux or + ``['C:\\Users\\username\\AppData\\Local\\Temp\\product.zip']`` on Windows) + :rtype: list """ + # Products are going to be removed one by one from this sequence once + # downloaded. + products = products[:] paths = [] # initiate retry loop start_time = datetime.now() diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 11662a18..f54fff86 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -32,7 +32,7 @@ from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_WAIT, Download, ) -from eodag.utils import get_progress_callback +from eodag.utils import get_progress_callback, path_to_uri from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -70,6 +70,8 @@ class HTTPDownload(Download): """ fs_path, record_filename = self._prepare_download(product, kwargs) if not fs_path or not record_filename: + if fs_path: + product.location = path_to_uri(fs_path) return fs_path # progress bar init @@ -80,7 +82,7 @@ class HTTPDownload(Download): # download assets if exist instead of remote_location try: - return self._download_assets( + fs_path = self._download_assets( product, fs_path.replace(".zip", ""), record_filename, @@ -88,6 +90,8 @@ class HTTPDownload(Download): progress_callback, kwargs ) + product.location = path_to_uri(fs_path) + return fs_path except NotAvailableError: pass @@ -218,8 +222,11 @@ class HTTPDownload(Download): ) new_fs_path = fs_path[: fs_path.index(".zip")] shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) return new_fs_path - return self._finalize(fs_path, kwargs) + product_path = self._finalize(fs_path, kwargs) + product.location = path_to_uri(product_path) + return product_path except NotAvailableError as e: if not getattr(self.config, "order_enabled", False): diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index 71876ea4..2f5628b9 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -29,7 +29,7 @@ from requests import HTTPError from eodag.api.product.metadata_mapping import OFFLINE_STATUS from eodag.plugins.download.aws import AwsDownload from eodag.plugins.download.http import HTTPDownload -from eodag.utils import get_progress_callback, urljoin +from eodag.utils import get_progress_callback, path_to_uri, urljoin from eodag.utils.exceptions import ( AuthenticationError, DownloadError, @@ -158,6 +158,7 @@ class S3RestDownload(AwsDownload): url_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() record_filename = os.path.join(download_records_dir, url_hash) if os.path.isfile(record_filename) and os.path.exists(product_local_path): + product.location = path_to_uri(product_local_path) return product_local_path # Remove the record file if product_local_path is absent (e.g. it was deleted while record wasn't) elif os.path.isfile(record_filename): @@ -215,4 +216,5 @@ class S3RestDownload(AwsDownload): fh.write(product.remote_location) logger.debug("Download recorded in %s", record_filename) + product.location = path_to_uri(product_local_path) return product_local_path diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index f48e3eb0..2c6f7e9d 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -463,7 +463,7 @@ def download_stac_item_by_id(catalogs, item_id, provider=None): product_path = eodag_api.download(product) - return product_path.replace("file://", "") + return product_path def get_stac_catalogs(url, root="/", catalogs=[], provider=None): diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 70798110..0c21649b 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -35,6 +35,7 @@ import warnings from collections import defaultdict from datetime import datetime, timezone from itertools import repeat, starmap +from pathlib import Path # All modules using these should import them from utils package from urllib.parse import ( # noqa; noqa @@ -43,8 +44,10 @@ from urllib.parse import ( # noqa; noqa urlencode, urljoin, urlparse, + urlsplit, urlunparse, ) +from urllib.request import url2pathname import click import shapefile @@ -203,6 +206,24 @@ def strip_accents(s): ) +def uri_to_path(uri): + """ + Convert a file URI (e.g. 'file:///tmp') to a local path (e.g. '/tmp') + """ + if not uri.startswith("file"): + raise ValueError("A file URI must be provided (e.g. 'file:///tmp'") + _, _, path, _, _ = urlsplit(uri) + # On Windows urlsplit returns the path starting with a slash ('/C:/User) + path = url2pathname(path) + # url2pathname removes it + return path + + +def path_to_uri(path): + """Convert a local absolute path to a file URI""" + return Path(path).as_uri() + + def mutate_dict_in_place(func, mapping): """Apply func to values of mapping. @@ -853,13 +874,13 @@ def _deprecated(reason="", version=None): @functools.wraps(callable) def wrapper(args, kwargs): - warnings.simplefilter("always", DeprecationWarning) - warnings.warn( - f"Call to deprecated {ctype} {cname} {reason_}{version_}", - category=DeprecationWarning, - stacklevel=2, - ) - warnings.simplefilter("default", DeprecationWarning) + with warnings.catch_warnings(): + warnings.simplefilter("always", DeprecationWarning) + warnings.warn( + f"Call to deprecated {ctype} {cname} {reason_}{version_}", + category=DeprecationWarning, + stacklevel=2, + ) return callable(args, **kwargs) return wrapper	providers chaining for search Actually, if a productType is not available for a provider, eodag will use the first matching provider with the highest priority. We can keep this behavior, and add the `search(providers=["theia"])` feature (see also #163 ). - this code will still look for products in the 1st available provider (peps in our case): ```python dag.set_preferred_provider("theia") dag.search(productType='S2_MSI_L1C') ``` - but this one would search only on theia and return nothing with a NotAvailable error, as S2_MSI_L1C is not available on theia: ```python dag.search(providers=["theia"], productType="S2_MSI_L1C") ``` We will have to describe that in more details in the tutorials, and I'd say that eodag should log at the warning level that the product searched is not available with the preferred provider. The tutorials should also make it clear that this is valid only for searching and not for downloading. TODOs: * [ ] Log when the requested products is not available with the preferred provider and that an alternative provider is used instead * [ ] Update the tutorials once #163 is fixed to describe the two ways to specify the providers in a search (`dag.set_preferred_provider` and `dag.search(providers=["theia", "peps"])`	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index 96807bc8..df74276a 100644 --- a/tests/context.py +++ b/tests/context.py @@ -44,7 +44,13 @@ from eodag.plugins.manager import PluginManager from eodag.plugins.search.base import Search from eodag.rest import server as eodag_http_server from eodag.rest.utils import eodag_api, get_date -from eodag.utils import get_geometry_from_various, get_timestamp, makedirs +from eodag.utils import ( + get_geometry_from_various, + get_timestamp, + makedirs, + path_to_uri, + uri_to_path, +) from eodag.utils.exceptions import ( AddressNotFound, AuthenticationError, diff --git a/tests/test_cli.py b/tests/test_cli.py index 94e07cc3..a4171dbb 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -427,7 +427,7 @@ class TestEodagCli(unittest.TestCase): TEST_RESOURCES_PATH, "eodag_search_result.geojson" ) config_path = os.path.join(TEST_RESOURCES_PATH, "file_config_override.yml") - dag.return_value.download_all.return_value = ["file:///fake_path"] + dag.return_value.download_all.return_value = ["/fake_path"] result = self.runner.invoke( eodag, ["download", "--search-results", search_results_path, "-f", config_path], @@ -435,7 +435,7 @@ class TestEodagCli(unittest.TestCase): dag.assert_called_once_with(user_conf_file_path=config_path) dag.return_value.deserialize.assert_called_once_with(search_results_path) self.assertEqual(dag.return_value.download_all.call_count, 1) - self.assertEqual("Downloaded file:///fake_path\n", result.output) + self.assertEqual("Downloaded /fake_path\n", result.output) # Testing the case when no downloaded path is returned dag.return_value.download_all.return_value = [None] diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index be2dee90..ca0a145c 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -18,15 +18,22 @@ import datetime import glob +import hashlib import multiprocessing import os import shutil +import time import unittest from pathlib import Path from eodag.api.product.metadata_mapping import ONLINE_STATUS from tests import TEST_RESOURCES_PATH, TESTS_DOWNLOAD_PATH -from tests.context import AuthenticationError, EODataAccessGateway +from tests.context import ( + AuthenticationError, + EODataAccessGateway, + SearchResult, + uri_to_path, +) THEIA_SEARCH_ARGS = [ "theia", @@ -148,8 +155,8 @@ class EndToEndBase(unittest.TestCase): """ search_criteria = { "productType": product_type, - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } if items_per_page: @@ -190,8 +197,8 @@ class EndToEndBase(unittest.TestCase): - Return all the products """ search_criteria = { - "startTimeFromAscendingNode": start, - "completionTimeFromAscendingNode": end, + "start": start, + "end": end, "geom": geom, } self.eodag.set_preferred_provider(provider) @@ -428,6 +435,196 @@ class TestEODagEndToEnd(EndToEndBase): self.assertGreater(len(results), 10) +class TestEODagEndToEndComplete(unittest.TestCase): + """Make real and complete test cases that search for products, download them and + extract them. There should be just a tiny number of these tests which can be quite + long to run. + + There must be a user conf file in the test resources folder named user_conf.yml + """ + + @classmethod + def setUpClass(cls): + + # use tests/resources/user_conf.yml if exists else default file ~/.config/eodag/eodag.yml + tests_user_conf = os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + if not os.path.isfile(tests_user_conf): + unittest.SkipTest("Missing user conf file with credentials") + cls.eodag = EODataAccessGateway( + user_conf_file_path=os.path.join(TEST_RESOURCES_PATH, "user_conf.yml") + ) + + # create TESTS_DOWNLOAD_PATH is not exists + if not os.path.exists(TESTS_DOWNLOAD_PATH): + os.makedirs(TESTS_DOWNLOAD_PATH) + + for provider, conf in cls.eodag.providers_config.items(): + # Change download directory to TESTS_DOWNLOAD_PATH for tests + if hasattr(conf, "download") and hasattr(conf.download, "outputs_prefix"): + conf.download.outputs_prefix = TESTS_DOWNLOAD_PATH + elif hasattr(conf, "api") and hasattr(conf.api, "outputs_prefix"): + conf.api.outputs_prefix = TESTS_DOWNLOAD_PATH + else: + # no outputs_prefix found for provider + pass + # Force all providers implementing RestoSearch and defining how to retrieve + # products by specifying the + # location scheme to use https, enabling actual downloading of the product + if ( + getattr(getattr(conf, "search", {}), "product_location_scheme", "https") + == "file" + ): + conf.search.product_location_scheme = "https" + + def tearDown(self): + """Clear the test directory""" + for p in Path(TESTS_DOWNLOAD_PATH).glob("*/"): + try: + os.remove(p) + except OSError: + shutil.rmtree(p) + + def test_end_to_end_complete_peps(self): + """Complete end-to-end test with PEPS for download and download_all""" + # Search for products that are ONLINE and as small as possible + today = datetime.date.today() + month_span = datetime.timedelta(weeks=4) + search_results, _ = self.eodag.search( + productType="S2_MSI_L1C", + start=(today - month_span).isoformat(), + end=today.isoformat(), + geom={"lonmin": 1, "latmin": 42, "lonmax": 5, "latmax": 46}, + items_per_page=100, + ) + prods_sorted_by_size = SearchResult( + sorted(search_results, key=lambda p: p.properties["resourceSize"]) + ) + prods_online = [ + p for p in prods_sorted_by_size if p.properties["storageStatus"] == "ONLINE" + ] + if len(prods_online) < 2: + unittest.skip( + "Not enough ONLINE products found, update the search criteria." + ) + + # Retrieve one product to work with + product = prods_online[0] + + prev_remote_location = product.remote_location + prev_location = product.location + # The expected product's archive filename is based on the product's title + expected_product_name = f"{product.properties['title']}.zip" + + # Download the product, but DON'T extract it + archive_file_path = self.eodag.download(product, extract=False) + + # The archive must have been downloaded + self.assertTrue(os.path.isfile(archive_file_path)) + # Its name must be the "{product_title}.zip" + self.assertIn( + expected_product_name, os.listdir(product.downloader.config.outputs_prefix) + ) + # Its size should be >= 5 KB + archive_size = os.stat(archive_file_path).st_size + self.assertGreaterEqual(archive_size, 5 * 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # That points to the downloaded archive + self.assertEqual(uri_to_path(product.location), archive_file_path) + # A .downloaded folder must have been created + record_dir = os.path.join(TESTS_DOWNLOAD_PATH, ".downloaded") + self.assertTrue(os.path.isdir(record_dir)) + # It must contain a file per product downloade, whose name is + # the MD5 hash of the product's remote location + expected_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() + record_file = os.path.join(record_dir, expected_hash) + self.assertTrue(os.path.isfile(record_file)) + # Its content must be the product's remote location + record_content = Path(record_file).read_text() + self.assertEqual(record_content, product.remote_location) + + # The product should not be downloaded again if the download method + # is executed again + previous_archive_file_path = archive_file_path + previous_location = product.location + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The paths should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(product.location, previous_location) + + # If we emulate that the product has just been found, it should not + # be downloaded again since the record file is still present. + product.location = product.remote_location + # Pretty much the same checks as with the previous step + previous_archive_file_path = archive_file_path + start_time = time.time() + archive_file_path = self.eodag.download(product, extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + # The returned path should be the same as before + self.assertEqual(archive_file_path, previous_archive_file_path) + self.assertEqual(uri_to_path(product.location), archive_file_path) + + # Remove the archive + os.remove(archive_file_path) + + # Now, the archive is removed but its associated record file + # still exists. Downloading the product again should really + # download it, if its location points to the remote location. + # The product should be automatically extracted. + product.location = product.remote_location + product_dir_path = self.eodag.download(product) + + # Its size should be >= 5 KB + downloaded_size = sum( + f.stat().st_size for f in Path(product_dir_path).glob("/") if f.is_file() + ) + self.assertGreaterEqual(downloaded_size, 5 2 10) + # The product remote_location should be the same + self.assertEqual(prev_remote_location, product.remote_location) + # However its location should have been update + self.assertNotEqual(prev_location, product.location) + # The location must follow the file URI scheme + self.assertTrue(product.location.startswith("file://")) + # The location must point to a SAFE directory + self.assertTrue(product.location.endswith("SAFE")) + # The path must point to a SAFE directory + self.assertTrue(os.path.isdir(product_dir_path)) + self.assertTrue(product_dir_path.endswith("SAFE")) + + # Remove the archive and extracted product and reset the product's location + os.remove(archive_file_path) + shutil.rmtree(Path(product_dir_path).parent) + product.location = product.remote_location + + # Now let's check download_all + products = prods_sorted_by_size[:2] + # Pass a copy because download_all empties the list + archive_paths = self.eodag.download_all(products[:], extract=False) + + # The returned paths must point to the downloaded archives + # Each product's location must be a URI path to the archive + for product, archive_path in zip(products, archive_paths): + self.assertTrue(os.path.isfile(archive_path)) + self.assertEqual(uri_to_path(product.location), archive_path) + + # Downloading the product again should not download them, since + # they are all already there. + prev_archive_paths = archive_paths + start_time = time.time() + archive_paths = self.eodag.download_all(products[:], extract=False) + end_time = time.time() + self.assertLess(end_time - start_time, 2) # Should be really fast (< 2s) + self.assertEqual(archive_paths, prev_archive_paths) + + # @unittest.skip("skip auto run") class TestEODagEndToEndWrongCredentials(EndToEndBase): """Make real case tests with wrong credentials. This assumes the existence of a @@ -462,7 +659,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) def test_end_to_end_good_apikey_wrong_credentials_aws_eos(self): @@ -489,7 +686,7 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], AWSEOS_SEARCH_ARGS[1:]) - ) + ), ) self.assertGreater(len(results), 0) one_product = results[0] @@ -525,5 +722,5 @@ class TestEODagEndToEndWrongCredentials(EndToEndBase): raise_errors=True, dict( zip(["productType", "start", "end", "geom"], USGS_SEARCH_ARGS[1:]) - ) + ), ) diff --git a/tests/units/test_core.py b/tests/units/test_core.py index cb6022e4..69cf9a3e 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -367,7 +367,7 @@ class TestCoreGeometry(unittest.TestCase): "lonmax": 2, "latmax": 52, } - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Bad dict with a missing key del geometry["lonmin"] self.assertRaises( @@ -378,10 +378,10 @@ class TestCoreGeometry(unittest.TestCase): ) # Tuple geometry = (0, 50, 2, 52) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List geometry = list(geometry) - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # List without 4 items geometry.pop() self.assertRaises( @@ -392,7 +392,7 @@ class TestCoreGeometry(unittest.TestCase): ) # WKT geometry = ref_geom_as_wkt - self.assertEquals(get_geometry_from_various([], geometry=geometry), ref_geom) + self.assertEqual(get_geometry_from_various([], geometry=geometry), ref_geom) # Some other shapely geom geometry = LineString([[0, 0], [1, 1]]) self.assertIsInstance( @@ -409,7 +409,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="FRA") ) self.assertIsInstance(geom_france, MultiPolygon) - self.assertEquals(len(geom_france), 3) # France + Guyana + Corsica + self.assertEqual(len(geom_france), 3) # France + Guyana + Corsica def test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs( self, @@ -444,7 +444,7 @@ class TestCoreGeometry(unittest.TestCase): locations_config, locations=dict(country="PA[A-Z]") ) self.assertIsInstance(geom_regex_pa, MultiPolygon) - self.assertEquals(len(geom_regex_pa), 2) + self.assertEqual(len(geom_regex_pa), 2) def test_get_geometry_from_various_locations_no_match_raises_error(self): """If the location search doesn't match any of the feature attribute a ValueError must be raised""" @@ -468,7 +468,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # France + Guyana + Corsica + somewhere over Poland - self.assertEquals(len(geom_combined), 4) + self.assertEqual(len(geom_combined), 4) geometry = { "lonmin": 0, "latmin": 50, @@ -480,7 +480,7 @@ class TestCoreGeometry(unittest.TestCase): ) self.assertIsInstance(geom_combined, MultiPolygon) # The bounding box overlaps with France inland - self.assertEquals(len(geom_combined), 3) + self.assertEqual(len(geom_combined), 3) class TestCoreSearch(unittest.TestCase): @@ -546,7 +546,6 @@ class TestCoreSearch(unittest.TestCase): "geometry": None, "productType": None, } - self.assertDictContainsSubset(expected, prepared_search) expected = set(["geometry", "productType", "auth", "search_plugin"]) self.assertSetEqual(expected, set(prepared_search)) @@ -557,11 +556,10 @@ class TestCoreSearch(unittest.TestCase): "end": "2020-02-01", } prepared_search = self.dag._prepare_search(base) - expected = { - "startTimeFromAscendingNode": base["start"], - "completionTimeFromAscendingNode": base["end"], - } - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["startTimeFromAscendingNode"], base["start"]) + self.assertEqual( + prepared_search["completionTimeFromAscendingNode"], base["end"] + ) def test__prepare_search_geom(self): """_prepare_search must handle geom, box and bbox""" @@ -608,8 +606,7 @@ class TestCoreSearch(unittest.TestCase): """_prepare_search must handle when a product type is given""" base = {"productType": "S2_MSI_L1C"} prepared_search = self.dag._prepare_search(base) - expected = {"productType": base["productType"]} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) def test__prepare_search_product_type_guess_it(self): """_prepare_search must guess a product type when required to""" @@ -621,8 +618,19 @@ class TestCoreSearch(unittest.TestCase): platformSerialIdentifier="S2A", ) prepared_search = self.dag._prepare_search(base) - expected = {"productType": "S2_MSI_L1C", base} - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], "S2_MSI_L1C") + + def test__prepare_search_remove_guess_kwargs(self): + """_prepare_search must remove the guess kwargs""" + # Uses guess_product_type to find the product matching + # the best the given params. + base = dict( + instrument="MSI", + platform="SENTINEL2", + platformSerialIdentifier="S2A", + ) + prepared_search = self.dag._prepare_search(base) + self.assertEqual(len(base.keys() & prepared_search.keys()), 0) def test__prepare_search_with_id(self): """_prepare_search must handle a search by id""" @@ -638,8 +646,8 @@ class TestCoreSearch(unittest.TestCase): "cloudCover": 10, } prepared_search = self.dag._prepare_search(base) - expected = base - self.assertDictContainsSubset(expected, prepared_search) + self.assertEqual(prepared_search["productType"], base["productType"]) + self.assertEqual(prepared_search["cloudCover"], base["cloudCover"]) def test__prepare_search_search_plugin_has_known_product_properties(self): """_prepare_search must attach the product properties to the search plugin""" diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index f43aaa84..0faf7704 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -90,23 +90,18 @@ class TestEOProduct(EODagTestCase): self.provider, self.eoproduct_props, productType=self.product_type ) geo_interface = geojson.loads(geojson.dumps(product)) - self.assertDictContainsSubset( - { - "type": "Feature", - "geometry": self._tuples_to_lists(geometry.mapping(self.geometry)), - }, - geo_interface, + self.assertEqual(geo_interface["type"], "Feature") + self.assertEqual( + geo_interface["geometry"], + self._tuples_to_lists(geometry.mapping(self.geometry)), ) - self.assertDictContainsSubset( - { - "eodag_provider": self.provider, - "eodag_search_intersection": self._tuples_to_lists( - geometry.mapping(product.search_intersection) - ), - "eodag_product_type": self.product_type, - }, - geo_interface["properties"], + properties = geo_interface["properties"] + self.assertEqual(properties["eodag_provider"], self.provider) + self.assertEqual( + properties["eodag_search_intersection"], + self._tuples_to_lists(geometry.mapping(product.search_intersection)), ) + self.assertEqual(properties["eodag_product_type"], self.product_type) def test_eoproduct_from_geointerface(self): """EOProduct must be build-able from its geo-interface""" @@ -295,7 +290,6 @@ class TestEOProduct(EODagTestCase): self.requests_http_get.assert_called_with( self.download_url, stream=True, auth=None, params={} ) - product_file_path = product_file_path[len("file://") :] download_records_dir = pathlib.Path(product_file_path).parent / ".downloaded" # A .downloaded folder should be created, including a text file that # lists the downloaded product by their url @@ -332,7 +326,7 @@ class TestEOProduct(EODagTestCase): # Download product_dir_path = product.download() - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) # The returned path must be a directory. self.assertTrue(product_dir_path.is_dir()) # Check that the extracted dir has at least one file, there are more @@ -379,7 +373,7 @@ class TestEOProduct(EODagTestCase): self.download_url, stream=True, auth=None, params={"fakeparam": "dummy"} ) # Check that "outputs_prefix" is respected. - product_dir_path = pathlib.Path(product_dir_path[len("file://") :]) + product_dir_path = pathlib.Path(product_dir_path) self.assertEqual(product_dir_path.parent.name, output_dir_name) # We've asked to extract the product so there should be a directory. self.assertTrue(product_dir_path.is_dir()) diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 28369fee..09dd221d 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -106,8 +106,8 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): info_message=mock.ANY, exception_message=mock.ANY, ) - self.assertEquals(estimate, self.sobloo_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.sobloo_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -144,7 +144,7 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -196,8 +196,8 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.awseos_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.awseos_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @mock.patch( @@ -232,7 +232,7 @@ class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): ) self.assertIsNone(estimate) - self.assertEquals(len(products), number_of_products) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) @@ -294,6 +294,6 @@ class TestSearchPluginODataV4Search(BaseSearchPluginTest): exception_message=mock.ANY, ) - self.assertEquals(estimate, self.onda_products_count) - self.assertEquals(len(products), number_of_products) + self.assertEqual(estimate, self.onda_products_count) + self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) diff --git a/tests/units/test_search_result.py b/tests/units/test_search_result.py index f1697117..d4e52303 100644 --- a/tests/units/test_search_result.py +++ b/tests/units/test_search_result.py @@ -32,9 +32,8 @@ class TestSearchResult(unittest.TestCase): def test_search_result_geo_interface(self): """SearchResult must provide a FeatureCollection geo-interface""" geo_interface = geojson.loads(geojson.dumps(self.search_result)) - self.assertDictContainsSubset( - {"type": "FeatureCollection", "features": []}, geo_interface - ) + self.assertEqual(geo_interface["type"], "FeatureCollection") + self.assertEqual(geo_interface["features"], []) def test_search_result_is_list_like(self): """SearchResult must provide a list interface""" diff --git a/tests/units/test_stac_reader.py b/tests/units/test_stac_reader.py index 8960ee81..f242fb38 100644 --- a/tests/units/test_stac_reader.py +++ b/tests/units/test_stac_reader.py @@ -47,10 +47,8 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.child_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.child_cat_len) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - items[0], - ) + self.assertEqual(items[0]["type"], "Feature") + self.assertEqual(items[0]["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_root_not_recursive(self): """fetch_stac_items from root must provide an empty list when no recursive""" @@ -62,23 +60,20 @@ class TestStacReader(unittest.TestCase): items = fetch_stac_items(self.root_cat, recursive=True) self.assertEqual(len(items), self.root_cat_len) for item in items: - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, item - ) + self.assertEqual(item["type"], "Feature") + self.assertEqual(item["collection"], "S2_MSI_L1C") def test_stac_reader_fetch_item(self): """fetch_stac_items from an item must return it""" item = fetch_stac_items(self.item) self.assertIsInstance(item, list) self.assertEqual(len(item), 1) - self.assertDictContainsSubset( - {"type": "Feature", "collection": "S2_MSI_L1C"}, - item[0], - ) + self.assertEqual(item[0]["type"], "Feature") + self.assertEqual(item[0]["collection"], "S2_MSI_L1C") def test_stact_reader_fetch_singlefile_catalog(self): """fetch_stact_items must return all the items from a single file catalog""" items = fetch_stac_items(self.singlefile_cat) self.assertIsInstance(items, list) self.assertEqual(len(items), self.singlefile_cat_len) - self.assertDictContainsSubset({"type": "Feature"}, items[0]) + self.assertEqual(items[0]["type"], "Feature") diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 58732712..ef865dff 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -16,10 +16,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import sys import unittest from datetime import datetime -from tests.context import get_timestamp +from tests.context import get_timestamp, path_to_uri, uri_to_path class TestUtils(unittest.TestCase): @@ -33,3 +34,21 @@ class TestUtils(unittest.TestCase): expected_dt = datetime.strptime(requested_date, date_format) actual_utc_dt = datetime.utcfromtimestamp(ts_in_secs) self.assertEqual(actual_utc_dt, expected_dt) + + def test_uri_to_path(self): + if sys.platform == "win32": + expected_path = r"C:\tmp\file.txt" + tested_uri = r"file:///C:/tmp/file.txt" + else: + expected_path = "/tmp/file.txt" + tested_uri = "file:///tmp/file.txt" + actual_path = uri_to_path(tested_uri) + self.assertEqual(actual_path, expected_path) + with self.assertRaises(ValueError): + uri_to_path("not_a_uri") + + def test_path_to_uri(self): + if sys.platform == "win32": + self.assertEqual(path_to_uri(r"C:\tmp\file.txt"), "file:///C:/tmp/file.txt") + else: + self.assertEqual(path_to_uri("/tmp/file.txt"), "file:///tmp/file.txt")	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_issue_reference", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 10 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@0632e4147f26119bea43e0672f6d06e75d216a20#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_cli.py::TestCore::test_core_object_locations_file_not_found", "tests/test_cli.py::TestCore::test_core_object_open_index_if_exists", "tests/test_cli.py::TestCore::test_core_object_set_default_locations_config", "tests/test_cli.py::TestCore::test_list_product_types_for_provider_ok", "tests/test_cli.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/test_cli.py::TestCore::test_list_product_types_ok", "tests/test_cli.py::TestCore::test_rebuild_index", "tests/test_cli.py::TestCore::test_supported_product_types_in_unit_test", "tests/test_cli.py::TestCore::test_supported_providers_in_unit_test", "tests/test_cli.py::TestEodagCli::test_eodag_download_missingcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_download_no_search_results_arg", "tests/test_cli.py::TestEodagCli::test_eodag_download_ok", "tests/test_cli.py::TestEodagCli::test_eodag_download_wrongcredentials", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_ok", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ko", "tests/test_cli.py::TestEodagCli::test_eodag_list_product_type_with_provider_ok", "tests/test_cli.py::TestEodagCli::test_eodag_search_all", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_geom_mutually_exclusive", "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_valid", "tests/test_cli.py::TestEodagCli::test_eodag_search_storage_arg", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_conf_file_inexistent", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_max_cloud_out_of_range", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_args", "tests/test_cli.py::TestEodagCli::test_eodag_search_without_producttype_arg", "tests/test_cli.py::TestEodagCli::test_eodag_with_only_verbose_opt", "tests/test_cli.py::TestEodagCli::test_eodag_without_args", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo_noresult", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda", "tests/units/test_search_result.py::TestSearchResult::test_search_result_from_feature_collection", "tests/units/test_search_result.py::TestSearchResult::test_search_result_geo_interface", "tests/units/test_search_result.py::TestSearchResult::test_search_result_is_list_like", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[ "tests/test_cli.py::TestEodagCli::test_eodag_search_bbox_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_geom_wkt_invalid", "tests/test_cli.py::TestEodagCli::test_eodag_search_with_cruncher", "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps_after_20161205", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sobloo", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_sobloo", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_child", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_item", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_not_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stac_reader_fetch_root_recursive", "tests/units/test_stac_reader.py::TestStacReader::test_stact_reader_fetch_singlefile_catalog" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-304	f32b8071ac367db96ff8f9f8709f0771f9d898f2	2021-06-15 16:30:26	f32b8071ac367db96ff8f9f8709f0771f9d898f2		diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index e356dd02..cd96491f 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -804,27 +804,6 @@ S2_MSI_L2A: collection: S2ST productType: S2MSI2A - S3_EFR: - productType: OL_1_EFR___ - collection: S3 - S3_ERR: - productType: OL_1_ERR___ - collection: S3 - S3_OLCI_L2LFR: - productType: OL_2_LFR___ - collection: S3 - S3_OLCI_L2LRR: - productType: OL_2_LRR___ - collection: S3 - S3_SLSTR_L1RBT: - productType: SL_1_RBT___ - collection: S3 - S3_SLSTR_L2LST: - productType: SL_2_LST___ - collection: S3 - S3_LAN: - productType: SR_2_LAN___ - collection: S3 GENERIC_PRODUCT_TYPE: productType: '{productType}' collection: '{collection}'	PEPS no longer supports S3 Attempting to use EODAG to download S3 data of any type from PEPs results in no search results returned as PEPS no longer supports S3 data. Confirmed directly by the PEPS help desk on 14.06.2021. "Comme l’indique l’actualité du 15 février sur notre flux RSS (https://peps-mission.cnes.fr/fr/peps-info-suppression-des-donnees-sentinel-3), PEPS ne diffuse plus de données S3." Therefore `dag.available_providers("S3_OLCI_L2LRR")` Should not return PEPS as a source.	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 17c4f06f..556277df 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -91,17 +91,17 @@ class TestCore(unittest.TestCase): "astraea_eod", "earth_search", ], - "S3_ERR": ["peps", "onda", "creodias"], - "S3_EFR": ["peps", "onda", "creodias"], - "S3_LAN": ["peps", "onda", "creodias"], + "S3_ERR": ["onda", "creodias"], + "S3_EFR": ["onda", "creodias"], + "S3_LAN": ["onda", "creodias"], "S3_SRA": ["onda", "creodias"], "S3_SRA_BS": ["onda", "creodias"], "S3_SRA_A_BS": ["onda", "creodias"], "S3_WAT": ["onda", "creodias"], - "S3_OLCI_L2LFR": ["peps", "onda", "creodias", "mundi"], - "S3_OLCI_L2LRR": ["peps", "onda", "creodias"], - "S3_SLSTR_L1RBT": ["peps", "onda", "creodias"], - "S3_SLSTR_L2LST": ["peps", "onda", "creodias"], + "S3_OLCI_L2LFR": ["onda", "creodias", "mundi"], + "S3_OLCI_L2LRR": ["onda", "creodias"], + "S3_SLSTR_L1RBT": ["onda", "creodias"], + "S3_SLSTR_L2LST": ["onda", "creodias"], "PLD_PAN": ["theia"], "PLD_XS": ["theia"], "PLD_BUNDLE": ["theia"],	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 1 }	2.2	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc", "apt-get install -y make", "apt-get install -y pandoc" ], "python": "3.7", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==24.2.0 bleach==6.0.0 boto3==1.33.13 botocore==1.33.13 cachetools==5.5.2 certifi @ file:///croot/certifi_1671487769961/work/certifi cffi==1.15.1 cfgv==3.3.1 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.2.7 cryptography==44.0.2 distlib==0.3.9 docutils==0.20.1 -e git+https://github.com/CS-SI/eodag.git@f32b8071ac367db96ff8f9f8709f0771f9d898f2#egg=eodag exceptiongroup==1.2.2 execnet==2.0.2 Faker==18.13.0 filelock==3.12.2 flake8==3.9.2 flasgger==0.9.7.1 Flask==2.2.5 geojson==3.2.0 identify==2.5.24 idna==3.10 importlib-metadata==6.7.0 importlib-resources==5.12.0 iniconfig==2.0.0 itsdangerous==2.1.2 jaraco.classes==3.2.3 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.17.3 keyring==24.1.1 lxml==5.3.1 Markdown==3.4.4 markdown-it-py==2.2.0 MarkupSafe==2.1.5 mccabe==0.6.1 mdurl==0.1.2 mistune==3.0.2 more-itertools==9.1.0 moto==4.2.14 nodeenv==1.9.1 nose==1.3.7 numpy==1.21.6 OWSLib==0.31.0 packaging==24.0 pandas==1.3.5 pkginfo==1.10.0 pkgutil_resolve_name==1.3.10 platformdirs==4.0.0 pluggy==1.2.0 ply==3.11 pre-commit==2.21.0 pycodestyle==2.7.0 pycparser==2.21 pyflakes==2.3.1 Pygments==2.17.2 pyproj==3.2.1 pyproject-api==1.5.3 pyrsistent==0.19.3 pyshp==2.3.1 pystac==1.5.0 pytest==7.4.4 pytest-asyncio==0.21.2 pytest-cov==4.1.0 pytest-mock==3.11.1 pytest-xdist==3.5.0 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.1 readme-renderer==37.3 requests==2.31.0 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==13.8.1 s3transfer==0.8.2 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.0.1 tox==4.8.0 tqdm==4.67.1 twine==4.0.2 types-PyYAML==6.0.12.12 typing_extensions==4.7.1 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.26.6 webencodings==0.5.1 Werkzeug==2.2.3 Whoosh==2.7.4 xarray==0.20.2 xmltodict==0.14.2 zipp==3.15.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==24.2.0 - bleach==6.0.0 - boto3==1.33.13 - botocore==1.33.13 - cachetools==5.5.2 - cffi==1.15.1 - cfgv==3.3.1 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.2.7 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.20.1 - exceptiongroup==1.2.2 - execnet==2.0.2 - faker==18.13.0 - filelock==3.12.2 - flake8==3.9.2 - flasgger==0.9.7.1 - flask==2.2.5 - geojson==3.2.0 - identify==2.5.24 - idna==3.10 - importlib-metadata==6.7.0 - importlib-resources==5.12.0 - iniconfig==2.0.0 - itsdangerous==2.1.2 - jaraco-classes==3.2.3 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.17.3 - keyring==24.1.1 - lxml==5.3.1 - markdown==3.4.4 - markdown-it-py==2.2.0 - markupsafe==2.1.5 - mccabe==0.6.1 - mdurl==0.1.2 - mistune==3.0.2 - more-itertools==9.1.0 - moto==4.2.14 - nodeenv==1.9.1 - nose==1.3.7 - numpy==1.21.6 - owslib==0.31.0 - packaging==24.0 - pandas==1.3.5 - pkginfo==1.10.0 - pkgutil-resolve-name==1.3.10 - platformdirs==4.0.0 - pluggy==1.2.0 - ply==3.11 - pre-commit==2.21.0 - pycodestyle==2.7.0 - pycparser==2.21 - pyflakes==2.3.1 - pygments==2.17.2 - pyproj==3.2.1 - pyproject-api==1.5.3 - pyrsistent==0.19.3 - pyshp==2.3.1 - pystac==1.5.0 - pytest==7.4.4 - pytest-asyncio==0.21.2 - pytest-cov==4.1.0 - pytest-mock==3.11.1 - pytest-xdist==3.5.0 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.1 - readme-renderer==37.3 - requests==2.31.0 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==13.8.1 - s3transfer==0.8.2 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.0.1 - tox==4.8.0 - tqdm==4.67.1 - twine==4.0.2 - types-pyyaml==6.0.12.12 - typing-extensions==4.7.1 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.26.6 - webencodings==0.5.1 - werkzeug==2.2.3 - whoosh==2.7.4 - xarray==0.20.2 - xmltodict==0.14.2 - zipp==3.15.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok" ]	[ "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex" ]	[ "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator" ]	[]	Apache License 2.0	null
CS-SI__eodag-367	3963758fc5fda86da4799a26eb64fbc610c444da	2021-11-26 15:47:22	d9f16f411fdf5eb6ad752f984782dd06ed882dc8		diff --git a/docs/_static/params_mapping_extra.csv b/docs/_static/params_mapping_extra.csv index b677c5ad..cf6647c4 100644 --- a/docs/_static/params_mapping_extra.csv +++ b/docs/_static/params_mapping_extra.csv @@ -10,7 +10,7 @@ herbaceousCover,,,:green:`queryable metadata`,,,,,,,,, iceCover,,,:green:`queryable metadata`,,,,,,,,, id,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,:green:`queryable metadata` orderLink,,,,,,,metadata only,,,,, -polarizationChannels,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,,metadata only,metadata only,,metadata only +polarizationChannels,metadata only,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,metadata only,,metadata only,metadata only,,:green:`queryable metadata` polarizationMode,,,,,metadata only,metadata only,,:green:`queryable metadata`,,metadata only,, processingBaseline,,,:green:`queryable metadata`,,,,,,,,, publishedAfter,,,:green:`queryable metadata`,,,,,,,,, diff --git a/docs/_static/params_mapping_opensearch.csv b/docs/_static/params_mapping_opensearch.csv index 562f7eec..dc598e7c 100644 --- a/docs/_static/params_mapping_opensearch.csv +++ b/docs/_static/params_mapping_opensearch.csv @@ -1,56 +1,56 @@ parameter,astraea_eod,aws_eos,creodias,earth_search,earth_search_cog,mundi,onda,peps,sobloo,theia,usgs,usgs_satapi_aws :abbr:`abstract ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Abstract. (String))`,metadata only,,metadata only,metadata only,metadata only,metadata only,,metadata only,,,,metadata only ":abbr:`accessConstraint ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations on obtaining the resource (String ))`",,,metadata only,,,metadata only,,metadata only,,metadata only,, -acquisitionStation,metadata only,,,metadata only,metadata only,metadata only,,,,metadata only,,metadata only -acquisitionSubType,metadata only,,,metadata only,metadata only,metadata only,,,,metadata only,,metadata only +acquisitionStation,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,,,metadata only,,:green:`queryable metadata` +acquisitionSubType,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,,,metadata only,,:green:`queryable metadata` acquisitionType,,,,,,metadata only,,metadata only,,,, antennaLookDirection,,,,,,metadata only,,,,metadata only,, archivingCenter,,,,,,metadata only,,,,metadata only,, -availabilityTime,metadata only,,,metadata only,metadata only,metadata only,,,metadata only,metadata only,,metadata only +availabilityTime,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,,metadata only,metadata only,,:green:`queryable metadata` :abbr:`classification ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Name of the handling restrictions on the resource or metadata (String ))`,,,,,,metadata only,,,,,, -cloudCover,metadata only,,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only +cloudCover,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata` completionTimeFromAscendingNode,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata` :abbr:`compositeType ([OpenSearch Parameters for Collection Search] Type of composite product expressed as time period that the composite product covers (e.g. P10D for a 10 day composite) (String))`,,,,,,metadata only,,,,,, -creationDate,,,,,,metadata only,metadata only,metadata only,metadata only,metadata only,, +creationDate,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,metadata only,metadata only,metadata only,metadata only,,:green:`queryable metadata` ":abbr:`dissemination ([OpenSearch Parameters for Collection Search] A string identifying the dissemination method (e.g. EUMETCast, EUMETCast-Europe, DataCentre) (String))`",,,,,,metadata only,,,,,, -:abbr:`doi ([OpenSearch Parameters for Collection Search] Digital Object Identifier identifying the product (see http://www.doi.org) (String))`,metadata only,,,metadata only,metadata only,metadata only,,,,,,metadata only -dopplerFrequency,metadata only,,,metadata only,metadata only,metadata only,,,,metadata only,,metadata only +:abbr:`doi ([OpenSearch Parameters for Collection Search] Digital Object Identifier identifying the product (see http://www.doi.org) (String))`,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,,,,,:green:`queryable metadata` +dopplerFrequency,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,,,metadata only,,:green:`queryable metadata` frame,,,,,,metadata only,,,,,, ":abbr:`hasSecurityConstraints ([OpenSearch Parameters for Collection Search] A string informing if the resource has any security constraints. Possible values: TRUE, FALSE (String))`",,,,,,metadata only,,,,,, highestLocation,,,,,,metadata only,,,,,, -illuminationAzimuthAngle,metadata only,,,metadata only,metadata only,metadata only,,,,metadata only,,metadata only -illuminationElevationAngle,metadata only,,,metadata only,metadata only,metadata only,,,,metadata only,,metadata only +illuminationAzimuthAngle,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,,,metadata only,,:green:`queryable metadata` +illuminationElevationAngle,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,,,metadata only,,:green:`queryable metadata` illuminationZenithAngle,,,,,,metadata only,,,,metadata only,, incidenceAngleVariation,,,,,,metadata only,,,,metadata only,, -":abbr:`instrument ([OpenSearch Parameters for Collection Search] A string identifying the instrument (e.g. MERIS, AATSR, ASAR, HRVIR. SAR). (String))`",metadata only,,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,,metadata only +":abbr:`instrument ([OpenSearch Parameters for Collection Search] A string identifying the instrument (e.g. MERIS, AATSR, ASAR, HRVIR. SAR). (String))`",metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,,:green:`queryable metadata` :abbr:`keyword ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Commonly used word(s) or formalised word(s) or phrase(s) used to describe the subject. (String))`,,,metadata only,,,metadata only,,metadata only,,metadata only,, :abbr:`language ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Language of the intellectual content of the metadata record (String ))`,,,,,,metadata only,,,,,, :abbr:`lineage ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] General explanation of the data producer’s knowledge about the lineage of a dataset. (String))`,,,,,,metadata only,,,,,, lowestLocation,,,,,,metadata only,,,,,, maximumIncidenceAngle,,,,,,metadata only,,,,metadata only,, minimumIncidenceAngle,,,,,,metadata only,,,,metadata only,, -modificationDate,,,metadata only,,,metadata only,,metadata only,,metadata only,, -orbitDirection,metadata only,,:green:`queryable metadata`,metadata only,metadata only,metadata only,metadata only,:green:`queryable metadata`,metadata only,,,metadata only -orbitNumber,metadata only,,:green:`queryable metadata`,metadata only,metadata only,metadata only,metadata only,:green:`queryable metadata`,metadata only,metadata only,,metadata only +modificationDate,metadata only,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,metadata only,,metadata only,,:green:`queryable metadata` +orbitDirection,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,metadata only,,,:green:`queryable metadata` +orbitNumber,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,metadata only,metadata only,,:green:`queryable metadata` ":abbr:`orbitType ([OpenSearch Parameters for Collection Search] A string identifying the platform orbit type (e.g. LEO, GEO) (String))`",,,,,,metadata only,,,,,, :abbr:`organisationName ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] A string identifying the name of the organization responsible for the resource (String))`,,,:green:`queryable metadata`,,,metadata only,,:green:`queryable metadata`,,metadata only,, :abbr:`organisationRole ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] The function performed by the responsible party (String ))`,,,,,,metadata only,,,,,, :abbr:`otherConstraint ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Other restrictions and legal prerequisites for accessing and using the resource or metadata. (String))`,,,,,,metadata only,,,,,, parentIdentifier,,,:green:`queryable metadata`,,,metadata only,,:green:`queryable metadata`,,metadata only,, -:abbr:`platform ([OpenSearch Parameters for Collection Search] A string with the platform short name (e.g. Sentinel-1) (String))`,metadata only,,metadata only,metadata only,metadata only,metadata only,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,,metadata only -:abbr:`platformSerialIdentifier ([OpenSearch Parameters for Collection Search] A string with the Platform serial identifier (String))`,metadata only,,:green:`queryable metadata`,metadata only,metadata only,metadata only,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,,metadata only +:abbr:`platform ([OpenSearch Parameters for Collection Search] A string with the platform short name (e.g. Sentinel-1) (String))`,metadata only,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,,:green:`queryable metadata` +:abbr:`platformSerialIdentifier ([OpenSearch Parameters for Collection Search] A string with the Platform serial identifier (String))`,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,metadata only,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,,:green:`queryable metadata` processingCenter,,,,,,metadata only,,metadata only,,,, processingDate,,,,,,metadata only,metadata only,,,metadata only,, -:abbr:`processingLevel ([OpenSearch Parameters for Collection Search] A string identifying the processing level applied to the entry (String))`,,,:green:`queryable metadata`,,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,, +:abbr:`processingLevel ([OpenSearch Parameters for Collection Search] A string identifying the processing level applied to the entry (String))`,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,,:green:`queryable metadata` processingMode,,,,,,metadata only,,,,metadata only,, processorName,,,,,,metadata only,,metadata only,,,, productQualityDegradationTag,,,,,,metadata only,,,,,, productQualityStatus,,,,,,metadata only,metadata only,metadata only,,,, -":abbr:`productType ([OpenSearch Parameters for Collection Search] A string identifying the entry type (e.g. ER02_SAR_IM__0P, MER_RR__1P, SM_SLC__1S, GES_DISC_AIRH3STD_V005) (String ))`",:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only -productVersion,,,,,,metadata only,metadata only,metadata only,,metadata only,, -:abbr:`publicationDate ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] The date when the resource was issued (Date time))`,,,metadata only,,,metadata only,,metadata only,metadata only,metadata only,, -resolution,metadata only,,:green:`queryable metadata`,metadata only,metadata only,metadata only,,:green:`queryable metadata`,,metadata only,,metadata only -sensorMode,metadata only,,:green:`queryable metadata`,metadata only,metadata only,metadata only,metadata only,:green:`queryable metadata`,metadata only,metadata only,,metadata only +":abbr:`productType ([OpenSearch Parameters for Collection Search] A string identifying the entry type (e.g. ER02_SAR_IM__0P, MER_RR__1P, SM_SLC__1S, GES_DISC_AIRH3STD_V005) (String ))`",:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata` +productVersion,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,metadata only,metadata only,,metadata only,,:green:`queryable metadata` +:abbr:`publicationDate ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] The date when the resource was issued (Date time))`,metadata only,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,metadata only,metadata only,metadata only,,:green:`queryable metadata` +resolution,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,,:green:`queryable metadata`,,metadata only,,:green:`queryable metadata` +sensorMode,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,metadata only,metadata only,,:green:`queryable metadata` ":abbr:`sensorType ([OpenSearch Parameters for Collection Search] A string identifying the sensor type. Suggested values are: OPTICAL, RADAR, ALTIMETRIC, ATMOSPHERIC, LIMB (String))`",,,,,,metadata only,,,,,, snowCover,,,:green:`queryable metadata`,,,metadata only,,:green:`queryable metadata`,,metadata only,, ":abbr:`spectralRange ([OpenSearch Parameters for Collection Search] A string identifying the sensor spectral range (e.g. INFRARED, NEAR-INFRARED, UV, VISIBLE) (String))`",,,,,,metadata only,,,,,, diff --git a/eodag/api/product/metadata_mapping.py b/eodag/api/product/metadata_mapping.py index e4bfa1aa..7f05a177 100644 --- a/eodag/api/product/metadata_mapping.py +++ b/eodag/api/product/metadata_mapping.py @@ -199,44 +199,6 @@ def format_metadata(search_param, args, kwargs): """ return int(1e3 get_timestamp(date_time)) - @staticmethod - def convert_to_rounded_wkt(value): - wkt_value = wkt.dumps(value, rounding_precision=COORDS_ROUNDING_PRECISION) - # If needed, simplify WKT to prevent too long request failure - tolerance = 0.1 - while len(wkt_value) > WKT_MAX_LEN and tolerance <= 1: - logger.debug( - "Geometry WKT is too long (%s), trying to simplify it with tolerance %s", - len(wkt_value), - tolerance, - ) - wkt_value = wkt.dumps( - value.simplify(tolerance), - rounding_precision=COORDS_ROUNDING_PRECISION, - ) - tolerance += 0.1 - if len(wkt_value) > WKT_MAX_LEN and tolerance > 1: - logger.warning("Failed to reduce WKT length lower than %s", WKT_MAX_LEN) - return wkt_value - - @staticmethod - def convert_to_bounds_lists(input_geom): - if isinstance(input_geom, MultiPolygon): - geoms = [geom for geom in input_geom] - # sort with larger one at first (stac-browser only plots first one) - geoms.sort(key=lambda x: x.area, reverse=True) - return [list(x.bounds[0:4]) for x in geoms] - else: - return [list(input_geom.bounds[0:4])] - - @staticmethod - def convert_to_geo_interface(geom): - return geojson.dumps(geom.__geo_interface__) - - @staticmethod - def convert_csv_list(values_list): - return ",".join([str(x) for x in values_list]) - @staticmethod def convert_to_iso_utc_datetime_from_milliseconds(timestamp): """Convert a timestamp in milliseconds (int) to its ISO8601 UTC format @@ -281,6 +243,44 @@ def format_metadata(search_param, args, kwargs): dt = dt.astimezone(UTC) return dt.isoformat()[:10] + @staticmethod + def convert_to_rounded_wkt(value): + wkt_value = wkt.dumps(value, rounding_precision=COORDS_ROUNDING_PRECISION) + # If needed, simplify WKT to prevent too long request failure + tolerance = 0.1 + while len(wkt_value) > WKT_MAX_LEN and tolerance <= 1: + logger.debug( + "Geometry WKT is too long (%s), trying to simplify it with tolerance %s", + len(wkt_value), + tolerance, + ) + wkt_value = wkt.dumps( + value.simplify(tolerance), + rounding_precision=COORDS_ROUNDING_PRECISION, + ) + tolerance += 0.1 + if len(wkt_value) > WKT_MAX_LEN and tolerance > 1: + logger.warning("Failed to reduce WKT length lower than %s", WKT_MAX_LEN) + return wkt_value + + @staticmethod + def convert_to_bounds_lists(input_geom): + if isinstance(input_geom, MultiPolygon): + geoms = [geom for geom in input_geom] + # sort with larger one at first (stac-browser only plots first one) + geoms.sort(key=lambda x: x.area, reverse=True) + return [list(x.bounds[0:4]) for x in geoms] + else: + return [list(input_geom.bounds[0:4])] + + @staticmethod + def convert_to_geo_interface(geom): + return geojson.dumps(geom.__geo_interface__) + + @staticmethod + def convert_csv_list(values_list): + return ",".join([str(x) for x in values_list]) + @staticmethod def convert_remove_extension(string): parts = string.split(".") @@ -365,6 +365,13 @@ def format_metadata(search_param, args, *kwargs): logger.error("Could not extract title infos from %s" % string) return NOT_AVAILABLE + # if stac extension colon separator `:` is in search search params, parse it to prevent issues with vformat + if re.search(r"{[a-zA-Z0-9_-]:[a-zA-Z0-9_-]}", search_param): + search_param = re.sub( + r"{([a-zA-Z0-9_-]):([a-zA-Z0-9_-])}", r"{\1_COLON_\2}", search_param + ) + kwargs = {k.replace(":", "_COLON_"): v for k, v in kwargs.items()} + return MetadataFormatter().vformat(search_param, args, kwargs) diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index d1e1181a..c62a2efc 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1489,6 +1489,35 @@ # This provider doesn't implement any pagination, let's just try to get the maximum number of # products available at once then, so we stick to 10_000. max_items_per_page: 10_000 + metadata_mapping: + # redefine the following mapppings as the provider does not support advanced queries/filtering, + # these parameters will not be queryable + doi: '$.properties."sci:doi"' + processingLevel: '$.properties."processing:level"' + platform: '$.properties.constellation' + platformSerialIdentifier: '$.properties.platform' + instrument: '$.properties.instruments' + # INSPIRE obligated OpenSearch Parameters for Collection Search (Table 4) + title: '$.id' + abstract: '$.properties.description' + resolution: '$.properties.gsd' + publicationDate: '$.properties.published' + # OpenSearch Parameters for Product Search (Table 5) + orbitNumber: '$.properties."sat:relative_orbit"' + orbitDirection: '$.properties."sat:orbit_state"' + cloudCover: '$.properties."eo:cloud_cover"' + sensorMode: '$.properties."sar:instrument_mode"' + creationDate: '$.properties.created' + modificationDate: '$.properties.updated' + productVersion: '$.properties.version' + # OpenSearch Parameters for Acquistion Parameters Search (Table 6) + availabilityTime: '$.properties.availabilityTime' + acquisitionStation: '$.properties.acquisitionStation' + acquisitionSubType: '$.properties.acquisitionSubType' + illuminationAzimuthAngle: '$.properties."view:sun_azimuth"' + illuminationElevationAngle: '$.properties."view:sun_elevation"' + polarizationChannels: '$.properties."sar:polarizations"' + dopplerFrequency: '$.properties."sar:frequency_band"' products: S1_SAR_GRD: productType: sentinel1_l1c_grd @@ -1564,39 +1593,43 @@ roles: - host description: USGS Landsatlook SAT API - url: https://landsatlook.usgs.gov/sat-api/ + url: https://landsatlook.usgs.gov/stac-server search: !plugin type: StacSearch - api_endpoint: https://landsatlook.usgs.gov/sat-api/collections/{collection}/items + api_endpoint: https://landsatlook.usgs.gov/stac-server/search pagination: - total_items_nb_key_path: '$.meta.found' # 2021/03/19: no more than 10_000 (if greater, returns a 500 error code) # but in practive if an Internal Server Error is returned for more than # about 500 products. max_items_per_page: 500 metadata_mapping: - productType: '$.collection' + cloudCover: + - '{{"query":{{"eo:cloud_cover":{{"lte":{cloudCover}}}}}}}' + - '$.properties."eo:cloud_cover"' + platformSerialIdentifier: + - '{{"query":{{"platform":{{"eq":"{platformSerialIdentifier}"}}}}}}' + - '$.properties.platform' assets: '{$.assets#recursive_sub_str(r"https?(.)landsatlook.usgs.gov/data/",r"s3\1usgs-landsat/")}' products: LANDSAT_C2L1: - collection: landsat-c2l1 + productType: landsat-c2l1 LANDSAT_C2L2_SR: - collection: landsat-c2l2-sr + productType: landsat-c2l2-sr LANDSAT_C2L2_ST: - collection: landsat-c2l2-st + productType: landsat-c2l2-st LANDSAT_C2L2ALB_BT: - collection: landsat-c2l2alb-bt + productType: landsat-c2l2alb-bt LANDSAT_C2L2ALB_SR: - collection: landsat-c2l2alb-sr + productType: landsat-c2l2alb-sr LANDSAT_C2L2ALB_ST: - collection: landsat-c2l2alb-st + productType: landsat-c2l2alb-st LANDSAT_C2L2ALB_TA: - collection: landsat-c2l2alb-ta + productType: landsat-c2l2alb-ta GENERIC_PRODUCT_TYPE: - collection: '{productType}' + productType: '{productType}' download: !plugin type: AwsDownload - base_uri: https://landsatlook.usgs.gov/sat-api/ + base_uri: https://landsatlook.usgs.gov/stac-server flatten_top_dirs: True auth: !plugin type: AwsAuth diff --git a/eodag/resources/stac_provider.yml b/eodag/resources/stac_provider.yml index 8121a70a..1708438c 100644 --- a/eodag/resources/stac_provider.yml +++ b/eodag/resources/stac_provider.yml @@ -26,7 +26,7 @@ search: discover_metadata: auto_discovery: true metadata_pattern: '^[a-zA-Z0-9_:-]+$' - search_param: '{{"{metadata}"="{{{metadata}}}"}}' + search_param: '{{{{"query":{{{{"{metadata}":{{{{"eq":"{{{metadata}}}" }}}} }}}} }}}}' metadata_path: '$.properties.*' metadata_mapping: # OpenSearch Parameters for Collection Search (Table 3) @@ -34,76 +34,77 @@ search: - '{{"collections":["{productType}"]}}' - '$.collection' doi: - - 'sci:doi' + - '{{"query":{{"sci:doi":{{"eq":"{doi}"}}}}}}' - '$.properties."sci:doi"' processingLevel: - - 'processing:level' + - '{{"query":{{"processing:level":{{"eq":"{processingLevel}"}}}}}}' - '$.properties."processing:level"' platform: - - 'constellation' + - '{{"query":{{"constellation":{{"eq":"{platform}"}}}}}}' - '$.properties.constellation' platformSerialIdentifier: - - 'platform' + - '{{"query":{{"platform":{{"eq":"{platformSerialIdentifier}"}}}}}}' - '$.properties.platform' instrument: - - 'instruments' + # to test + - '{{"query":{{"instruments":{{"eq":"{instrument}"}}}}}}' - '$.properties.instruments' # INSPIRE obligated OpenSearch Parameters for Collection Search (Table 4) title: '$.id' abstract: '$.properties.description' resolution: - - 'gsd' + - '{{"query":{{"gsd":{{"eq":"{resolution}"}}}}}}' - '$.properties.gsd' publicationDate: - - 'published' + - '{{"query":{{"published":{{"eq":"{publicationDate}"}}}}}}' - '$.properties.published' # OpenSearch Parameters for Product Search (Table 5) orbitNumber: - - 'sat:relative_orbit' + - '{{"query":{{"sat:relative_orbit":{{"eq":"{orbitNumber}"}}}}}}' - '$.properties."sat:relative_orbit"' orbitDirection: - - 'sat:orbit_state' + - '{{"query":{{"sat:orbit_state":{{"eq":"{orbitDirection}"}}}}}}' - '$.properties."sat:orbit_state"' cloudCover: - - 'eo:cloud_cover' + - '{{"query":{{"eo:cloud_cover":{{"lte":"{cloudCover}"}}}}}}' - '$.properties."eo:cloud_cover"' sensorMode: - - 'sar:instrument_mode' + - '{{"query":{{"sar:instrument_mode":{{"eq":"{sensorMode}"}}}}}}' - '$.properties."sar:instrument_mode"' creationDate: - - 'created' + - '{{"query":{{"created":{{"eq":"{creationDate}"}}}}}}' - '$.properties.created' modificationDate: - - 'updated' + - '{{"query":{{"updated":{{"eq":"{modificationDate}"}}}}}}' - '$.properties.updated' productVersion: - - 'version' + - '{{"query":{{"version":{{"eq":"{productVersion}"}}}}}}' - '$.properties.version' # OpenSearch Parameters for Acquistion Parameters Search (Table 6) availabilityTime: - - 'availabilityTime' + - '{{"query":{{"availabilityTime":{{"eq":"{availabilityTime}"}}}}}}' - '$.properties.availabilityTime' acquisitionStation: - - 'acquisitionStation' + - '{{"query":{{"acquisitionStation":{{"eq":"{acquisitionStation}"}}}}}}' - '$.properties.acquisitionStation' acquisitionSubType: - - 'acquisitionSubType' + - '{{"query":{{"acquisitionSubType":{{"eq":"{acquisitionSubType}"}}}}}}' - '$.properties.acquisitionSubType' startTimeFromAscendingNode: '$.properties.datetime' completionTimeFromAscendingNode: - '{{"datetime":"{startTimeFromAscendingNode#to_iso_utc_datetime}/{completionTimeFromAscendingNode#to_iso_utc_datetime}"}}' - '$.properties.end_datetime' illuminationAzimuthAngle: - - 'view:sun_azimuth' + - '{{"query":{{"view:sun_azimuth":{{"eq":"{illuminationAzimuthAngle}"}}}}}}' - '$.properties."view:sun_azimuth"' illuminationElevationAngle: - - 'view:sun_elevation' + - '{{"query":{{"view:sun_elevation":{{"eq":"{illuminationElevationAngle}"}}}}}}' - '$.properties."view:sun_elevation"' polarizationChannels: - - 'sar:polarizations' + - '{{"query":{{"sar:polarizations":{{"eq":"{polarizationChannels}"}}}}}}' - '$.properties."sar:polarizations"' dopplerFrequency: - - 'sar:frequency_band' + - '{{"query":{{"sar:frequency_band":{{"eq":"{dopplerFrequency}"}}}}}}' - '$.properties."sar:frequency_band"' # Custom parameters (not defined in the base document referenced above) id: @@ -116,8 +117,6 @@ search: quicklook: '$.assets.quicklook.href' thumbnail: '$.assets.thumbnail.href' # storageStatus set to ONLINE for consistency between providers - storageStatus: - - 'storageStatus' - - '{$.null#replace_str("Not Available","ONLINE")}' + storageStatus: '{$.null#replace_str("Not Available","ONLINE")}' # Normalization code moves assets from properties to product's attr assets: '$.assets'	implement STAC API Query fragment For STAC providers, STAC API Item Search has limited filtering capabilities (`collections`, `ids`, `bbox` / `intersects`, `datetime`). However, it does not contain a formalized way to filter based on arbitrary fields in an Item. For example, there is no way to express the filter "`item.properties.eo:cloud_cover` is less than 10" This can be improved using properties filtering provided by the [STAC API - Query Fragment](https://github.com/radiantearth/stac-api-spec/blob/master/fragments/query/README.md) Note that it is planned to be deprecated in favor of [STAC API - Filter Fragment](https://github.com/radiantearth/stac-api-spec/blob/master/fragments/filter/README.md), providing CQL filtering. But for the moment, available STAC API provider do not support it.	CS-SI/eodag	diff --git a/tests/units/test_metadata_mapping.py b/tests/units/test_metadata_mapping.py index e510ed38..fc19611f 100644 --- a/tests/units/test_metadata_mapping.py +++ b/tests/units/test_metadata_mapping.py @@ -17,7 +17,7 @@ # limitations under the License. import unittest -from tests.context import format_metadata +from tests.context import format_metadata, get_geometry_from_various class TestMetadataFormatter(unittest.TestCase): @@ -39,6 +39,17 @@ class TestMetadataFormatter(unittest.TestCase): "1618956000000", ) + def test_convert_to_iso_utc_datetime_from_milliseconds(self): + to_format = "{fieldname#to_iso_utc_datetime_from_milliseconds}" + self.assertEqual( + format_metadata(to_format, fieldname=1619029639123), + "2021-04-21T18:27:19.123Z", + ) + self.assertEqual( + format_metadata(to_format, fieldname=1618963200000), + "2021-04-21T00:00:00.000Z", + ) + def test_convert_to_iso_utc_datetime(self): to_format = "{fieldname#to_iso_utc_datetime}" self.assertEqual( @@ -76,13 +87,111 @@ class TestMetadataFormatter(unittest.TestCase): "2021-04-20", ) - def test_convert_to_iso_utc_datetime_from_milliseconds(self): - to_format = "{fieldname#to_iso_utc_datetime_from_milliseconds}" + def test_convert_to_rounded_wkt(self): + to_format = "{fieldname#to_rounded_wkt}" + geom = get_geometry_from_various(geometry="POINT (0.11111 1.22222222)") self.assertEqual( - format_metadata(to_format, fieldname=1619029639123), - "2021-04-21T18:27:19.123Z", + format_metadata(to_format, fieldname=geom), + "POINT (0.1111 1.2222)", + ) + + def test_convert_to_bounds_lists(self): + to_format = "{fieldname#to_bounds_lists}" + geom = get_geometry_from_various( + geometry="""MULTIPOLYGON ( + ((1.23 43.42, 1.23 43.76, 1.68 43.76, 1.68 43.42, 1.23 43.42)), + ((2.23 43.42, 2.23 43.76, 3.68 43.76, 3.68 43.42, 2.23 43.42)) + )""" ) self.assertEqual( - format_metadata(to_format, fieldname=1618963200000), - "2021-04-21T00:00:00.000Z", + format_metadata(to_format, fieldname=geom), + "[[2.23, 43.42, 3.68, 43.76], [1.23, 43.42, 1.68, 43.76]]", + ) + + def test_convert_to_geo_interface(self): + to_format = "{fieldname#to_geo_interface}" + geom = get_geometry_from_various(geometry="POINT (0.11 1.22)") + self.assertEqual( + format_metadata(to_format, fieldname=geom), + '{"type": "Point", "coordinates": [0.11, 1.22]}', + ) + + def test_convert_csv_list(self): + to_format = "{fieldname#csv_list}" + self.assertEqual( + format_metadata(to_format, fieldname=[1, 2, 3]), + "1,2,3", + ) + + def test_convert_remove_extension(self): + to_format = "{fieldname#remove_extension}" + self.assertEqual( + format_metadata(to_format, fieldname="foo.bar"), + "foo", + ) + + def test_convert_get_group_name(self): + to_format = ( + "{fieldname#get_group_name((?P<this_is_foo>foo)\|(?P<that_is_bar>bar))}" + ) + self.assertEqual( + format_metadata(to_format, fieldname="foo"), + "this_is_foo", + ) + + def test_convert_replace_str(self): + to_format = r"{fieldname#replace_str(r'(.) is (.)',r'\1 was \2...')}" + self.assertEqual( + format_metadata(to_format, fieldname="this is foo"), + "this was foo...", + ) + + def test_convert_recursive_sub_str(self): + to_format = r"{fieldname#recursive_sub_str(r'(.) is (.)',r'\1 was \2...')}" + self.assertEqual( + format_metadata( + to_format, fieldname=[{"a": "this is foo", "b": [{"c": "that is bar"}]}] + ), + "[{'a': 'this was foo...', 'b': [{'c': 'that was bar...'}]}]", + ) + + def test_convert_dict_update(self): + to_format = '{fieldname#dict_update([["b",[["href","bar"],["title","baz"]]]])}' + self.assertEqual( + format_metadata(to_format, fieldname={"a": {"title": "foo"}}), + "{'a': {'title': 'foo'}, 'b': {'href': 'bar', 'title': 'baz'}}", + ) + + def test_convert_slice_str(self): + to_format = "{fieldname#slice_str(1,12,2)}" + self.assertEqual( + format_metadata(to_format, fieldname="abcdefghijklmnop"), + "bdfhjl", + ) + + def test_convert_fake_l2a_title_from_l1c(self): + to_format = "{fieldname#fake_l2a_title_from_l1c}" + self.assertEqual( + format_metadata( + to_format, + fieldname="S2B_MSIL1C_20210427T103619_N0300_R008_T31TCJ_20210427T124539", + ), + "S2B_MSIL2A_20210427T103619____________T31TCJ________________", + ) + + def test_convert_s2msil2a_title_to_aws_productinfo(self): + to_format = "{fieldname#s2msil2a_title_to_aws_productinfo}" + self.assertEqual( + format_metadata( + to_format, + fieldname="S2A_MSIL2A_20201201T100401_N0214_R122_T32SNA_20201201T114520", + ), + "https://roda.sentinel-hub.com/sentinel-s2-l2a/tiles/32/S/NA/2020/12/1/0/{collection}.json", + ) + + def test_format_stac_extension_parameter(self): + to_format = "{some_extension:a_parameter}" + self.assertEqual( + format_metadata(to_format, **{"some_extension:a_parameter": "value"}), + "value", )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 0 }, "num_modified_files": 5 }	2.3	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "nose", "nose-cov", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc make pandoc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 cov-core==1.15.0 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@3963758fc5fda86da4799a26eb64fbc610c444da#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 nose-cov==1.6 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - cov-core==1.15.0 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - nose-cov==1.6 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_format_stac_extension_parameter" ]	[ "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_bounds_lists" ]	[ "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_csv_list", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_datetime_to_timestamp_milliseconds", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_dict_update", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_fake_l2a_title_from_l1c", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_get_group_name", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_recursive_sub_str", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_remove_extension", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_replace_str", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_s2msil2a_title_to_aws_productinfo", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_slice_str", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_geo_interface", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_date", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_utc_datetime", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_utc_datetime_from_milliseconds", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_rounded_wkt" ]	[]	Apache License 2.0	null
CS-SI__eodag-380	6e6e6890a52ac8ad4d0f989f41a726b35d9cf5d8	2021-12-22 16:01:32	d9f16f411fdf5eb6ad752f984782dd06ed882dc8		diff --git a/eodag/api/core.py b/eodag/api/core.py index 0ce2f7fe..7d5d039b 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -17,6 +17,7 @@ # limitations under the License. import logging import os +import re import shutil from operator import itemgetter @@ -1055,6 +1056,31 @@ class EODataAccessGateway(object): # be returned as a search result if there was no search extent (because we # will not try to do an intersection) for eo_product in res: + # if product_type is not defined, try to guess using properties + if eo_product.product_type is None: + pattern = re.compile(r"[^\w,]+") + try: + guesses = self.guess_product_type( + **{ + # k:str(v) for k,v in eo_product.properties.items() + k: pattern.sub("", str(v).upper()) + for k, v in eo_product.properties.items() + if k + in [ + "instrument", + "platform", + "platformSerialIdentifier", + "processingLevel", + "sensorType", + "keywords", + ] + and v is not None + } + ) + except NoMatchingProductType: + pass + else: + eo_product.product_type = guesses[0] if eo_product.search_intersection is not None: download_plugin = self._plugins_manager.get_download_plugin( eo_product	prevent mixed results between earth_search and earth_search_cog See #362 . @GriffinBabe pointed out that if we omit to specify the product type when searching on `earth_search_cog`, we get also results from `earth_search` that cannot be downloaded with the wrong download plugin: ```py from eodag import EODataAccessGateway dag = EODataAccessGateway() dag.set_preferred_provider("earth_search_cog") # Here I forget to specify which productType I am looking for cog_products, _ = dag.search() path = dag.download(cog_products[0]) ``` Raises ``` requests.exceptions.InvalidSchema: No connection adapters were found for 's3://sentinel-s2-l2a/tiles/42/F/UK/2021/11/23/0/qi/L2A_PVI.jp2' ``` To prevent this, we'd have to: - [ ] only accept `S2_MSI_L2A_COG` product type with `earth_search_cog` - [ ] filter out `S2_MSI_L2A_COG` product type with `earth_search`	CS-SI/eodag	diff --git a/tests/integration/test_search_stac_static.py b/tests/integration/test_search_stac_static.py index ee3483b0..71a31b4c 100644 --- a/tests/integration/test_search_stac_static.py +++ b/tests/integration/test_search_stac_static.py @@ -83,8 +83,8 @@ class TestSearchStacStatic(unittest.TestCase): self.assertIsInstance(items, SearchResult) self.assertEqual(len(items), self.child_cat_len) self.assertEqual(items[0].provider, self.stac_provider) - # if no product_type is provided, product_type is None - self.assertIsNone(items[0].product_type) + # if no product_type is provided, product_type should be guessed from properties + self.assertEqual(items[0].product_type, "S2_MSI_L1C") def test_search_stac_static_load_root_not_recursive(self): """load_stac_items from root must provide an empty list when no recursive""" @@ -120,8 +120,8 @@ class TestSearchStacStatic(unittest.TestCase): self.assertIsInstance(item, SearchResult) self.assertEqual(len(item), 1) self.assertEqual(item[0].provider, self.stac_provider) - # if no product_type is provided, product_type is None - self.assertIsNone(item[0].product_type) + # if no product_type is provided, product_type should be guessed from properties + self.assertEqual(item[0].product_type, "S2_MSI_L1C") def test_search_stac_static_load_item_updated_provider(self): """load_stac_items from a single item using updated provider"""	{ "commit_name": "merge_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 1 }, "num_modified_files": 1 }	2.3	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y make pandoc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@6e6e6890a52ac8ad4d0f989f41a726b35d9cf5d8#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_load_child", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_load_item" ]	[]	[ "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_by_cloudcover", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_by_date", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_by_geom", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_by_property", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_crunch_filter_date", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_crunch_filter_lastest_by_name", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_crunch_filter_overlap", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_crunch_filter_property", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_load_item_updated_provider", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_load_root_not_recursive", "tests/integration/test_search_stac_static.py::TestSearchStacStatic::test_search_stac_static_load_root_recursive" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-380
CS-SI__eodag-381	df05c353836883546e47db4057123ac501e003c6	2021-12-22 16:19:59	d9f16f411fdf5eb6ad752f984782dd06ed882dc8		diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index dec68622..315bf26f 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -10,9 +10,10 @@ Logging .. automodule:: eodag.utils.logging :members: -Progress callback +Callbacks ----------------- +.. autofunction:: eodag.utils.DownloadedCallback .. autofunction:: eodag.utils.ProgressCallback Notebook diff --git a/eodag/api/core.py b/eodag/api/core.py index 7d5d039b..e4f03232 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -1172,6 +1172,7 @@ class EODataAccessGateway(object): def download_all( self, search_result, + downloaded_callback=None, progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, @@ -1181,6 +1182,13 @@ class EODataAccessGateway(object): :param search_result: A collection of EO products resulting from a search :type search_result: :class:`~eodag.api.search_result.SearchResult` + :param downloaded_callback: (optional) A method or a callable object which takes + as parameter the ``product``. You can use the base class + :class:`~eodag.utils.DownloadedCallback` and override + its ``__call__`` method. Will be called each time a product + finishes downloading + :type downloaded_callback: Callable[[:class:`~eodag.api.product._product.EOProduct`], None] + or None :param progress_callback: (optional) A method or a callable object which takes a current size and a maximum size as inputs and handle progress bar @@ -1211,6 +1219,7 @@ class EODataAccessGateway(object): ) paths = download_plugin.download_all( search_result, + downloaded_callback=downloaded_callback, progress_callback=progress_callback, wait=wait, timeout=timeout, diff --git a/eodag/plugins/apis/base.py b/eodag/plugins/apis/base.py index 051a26d7..d49661e2 100644 --- a/eodag/plugins/apis/base.py +++ b/eodag/plugins/apis/base.py @@ -78,6 +78,8 @@ class Api(PluginTopic): :param product: The EO product to download :type product: :class:`~eodag.api.product._product.EOProduct` + :param auth: (optional) The configuration of a plugin of type Authentication + :type auth: :class:`~eodag.config.PluginConfig` :param progress_callback: (optional) A progress callback :type progress_callback: :class:`~eodag.utils.ProgressCallback` :param wait: (optional) If download fails, wait time in minutes between two download tries @@ -103,6 +105,7 @@ class Api(PluginTopic): self, products, auth=None, + downloaded_callback=None, progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, @@ -113,6 +116,15 @@ class Api(PluginTopic): :param products: Products to download :type products: :class:`~eodag.api.search_result.SearchResult` + :param auth: (optional) The configuration of a plugin of type Authentication + :type auth: :class:`~eodag.config.PluginConfig` + :param downloaded_callback: (optional) A method or a callable object which takes + as parameter the ``product``. You can use the base class + :class:`~eodag.utils.DownloadedCallback` and override + its ``__call__`` method. Will be called each time a product + finishes downloading + :type downloaded_callback: Callable[[:class:`~eodag.api.product._product.EOProduct`], None] + or None :param progress_callback: (optional) A progress callback :type progress_callback: :class:`~eodag.utils.ProgressCallback` :param wait: (optional) If download fails, wait time in minutes between two download tries diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index bcc06c64..39804a91 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -276,6 +276,7 @@ class UsgsApi(Download, Api): self, products, auth=None, + downloaded_callback=None, progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, @@ -287,6 +288,7 @@ class UsgsApi(Download, Api): return super(UsgsApi, self).download_all( products, auth=auth, + downloaded_callback=downloaded_callback, progress_callback=progress_callback, wait=wait, timeout=timeout, diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 0a8e2632..3202c76a 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -862,10 +862,21 @@ class AwsDownload(Download): logger.debug("Downloading %s to %s" % (chunk.key, product_path)) return product_path - def download_all(self, products, auth=None, progress_callback=None, kwargs): + def download_all( + self, + products, + auth=None, + downloaded_callback=None, + progress_callback=None, + kwargs + ): """ download_all using parent (base plugin) method """ return super(AwsDownload, self).download_all( - products, auth=auth, progress_callback=progress_callback, kwargs + products, + auth=auth, + downloaded_callback=downloaded_callback, + progress_callback=progress_callback, + kwargs ) diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index 16b181f3..99fef16c 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -370,6 +370,7 @@ class Download(PluginTopic): self, products, auth=None, + downloaded_callback=None, progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, @@ -383,6 +384,15 @@ class Download(PluginTopic): :param products: Products to download :type products: :class:`~eodag.api.search_result.SearchResult` + :param auth: (optional) The configuration of a plugin of type Authentication + :type auth: :class:`~eodag.config.PluginConfig` + :param downloaded_callback: (optional) A method or a callable object which takes + as parameter the ``product``. You can use the base class + :class:`~eodag.utils.DownloadedCallback` and override + its ``__call__`` method. Will be called each time a product + finishes downloading + :type downloaded_callback: Callable[[:class:`~eodag.api.product._product.EOProduct`], None] + or None :param progress_callback: (optional) A progress callback :type progress_callback: :class:`~eodag.utils.ProgressCallback` :param wait: (optional) If download fails, wait time in minutes between two download tries @@ -448,6 +458,9 @@ class Download(PluginTopic): ) ) + if downloaded_callback: + downloaded_callback(product) + # product downloaded, to not retry it products.remove(product) bar(1) diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index c73fcb3c..0fdcd732 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -416,6 +416,7 @@ class HTTPDownload(Download): self, products, auth=None, + downloaded_callback=None, progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, @@ -427,6 +428,7 @@ class HTTPDownload(Download): return super(HTTPDownload, self).download_all( products, auth=auth, + downloaded_callback=downloaded_callback, progress_callback=progress_callback, wait=wait, timeout=timeout, diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 90ccf43c..03ac665d 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -400,6 +400,18 @@ def get_timestamp(date_time): return dt.timestamp() +class DownloadedCallback: + """Example class for callback after each download in :meth:`~eodag.api.core.EODataAccessGateway.download_all`""" + + def __call__(self, product): + """Callback + + :param product: The downloaded EO product + :type product: :class:`~eodag.api.product._product.EOProduct` + """ + logger.debug("Download finished for the product %s", product) + + class ProgressCallback(tqdm): """A callable used to render progress to users for long running processes.	download_all callback [Original report](https://bitbucket.org/geostorm/eodag/issue/121) by me. ---------------------------------------- When using download_all, provide the availability to launch a callback when each individual download in finished	CS-SI/eodag	diff --git a/tests/__init__.py b/tests/__init__.py index e2afd291..5cf15b0d 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -16,7 +16,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +import io import os +import pathlib import random import shutil import tempfile @@ -29,7 +31,10 @@ from owslib.etree import etree from owslib.ows import ExceptionReport from shapely import wkt +from eodag import config +from eodag.api.product import EOProduct from eodag.api.product.metadata_mapping import DEFAULT_METADATA_MAPPING +from eodag.plugins.download.http import HTTPDownload jp = os.path.join dirn = os.path.dirname @@ -278,3 +283,80 @@ class EODagTestCase(unittest.TestCase): } ) return mock.DEFAULT + + def _dummy_product( + self, provider=None, properties=None, productType=None, kwargs + ): + return EOProduct( + self.provider if provider is None else provider, + self.eoproduct_props if properties is None else properties, + productType=self.product_type if productType is None else productType, + kwargs, + ) + + def _dummy_downloadable_product( + self, + product=None, + base_uri=None, + outputs_prefix=None, + extract=None, + delete_archive=None, + ): + self._set_download_simulation() + dl_config = config.PluginConfig.from_mapping( + { + "base_uri": "fake_base_uri" if base_uri is None else base_uri, + "outputs_prefix": tempfile.gettempdir() + if outputs_prefix is None + else outputs_prefix, + "extract": True if extract is None else extract, + "delete_archive": False if delete_archive is None else delete_archive, + } + ) + downloader = HTTPDownload(provider=self.provider, config=dl_config) + if product is None: + product = self._dummy_product() + product.register_downloader(downloader, None) + return product + + def _clean_product(self, product_path): + product_path = pathlib.Path(product_path) + download_records_dir = product_path.parent / ".downloaded" + product_zip = product_path.parent / (product_path.name + ".zip") + shutil.rmtree(product_path) + shutil.rmtree(download_records_dir) + if os.path.exists(product_zip): + os.remove(product_zip) + + def _set_download_simulation(self): + self.requests_http_get.return_value = self._download_response_archive() + + def _download_response_archive(self): + class Response(object): + """Emulation of a response to requests.get method for a zipped product""" + + def __init__(response): + # Using a zipped product file + with open(self.local_product_as_archive_path, "rb") as fh: + response.__zip_buffer = io.BytesIO(fh.read()) + cl = response.__zip_buffer.getbuffer().nbytes + response.headers = {"content-length": cl} + + def __enter__(response): + return response + + def __exit__(response, args): + pass + + def iter_content(response, kwargs): + with response.__zip_buffer as fh: + while True: + chunk = fh.read(kwargs["chunk_size"]) + if not chunk: + break + yield chunk + + def raise_for_status(response): + pass + + return Response() diff --git a/tests/context.py b/tests/context.py index 96aca67b..1a31b578 100644 --- a/tests/context.py +++ b/tests/context.py @@ -53,6 +53,7 @@ from eodag.utils import ( path_to_uri, ProgressCallback, uri_to_path, + DownloadedCallback, ) from eodag.utils.exceptions import ( AddressNotFound, diff --git a/tests/integration/test_core_search_results.py b/tests/integration/test_core_search_results.py index db49dea3..e14f7e25 100644 --- a/tests/integration/test_core_search_results.py +++ b/tests/integration/test_core_search_results.py @@ -20,16 +20,16 @@ import json import os import shutil import tempfile -import unittest from shapely import geometry -from tests import TEST_RESOURCES_PATH +from tests import TEST_RESOURCES_PATH, EODagTestCase from tests.context import EODataAccessGateway, EOProduct, SearchResult -class TestCoreSearchResults(unittest.TestCase): +class TestCoreSearchResults(EODagTestCase): def setUp(self): + super(TestCoreSearchResults, self).setUp() self.dag = EODataAccessGateway() self.maxDiff = None self.geojson_repr = { @@ -203,3 +203,27 @@ class TestCoreSearchResults(unittest.TestCase): self.assertIn(1, ss_len) self.assertIn(2, ss_len) self.assertIn(3, ss_len) + + def test_empty_search_result_return_empty_list(self): + products_paths = self.dag.download_all(None) + self.assertFalse(products_paths) + + def test_download_all_callback(self): + product = self._dummy_downloadable_product() + search_result = SearchResult([product]) + + def downloaded_callback_func(product): + self.assertTrue(product in search_result) + downloaded_callback_func.times_called += 1 + + downloaded_callback_func.times_called = 0 + + try: + self.assertEqual(downloaded_callback_func.times_called, 0) + products_paths = self.dag.download_all( + search_result, downloaded_callback=downloaded_callback_func + ) + self.assertEqual(downloaded_callback_func.times_called, len(search_result)) + finally: + for product_path in products_paths: + self._clean_product(product_path) diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index a72428a9..72d6ff7d 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -40,9 +40,7 @@ class TestEOProduct(EODagTestCase): def test_eoproduct_search_intersection_geom(self): """EOProduct search_intersection attr must be it's geom when no bbox_or_intersect param given""" # noqa - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) + product = self._dummy_product() self.assertEqual(product.geometry, product.search_intersection) def test_eoproduct_search_intersection_none(self): @@ -60,10 +58,7 @@ class TestEOProduct(EODagTestCase): ] ], } - product = EOProduct( - self.provider, - self.eoproduct_props, - productType=self.product_type, + product = self._dummy_product( geometry=geometry.Polygon( ( (10.469970703124998, 3.9957805129630373), @@ -77,18 +72,12 @@ class TestEOProduct(EODagTestCase): def test_eoproduct_default_driver_unsupported_product_type(self): """EOProduct driver attr must be NoDriver if its product type is not associated with a eodag dataset driver""" # noqa - product = EOProduct( - self.provider, - self.eoproduct_props, - productType=self.NOT_ASSOCIATED_PRODUCT_TYPE, - ) + product = self._dummy_product(productType=self.NOT_ASSOCIATED_PRODUCT_TYPE) self.assertIsInstance(product.driver, NoDriver) def test_eoproduct_geointerface(self): """EOProduct must provide a geo-interface with a set of specific properties""" - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) + product = self._dummy_product() geo_interface = geojson.loads(geojson.dumps(product)) self.assertEqual(geo_interface["type"], "Feature") self.assertEqual( @@ -105,9 +94,7 @@ class TestEOProduct(EODagTestCase): def test_eoproduct_from_geointerface(self): """EOProduct must be build-able from its geo-interface""" - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) + product = self._dummy_product() same_product = EOProduct.from_geojson(geojson.loads(geojson.dumps(product))) self.assertSequenceEqual( [ @@ -138,9 +125,7 @@ class TestEOProduct(EODagTestCase): def test_eoproduct_get_quicklook_no_quicklook_url(self): """EOProduct.get_quicklook must return an empty string if no quicklook property""" # noqa - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) + product = self._dummy_product() product.properties["quicklook"] = None quicklook_file_path = product.get_quicklook() @@ -148,9 +133,7 @@ class TestEOProduct(EODagTestCase): def test_eoproduct_get_quicklook_http_error(self): """EOProduct.get_quicklook must return an empty string if there was an error during retrieval""" # noqa - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) + product = self._dummy_product() product.properties["quicklook"] = "https://fake.url.to/quicklook" self.requests_http_get.return_value.__enter__.return_value.raise_for_status.side_effect = ( # noqa @@ -172,9 +155,7 @@ class TestEOProduct(EODagTestCase): def test_eoproduct_get_quicklook_ok(self): """EOProduct.get_quicklook must return the path to the successfully downloaded quicklook""" # noqa - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) + product = self._dummy_product() product.properties["quicklook"] = "https://fake.url.to/quicklook" self.requests_http_get.return_value = self._quicklook_response() @@ -224,9 +205,7 @@ class TestEOProduct(EODagTestCase): os.mkdir(quicklook_dir) with open(existing_quicklook_file_path, "wb") as fh: fh.write(b"content") - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) + product = self._dummy_product() product.properties["quicklook"] = "https://fake.url.to/quicklook" mock_downloader = mock.MagicMock( spec_set=Download(provider=self.provider, config=None) @@ -273,20 +252,7 @@ class TestEOProduct(EODagTestCase): def test_eoproduct_download_http_default(self): """eoproduct.download must save the product at outputs_prefix and create a .downloaded dir""" # noqa # Setup - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) - self.requests_http_get.return_value = self._download_response_archive() - dl_config = config.PluginConfig.from_mapping( - { - "base_uri": "fake_base_uri", - "outputs_prefix": tempfile.gettempdir(), - "delete_archive": False, - } - ) - downloader = HTTPDownload(provider=self.provider, config=dl_config) - product.register_downloader(downloader, None) - + product = self._dummy_downloadable_product() try: # Download product_dir_path = product.download() @@ -313,65 +279,33 @@ class TestEOProduct(EODagTestCase): self.assertTrue(zipfile.is_zipfile(product_zip)) finally: # Teardown - os.remove(product_zip) - shutil.rmtree(product_dir_path) - os.remove(str(records_file)) - os.rmdir(str(download_records_dir)) + self._clean_product(product_dir_path) def test_eoproduct_download_http_delete_archive(self): """eoproduct.download must delete the downloaded archive""" # noqa # Setup - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) - self.requests_http_get.return_value = self._download_response_archive() - dl_config = config.PluginConfig.from_mapping( - { - "base_uri": "fake_base_uri", - "outputs_prefix": tempfile.gettempdir(), - "delete_archive": True, - } - ) - downloader = HTTPDownload(provider=self.provider, config=dl_config) - product.register_downloader(downloader, None) - + product = self._dummy_downloadable_product(delete_archive=True) try: # Download product_dir_path = product.download() - product_dir_path = pathlib.Path(product_dir_path) # Check that the mocked request was properly called. self.requests_http_get.assert_called_with( self.download_url, stream=True, auth=None, params={} ) - download_records_dir = product_dir_path.parent / ".downloaded" # Check that the product's directory exists. self.assertTrue(os.path.isdir(product_dir_path)) # Check that the ZIP file was deleted there - product_zip = product_dir_path.parent / (product_dir_path.name + ".zip") + _product_dir_path = pathlib.Path(product_dir_path) + product_zip = _product_dir_path.parent / (_product_dir_path.name + ".zip") self.assertFalse(os.path.exists(product_zip)) finally: # Teardown - shutil.rmtree(product_dir_path) - shutil.rmtree(download_records_dir) + self._clean_product(product_dir_path) def test_eoproduct_download_http_extract(self): """eoproduct.download over must be able to extract a product""" # Setup - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) - self.requests_http_get.return_value = self._download_response_archive() - dl_config = config.PluginConfig.from_mapping( - { - "base_uri": "fake_base_uri", - "outputs_prefix": tempfile.gettempdir(), - "extract": True, - "delete_archive": False, - } - ) - downloader = HTTPDownload(provider=self.provider, config=dl_config) - product.register_downloader(downloader, None) - + product = self._dummy_downloadable_product(extract=True) try: # Download product_dir_path = product.download() @@ -384,21 +318,15 @@ class TestEOProduct(EODagTestCase): # The zip file should is around product_zip_file = product_dir_path.with_suffix(".SAFE.zip") self.assertTrue(product_zip_file.is_file) - - download_records_dir = pathlib.Path(product_dir_path).parent / ".downloaded" finally: # Teardown - shutil.rmtree(str(product_dir_path)) - os.remove(str(product_zip_file)) - shutil.rmtree(str(download_records_dir)) + self._clean_product(product_dir_path) def test_eoproduct_download_http_dynamic_options(self): """eoproduct.download must accept the download options to be set automatically""" # Setup - product = EOProduct( - self.provider, self.eoproduct_props, productType=self.product_type - ) - self.requests_http_get.return_value = self._download_response_archive() + product = self._dummy_product() + self._set_download_simulation() dl_config = config.PluginConfig.from_mapping( {"base_uri": "fake_base_uri", "outputs_prefix": "will_be_overriden"} ) @@ -409,7 +337,7 @@ class TestEOProduct(EODagTestCase): output_dir = pathlib.Path(tempfile.gettempdir()) / output_dir_name try: if output_dir.is_dir(): - shutil.rmtree(str(output_dir)) + shutil.rmtree(output_dir) output_dir.mkdir() # Download @@ -435,34 +363,4 @@ class TestEOProduct(EODagTestCase): self.assertTrue(product_zip_file.is_file) finally: # Teardown (all the created files are within outputs_prefix) - shutil.rmtree(str(output_dir)) - - def _download_response_archive(self): - class Response(object): - """Emulation of a response to requests.get method for a zipped product""" - - def __init__(response): - # Using a zipped product file - with open(self.local_product_as_archive_path, "rb") as fh: - response.__zip_buffer = io.BytesIO(fh.read()) - cl = response.__zip_buffer.getbuffer().nbytes - response.headers = {"content-length": cl} - - def __enter__(response): - return response - - def __exit__(response, args): - pass - - def iter_content(response, **kwargs): - with response.__zip_buffer as fh: - while True: - chunk = fh.read(kwargs["chunk_size"]) - if not chunk: - break - yield chunk - - def raise_for_status(response): - pass - - return Response() + shutil.rmtree(output_dir) diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 62a5b713..d7757876 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -23,6 +23,7 @@ from datetime import datetime from io import StringIO from tests.context import ( + DownloadedCallback, ProgressCallback, get_timestamp, merge_mappings, @@ -74,6 +75,15 @@ class TestUtils(unittest.TestCase): else: self.assertEqual(path_to_uri("/tmp/file.txt"), "file:///tmp/file.txt") + def test_downloaded_callback(self): + """DownloadedCallback instance is callable with product as parameter""" + downloaded_callback = DownloadedCallback() + self.assertTrue(callable(downloaded_callback)) + try: + downloaded_callback(product=None) + except TypeError as e: + self.fail(f"DownloadedCallback got an error when called: {e}") + def test_progresscallback_init(self): """ProgressCallback can be instantiated using defaults values""" with ProgressCallback() as bar:	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 8 }	2.3	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@df05c353836883546e47db4057123ac501e003c6#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_core_deserialize_search_results", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_core_serialize_search_results", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_download_all_callback", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_empty_search_result_return_empty_list", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_group_by_extent", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_delete_archive", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom", "tests/units/test_utils.py::TestUtils::test_downloaded_callback", "tests/units/test_utils.py::TestUtils::test_merge_mappings", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_progresscallback_copy", "tests/units/test_utils.py::TestUtils::test_progresscallback_disable", "tests/units/test_utils.py::TestUtils::test_progresscallback_init", "tests/units/test_utils.py::TestUtils::test_progresscallback_init_customize", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-405	d9f16f411fdf5eb6ad752f984782dd06ed882dc8	2022-02-22 18:02:30	d9f16f411fdf5eb6ad752f984782dd06ed882dc8		diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 3202c76a..fe7d3573 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -253,11 +253,17 @@ class AwsDownload(Download): bucket_names_and_prefixes = [self.get_bucket_name_and_prefix(product)] # add complementary urls - for complementary_url_key in product_conf.get("complementary_url_key", []): - bucket_names_and_prefixes.append( - self.get_bucket_name_and_prefix( - product, product.properties[complementary_url_key] + try: + for complementary_url_key in product_conf.get("complementary_url_key", []): + bucket_names_and_prefixes.append( + self.get_bucket_name_and_prefix( + product, product.properties[complementary_url_key] + ) ) + except KeyError: + logger.error( + "complementary_url_key %s is missing in %s properties" + % (complementary_url_key, product.properties["id"]) ) # authenticate @@ -566,13 +572,18 @@ class AwsDownload(Download): def check_manifest_file_list(self, product_path): """Checks if products listed in manifest.safe exist""" - manifest_path = [ + manifest_path_list = [ os.path.join(d, x) for d, _, f in os.walk(product_path) for x in f if x == "manifest.safe" - ][0] - safe_path = os.path.dirname(manifest_path) + ] + if len(manifest_path_list) == 0: + raise FileNotFoundError( + f"No manifest.safe could be found in {product_path}" + ) + else: + safe_path = os.path.dirname(manifest_path_list[0]) root = etree.parse(os.path.join(safe_path, "manifest.safe")).getroot() for safe_file in root.xpath("//fileLocation"): @@ -587,13 +598,18 @@ class AwsDownload(Download): """Add missing dirs to downloaded product""" try: logger.debug("Finalize SAFE product") - manifest_path = [ + manifest_path_list = [ os.path.join(d, x) for d, _, f in os.walk(product_path) for x in f if x == "manifest.safe" - ][0] - safe_path = os.path.dirname(manifest_path) + ] + if len(manifest_path_list) == 0: + raise FileNotFoundError( + f"No manifest.safe could be found in {product_path}" + ) + else: + safe_path = os.path.dirname(manifest_path_list[0]) # create empty missing dirs auxdata_path = os.path.join(safe_path, "AUX_DATA") @@ -868,7 +884,7 @@ class AwsDownload(Download): auth=None, downloaded_callback=None, progress_callback=None, - kwargs + kwargs, ): """ download_all using parent (base plugin) method @@ -878,5 +894,5 @@ class AwsDownload(Download): auth=auth, downloaded_callback=downloaded_callback, progress_callback=progress_callback, - kwargs + kwargs, ) diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 0dc859d4..2b792b94 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1718,7 +1718,7 @@ platformSerialIdentifier: '$.id.`split(_, 0, -1)`' polarizationMode: '$.id.`sub(/.{14}([A-Z]{2})./, \\1)`' productPath: \| - $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL1C_([0-9]{4})(0?)([1-9]+)([0-9]{2})(T.)/, products/\\2/\\4/\\5/\\1_MSIL1C_\\2\\3\\4\\5\\6)` + $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL1C_([0-9]{4})([0-9]{2})([0-9]{2})(T.)/, products/\\2/\\3/\\4/\\1_MSIL1C_\\2\\3\\4\\5)`.`sub(/\/0+/, \/)` tilePath: \| $.assets.info.href.`sub(/./sentinel-s2-l1c\/(tiles\/.)\/tileInfo\.json/, \\1)` S2_MSI_L2A: @@ -1728,7 +1728,7 @@ platformSerialIdentifier: '$.id.`split(_, 0, -1)`' polarizationMode: '$.id.`sub(/.{14}([A-Z]{2})./, \\1)`' productPath: \| - $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL2A_([0-9]{4})(0?)([1-9]+)([0-9]{2})(T.)/, products/\\2/\\4/\\5/\\1_MSIL2A_\\2\\3\\4\\5\\6)` + $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL2A_([0-9]{4})([0-9]{2})([0-9]{2})(T.)/, products/\\2/\\3/\\4/\\1_MSIL2A_\\2\\3\\4\\5)`.`sub(/\/0+/, \/)` tilePath: \| $.assets.info.href.`sub(/./sentinel-s2-l2a\/(tiles\/.)\/tileInfo\.json/, \\1)` L8_OLI_TIRS_C1L1:	finalize_s2_safe_product: Fails when missing manifest.safe When downloading some products on `earth_search`, I have a problem when converting the product format to `.SAFE`: ![2022-02-22_14h47_27](https://user-images.githubusercontent.com/67311115/155145249-38c42542-b9bd-4888-9573-2f7d1cc24c6d.png) ![2022-02-22_14h48_44](https://user-images.githubusercontent.com/67311115/155145285-a0e12781-4c2d-442b-8910-48ddae12dd7a.png) It seems some L2A products are arriving without the `manifest.safe` file 😓 Environment: Python version: Python 3.7.12 EODAG version: eodag (Earth Observation Data Access Gateway): version 2.3.4	CS-SI/eodag	diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 405451a8..47ced2e6 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -297,3 +297,50 @@ class TestSearchPluginODataV4Search(BaseSearchPluginTest): self.assertEqual(estimate, self.onda_products_count) self.assertEqual(len(products), number_of_products) self.assertIsInstance(products[0], EOProduct) + + +class TestSearchPluginStacSearch(BaseSearchPluginTest): + @mock.patch("eodag.plugins.search.qssearch.StacSearch._request", autospec=True) + def test_plugins_search_stacsearch_mapping_earthsearch(self, mock__request): + """The metadata mapping for earth_search should return well formatted results""" # noqa + + geojson_geometry = self.search_criteria_s2_msi_l1c["geometry"].__geo_interface__ + + mock__request.return_value = mock.Mock() + mock__request.return_value.json.side_effect = [ + { + "context": {"page": 1, "limit": 2, "matched": 1, "returned": 2}, + }, + { + "features": [ + { + "id": "foo", + "geometry": geojson_geometry, + "properties": { + "sentinel:product_id": "S2B_MSIL1C_20201009T012345_N0209_R008_T31TCJ_20201009T123456", + }, + }, + { + "id": "bar", + "geometry": geojson_geometry, + "properties": { + "sentinel:product_id": "S2B_MSIL1C_20200910T012345_N0209_R008_T31TCJ_20200910T123456", + }, + }, + ] + }, + ] + + search_plugin = self.get_search_plugin(self.product_type, "earth_search") + + products, estimate = search_plugin.query( + page=1, items_per_page=2, auth=None, **self.search_criteria_s2_msi_l1c + ) + self.assertEqual( + products[0].properties["productPath"], + "products/2020/10/9/S2B_MSIL1C_20201009T012345_N0209_R008_T31TCJ_20201009T123456", + ) + self.assertEqual( + products[1].properties["productPath"], + "products/2020/9/10/S2B_MSIL1C_20200910T012345_N0209_R008_T31TCJ_20200910T123456", + )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_media", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 2 }	2.3	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 -e git+https://github.com/CS-SI/eodag.git@d9f16f411fdf5eb6ad752f984782dd06ed882dc8#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 nose==1.3.7 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - nose==1.3.7 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_mapping_earthsearch" ]	[]	[ "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda" ]	[]	Apache License 2.0	null
CS-SI__eodag-458	909354f089a3e08e810a194a10ce54858b26e6f6	2022-05-16 11:32:46	be026c211b2ef28ceb8b13437a8eceaf4245689b	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 11m 23s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") +49s 252 tests ±0 250 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") ±0 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 504 runs ±0 500 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") ±0 4 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit 77ae4d8d. ± Comparison against base commit be026c21. github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `79%` \| :white_check_mark: \| \| eodag/api/search\_result.py \| `84%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `79%` \| :white_check_mark: \| \| eodag/api/search\_result.py \| `84%` \| :white_check_mark: \| \| eodag/api/search\_result.py \| `83%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 77ae4d8de012d2375a443d5a10c46c25449bcbf1 </p>	diff --git a/docs/api_reference/searchresult.rst b/docs/api_reference/searchresult.rst index cb845cfe..7957cf0b 100644 --- a/docs/api_reference/searchresult.rst +++ b/docs/api_reference/searchresult.rst @@ -22,6 +22,7 @@ Crunch SearchResult.filter_latest_by_name SearchResult.filter_overlap SearchResult.filter_property + SearchResult.filter_online Conversion ---------- @@ -41,4 +42,4 @@ Interface SearchResult.__geo_interface__ .. autoclass:: SearchResult - :members: crunch, filter_date, filter_latest_intersect, filter_latest_by_name, filter_overlap, filter_property, from_geojson, as_geojson_object, as_shapely_geometry_object, as_wkt_object, __geo_interface__ + :members: crunch, filter_date, filter_latest_intersect, filter_latest_by_name, filter_overlap, filter_property, filter_online, from_geojson, as_geojson_object, as_shapely_geometry_object, as_wkt_object, __geo_interface__ diff --git a/docs/notebooks/api_user_guide/6_crunch.ipynb b/docs/notebooks/api_user_guide/6_crunch.ipynb index 5c9b45cb..5c4d429b 100644 --- a/docs/notebooks/api_user_guide/6_crunch.ipynb +++ b/docs/notebooks/api_user_guide/6_crunch.ipynb @@ -470,6 +470,51 @@ "all([p.properties[\"cloudCover\"] < 1 for p in filtered_products])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter for online products" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes you may want to avoid ordering OFFLINE products, and only download the one marked ONLINE.\n", + "\n", + "You can already filter for online products using [FilterProperty](../../plugins_reference/generated/eodag.plugins.crunch.filter_property.FilterProperty.rst#eodag.plugins.crunch.filter_property.FilterProperty) like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_products = search_results.crunch(\n", + " FilterProperty(dict(storageStatus=\"ONLINE\", operator=\"eq\"))\n", + ")\n", + "print(f\"{len(search_results) - len(filtered_products)} products are online.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this code do the job, it is quite verbose. The better way is to use [SearchResult.filter_online()](../../api_reference/searchresult.rst#eodag.api.search_result.SearchResult.filter_online)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_products = search_results.filter_online()\n", + "print(f\"{len(search_results) - len(filtered_products)} products are online.\")" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/eodag/api/search_result.py b/eodag/api/search_result.py index 182e7e54..eaea0653 100644 --- a/eodag/api/search_result.py +++ b/eodag/api/search_result.py @@ -102,6 +102,13 @@ class SearchResult(UserList): """ return self.crunch(FilterProperty(dict(operator=operator, **search_property))) + def filter_online(self): + """ + Use cruncher :class:`~eodag.plugins.crunch.filter_property.FilterProperty`, + filter for online products. + """ + return self.filter_property(storageStatus="ONLINE") + @staticmethod def from_geojson(feature_collection): """Builds an :class:`~eodag.api.search_result.SearchResult` object from its representation as geojson	Avoid trying to order an OFFLINE product From @remi-braun in https://github.com/CS-SI/eodag/issues/214 > Maybe add the LTA order as a parameter, as it is not needed in every usecase (sometimes, sb just want to download everything he can without triggering any LTA retrieval) LTA: Long Term Archive The `HTTPDownload` plugin (used by creodias, onda, theia, peps, sobloo) can automatically try to order a product if its status is OFFLINE, and can download it afterwards. The feature request is to add a way to avoid trying to order an OFFLINE product. This could be done in at least two complementary ways: 1. Document the way to filter out OFFLINE products from the result of a search. This can be done as follows currently: ```python from eodag.plugins.crunch.filter_property import FilterProperty dag.set_preferred_provider("peps") prods, _ = dag.search(...) online_prods = prods.crunch(FilterProperty({"storageStatus": "ONLINE", "operator": "eq"})) dag.download_all(online_prods) ``` 2. Add a parameter to pass as a kwarg to `EODataAccessGateway.download_all` (not sure it's relevant for `download`), e.g. `skip_offline`, that would be passed to the HTTPPlugin and, if `True`, would stop trying to download this product, which would lead to `download_all` trying to download the next product.	CS-SI/eodag	diff --git a/tests/units/test_search_result.py b/tests/units/test_search_result.py index 608cd86f..e85e3233 100644 --- a/tests/units/test_search_result.py +++ b/tests/units/test_search_result.py @@ -22,13 +22,34 @@ from collections import UserList import geojson from shapely.geometry.collection import GeometryCollection -from tests.context import SearchResult +from tests.context import EOProduct, SearchResult class TestSearchResult(unittest.TestCase): def setUp(self): super(TestSearchResult, self).setUp() self.search_result = SearchResult([]) + self.search_result2 = SearchResult( + [ + EOProduct( + provider=None, + properties={"geometry": "POINT (0 0)", "storageStatus": "ONLINE"}, + ), + EOProduct( + provider=None, + properties={"geometry": "POINT (0 0)", "storageStatus": "OFFLINE"}, + ), + ] + ) + + def test_search_result_filter_online(self): + """SearchResult.filter_online must only keep online results""" + filtered_products = self.search_result2.filter_online() + origin_size = len(self.search_result2) + filtered_size = len(filtered_products) + self.assertFalse(origin_size == filtered_size) + for product in filtered_products: + assert product.properties["storageStatus"] == "ONLINE" def test_search_result_geo_interface(self): """SearchResult must provide a FeatureCollection geo-interface"""	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 3 }	2.4	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@909354f089a3e08e810a194a10ce54858b26e6f6#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==4.1.1 pytest-metadata==3.1.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.30.0 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.4.1.dev19+g909354f0 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==4.1.1 - pytest-metadata==3.1.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.30.0 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_search_result.py::TestSearchResult::test_search_result_filter_online" ]	[]	[ "tests/units/test_search_result.py::TestSearchResult::test_search_result_as_geojson_object", "tests/units/test_search_result.py::TestSearchResult::test_search_result_as_shapely_geometry_object", "tests/units/test_search_result.py::TestSearchResult::test_search_result_as_wkt_object", "tests/units/test_search_result.py::TestSearchResult::test_search_result_from_feature_collection", "tests/units/test_search_result.py::TestSearchResult::test_search_result_geo_interface", "tests/units/test_search_result.py::TestSearchResult::test_search_result_is_list_like" ]	[]	Apache License 2.0	null
CS-SI__eodag-480	54a51822ae350e54d2b7d353db377b6ff2886518	2022-07-05 13:23:09	158fac82232af978700e8f829bd5b7e227774a4c	github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `80%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `79%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 3daa8ff24c4f12d0f64328f8d266f223eb4f0f1f </p> github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 10m 19s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") -46s 253 tests ±0 251 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") ±0 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 506 runs ±0 502 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") ±0 4 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit 59ba44f9. ± Comparison against base commit 54a51822. [test-results]:data:application/gzip;base64,H4sIAAhAxGIC/02M0Q7DEBRAf6XxvAdcpPYzC0oia2tRnpb9+0yxPp5zkvNGzq/2QPeJ3iZ0ZJ8GLDmq5MNeUBBZREmpRg6dHkc2piryV0//aotTOOXXIvAQNsYQm4l5/z05Fg36kmM6zHlknS/DytefCdvmUwHEpVaMOak0CK1nIjDWQIXGEsDZmVK7MKDKoM8Xu2tuoQUBAAA=	diff --git a/CHANGES.rst b/CHANGES.rst index 964bb480..5762d991 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,11 @@ Release history --------------- +2.5.2 (2022-07-05) +++++++++++++++++++ + +* Fixes missing ``productPath`` property for some ``earth_search`` products (:pull:`480`) + 2.5.1 (2022-06-27) ++++++++++++++++++ diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 1bd6eb37..d9080757 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1725,7 +1725,7 @@ platformSerialIdentifier: '$.id.`split(_, 0, -1)`' polarizationMode: '$.id.`sub(/.{14}([A-Z]{2})./, \\1)`' productPath: \| - $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL1C_([0-9]{4})([0-9]{2})([0-9]{2})(T.)/, products/\\2/\\3/\\4/\\1_MSIL1C_\\2\\3\\4\\5)`.`sub(/\/0+/, \/)` + $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL1C_([0-9]{4})([0-9]{2})([0-9]{2})(T.)/, products!\\2!\\3!\\4!\\1_MSIL1C_\\2\\3\\4\\5)`.`sub(/!0/, /)` tilePath: \| $.assets.info.href.`sub(/./sentinel-s2-l1c\/(tiles\/.)\/tileInfo\.json/, \\1)` S2_MSI_L2A: @@ -1735,7 +1735,7 @@ platformSerialIdentifier: '$.id.`split(_, 0, -1)`' polarizationMode: '$.id.`sub(/.{14}([A-Z]{2})./, \\1)`' productPath: \| - $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL2A_([0-9]{4})([0-9]{2})([0-9]{2})(T.)/, products/\\2/\\3/\\4/\\1_MSIL2A_\\2\\3\\4\\5)`.`sub(/\/0+/, \/)` + $.properties."sentinel:product_id".`sub(/([S2AB]{3})_MSIL2A_([0-9]{4})([0-9]{2})([0-9]{2})(T.)/, products!\\2!\\3!\\4!\\1_MSIL2A_\\2\\3\\4\\5)`.`sub(/!0/, /)` tilePath: \| $.assets.info.href.`sub(/./sentinel-s2-l2a\/(tiles\/.)\/tileInfo\.json/, \\1)` L8_OLI_TIRS_C1L1: diff --git a/pyproject.toml b/pyproject.toml index f002f4b6..730b048d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,4 +3,4 @@ requires = ["setuptools>=45", "setuptools_scm[toml]>=6.2,<7"] build-backend = "setuptools.build_meta" [tool.setuptools_scm] -fallback_version = "2.5.2.dev0" +fallback_version = "2.5.3.dev0"	Earth Search: Missing ProductPath in some downloaded product Bug linked to #423. Sorry to reopen again this issue, but I have encountered new products where it fails 😅 I am recreating a new issue to be more precise. Describe the bug Some products are missing a ProductPath in their properties, leading to failure to find the manifest file `FileNotFoundError: No manifest.safe could be found in D:\_EODOWNLOAD\OUTPUT\GRAND_EST\L2A\S2B_MSIL2A_20211229T130249_N0301_R095_T24MUV_20211229T151639` ![2022-03-16_09h17_21](https://user-images.githubusercontent.com/67311115/158546345-4ce55c5e-d57d-4c8e-afff-25029c837625.png) This comes from the fact that the ProductPath is missing and its url is replaced by the product location in `get_bucket_name_and_prefix` (in `aws.py`). This leads to a wrong name and bucket: ```python print( self.get_bucket_name_and_prefix( product, product.properties["productPath"] ) ) ('v0', '/collections/sentinel-s2-l2a/items/S2A_32TLT_20211016_0_L2A') ``` Code To Reproduce ```py import os import tempfile from eodag import EODataAccessGateway, setup_logging from eodag.config import override_config_from_file from eodownload import dir_utils from eodownload.dir_utils import get_data_directory OUTPUT = r"D:\" os.environ["EODAG__EARTH_SEARCH__DOWNLOAD__EXTRACT"] = "false" os.environ["EODAG__EARTH_SEARCH__DOWNLOAD__OUTPUTS_PREFIX"] = OUTPUT os.environ["EODAG__EARTH_SEARCH__AUTH__CREDENTIALS__AWS_ACCESS_KEY_ID"] = "***" os.environ["EODAG__EARTH_SEARCH__AUTH__CREDENTIALS__AWS_SECRET_ACCESS_KEY"] = "*" os.environ["EODAG__EARTH_SEARCH__AUTH__CREDENTIALS__AWS_PROFILE"] = "*" if __name__ == "__main__": setup_logging(2) # DEBUG level with tempfile.TemporaryDirectory() as tmp: # Create gateway dag = EODataAccessGateway() dag.set_preferred_provider("earth_search") # Query all products = dag.search_all( start="2021-12-28", end="2021-12-30", { "productType": "S2_MSI_L2A", "geom": "POINT (-40.30881686219417 -5.018666560087038)" } ) # Download all dag.download_all(products) ``` ``` 2022-06-30 15:46:48,821 eodag.config [INFO ] Loading user configuration from: C:\Users\rbraun\.config\eodag\eodag.yml 2022-06-30 15:46:48,830 eodag.config [INFO ] aws_s3_sentinel2_l1c: unknown provider found in user conf, trying to use provided configuration 2022-06-30 15:46:48,830 eodag.config [WARNING ] aws_s3_sentinel2_l1c skipped: could not be loaded from user configuration 2022-06-30 15:46:48,965 eodag.core [INFO ] usgs: provider needing auth for search has been pruned because no crendentials could be found 2022-06-30 15:46:48,965 eodag.core [INFO ] aws_eos: provider needing auth for search has been pruned because no crendentials could be found 2022-06-30 15:46:49,114 eodag.core [INFO ] Locations configuration loaded from C:\Users\rbraun\.config\eodag\locations.yml 2022-06-30 15:46:49,763 eodag.core [INFO ] Searching product type 'S2_MSI_L2A' on provider: earth_search 2022-06-30 15:46:49,763 eodag.core [INFO ] Iterate search over multiple pages: page #1 2022-06-30 15:46:49,856 eodag.plugins.search.qssearch [INFO ] Sending search request: https://earth-search.aws.element84.com/v0/search 2022-06-30 15:46:50,392 eodag.core [INFO ] Found 1 result(s) on provider 'earth_search' 2022-06-30 15:46:50,392 eodag.core [INFO ] Downloading 1 products Downloaded products: 0%\| \| 0/1 [00:00<?, ?product/s] 0.00B [00:00, ?B/s] S2B_24MUV_20211229_1_L2A: 0.00B [00:00, ?B/s]2022-06-30 15:46:50,394 eodag.plugins.download.base [INFO ] Download url: https://earth-search.aws.element84.com/v0/collections/sentinel-s2-l2a/items/S2B_24MUV_20211229_1_L2A 2022-06-30 15:46:50,394 eodag.plugins.download.aws [ERROR ] complementary_url_key productPath is missing in S2B_24MUV_20211229_1_L2A properties S2B_24MUV_20211229_1_L2A: 100%\|██████████\| 407M/407M [00:27<00:00, 8.57MB/s]2022-06-30 15:47:22,499 eodag.plugins.download.aws [ERROR ] Could not finalize SAFE product from downloaded data Traceback (most recent call last): File "C:\Users\rbraun\Anaconda3\envs\eodownload\lib\site-packages\eodag\plugins\download\aws.py", line 657, in finalize_s2_safe_product f"No manifest.safe could be found in {product_path}" FileNotFoundError: No manifest.safe could be found in D:\_EODOWNLOAD\OUTPUT\GRAND_EST\L2A\S2B_MSIL2A_20211229T130249_N0301_R095_T24MUV_20211229T151639 2022-06-30 15:47:22,500 eodag.plugins.download.base [WARNING ] A problem occurred during download of product: EOProduct(id=S2B_24MUV_20211229_1_L2A, provider=earth_search). Skipping it S2B_24MUV_20211229_1_L2A: 100%\|██████████\| 407M/407M [00:27<00:00, 14.6MB/s] 2022-06-30 15:47:22,503 eodag.plugins.download.base [INFO ] [Retry #1, 0/1 D/L] Waiting 87s until next download try (retry every 2' for 20') ``` Output Compete output obtained with maximal verbosity. Environment: - Python version: Python 3.7.12 - EODAG version: eodag (Earth Observation Data Access Gateway): version 2.5.1	CS-SI/eodag	diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 636c92e3..f8e0f7d7 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -327,6 +327,13 @@ class TestSearchPluginStacSearch(BaseSearchPluginTest): "sentinel:product_id": "S2B_MSIL1C_20200910T012345_N0209_R008_T31TCJ_20200910T123456", }, }, + { + "id": "bar", + "geometry": geojson_geometry, + "properties": { + "sentinel:product_id": "S2B_MSIL1C_20201010T012345_N0209_R008_T31TCJ_20201010T123456", + }, + }, ] }, ] @@ -344,3 +351,7 @@ class TestSearchPluginStacSearch(BaseSearchPluginTest): products[1].properties["productPath"], "products/2020/9/10/S2B_MSIL1C_20200910T012345_N0209_R008_T31TCJ_20200910T123456", ) + self.assertEqual( + products[2].properties["productPath"], + "products/2020/10/10/S2B_MSIL1C_20201010T012345_N0209_R008_T31TCJ_20201010T123456", + )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_issue_reference", "has_media", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 3 }	2.5	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[tutorials]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	affine==2.4.0 anyio==4.9.0 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 arrow==1.3.0 asttokens==3.0.0 async-lru==2.0.5 attrs==25.3.0 babel==2.17.0 beautifulsoup4==4.13.3 bleach==6.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 branca==0.8.1 certifi==2025.1.31 cffi==1.17.1 charset-normalizer==3.4.1 click==8.1.8 click-plugins==1.1.1 cligj==0.7.2 comm==0.2.2 contourpy==1.3.0 coverage==7.8.0 cycler==0.12.1 debugpy==1.8.13 decorator==5.2.1 defusedxml==0.7.1 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@54a51822ae350e54d2b7d353db377b6ff2886518#egg=eodag eodag-cube==0.3.1 exceptiongroup==1.2.2 execnet==2.1.1 executing==2.2.0 fastjsonschema==2.21.1 flasgger==0.9.7.1 Flask==3.1.0 folium==0.19.5 fonttools==4.56.0 fqdn==1.5.1 geojson==3.2.0 grpcio==1.71.0 h11==0.14.0 httpcore==1.0.7 httpx==0.28.1 idna==3.10 imageio==2.37.0 importlib_metadata==8.6.1 importlib_resources==6.5.2 iniconfig==2.1.0 ipykernel==6.29.5 ipyleaflet==0.19.2 ipython==8.18.1 ipywidgets==8.1.5 isoduration==20.11.0 itsdangerous==2.2.0 jedi==0.19.2 Jinja2==3.1.6 jmespath==1.0.1 json5==0.10.0 jsonpath-ng==1.7.0 jsonpointer==3.0.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 jupyter==1.1.1 jupyter-console==6.6.3 jupyter-events==0.12.0 jupyter-leaflet==0.19.2 jupyter-lsp==2.2.5 jupyter_client==8.6.3 jupyter_core==5.7.2 jupyter_server==2.15.0 jupyter_server_terminals==0.5.3 jupyterlab==4.3.6 jupyterlab_pygments==0.3.0 jupyterlab_server==2.27.3 jupyterlab_widgets==3.0.13 kiwisolver==1.4.7 lxml==5.3.1 Markdown==3.7 MarkupSafe==3.0.2 matplotlib==3.9.4 matplotlib-inline==0.1.7 mistune==3.1.3 nbclient==0.10.2 nbconvert==7.16.6 nbformat==5.10.4 nest-asyncio==1.6.0 notebook==7.3.3 notebook_shim==0.2.4 numpy==2.0.2 overrides==7.7.0 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 pandocfilters==1.5.1 parso==0.8.4 pexpect==4.9.0 pillow==11.1.0 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 prometheus_client==0.21.1 prompt_toolkit==3.0.50 protobuf==3.20.0 psutil==7.0.0 ptyprocess==0.7.0 pure_eval==0.2.3 pycparser==2.22 Pygments==2.19.1 pyparsing==3.2.3 pyproj==3.6.1 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-json-logger==3.3.0 pytz==2025.2 PyYAML==6.0.2 pyzmq==26.3.0 rasterio==1.4.3 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 rioxarray==0.15.0 rpds-py==0.24.0 s3transfer==0.11.4 Send2Trash==1.8.3 shapely==2.0.7 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 stack-data==0.6.3 terminado==0.18.1 tinycss2==1.4.0 tomli==2.2.1 tornado==6.4.2 tqdm==4.67.1 traitlets==5.14.3 traittypes==0.2.1 types-python-dateutil==2.9.0.20241206 typing_extensions==4.13.0 tzdata==2025.2 uri-template==1.3.0 urllib3==1.26.20 usgs==0.3.5 wcwidth==0.2.13 webcolors==24.11.1 webencodings==0.5.1 websocket-client==1.8.0 Werkzeug==3.1.3 Whoosh==2.7.4 widgetsnbextension==4.0.13 xarray==2024.7.0 xyzservices==2025.1.0 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - affine==2.4.0 - anyio==4.9.0 - argon2-cffi==23.1.0 - argon2-cffi-bindings==21.2.0 - arrow==1.3.0 - asttokens==3.0.0 - async-lru==2.0.5 - attrs==25.3.0 - babel==2.17.0 - beautifulsoup4==4.13.3 - bleach==6.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - branca==0.8.1 - certifi==2025.1.31 - cffi==1.17.1 - charset-normalizer==3.4.1 - click==8.1.8 - click-plugins==1.1.1 - cligj==0.7.2 - comm==0.2.2 - contourpy==1.3.0 - coverage==7.8.0 - cycler==0.12.1 - debugpy==1.8.13 - decorator==5.2.1 - defusedxml==0.7.1 - ecmwf-api-client==1.6.5 - eodag==2.5.1 - eodag-cube==0.3.1 - exceptiongroup==1.2.2 - execnet==2.1.1 - executing==2.2.0 - fastjsonschema==2.21.1 - flasgger==0.9.7.1 - flask==3.1.0 - folium==0.19.5 - fonttools==4.56.0 - fqdn==1.5.1 - geojson==3.2.0 - grpcio==1.71.0 - h11==0.14.0 - httpcore==1.0.7 - httpx==0.28.1 - idna==3.10 - imageio==2.37.0 - importlib-metadata==8.6.1 - importlib-resources==6.5.2 - iniconfig==2.1.0 - ipykernel==6.29.5 - ipyleaflet==0.19.2 - ipython==8.18.1 - ipywidgets==8.1.5 - isoduration==20.11.0 - itsdangerous==2.2.0 - jedi==0.19.2 - jinja2==3.1.6 - jmespath==1.0.1 - json5==0.10.0 - jsonpath-ng==1.7.0 - jsonpointer==3.0.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - jupyter==1.1.1 - jupyter-client==8.6.3 - jupyter-console==6.6.3 - jupyter-core==5.7.2 - jupyter-events==0.12.0 - jupyter-leaflet==0.19.2 - jupyter-lsp==2.2.5 - jupyter-server==2.15.0 - jupyter-server-terminals==0.5.3 - jupyterlab==4.3.6 - jupyterlab-pygments==0.3.0 - jupyterlab-server==2.27.3 - jupyterlab-widgets==3.0.13 - kiwisolver==1.4.7 - lxml==5.3.1 - markdown==3.7 - markupsafe==3.0.2 - matplotlib==3.9.4 - matplotlib-inline==0.1.7 - mistune==3.1.3 - nbclient==0.10.2 - nbconvert==7.16.6 - nbformat==5.10.4 - nest-asyncio==1.6.0 - notebook==7.3.3 - notebook-shim==0.2.4 - numpy==2.0.2 - overrides==7.7.0 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - pandocfilters==1.5.1 - parso==0.8.4 - pexpect==4.9.0 - pillow==11.1.0 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - prometheus-client==0.21.1 - prompt-toolkit==3.0.50 - protobuf==3.20.0 - psutil==7.0.0 - ptyprocess==0.7.0 - pure-eval==0.2.3 - pycparser==2.22 - pygments==2.19.1 - pyparsing==3.2.3 - pyproj==3.6.1 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-json-logger==3.3.0 - pytz==2025.2 - pyyaml==6.0.2 - pyzmq==26.3.0 - rasterio==1.4.3 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - rfc3339-validator==0.1.4 - rfc3986-validator==0.1.1 - rioxarray==0.15.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - send2trash==1.8.3 - shapely==2.0.7 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - stack-data==0.6.3 - terminado==0.18.1 - tinycss2==1.4.0 - tomli==2.2.1 - tornado==6.4.2 - tqdm==4.67.1 - traitlets==5.14.3 - traittypes==0.2.1 - types-python-dateutil==2.9.0.20241206 - typing-extensions==4.13.0 - tzdata==2025.2 - uri-template==1.3.0 - urllib3==1.26.20 - usgs==0.3.5 - wcwidth==0.2.13 - webcolors==24.11.1 - webencodings==0.5.1 - websocket-client==1.8.0 - werkzeug==3.1.3 - whoosh==2.7.4 - widgetsnbextension==4.0.13 - xarray==2024.7.0 - xyzservices==2025.1.0 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_mapping_earthsearch" ]	[]	[ "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-480
CS-SI__eodag-490	0a782d7f476a993170f7d60c006be09b4cfa2b47	2022-07-26 13:06:01	158fac82232af978700e8f829bd5b7e227774a4c	github-actions[bot]: ## Unit Test Results     1 files -     1     1 suites - 1 4m 47s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") - 6m 50s 268 tests +    1 265 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") ±    0 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") +1 268 runs - 266 265 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") - 265 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") - 2 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") +1 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/7520866179). Results for commit e0e2c5a0. ± Comparison against base commit 0a782d7f. [test-results]:data:application/gzip;base64,H4sIAEzo32IC/1XMQQ6DIBCF4asY1l2Aggy9TEOmQ0Kq0iCsmt69o7Zql//3kvcSIQ40i2ujLo2Yayx73Gv2JaaJswXLwFNZxraHX93miriSOegRnwvtEHwcvp8bUM4ps0iWXKfjc4n/y02Ox7VPh2uf/zCNYywcgiS1aLyUaIGkBXC9ca4j0/UayCrUSoOyQbw/wjNTowUBAAA= github-actions[bot]: <strong></strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `80%` \| :white_check_mark: \| \| eodag/api/product/\_product.py \| `83%` \| :white_check_mark: \| \| tests/units/test\_eoproduct.py \| `98%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against e0e2c5a00c78e0788965993e53648e71c414817f </p>	diff --git a/eodag/api/product/_product.py b/eodag/api/product/_product.py index 683870ba..45162a1f 100644 --- a/eodag/api/product/_product.py +++ b/eodag/api/product/_product.py @@ -282,7 +282,7 @@ class EOProduct(object): # update units as bar may have been previously used for extraction progress_callback.unit = "B" progress_callback.unit_scale = True - progress_callback.desc = self.properties.get("id", "") + progress_callback.desc = str(self.properties.get("id", "")) progress_callback.refresh() fs_path = self.downloader.download(	progress bar and int id If product id is an integer, it makes download progress bar crash: ```py >>> from eodag import EODataAccessGateway >>> dag = EODataAccessGateway() >>> dag.set_preferred_provider("usgs") >>> prods, _ = dag.search(productType="SENTINEL_2A") >>> prods[0].properties["id"] 18933649 >>> prods[0].download() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/sylvain/workspace/eodag/eodag/api/product/_product.py", line 286, in download progress_callback.refresh() File "/home/sylvain/.virtualenvs/eodag-dev/lib/python3.8/site-packages/tqdm/std.py", line 1361, in refresh self.display() File "/home/sylvain/.virtualenvs/eodag-dev/lib/python3.8/site-packages/tqdm/std.py", line 1509, in display self.sp(self.__str__() if msg is None else msg) File "/home/sylvain/.virtualenvs/eodag-dev/lib/python3.8/site-packages/tqdm/std.py", line 1165, in __str__ return self.format_meter(**self.format_dict) File "/home/sylvain/.virtualenvs/eodag-dev/lib/python3.8/site-packages/tqdm/std.py", line 475, in format_meter bool_prefix_colon_already = (prefix[-2:] == ": ") TypeError: 'int' object is not subscriptable ``` It should be cast to `str` before set as progress bar prefix.	CS-SI/eodag	diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index 7829c168..045edf0d 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -34,6 +34,7 @@ from tests.context import ( EOProduct, HTTPDownload, NoDriver, + ProgressCallback, config, ) from tests.utils import mock @@ -44,6 +45,12 @@ class TestEOProduct(EODagTestCase): def setUp(self): super(TestEOProduct, self).setUp() + self.output_dir = tempfile.mkdtemp() + + def tearDown(self): + super(TestEOProduct, self).tearDown() + if os.path.isdir(self.output_dir): + shutil.rmtree(self.output_dir) def test_eoproduct_search_intersection_geom(self): """EOProduct search_intersection attr must be it's geom when no bbox_or_intersect param given""" # noqa @@ -383,3 +390,26 @@ class TestEOProduct(EODagTestCase): finally: # Teardown (all the created files are within outputs_prefix) shutil.rmtree(output_dir) + + def test_eoproduct_download_progress_bar(self): + """eoproduct.download must show a progress bar""" + product = self._dummy_downloadable_product() + product.properties["id"] = 12345 + progress_callback = ProgressCallback() + + # progress bar did not start + self.assertEqual(progress_callback.n, 0) + + # extract=true would replace bar desc with extraction status + product.download( + progress_callback=progress_callback, + outputs_prefix=self.output_dir, + extract=False, + ) + + # should be product id cast to str + self.assertEqual(progress_callback.desc, "12345") + + # progress bar finished + self.assertEqual(progress_callback.n, progress_callback.total) + self.assertGreater(progress_callback.total, 0)	{ "commit_name": "merge_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 0, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 1 }	2.5	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[tutorials]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	affine==2.4.0 anyio==4.9.0 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 arrow==1.3.0 asttokens==3.0.0 async-lru==2.0.5 attrs==25.3.0 babel==2.17.0 beautifulsoup4==4.13.3 bleach==6.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 branca==0.8.1 certifi==2025.1.31 cffi==1.17.1 charset-normalizer==3.4.1 click==8.1.8 click-plugins==1.1.1 cligj==0.7.2 comm==0.2.2 contourpy==1.3.0 coverage==7.8.0 cycler==0.12.1 debugpy==1.8.13 decorator==5.2.1 defusedxml==0.7.1 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@0a782d7f476a993170f7d60c006be09b4cfa2b47#egg=eodag eodag-cube==0.3.1 exceptiongroup==1.2.2 execnet==2.1.1 executing==2.2.0 fastjsonschema==2.21.1 flasgger==0.9.7.1 Flask==3.1.0 folium==0.19.5 fonttools==4.56.0 fqdn==1.5.1 geojson==3.2.0 grpcio==1.71.0 h11==0.14.0 httpcore==1.0.7 httpx==0.28.1 idna==3.10 imageio==2.37.0 importlib_metadata==8.6.1 importlib_resources==6.5.2 iniconfig==2.1.0 ipykernel==6.29.5 ipyleaflet==0.19.2 ipython==8.18.1 ipywidgets==8.1.5 isoduration==20.11.0 itsdangerous==2.2.0 jedi==0.19.2 Jinja2==3.1.6 jmespath==1.0.1 json5==0.10.0 jsonpath-ng==1.7.0 jsonpointer==3.0.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 jupyter==1.1.1 jupyter-console==6.6.3 jupyter-events==0.12.0 jupyter-leaflet==0.19.2 jupyter-lsp==2.2.5 jupyter_client==8.6.3 jupyter_core==5.7.2 jupyter_server==2.15.0 jupyter_server_terminals==0.5.3 jupyterlab==4.3.6 jupyterlab_pygments==0.3.0 jupyterlab_server==2.27.3 jupyterlab_widgets==3.0.13 kiwisolver==1.4.7 lxml==5.3.1 Markdown==3.7 MarkupSafe==3.0.2 matplotlib==3.9.4 matplotlib-inline==0.1.7 mistune==3.1.3 nbclient==0.10.2 nbconvert==7.16.6 nbformat==5.10.4 nest-asyncio==1.6.0 notebook==7.3.3 notebook_shim==0.2.4 numpy==2.0.2 overrides==7.7.0 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 pandocfilters==1.5.1 parso==0.8.4 pexpect==4.9.0 pillow==11.1.0 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 prometheus_client==0.21.1 prompt_toolkit==3.0.50 protobuf==3.20.0 psutil==7.0.0 ptyprocess==0.7.0 pure_eval==0.2.3 pycparser==2.22 Pygments==2.19.1 pyparsing==3.2.3 pyproj==3.6.1 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-json-logger==3.3.0 pytz==2025.2 PyYAML==6.0.2 pyzmq==26.3.0 rasterio==1.4.3 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 rioxarray==0.15.0 rpds-py==0.24.0 s3transfer==0.11.4 Send2Trash==1.8.3 shapely==2.0.7 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 stack-data==0.6.3 terminado==0.18.1 tinycss2==1.4.0 tomli==2.2.1 tornado==6.4.2 tqdm==4.67.1 traitlets==5.14.3 traittypes==0.2.1 types-python-dateutil==2.9.0.20241206 typing_extensions==4.13.0 tzdata==2025.2 uri-template==1.3.0 urllib3==1.26.20 usgs==0.3.5 wcwidth==0.2.13 webcolors==24.11.1 webencodings==0.5.1 websocket-client==1.8.0 Werkzeug==3.1.3 Whoosh==2.7.4 widgetsnbextension==4.0.13 xarray==2024.7.0 xyzservices==2025.1.0 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - affine==2.4.0 - anyio==4.9.0 - argon2-cffi==23.1.0 - argon2-cffi-bindings==21.2.0 - arrow==1.3.0 - asttokens==3.0.0 - async-lru==2.0.5 - attrs==25.3.0 - babel==2.17.0 - beautifulsoup4==4.13.3 - bleach==6.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - branca==0.8.1 - certifi==2025.1.31 - cffi==1.17.1 - charset-normalizer==3.4.1 - click==8.1.8 - click-plugins==1.1.1 - cligj==0.7.2 - comm==0.2.2 - contourpy==1.3.0 - coverage==7.8.0 - cycler==0.12.1 - debugpy==1.8.13 - decorator==5.2.1 - defusedxml==0.7.1 - ecmwf-api-client==1.6.5 - eodag==2.5.3.dev15+g0a782d7f - eodag-cube==0.3.1 - exceptiongroup==1.2.2 - execnet==2.1.1 - executing==2.2.0 - fastjsonschema==2.21.1 - flasgger==0.9.7.1 - flask==3.1.0 - folium==0.19.5 - fonttools==4.56.0 - fqdn==1.5.1 - geojson==3.2.0 - grpcio==1.71.0 - h11==0.14.0 - httpcore==1.0.7 - httpx==0.28.1 - idna==3.10 - imageio==2.37.0 - importlib-metadata==8.6.1 - importlib-resources==6.5.2 - iniconfig==2.1.0 - ipykernel==6.29.5 - ipyleaflet==0.19.2 - ipython==8.18.1 - ipywidgets==8.1.5 - isoduration==20.11.0 - itsdangerous==2.2.0 - jedi==0.19.2 - jinja2==3.1.6 - jmespath==1.0.1 - json5==0.10.0 - jsonpath-ng==1.7.0 - jsonpointer==3.0.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - jupyter==1.1.1 - jupyter-client==8.6.3 - jupyter-console==6.6.3 - jupyter-core==5.7.2 - jupyter-events==0.12.0 - jupyter-leaflet==0.19.2 - jupyter-lsp==2.2.5 - jupyter-server==2.15.0 - jupyter-server-terminals==0.5.3 - jupyterlab==4.3.6 - jupyterlab-pygments==0.3.0 - jupyterlab-server==2.27.3 - jupyterlab-widgets==3.0.13 - kiwisolver==1.4.7 - lxml==5.3.1 - markdown==3.7 - markupsafe==3.0.2 - matplotlib==3.9.4 - matplotlib-inline==0.1.7 - mistune==3.1.3 - nbclient==0.10.2 - nbconvert==7.16.6 - nbformat==5.10.4 - nest-asyncio==1.6.0 - notebook==7.3.3 - notebook-shim==0.2.4 - numpy==2.0.2 - overrides==7.7.0 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - pandocfilters==1.5.1 - parso==0.8.4 - pexpect==4.9.0 - pillow==11.1.0 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - prometheus-client==0.21.1 - prompt-toolkit==3.0.50 - protobuf==3.20.0 - psutil==7.0.0 - ptyprocess==0.7.0 - pure-eval==0.2.3 - pycparser==2.22 - pygments==2.19.1 - pyparsing==3.2.3 - pyproj==3.6.1 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-json-logger==3.3.0 - pytz==2025.2 - pyyaml==6.0.2 - pyzmq==26.3.0 - rasterio==1.4.3 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - rfc3339-validator==0.1.4 - rfc3986-validator==0.1.1 - rioxarray==0.15.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - send2trash==1.8.3 - shapely==2.0.7 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - stack-data==0.6.3 - terminado==0.18.1 - tinycss2==1.4.0 - tomli==2.2.1 - tornado==6.4.2 - tqdm==4.67.1 - traitlets==5.14.3 - traittypes==0.2.1 - types-python-dateutil==2.9.0.20241206 - typing-extensions==4.13.0 - tzdata==2025.2 - uri-template==1.3.0 - urllib3==1.26.20 - usgs==0.3.5 - wcwidth==0.2.13 - webcolors==24.11.1 - webencodings==0.5.1 - websocket-client==1.8.0 - werkzeug==3.1.3 - whoosh==2.7.4 - widgetsnbextension==4.0.13 - xarray==2024.7.0 - xyzservices==2025.1.0 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_progress_bar" ]	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none" ]	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_delete_archive", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-490
CS-SI__eodag-508	b57fc768c1a08e1e255b8fa44b3785255bc8ca23	2022-09-16 17:00:19	158fac82232af978700e8f829bd5b7e227774a4c	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 7m 14s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") -42s 285 tests +3 282 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +3 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 570 runs +6 564 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +6 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit 3ecaa30d. ± Comparison against base commit b57fc768. [test-results]:data:application/gzip;base64,H4sIAEmtJGMC/03MSw7CIBSF4a00jB1A4YK4GcOrCbEthseoce8iCnb4fyc5B1r86hK6TfNlQqn4PMKWqLIPe01GWYU65TZeodc9FWMazX96+GclOmBRfq2AB7gYQ/xJLPvnE0SPfgmcDfk+8t6nw9bnPxO2zecaiDqjFMUWrKaOEOHAMqs5cImVkASYdMA11uj1BtZO9fMFAQAA github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `83%` \| :white_check_mark: \| \| eodag/plugins/apis/usgs.py \| `79%` \| :white_check_mark: \| \| eodag/plugins/download/base.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_apis\_plugins.py \| `98%` \| :white_check_mark: \| \| tests/units/test\_core.py \| `98%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| \| eodag/plugins/apis/usgs.py \| `79%` \| :white_check_mark: \| \| eodag/plugins/download/base.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_apis\_plugins.py \| `98%` \| :white_check_mark: \| \| tests/units/test\_core.py \| `98%` \| :white_check_mark: \| \| eodag/plugins/apis/usgs.py \| `79%` \| :white_check_mark: \| \| eodag/plugins/download/base.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_apis\_plugins.py \| `98%` \| :white_check_mark: \| \| tests/units/test\_core.py \| `98%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 3ecaa30d5db3e117e5d4db65690a791549e56b0b </p>	diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 5a7162bd..ecf1b113 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -19,6 +19,7 @@ import copy import logging import shutil import tarfile +import zipfile import requests from jsonpath_ng.ext import parse @@ -35,7 +36,6 @@ from eodag.plugins.apis.base import Api from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_TIMEOUT, DEFAULT_DOWNLOAD_WAIT, - DEFAULT_STREAM_REQUESTS_TIMEOUT, Download, ) from eodag.utils import ( @@ -52,14 +52,11 @@ logger = logging.getLogger("eodag.plugins.apis.usgs") class UsgsApi(Download, Api): """A plugin that enables to query and download data on the USGS catalogues""" - def query( - self, product_type=None, items_per_page=None, page=None, count=True, kwargs - ): - """Search for data on USGS catalogues""" - product_type = kwargs.get("productType") - if product_type is None: - return [], 0 + def authenticate(self): + """Login to usgs api + :raises: :class:`~eodag.utils.exceptions.AuthenticationError` + """ for i in range(2): try: api.login( @@ -77,6 +74,16 @@ class UsgsApi(Download, Api): "Please check your USGS credentials." ) from None + def query( + self, product_type=None, items_per_page=None, page=None, count=True, kwargs + ): + """Search for data on USGS catalogues""" + product_type = kwargs.get("productType") + if product_type is None: + return [], 0 + + self.authenticate() + product_type_def_params = self.config.products.get( product_type, self.config.products[GENERIC_PRODUCT_TYPE] ) @@ -108,8 +115,7 @@ class UsgsApi(Download, Api): else: lower_left, upper_right = None, None try: - results = api.scene_search( - usgs_dataset, + api_search_kwargs = dict( start_date=start_date, end_date=end_date, ll=lower_left, @@ -117,6 +123,29 @@ class UsgsApi(Download, Api): max_results=items_per_page, starting_number=(1 + (page - 1) * items_per_page), ) + logger.info( + f"Sending search request for {usgs_dataset} with {api_search_kwargs}" + ) + + results = api.scene_search(usgs_dataset, api_search_kwargs) + + # update results with storage info from download_options() + results_by_entity_id = { + res["entityId"]: res for res in results["data"]["results"] + } + logger.debug( + f"Adapting {len(results_by_entity_id)} plugin results to eodag product representation" + ) + download_options = api.download_options( + usgs_dataset, list(results_by_entity_id.keys()) + ) + if download_options.get("data", None) is not None: + for download_option in download_options["data"]: + if "dds" in download_option["downloadSystem"]: + results_by_entity_id[download_option["entityId"]].update( + download_option + ) + results["data"]["results"] = list(results_by_entity_id.values()) # Same method as in base.py, Search.__init__() # Prepare the metadata mapping @@ -145,10 +174,9 @@ class UsgsApi(Download, Api): ) except USGSError as e: logger.warning( - "Product type %s does not exist on USGS EE catalog", - usgs_dataset, + f"Product type {usgs_dataset} does not exist on USGS EE catalog" ) - logger.warning("Skipping error: %s", e) + logger.warning(f"Skipping error: {e}") api.logout() if final: @@ -160,7 +188,15 @@ class UsgsApi(Download, Api): return final, total_results - def download(self, product, auth=None, progress_callback=None, kwargs): + def download( + self, + product, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): """Download data from USGS catalogues""" if progress_callback is None: @@ -169,130 +205,133 @@ class UsgsApi(Download, Api): ) progress_callback = ProgressCallback(disable=True) + outputs_extension = self.config.products.get( + product.product_type, self.config.products[GENERIC_PRODUCT_TYPE] + ).get("outputs_extension", ".tar.gz") + fs_path, record_filename = self._prepare_download( product, progress_callback=progress_callback, - outputs_extension=".tar.gz", - kwargs + outputs_extension=outputs_extension, + kwargs, ) if not fs_path or not record_filename: if fs_path: product.location = path_to_uri(fs_path) return fs_path - try: - api.login( - getattr(self.config, "credentials", {}).get("username", ""), - getattr(self.config, "credentials", {}).get("password", ""), - save=True, + self.authenticate() + + if "dds" in product.properties.get("downloadSystem", ""): + raise NotAvailableError( + f"No USGS products found for {product.properties['id']}" ) - except USGSError: - raise AuthenticationError("Please check your USGS credentials.") from None - download_options = api.download_options( - product.properties["productType"], product.properties["id"] + download_request = api.download_request( + product.properties["productType"], + product.properties["entityId"], + product.properties["productId"], ) + req_urls = [] try: - product_ids = [ - p["id"] - for p in download_options["data"] - if p["downloadSystem"] == "dds" - ] + if len(download_request["data"]["preparingDownloads"]) > 0: + req_urls.extend( + [x["url"] for x in download_request["data"]["preparingDownloads"]] + ) + else: + req_urls.extend( + [x["url"] for x in download_request["data"]["availableDownloads"]] + ) except KeyError as e: raise NotAvailableError( - "%s not found in %s's products" % (e, product.properties["id"]) + f"{e} not found in {product.properties['id']} download_request" ) - if not product_ids: - raise NotAvailableError( - "No USGS products found for %s" % product.properties["id"] - ) - - req_urls = [] - for product_id in product_ids: - download_request = api.download_request( - product.properties["productType"], product.properties["id"], product_id - ) - try: - if len(download_request["data"]["preparingDownloads"]) > 0: - req_urls.extend( - [ - x["url"] - for x in download_request["data"]["preparingDownloads"] - ] - ) - else: - req_urls.extend( - [ - x["url"] - for x in download_request["data"]["availableDownloads"] - ] - ) - except KeyError as e: - raise NotAvailableError( - "%s not found in %s download_request" - % (e, product.properties["id"]) - ) - if len(req_urls) > 1: logger.warning( - "%s usgs products found for %s. Only first will be downloaded" - % (len(req_urls), product.properties["id"]) + f"{len(req_urls)} usgs products found for {product.properties['id']}. Only first will be downloaded" ) elif not req_urls: raise NotAvailableError( - "No usgs request url was found for %s" % product.properties["id"] + f"No usgs request url was found for {product.properties['id']}" ) req_url = req_urls[0] progress_callback.reset() - with requests.get( - req_url, - stream=True, - timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, - ) as stream: + logger.debug(f"Downloading {req_url}") + + @self._download_retry(product, wait, timeout) + def download_request(product, fs_path, progress_callback, kwargs): try: - stream.raise_for_status() - except RequestException: - import traceback as tb + with requests.get( + req_url, + stream=True, + timeout=wait * 60, + ) as stream: + try: + stream.raise_for_status() + except RequestException: + import traceback as tb - logger.error( - "Error while getting resource :\n%s", - tb.format_exc(), - ) - else: - stream_size = int(stream.headers.get("content-length", 0)) - progress_callback.reset(total=stream_size) - with open(fs_path, "wb") as fhandle: - for chunk in stream.iter_content(chunk_size=64 * 1024): - if chunk: - fhandle.write(chunk) - progress_callback(len(chunk)) + logger.error( + f"Error while getting resource :\n{tb.format_exc()}", + ) + else: + stream_size = int(stream.headers.get("content-length", 0)) + progress_callback.reset(total=stream_size) + with open(fs_path, "wb") as fhandle: + for chunk in stream.iter_content(chunk_size=64 * 1024): + if chunk: + fhandle.write(chunk) + progress_callback(len(chunk)) + except requests.exceptions.Timeout as e: + raise NotAvailableError(str(e)) + + download_request(product, fs_path, progress_callback, kwargs) with open(record_filename, "w") as fh: fh.write(product.properties["downloadLink"]) - logger.debug("Download recorded in %s", record_filename) + logger.debug(f"Download recorded in {record_filename}") api.logout() - # Check that the downloaded file is really a tar file - if not tarfile.is_tarfile(fs_path): + # Check downloaded file format + if (outputs_extension == ".tar.gz" and tarfile.is_tarfile(fs_path)) or ( + outputs_extension == ".zip" and zipfile.is_zipfile(fs_path) + ): + product_path = self._finalize( + fs_path, + progress_callback=progress_callback, + outputs_extension=outputs_extension, + kwargs, + ) + product.location = path_to_uri(product_path) + return product_path + elif tarfile.is_tarfile(fs_path): + logger.info( + "Downloaded product detected as a tar File, but was was expected to be a zip file" + ) + new_fs_path = fs_path[: fs_path.index(outputs_extension)] + ".tar.gz" + shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) + return new_fs_path + elif zipfile.is_zipfile(fs_path): + logger.info( + "Downloaded product detected as a zip File, but was was expected to be a tar file" + ) + new_fs_path = fs_path[: fs_path.index(outputs_extension)] + ".zip" + shutil.move(fs_path, new_fs_path) + product.location = path_to_uri(new_fs_path) + return new_fs_path + else: logger.warning( - "Downloaded product is not a tar File. Please check its file type before using it" + "Downloaded product is not a tar or a zip File. Please check its file type before using it" ) - new_fs_path = fs_path[: fs_path.index(".tar.gz")] + new_fs_path = fs_path[: fs_path.index(outputs_extension)] shutil.move(fs_path, new_fs_path) product.location = path_to_uri(new_fs_path) return new_fs_path - product_path = self._finalize( - fs_path, - progress_callback=progress_callback, - outputs_extension=".tar.gz", - kwargs - ) - product.location = path_to_uri(product_path) - return product_path def download_all( self, @@ -302,7 +341,7 @@ class UsgsApi(Download, Api): progress_callback=None, wait=DEFAULT_DOWNLOAD_WAIT, timeout=DEFAULT_DOWNLOAD_TIMEOUT, - kwargs + kwargs, ): """ Download all using parent (base plugin) method @@ -314,5 +353,5 @@ class UsgsApi(Download, Api): progress_callback=progress_callback, wait=wait, timeout=timeout, - kwargs + **kwargs, ) diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index 10130749..d3bd7b6e 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -81,7 +81,7 @@ class Download(PluginTopic): def __init__(self, provider, config): super(Download, self).__init__(provider, config) - self.authenticate = bool(getattr(self.config, "authenticate", False)) + self._authenticate = bool(getattr(self.config, "authenticate", False)) def download( self, @@ -598,11 +598,14 @@ class Download(PluginTopic): ) logger.debug(not_available_info) # Retry-After info from Response header - retry_server_info = self.stream.headers.get("Retry-After", "") - if retry_server_info: - logger.debug( - f"[{self.provider} response] Retry-After: {retry_server_info}" + if hasattr(self, "stream"): + retry_server_info = self.stream.headers.get( + "Retry-After", "" ) + if retry_server_info: + logger.debug( + f"[{self.provider} response] Retry-After: {retry_server_info}" + ) logger.info(retry_info) nb_info.display_html(retry_info) sleep(wait_seconds) diff --git a/eodag/resources/product_types.yml b/eodag/resources/product_types.yml index e453de30..96a4eb02 100644 --- a/eodag/resources/product_types.yml +++ b/eodag/resources/product_types.yml @@ -187,6 +187,20 @@ LANDSAT_C2L1: title: Landsat Collection 2 Level-1 Product missionStartDate: "1972-07-25T00:00:00Z" +LANDSAT_C2L2: + abstract: \| + Collection 2 Landsat OLI/TIRS Level-2 Science Products (L2SP) include + Surface Reflectance and Surface Temperature scene-based products. + instrument: OLI,TIRS + platform: LANDSAT + platformSerialIdentifier: L8,L9 + processingLevel: L1 + keywords: OLI,TIRS,LANDSAT,L8,L9,L2,C2,COLLECTION2 + sensorType: OPTICAL + license: proprietary + title: Landsat OLI and TIRS Collection 2 Level-2 Science Products 30-meter multispectral data. + missionStartDate: "2013-02-11T00:00:00Z" + LANDSAT_C2L2_SR: abstract: \| The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected @@ -269,6 +283,101 @@ LANDSAT_C2L2ALB_TA: title: Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product missionStartDate: "1982-08-22T00:00:00Z" +LANDSAT_TM_C1: + abstract: \| + Landsat 4-5 TM image data files consist of seven spectral bands (See band designations). + The resolution is 30 meters for bands 1 to 7. + (Thermal infrared band 6 was collected at 120 meters, but was resampled to 30 meters.) + The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). + instrument: TM + platform: LANDSAT + platformSerialIdentifier: L4,L5 + processingLevel: L1 + keywords: TM,LANDSAT,L4,L5,L1,C1,COLLECTION1 + sensorType: OPTICAL + license: proprietary + title: Landsat 4-5 Thematic Mapper (TM) Collection-1 Level-1 Data Products + missionStartDate: "1982-08-22T00:00:00Z" + missionEndDate: "2013-06-06T00:00:00Z" + +LANDSAT_TM_C2L1: + abstract: \| + Landsat 4-5 TM image data files consist of seven spectral bands (See band designations). + The resolution is 30 meters for bands 1 to 7. + (Thermal infrared band 6 was collected at 120 meters, but was resampled to 30 meters.) + The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). + instrument: TM + platform: LANDSAT + platformSerialIdentifier: L4,L5 + processingLevel: L1 + keywords: TM,LANDSAT,L4,L5,L1,C2,COLLECTION2 + sensorType: OPTICAL + license: proprietary + title: Landsat 4-5 Thematic Mapper (TM) Collection-2 Level-1 Data Products + missionStartDate: "1982-08-22T00:00:00Z" + missionEndDate: "2013-06-06T00:00:00Z" + +LANDSAT_TM_C2L2: + abstract: \| + Collection 2 Landsat 4-5 Thematic Mapper (TM) Level-2 Science Products (L2SP) include + Surface Reflectance and Surface Temperature scene-based products. + instrument: TM + platform: LANDSAT + platformSerialIdentifier: L4,L5 + processingLevel: L2 + keywords: TM,LANDSAT,L4,L5,L2,C2,COLLECTION2 + sensorType: OPTICAL + license: proprietary + title: Landsat 4-5 Thematic Mapper (TM) Collection-2 Level-2 Data Products + missionStartDate: "1982-08-22T00:00:00Z" + missionEndDate: "2013-06-06T00:00:00Z" + +LANDSAT_ETM_C1: + abstract: \| + Landsat 7 ETM+ images consist of eight spectral bands with a spatial resolution of 30 meters for bands 1 to 7. + The panchromatic band 8 has a resolution of 15 meters. All bands can collect one of two gain settings (high or low) + for increased radiometric sensitivity and dynamic range, while Band 6 collects both high and low gain for all + scenes. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). + instrument: ETM+ + platform: LANDSAT + platformSerialIdentifier: L7 + processingLevel: L1 + keywords: ETM,ETM+,LANDSAT,L7,L1,C1,COLLECTION1 + sensorType: OPTICAL + license: proprietary + title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-1 Level-1 data from Landsat 7 + missionStartDate: "1999-04-15T00:00:00Z" + +LANDSAT_ETM_C2L1: + abstract: \| + Landsat 7 ETM+ images consist of eight spectral bands with a spatial resolution of 30 meters for bands 1 to 7. + The panchromatic band 8 has a resolution of 15 meters. All bands can collect one of two gain settings (high or low) + for increased radiometric sensitivity and dynamic range, while Band 6 collects both high and low gain for all + scenes. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). + instrument: ETM+ + platform: LANDSAT + platformSerialIdentifier: L7 + processingLevel: L1 + keywords: ETM,ETM+,LANDSAT,L7,L1,C2,COLLECTION2 + sensorType: OPTICAL + license: proprietary + title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-2 Level-1 data from Landsat 7 + missionStartDate: "1999-04-15T00:00:00Z" + +LANDSAT_ETM_C2L2: + abstract: \| + Collection 2 Landsat 7 ETM+ Level-2 Science Products (L2SP) include Surface Reflectance and Surface Temperature + scene-based products. + instrument: ETM+ + platform: LANDSAT + platformSerialIdentifier: L7 + processingLevel: L2 + keywords: ETM,ETM+,LANDSAT,L7,L2,C2,COLLECTION2 + sensorType: OPTICAL + license: proprietary + title: Enhanced Thematic Mapper Plus (ETM+) 15- to 30-meter multispectral Collection-2 Level-2 data from Landsat 7 + missionStartDate: "1999-04-15T00:00:00Z" + # MODIS ----------------------------------------------------------------------- MODIS_MCD43A4: abstract: \| diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 7094127f..1f60a8a3 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -31,7 +31,7 @@ max_items_per_page: 5000 total_items_nb_key_path: '$.totalHits' metadata_mapping: - id: '$.entityId' + id: '$.displayId' geometry: '$.spatialBounds' productType: '$.productType' title: '$.displayId' @@ -39,15 +39,49 @@ cloudCover: '$.cloudCover' startTimeFromAscendingNode: '$.temporalCoverage.startDate' completionTimeFromAscendingNode: '$.temporalCoverage.endDate' - quicklook: '$.browse[0].thumbnailPath' - # storageStatus set to ONLINE for consistency between providers - storageStatus: '{$.null#replace_str("Not Available","ONLINE")}' - downloadLink: 'https://dds.cr.usgs.gov/{id}' + publicationDate: '$.publishDate' + thumbnail: '$.browse[0].thumbnailPath' + quicklook: '$.browse[0].browsePath' + storageStatus: '{$.available#get_group_name((?P<ONLINE>True)\|(?P<OFFLINE>False))}' + downloadLink: 'https://earthexplorer.usgs.gov/download/external/options/{productType}/{entityId}/M2M/' + # metadata needed for download + entityId: '$.entityId' + productId: '$.id' extract: True + order_enabled: true products: - # datasets list http://kapadia.github.io/usgs/_sources/reference/catalog/ee.txt + # datasets list http://kapadia.github.io/usgs/_sources/reference/catalog/ee.txt may be outdated + # see also https://dds.cr.usgs.gov/ee-data/coveragemaps/shp/ee/ L8_OLI_TIRS_C1L1: dataset: LANDSAT_8_C1 + outputs_extension: .tar.gz + LANDSAT_C2L1: + dataset: landsat_ot_c2_l1 + outputs_extension: .tar.gz + LANDSAT_C2L2: + dataset: landsat_ot_c2_l2 + outputs_extension: .tar.gz + LANDSAT_TM_C1: + dataset: landsat_tm_c1 + outputs_extension: .tar.gz + LANDSAT_TM_C2L1: + dataset: landsat_tm_c2_l1 + outputs_extension: .tar.gz + LANDSAT_TM_C2L2: + dataset: landsat_tm_c2_l2 + outputs_extension: .tar.gz + LANDSAT_ETM_C1: + dataset: landsat_etm_c1 + outputs_extension: .tar.gz + LANDSAT_ETM_C2L1: + dataset: landsat_etm_c2_l1 + outputs_extension: .tar.gz + LANDSAT_ETM_C2L2: + dataset: landsat_etm_c2_l2 + outputs_extension: .tar.gz + S2_MSI_L1C: + dataset: SENTINEL_2A + outputs_extension: .zip GENERIC_PRODUCT_TYPE: dataset: '{productType}'	Add S2_MSI_L1C productType to the USGS provider When trying to download Sentinel2 images using the USGS provider, the "S2_MSI_L1C" product type is not recognized, even though it is available using the earth explorer website. It would be great to add this one (and more precisely in my case I'd like that because I only got a bunch of failure when trying to do that using the peps provider...).	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index cb294dfd..1428ddf0 100644 --- a/tests/context.py +++ b/tests/context.py @@ -52,7 +52,11 @@ from eodag.plugins.crunch.filter_latest_tpl_name import FilterLatestByName from eodag.plugins.crunch.filter_property import FilterProperty from eodag.plugins.crunch.filter_overlap import FilterOverlap from eodag.plugins.download.aws import AwsDownload -from eodag.plugins.download.base import Download, DEFAULT_STREAM_REQUESTS_TIMEOUT +from eodag.plugins.download.base import ( + Download, + DEFAULT_STREAM_REQUESTS_TIMEOUT, + DEFAULT_DOWNLOAD_WAIT, +) from eodag.plugins.download.http import HTTPDownload from eodag.plugins.manager import PluginManager from eodag.plugins.search.base import Search @@ -83,3 +87,4 @@ from eodag.utils.exceptions import ( ) from eodag.utils.stac_reader import fetch_stac_items, HTTP_REQ_TIMEOUT from tests import TESTS_DOWNLOAD_PATH, TEST_RESOURCES_PATH +from usgs.api import USGSAuthExpiredError, USGSError diff --git a/tests/units/test_apis_plugins.py b/tests/units/test_apis_plugins.py index d9b4304c..2e2b2980 100644 --- a/tests/units/test_apis_plugins.py +++ b/tests/units/test_apis_plugins.py @@ -21,13 +21,23 @@ import unittest from tempfile import TemporaryDirectory from unittest import mock +import responses from ecmwfapi.api import ANONYMOUS_APIKEY_VALUES +from shapely.geometry import shape from tests.context import ( + DEFAULT_DOWNLOAD_WAIT, + ONLINE_STATUS, + AuthenticationError, EODataAccessGateway, + EOProduct, + NotAvailableError, PluginManager, SearchResult, + USGSAuthExpiredError, + USGSError, ValidationError, + get_geometry_from_various, load_default_config, parse_qsl, path_to_uri, @@ -317,3 +327,251 @@ class TestApisPluginEcmwfApi(BaseApisPluginTest): ) assert mock_ecmwfdataserver_retrieve.call_count == 2 assert len(paths) == 2 + + +class TestApisPluginUsgsApi(BaseApisPluginTest): + def setUp(self): + self.provider = "usgs" + self.api_plugin = self.get_search_plugin(provider=self.provider) + + @mock.patch("usgs.api.login", autospec=True) + @mock.patch("usgs.api.logout", autospec=True) + def test_plugins_apis_usgs_authenticate(self, mock_api_logout, mock_api_login): + """UsgsApi.authenticate must return try to login""" + # no credential + self.api_plugin.authenticate() + mock_api_login.assert_called_once_with("", "", save=True) + mock_api_logout.assert_not_called() + mock_api_login.reset_mock() + + # with credentials + self.api_plugin.config.credentials = { + "username": "foo", + "password": "bar", + } + self.api_plugin.authenticate() + mock_api_login.assert_called_once_with("foo", "bar", save=True) + mock_api_logout.assert_not_called() + mock_api_login.reset_mock() + + # with USGSAuthExpiredError + mock_api_login.side_effect = [USGSAuthExpiredError(), None] + self.api_plugin.authenticate() + self.assertEqual(mock_api_login.call_count, 2) + self.assertEqual(mock_api_logout.call_count, 1) + mock_api_login.reset_mock() + mock_api_logout.reset_mock() + + # with invalid credentials / USGSError + mock_api_login.side_effect = USGSError() + with self.assertRaises(AuthenticationError): + self.api_plugin.authenticate() + self.assertEqual(mock_api_login.call_count, 1) + mock_api_logout.assert_not_called() + + @mock.patch("usgs.api.login", autospec=True) + @mock.patch("usgs.api.logout", autospec=True) + @mock.patch( + "usgs.api.scene_search", + autospec=True, + return_value={ + "data": { + "results": [ + { + "browse": [ + { + "browsePath": "https://path/to/quicklook.jpg", + "thumbnailPath": "https://path/to/thumbnail.jpg", + }, + {}, + {}, + ], + "cloudCover": "77.46", + "entityId": "LC81780382020041LGN00", + "displayId": "LC08_L1GT_178038_20200210_20200224_01_T2", + "spatialBounds": { + "type": "Polygon", + "coordinates": [ + [ + [28.03905, 30.68073], + [28.03905, 32.79057], + [30.46294, 32.79057], + [30.46294, 30.68073], + [28.03905, 30.68073], + ] + ], + }, + "temporalCoverage": { + "endDate": "2020-02-10 00:00:00", + "startDate": "2020-02-10 00:00:00", + }, + "publishDate": "2020-02-10 08:24:46", + }, + ], + "recordsReturned": 5, + "totalHits": 139, + "startingNumber": 6, + "nextRecord": 11, + } + }, + ) + @mock.patch( + "usgs.api.download_options", + autospec=True, + return_value={ + "data": [ + { + "id": "5e83d0b8e7f6734c", + "entityId": "LC81780382020041LGN00", + "available": True, + "filesize": 9186067, + "productName": "LandsatLook Natural Color Image", + "downloadSystem": "dds", + }, + { + "entityId": "LC81780382020041LGN00", + "available": False, + "downloadSystem": "wms", + }, + ] + }, + ) + def test_plugins_apis_usgs_query( + self, + mock_api_download_options, + mock_api_scene_search, + mock_api_logout, + mock_api_login, + ): + """UsgsApi.query must search using usgs api""" + + search_kwargs = { + "productType": "L8_OLI_TIRS_C1L1", + "startTimeFromAscendingNode": "2020-02-01", + "completionTimeFromAscendingNode": "2020-02-10", + "geometry": get_geometry_from_various(geometry=[10, 20, 30, 40]), + "items_per_page": 5, + "page": 2, + } + search_results, total_count = self.api_plugin.query(*search_kwargs) + mock_api_scene_search.assert_called_once_with( + "LANDSAT_8_C1", + start_date="2020-02-01", + end_date="2020-02-10", + ll={"longitude": 10.0, "latitude": 20.0}, + ur={"longitude": 30.0, "latitude": 40.0}, + max_results=5, + starting_number=6, + ) + self.assertEqual(search_results[0].provider, "usgs") + self.assertEqual(search_results[0].product_type, "L8_OLI_TIRS_C1L1") + self.assertEqual( + search_results[0].geometry, + shape( + mock_api_scene_search.return_value["data"]["results"][0][ + "spatialBounds" + ] + ), + ) + self.assertEqual( + search_results[0].properties["id"], + mock_api_scene_search.return_value["data"]["results"][0]["displayId"], + ) + self.assertEqual( + search_results[0].properties["cloudCover"], + float( + mock_api_scene_search.return_value["data"]["results"][0]["cloudCover"] + ), + ) + self.assertEqual(search_results[0].properties["storageStatus"], ONLINE_STATUS) + self.assertEqual( + total_count, mock_api_scene_search.return_value["data"]["totalHits"] + ) + + @mock.patch("usgs.api.login", autospec=True) + @mock.patch("usgs.api.logout", autospec=True) + @mock.patch("usgs.api.download_request", autospec=True) + def test_plugins_apis_usgs_download( + self, + mock_api_download_request, + mock_api_logout, + mock_api_login, + ): + """UsgsApi.download must donwload using usgs api""" + + @responses.activate + def run(): + + product = EOProduct( + "peps", + dict( + geometry="POINT (0 0)", + title="dummy_product", + id="dummy", + entityId="dummyEntityId", + productId="dummyProductId", + productType="L8_OLI_TIRS_C1L1", + ), + ) + product.location = product.remote_location = product.properties[ + "downloadLink" + ] = "http://somewhere" + product.properties["id"] = "someproduct" + + responses.add( + responses.GET, + "http://path/to/product", + status=200, + content_type="application/octet-stream", + body=b"something", + auto_calculate_content_length=True, + match=[ + responses.matchers.request_kwargs_matcher( + dict(stream=True, timeout=DEFAULT_DOWNLOAD_WAIT 60) + ) + ], + ) + + # missing download_request return value + with self.assertRaises(NotAvailableError): + self.api_plugin.download(product, outputs_prefix=self.tmp_home_dir.name) + + # download 1 available product + mock_api_download_request.return_value = { + "data": {"preparingDownloads": [{"url": "http://path/to/product"}]} + } + + path = self.api_plugin.download( + product, outputs_prefix=self.tmp_home_dir.name + ) + + self.assertEqual(len(responses.calls), 1) + responses.calls.reset() + + self.assertEqual( + path, os.path.join(self.tmp_home_dir.name, "dummy_product") + ) + self.assertTrue(os.path.isfile(path)) + + # check file extension + # tar + os.remove(path) + with mock.patch("tarfile.is_tarfile", return_value=True, autospec=True): + path = self.api_plugin.download( + product, outputs_prefix=self.tmp_home_dir.name, extract=False + ) + self.assertEqual( + path, os.path.join(self.tmp_home_dir.name, "dummy_product.tar.gz") + ) + # zip + os.remove(path) + product.product_type = "S2_MSI_L1C" + with mock.patch("zipfile.is_zipfile", return_value=True, autospec=True): + path = self.api_plugin.download( + product, outputs_prefix=self.tmp_home_dir.name, extract=False + ) + self.assertEqual( + path, os.path.join(self.tmp_home_dir.name, "dummy_product.zip") + ) + + run() diff --git a/tests/units/test_core.py b/tests/units/test_core.py index edaac541..9df038b6 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -86,13 +86,20 @@ class TestCore(TestCoreBase): "earth_search", "earth_search_gcs", ], - "LANDSAT_C2L1": ["usgs_satapi_aws", "astraea_eod"], + "LANDSAT_C2L1": ["usgs_satapi_aws", "astraea_eod", "usgs"], + "LANDSAT_C2L2": ["usgs"], "LANDSAT_C2L2_SR": ["usgs_satapi_aws"], "LANDSAT_C2L2_ST": ["usgs_satapi_aws"], "LANDSAT_C2L2ALB_BT": ["usgs_satapi_aws"], "LANDSAT_C2L2ALB_SR": ["usgs_satapi_aws"], "LANDSAT_C2L2ALB_ST": ["usgs_satapi_aws"], "LANDSAT_C2L2ALB_TA": ["usgs_satapi_aws"], + "LANDSAT_TM_C1": ["usgs"], + "LANDSAT_TM_C2L1": ["usgs"], + "LANDSAT_TM_C2L2": ["usgs"], + "LANDSAT_ETM_C1": ["usgs"], + "LANDSAT_ETM_C2L1": ["usgs"], + "LANDSAT_ETM_C2L2": ["usgs"], "S1_SAR_GRD": [ "peps", "sobloo", @@ -130,6 +137,7 @@ class TestCore(TestCoreBase): "astraea_eod", "earth_search", "earth_search_gcs", + "usgs", ], "S3_ERR": ["onda", "creodias"], "S3_EFR": ["onda", "creodias"],	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 3, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 4 }	2.5	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@b57fc768c1a08e1e255b8fa44b3785255bc8ca23#egg=eodag exceptiongroup==1.2.2 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==4.1.1 pytest-metadata==3.1.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.5.3.dev27+gb57fc768 - exceptiongroup==1.2.2 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==4.1.1 - pytest-metadata==3.1.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_download", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_query" ]	[]	[ "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download_all", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_mandatory_params_missing", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_custom_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_without_producttype", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator" ]	[]	Apache License 2.0	null
CS-SI__eodag-524	58335e728fff5b98bd85b96fead271429773a593	2022-10-11 13:31:50	29c1474e5016cd8158c890ec5210af2062c73a49	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 9m 44s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") +57s 295 tests +2 292 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 590 runs +4 584 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +4 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit eaebc66c. ± Comparison against base commit 58335e72. [test-results]:data:application/gzip;base64,H4sIAEZyRWMC/03NSQ6DMAyF4augrLuAkIlepgomlqIyVBlWqHevGxrK8v8sPe8M/ewiuzf81rCYfTpjysEmv62U0ggCOqVyHGStR8wAhfifnv5F1J+A1s8E7QkuhC38JOT1uymHGnXy+HnIsahqXwZLX/dgWxafKJizbgSlQPeiVV2HWo04TD2OhgshWmkAkXPU7P0Bp8XO1QUBAAA= github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `83%` \| :white_check_mark: \| \| eodag/plugins/download/aws.py \| `48%` \| :x: \| \| eodag/plugins/download/s3rest.py \| `18%` \| :x: \| \| eodag/utils/\_\_init\_\_.py \| `89%` \| :white_check_mark: \| \| tests/units/test\_download\_plugins.py \| `95%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `94%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `83%` \| :white_check_mark: \| \| eodag/plugins/download/aws.py \| `48%` \| :x: \| \| eodag/plugins/download/s3rest.py \| `18%` \| :x: \| \| eodag/utils/\_\_init\_\_.py \| `89%` \| :white_check_mark: \| \| tests/units/test\_download\_plugins.py \| `95%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `94%` \| :white_check_mark: \| \| eodag/plugins/download/aws.py \| `48%` \| :x: \| \| eodag/plugins/download/s3rest.py \| `18%` \| :x: \| \| eodag/utils/\_\_init\_\_.py \| `88%` \| :white_check_mark: \| \| tests/units/test\_download\_plugins.py \| `94%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `94%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against eaebc66c7340611f76bf9d3fb82444058cff22f7 </p>	diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index e27ffe9c..e5d9d2a9 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -34,7 +34,12 @@ from eodag.api.product.metadata_mapping import ( properties_from_xml, ) from eodag.plugins.download.base import Download -from eodag.utils import ProgressCallback, path_to_uri, rename_subfolder, urlparse +from eodag.utils import ( + ProgressCallback, + get_bucket_name_and_prefix, + path_to_uri, + rename_subfolder, +) from eodag.utils.exceptions import AuthenticationError, DownloadError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT @@ -601,22 +606,20 @@ class AwsDownload(Download): """ if not url: url = product.location - bucket, prefix = None, None - # Assume the bucket is encoded into the product location as a URL or given as the 'default_bucket' config - # param - scheme, netloc, path, params, query, fragment = urlparse(url) - if not scheme or scheme == "s3": + + bucket_path_level = getattr(self.config, "bucket_path_level", None) + + bucket, prefix = get_bucket_name_and_prefix( + url=url, bucket_path_level=bucket_path_level + ) + + if bucket is None: bucket = ( - netloc - if netloc - else getattr(self.config, "products", {}) + getattr(self.config, "products", {}) .get(product.product_type, {}) .get("default_bucket", "") ) - prefix = path.strip("/") - elif scheme in ("http", "https", "ftp"): - parts = path.split("/") - bucket, prefix = parts[1], "/{}".format("/".join(parts[2:])) + return bucket, prefix def check_manifest_file_list(self, product_path): diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index db92da7a..fa2d0f65 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -27,10 +27,15 @@ import requests from requests import RequestException from eodag.api.product.metadata_mapping import OFFLINE_STATUS -from eodag.plugins.download.aws import AwsDownload -from eodag.plugins.download.base import DEFAULT_STREAM_REQUESTS_TIMEOUT +from eodag.plugins.download.base import DEFAULT_STREAM_REQUESTS_TIMEOUT, Download from eodag.plugins.download.http import HTTPDownload -from eodag.utils import ProgressCallback, path_to_uri, unquote, urljoin +from eodag.utils import ( + ProgressCallback, + get_bucket_name_and_prefix, + path_to_uri, + unquote, + urljoin, +) from eodag.utils.exceptions import ( AuthenticationError, DownloadError, @@ -42,7 +47,7 @@ from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT logger = logging.getLogger("eodag.plugins.download.s3rest") -class S3RestDownload(AwsDownload): +class S3RestDownload(Download): """Http download on S3-like object storage location for example using Mundi REST API (free account) https://mundiwebservices.com/keystoneapi/uploads/documents/CWS-DATA-MUT-087-EN-Mundi_Download_v1.1.pdf#page=13 @@ -78,7 +83,9 @@ class S3RestDownload(AwsDownload): progress_callback = ProgressCallback(disable=True) # get bucket urls - bucket_name, prefix = self.get_bucket_name_and_prefix(product) + bucket_name, prefix = get_bucket_name_and_prefix( + url=product.location, bucket_path_level=self.config.bucket_path_level + ) if ( bucket_name is None diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 0302a9b2..df79e7aa 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1261,7 +1261,7 @@ url: https://mundiwebservices.com/ search: !plugin type: QueryStringSearch - api_endpoint: 'https://mundiwebservices.com/acdc/catalog/proxy/search/{collection}/opensearch' + api_endpoint: 'https://{collection}.browse.catalog.mundiwebservices.com/opensearch' need_auth: false result_type: 'xml' results_entry: '//ns:entry' @@ -1390,6 +1390,7 @@ base_uri: 'https://mundiwebservices.com/dp' extract: true auth_error_code: 401 + bucket_path_level: 0 auth: !plugin type: HTTPHeaderAuth headers: diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index de74e38b..40234376 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -1028,3 +1028,33 @@ def cached_parse(str_to_parse): :rtype: :class:`jsonpath_ng.JSONPath` """ return parse(str_to_parse) + + +def get_bucket_name_and_prefix(url=None, bucket_path_level=None): + """Extract bucket name and prefix from URL + + :param url: (optional) URL to use as product.location + :type url: str + :param bucket_path_level: (optional) bucket location index in path.split('/') + :type bucket_path_level: int + :returns: bucket_name and prefix as str + :rtype: tuple + """ + bucket, prefix = None, None + + scheme, netloc, path, params, query, fragment = urlparse(url) + subdomain = netloc.split(".")[0] + path = path.strip("/") + + if scheme and bucket_path_level is None: + bucket = subdomain + prefix = path + elif not scheme and bucket_path_level is None: + prefix = path + elif bucket_path_level is not None: + parts = path.split("/") + bucket, prefix = parts[bucket_path_level], "/".join( + parts[(bucket_path_level + 1) :] + ) + + return bucket, prefix	Capella Bucket Download issue Bucket Issue Hi there, I'm trying to download Capella's Open data from [this stac index]](https://www.stacindex.org/catalogs/capella-space-open-data#/BrVjRL9LGRjg8ipuaKLhdHr1kD4sbvyd8QTYGwbMTTtkR4fzPd4yeiApxyrQkiq/LGziSG3zruu42mUQkBqrXkRPLdBZyfQ9WBqmakLW8v6Q2jtwGXLNgDPJyeVfFR6XkZHa8qL5E75MA6d8vzhnxgNLQVUuVFNrq?t=2). But I got a "NoSuchKey" error from the "bucket_name" = "data" I tried unsuccesfull workarounds... If someone got an idea, it's very welcome. Thanks, and thanks a lot EOdag devs! Ari Code To Reproduce ```py from eodag import setup_logging setup_logging(verbose=3) from eodag.api.core import EODataAccessGateway # Create an EODAG custom STAC provider dag = EODataAccessGateway() # Add the custom STAC provider + output + login an password catalog_path = "https://capella-open-data.s3.us-west-2.amazonaws.com/stac/capella-open-data-by-product-type/capella-open-data-slc/collection.json" # catalog_path = 'https://capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json' base_uri = "https://capella-open-data.s3.us-west-2.amazonaws.com" outputs_prefix = r"D:\Radar\data\EODAG" aws_access_key_id = "change_me" aws_secret_access_key = "change_me" dag.update_providers_config(""" capella: search: type: StaticStacSearch api_endpoint: %s products: GENERIC_PRODUCT_TYPE: productType: '{productType}' download: type: AwsDownload base_uri: %s flatten_top_dirs: True outputs_prefix: %s auth: type: AwsAuth credentials: aws_access_key_id: %s aws_secret_access_key: %s """ % (catalog_path, base_uri, outputs_prefix, aws_access_key_id, aws_secret_access_key )) # # Set the custom STAC provider as preferred dag.set_preferred_provider("capella") # Query every product from inside the catalog # all_products, _ = dag.search() capella_prod, _ = dag.search(start="2021-12-01", end="2021-12-15", {"sar:product_type": "SLC", "sensorMode": "spotlight"}) # , locations=dict(country="BRA"), start="2021-01-01", end="2020-05-30", items_per_page=50) # [(key, asset["href"]) for key, asset in capella_prod[0].assets.items()] paths = dag.download_all(capella_prod) ``` TroubleShoot ```py 2022-10-07 16:47:56,830 eodag.config [INFO ] (config ) Loading user configuration from: C:\Users\ajeannin\.config\eodag\eodag.yml 2022-10-07 16:47:56,923 eodag.core [INFO ] (core ) usgs: provider needing auth for search has been pruned because no crendentials could be found 2022-10-07 16:47:56,923 eodag.core [INFO ] (core ) aws_eos: provider needing auth for search has been pruned because no crendentials could be found 2022-10-07 16:47:57,010 eodag.core [DEBUG ] (core ) Opening product types index in C:\Users\ajeannin\.config\eodag\.index 2022-10-07 16:47:57,020 eodag.core [INFO ] (core ) Locations configuration loaded from C:\Users\ajeannin\.config\eodag\locations.yml 2022-10-07 16:47:57,022 eodag.config [INFO ] (config ) capella: unknown provider found in user conf, trying to use provided configuration 2022-10-07 16:47:57,110 eodag.core [INFO ] (core ) No product type could be guessed with provided arguments 2022-10-07 16:47:57,686 eodag.core [INFO ] (core ) Searching product type 'None' on provider: capella 2022-10-07 16:47:57,686 eodag.core [DEBUG ] (core ) Using plugin class for search: StaticStacSearch 2022-10-07 16:47:58,682 eodag.utils.stac_reader [DEBUG ] (stac_reader ) Fetching 125 items 2022-10-07 16:47:58,683 eodag.utils.stac_reader [DEBUG ] (stac_reader ) read_local_json is not the right STAC opener 2022-10-07 16:48:00,570 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Getting genric provider product type definition parameters for None 2022-10-07 16:48:00,571 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Building the query string that will be used for search 2022-10-07 16:48:00,571 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Retrieving queryable metadata from metadata_mapping 2022-10-07 16:48:00,609 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Next page URL could not be collected 2022-10-07 16:48:00,626 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Next page Query-object could not be collected 2022-10-07 16:48:00,643 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Next page merge could not be collected 2022-10-07 16:48:00,643 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Adapting 125 plugin results to eodag product representation 2022-10-07 16:48:02,877 eodag.plugins.crunch.filter_date [DEBUG ] (filter_date ) Start filtering by date 2022-10-07 16:48:02,893 eodag.plugins.crunch.filter_date [INFO ] (filter_date ) Finished filtering products. 4 resulting products 2022-10-07 16:48:02,893 eodag.plugins.crunch.filter_overlap [DEBUG ] (filter_overlap ) Start filtering for overlapping products 2022-10-07 16:48:02,893 eodag.plugins.crunch.filter_overlap [WARNING ] (filter_overlap ) geometry not found in cruncher arguments, filtering disabled. 2022-10-07 16:48:02,893 eodag.plugins.crunch.filter_property [DEBUG ] (filter_property ) Start filtering for products matching operator.eq(product.properties['sar:product_type'], SLC) 2022-10-07 16:48:02,893 eodag.plugins.crunch.filter_property [INFO ] (filter_property ) Finished filtering products. 4 resulting products 2022-10-07 16:48:02,893 eodag.plugins.crunch.filter_property [DEBUG ] (filter_property ) Start filtering for products matching operator.eq(product.properties['sensorMode'], spotlight) 2022-10-07 16:48:02,893 eodag.plugins.crunch.filter_property [INFO ] (filter_property ) Finished filtering products. 2 resulting products 2022-10-07 16:48:02,898 eodag.core [INFO ] (core ) Found 2 result(s) on provider 'capella' 2022-10-07 16:48:02,898 eodag.core [INFO ] (core ) Downloading 2 products Downloaded products: 0%\| \| 0/2 [00:00<?, ?product/s] 0.00B [00:00, ?B/s] CAPELLA_C03_SP_SLC_HH_20211214232643_20211214232646: 0.00B [00:00, ?B/s]2022-10-07 16:48:02,900 eodag.plugins.download.base [INFO ] (base ) Download url: https://capella-open-data.s3.us-west-2.amazonaws.com/collections/capella-open-data-spotlight/items/CAPELLA_C03_SP_SLC_HH_20211214232643_20211214232646 2022-10-07 16:48:03,847 eodag.plugins.download.aws [WARNING ] (aws ) Unexpected error: An error occurred (NoSuchKey) when calling the ListObjects operation: The specified key does not exist. 2022-10-07 16:48:03,847 eodag.plugins.download.aws [WARNING ] (aws ) Skipping data//2021/12/14/CAPELLA_C03_SP_SLC_HH_20211214232643_20211214232646/CAPELLA_C03_SP_SLC_HH_20211214232643_20211214232646.tif 2022-10-07 16:48:04,678 eodag.plugins.download.aws [WARNING ] (aws ) Unexpected error: An error occurred (NoSuchKey) when calling the ListObjects operation: The specified key does not exist. 2022-10-07 16:48:04,678 eodag.plugins.download.aws [WARNING ] (aws ) Skipping data//2021/12/14/CAPELLA_C03_SP_SLC_HH_20211214232643_20211214232646/CAPELLA_C03_SP_SLC_HH_20211214232643_20211214232646_extended.json 2022-10-07 16:48:05,496 eodag.plugins.download.aws [WARNING ] (aws ) Unexpected error: An error occurred (NoSuchKey) when calling the ListObjects operation: The specified key does not exist. 2022-10-07 16:48:05,496 eodag.plugins.download.aws [WARNING ] (aws ) Skipping data//2021/12/14/CAPELLA_C03_SP_GEO_HH_20211214232633_20211214232656/CAPELLA_C03_SP_GEO_HH_20211214232633_20211214232656_preview.tif 2022-10-07 16:48:06,301 eodag.plugins.download.aws [WARNING ] (aws ) Unexpected error: An error occurred (NoSuchKey) when calling the ListObjects operation: The specified key does not exist. 2022-10-07 16:48:06,301 eodag.plugins.download.aws [WARNING ] (aws ) Skipping data//2021/12/14/CAPELLA_C03_SP_GEO_HH_20211214232633_20211214232656/CAPELLA_C03_SP_GEO_HH_20211214232633_20211214232656_thumb.png 2022-10-07 16:48:06,302 eodag.plugins.download.base [ERROR ] (base ) Stopped because of credentials problems with provider capella Traceback (most recent call last): File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\plugins\download\base.py", line 456, in download_all product.download( File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\api\product\_product.py", line 288, in download fs_path = self.downloader.download( File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\plugins\download\aws.py", line 339, in download raise AuthenticationError(", ".join(auth_error_messages)) eodag.utils.exceptions.AuthenticationError: An error occurred (NoSuchKey) when calling the ListObjects operation: The specified key does not exist. Downloaded products: 0%\| \| 0/2 [00:03<?, ?product/s] Traceback (most recent call last): File "D:\PythonCode\radar_dev\radar_dev\utils.py", line 48, in <module> paths = dag.download_all(capella_prod) File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\api\core.py", line 1288, in download_all paths = download_plugin.download_all( File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\plugins\download\aws.py", line 920, in download_all return super(AwsDownload, self).download_all( File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\plugins\download\base.py", line 456, in download_all product.download( File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\api\product\_product.py", line 288, in download fs_path = self.downloader.download( File "C:\Users\ajeannin\Anaconda3\envs\radar_dev\lib\site-packages\eodag\plugins\download\aws.py", line 339, in download raise AuthenticationError(", ".join(auth_error_messages)) eodag.utils.exceptions.AuthenticationError: An error occurred (NoSuchKey) when calling the ListObjects operation: The specified key does not exist. CAPELLA_C03_SP_SLC_HH_20211214232643_20211214232646: 0.00B [00:03, ?B/s] Process finished with exit code 1 ``` Environment:** - Conda version: 22.9.0 - Python version: 3.7 - EODAG version: `eodag version` tells me ```py (radar_dev) λ eodag version eodag (Earth Observation Data Access Gateway): version 0.0.0 ``` But I think I got the 2.5.0 version as it is the latest on Conda env (should I open a new issue ?)	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index 1428ddf0..f23c149a 100644 --- a/tests/context.py +++ b/tests/context.py @@ -61,6 +61,7 @@ from eodag.plugins.download.http import HTTPDownload from eodag.plugins.manager import PluginManager from eodag.plugins.search.base import Search from eodag.utils import ( + get_bucket_name_and_prefix, get_geometry_from_various, get_timestamp, makedirs, diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index 40839b23..3b3c480b 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -314,3 +314,40 @@ class TestDownloadPluginHttpRetry(BaseDownloadPluginTest): self.assertEqual(len(responses.calls), 1) run() + + +class TestDownloadPluginAws(BaseDownloadPluginTest): + def setUp(self): + super(TestDownloadPluginAws, self).setUp() + self.product = EOProduct( + "astraea_eod", + dict( + geometry="POINT (0 0)", + title="dummy_product", + id="dummy", + ), + productType="S2_MSI_L2A", + ) + + def test_plugins_download_aws_get_bucket_prefix(self): + """AwsDownload.get_bucket_name_and_prefix() must extract bucket & prefix from location""" + + plugin = self.get_download_plugin(self.product) + plugin.config.products["S2_MSI_L2A"]["default_bucket"] = "default_bucket" + self.product.properties["id"] = "someproduct" + + self.product.location = ( + self.product.remote_location + ) = "http://somebucket.somehost.com/path/to/some/product" + bucket, prefix = plugin.get_bucket_name_and_prefix(self.product) + self.assertEqual((bucket, prefix), ("somebucket", "path/to/some/product")) + + plugin.config.bucket_path_level = 1 + bucket, prefix = plugin.get_bucket_name_and_prefix(self.product) + self.assertEqual((bucket, prefix), ("to", "some/product")) + del plugin.config.bucket_path_level + + bucket, prefix = plugin.get_bucket_name_and_prefix( + self.product, url="/somewhere/else" + ) + self.assertEqual((bucket, prefix), ("default_bucket", "somewhere/else")) diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index f07a3c71..d5596270 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -25,6 +25,7 @@ from io import StringIO from tests.context import ( DownloadedCallback, ProgressCallback, + get_bucket_name_and_prefix, get_timestamp, merge_mappings, path_to_uri, @@ -162,3 +163,51 @@ class TestUtils(unittest.TestCase): mapping = {"keyA": True} merge_mappings(mapping, {"keya": "bar"}) self.assertEqual(mapping, {"keyA": True}) + + def test_get_bucket_name_and_prefix(self): + """get_bucket_name_and_prefix must extract bucket and prefix from url""" + self.assertEqual( + get_bucket_name_and_prefix( + "s3://sentinel-s2-l1c/tiles/50/R/LR/2021/6/8/0/B02.jp2" + ), + ("sentinel-s2-l1c", "tiles/50/R/LR/2021/6/8/0/B02.jp2"), + ) + self.assertEqual( + get_bucket_name_and_prefix( + "https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/44/2022/10/S2B_20221011/B01.tif" + ), + ( + "sentinel-cogs", + "sentinel-s2-l2a-cogs/44/2022/10/S2B_20221011/B01.tif", + ), + ) + self.assertEqual( + get_bucket_name_and_prefix( + "https://obs.eu-de.otc.t-systems.com/s2-l1c-2021-q4/30/T/2021/11/14/S2A_MSIL1C_20211114T110321_N0301", + 0, + ), + ("s2-l1c-2021-q4", "30/T/2021/11/14/S2A_MSIL1C_20211114T110321_N0301"), + ) + self.assertEqual( + get_bucket_name_and_prefix( + "products/2021/1/5/S2A_MSIL1C_20210105T105441_N0209_R051_T30TYN_20210105T130129", + ), + ( + None, + "products/2021/1/5/S2A_MSIL1C_20210105T105441_N0209_R051_T30TYN_20210105T130129", + ), + ) + self.assertEqual( + get_bucket_name_and_prefix( + "bucket/path/to/product", + 0, + ), + ("bucket", "path/to/product"), + ) + self.assertEqual( + get_bucket_name_and_prefix( + "http://foo/bar/bucket/path/to/product", + 1, + ), + ("bucket", "path/to/product"), + )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_media", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 4 }	2.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@58335e728fff5b98bd85b96fead271429773a593#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 Markdown==3.7 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==4.1.1 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.6.1.dev3+g58335e72 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown==3.7 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==4.1.1 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_utils.py::TestUtils::test_downloaded_callback", "tests/units/test_utils.py::TestUtils::test_get_bucket_name_and_prefix", "tests/units/test_utils.py::TestUtils::test_merge_mappings", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_progresscallback_copy", "tests/units/test_utils.py::TestUtils::test_progresscallback_disable", "tests/units/test_utils.py::TestUtils::test_progresscallback_init", "tests/units/test_utils.py::TestUtils::test_progresscallback_init_customize", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[]	[]	Apache License 2.0	null
CS-SI__eodag-545	837c127b5068fd87a99b875206b95d44e48fdb50	2022-11-03 16:32:25	29c1474e5016cd8158c890ec5210af2062c73a49	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 9m 47s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") + 1m 42s 300 tests +1 297 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +1 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 600 runs +2 594 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit d1db0582. ± Comparison against base commit 837c127b. [test-results]:data:application/gzip;base64,H4sIADnwY2MC/02MSw7DIAwFrxKx7sKQQEgvU5mfhJpPRWAV9e6FtpAsZ/w8B3F+tju5d+zWkT352MCkgNFva0YuxyzyKZZjD1DpsSety34aT/X0r7JqwqGfszifbAhb+JuQ1tIUUKEm+TQ08yuKypfgl689vS2LjxmIoUYBl4yPqKigxglLAWXvAJVCpqxiPUg7kPcH9xw55gUBAAA= github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `84%` \| :white_check_mark: \| \| eodag/api/core.py \| `88%` \| :white_check_mark: \| \| tests/units/test\_core.py \| `98%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `94%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `83%` \| :white_check_mark: \| \| eodag/api/core.py \| `88%` \| :white_check_mark: \| \| tests/units/test\_core.py \| `98%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `94%` \| :white_check_mark: \| \| eodag/api/core.py \| `87%` \| :white_check_mark: \| \| tests/units/test\_core.py \| `98%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `94%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against d1db058257ab161df6e10a83f0abba2beb2308e4 </p>	diff --git a/eodag/api/core.py b/eodag/api/core.py index cd5d696b..a88a3212 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -1708,7 +1708,7 @@ class EODataAccessGateway(object): If the metadata mapping for ``downloadLink`` is set to something that can be interpreted as a link on a local filesystem, the download is skipped (by now, only a link starting - with ``file://`` is supported). Therefore, any user that knows how to extract + with ``file:/`` is supported). Therefore, any user that knows how to extract product location from product metadata on a provider can override the ``downloadLink`` metadata mapping in the right way. For example, using the environment variable: @@ -1742,7 +1742,7 @@ class EODataAccessGateway(object): :raises: :class:`~eodag.utils.exceptions.PluginImplementationError` :raises: :class:`RuntimeError` """ - if product.location.startswith("file://"): + if product.location.startswith("file:/"): logger.info("Local product detected. Download skipped") return uri_to_path(product.location) if product.downloader is None:	accepts all file URI formats EODAG should support `file:/path/to/file` formatting the same way it already supports `file:///path/to/file`. See https://datatracker.ietf.org/doc/html/rfc8089#appendix-B	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index f3514a29..c109a695 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -1498,3 +1498,34 @@ class TestCoreSearch(TestCoreBase): dag.set_preferred_provider("dummy_provider") dag.search_all(productType="S2_MSI_L1C", items_per_page=7) self.assertEqual(mocked_search_iter_page.call_args[1]["items_per_page"], 7) + + +class TestCoreDownload(TestCoreBase): + @classmethod + def setUpClass(cls): + super(TestCoreDownload, cls).setUpClass() + cls.dag = EODataAccessGateway() + + def test_download_local_product(self): + """download must skip local products""" + product = EOProduct("dummy", dict(geometry="POINT (0 0)", id="dummy_product")) + + product.location = "file:///some/path" + with self.assertLogs(level="INFO") as cm: + self.dag.download(product) + self.assertIn("Local product detected. Download skipped", str(cm.output)) + + product.location = "file:/some/path" + with self.assertLogs(level="INFO") as cm: + self.dag.download(product) + self.assertIn("Local product detected. Download skipped", str(cm.output)) + + product.location = "file:///c:/some/path" + with self.assertLogs(level="INFO") as cm: + self.dag.download(product) + self.assertIn("Local product detected. Download skipped", str(cm.output)) + + product.location = "file:/c:/some/path" + with self.assertLogs(level="INFO") as cm: + self.dag.download(product) + self.assertIn("Local product detected. Download skipped", str(cm.output)) diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index d5596270..8616d6c2 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -62,11 +62,15 @@ class TestUtils(unittest.TestCase): if sys.platform == "win32": expected_path = r"C:\tmp\file.txt" tested_uri = r"file:///C:/tmp/file.txt" + other_tested_uri = r"file:/C:/tmp/file.txt" else: expected_path = "/tmp/file.txt" tested_uri = "file:///tmp/file.txt" + other_tested_uri = "file:/tmp/file.txt" actual_path = uri_to_path(tested_uri) self.assertEqual(actual_path, expected_path) + actual_path = uri_to_path(other_tested_uri) + self.assertEqual(actual_path, expected_path) with self.assertRaises(ValueError): uri_to_path("not_a_uri")	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_hyperlinks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 1 }	2.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@837c127b5068fd87a99b875206b95d44e48fdb50#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-metadata==3.1.1 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.6.2.dev6+g837c127b - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-metadata==3.1.1 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCoreDownload::test_download_local_product" ]	[]	[ "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_utils.py::TestUtils::test_downloaded_callback", "tests/units/test_utils.py::TestUtils::test_get_bucket_name_and_prefix", "tests/units/test_utils.py::TestUtils::test_merge_mappings", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_progresscallback_copy", "tests/units/test_utils.py::TestUtils::test_progresscallback_disable", "tests/units/test_utils.py::TestUtils::test_progresscallback_init", "tests/units/test_utils.py::TestUtils::test_progresscallback_init_customize", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[]	Apache License 2.0	null
CS-SI__eodag-554	12e3cdc83bb46a2384b2821e28833b0fdb15c799	2022-11-16 10:38:04	29c1474e5016cd8158c890ec5210af2062c73a49	github-actions[bot]: <strong></strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `84%` \| :white_check_mark: \| \| eodag/plugins/search/qssearch.py \| `76%` \| :white_check_mark: \| \| tests/units/test\_core.py \| `98%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 25333fec73fbfc8328fbe55c3d6282eb839dfc31 </p> github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 9m 25s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") +22s 319 tests +1 316 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +1 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 638 runs +2 632 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit 557a6f3f. ± Comparison against base commit 12e3cdc8. [test-results]:data:application/gzip;base64,H4sIAKK/dGMC/03MSw6DIBSF4a0Yxh3I6wrdTENBElKVhsfIdO8FK9Th/53k7Mi6ZY7oPpDbgGJ2qYfJQSXnt5IceIEypTpSLFs9Ytb6IPjTy70rdbDKLQXGDnMIPpwS8lY/gYoz2iVQ0uX3CK0vh0df/7RfV5dKIM4nBZZaozCbjWTwFIRYIQUfp5ExEKCxxJihzxcUSlynBQEAAA==	diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index f349b760..a9625b53 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -597,8 +597,7 @@ class QueryStringSearch(Search): "instance:".format(self.provider, self.__class__.__name__), ) except RequestError: - # Signal the end of iteration (see PEP-479) - return + return [] else: next_page_url_key_path = self.config.pagination.get( "next_page_url_key_path", None	Connection timeout bug in search API Describe the bug No result from provider 'creodias' due to an error during the search. `urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x7f0614fa07f0>, 'Connection to finder.creodias.eu timed out. (connect timeout=5)')` Timeout error is not properly handled and propagated back. The error (len() of None) appears already inside the eodag code: ``` normalize_remaining_count = len(results) TypeError: object of type 'NoneType' has no len() ``` Code To Reproduce `all_products = session.search_all(search_criteria)` -> with the specific search and creodias provider for example. Output ``` [2022-11-14 23:38:30,795][eodag.core][INFO] - Searching product type 'S2_MSI_L1C' on provider: creodias [2022-11-14 23:38:30,795][eodag.core][INFO] - Iterate search over multiple pages: page #1 [2022-11-14 23:38:30,795][eodag.plugins.search.qssearch][INFO] - Sending search request: https://finder.creodias.eu/resto/api/collections/Sentinel2/search.json?startDate=2019-05-01T00:00:00&completionDate=2019-10-01T00:00:00&geometry=POLYGON ((9.1010 46.6489, 9.0945 46.6490, 9.0946 46.6534, 9.1011 46.6534, 9.1010 46.6489))&productType=L1C&maxRecords=2000&page=1 [2022-11-14 23:38:35,883][eodag.plugins.search.qssearch][ERROR] - Skipping error while searching for creodias QueryStringSearch instance: Traceback (most recent call last): File ".../lib/python3.9/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File ".../lib/python3.9/site-packages/urllib3/util/connection.py", line 95, in create_connection raise err File ".../lib/python3.9/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) socket.timeout: timed out During handling of the above exception, another exception occurred: Traceback (most recent call last): File ".../lib/python3.9/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File ".../lib/python3.9/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File ".../lib/python3.9/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File ".../lib/python3.9/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File ".../lib/python3.9/site-packages/urllib3/connection.py", line 179, in _new_conn raise ConnectTimeoutError( urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x7f0614fa07f0>, 'Connection to finder.creodias.eu timed out. (connect timeout=5)') During handling of the above exception, another exception occurred: Traceback (most recent call last): File ".../lib/python3.9/site-packages/requests/adapters.py", line 489, in send resp = conn.urlopen( File ".../lib/python3.9/site-packages/urllib3/connectionpool.py", line 787, in urlopen retries = retries.increment( File ".../lib/python3.9/site-packages/urllib3/util/retry.py", line 592, in increment raise MaxRetryError(_pool, url, error or ResponseError(cause)) urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='finder.creodias.eu', port=443): Max retries exceeded with url: /resto/api/collections/Sentinel2/search.json?startDate=2019-05-01T00:00:00&completionDate=2019-10-01T00:00:00&geometry=POLYGON%20((9.1010%2046.6489,%209.0945%2046.6490,%209.0946%2046.6534,%209.1011%2046.6534,%209.1010%2046.6489))&productType=L1C&maxRecords=2000&page=1 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f0614fa07f0>, 'Connection to finder.creodias.eu timed out. (connect timeout=5)')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File ".../lib/python3.9/site-packages/eodag/plugins/search/qssearch.py", line 864, in _request response = requests.get(url, timeout=HTTP_REQ_TIMEOUT, kwargs) File ".../lib/python3.9/site-packages/requests/api.py", line 73, in get return request("get", url, params=params, kwargs) File ".../lib/python3.9/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, kwargs) File ".../lib/python3.9/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, send_kwargs) File ".../lib/python3.9/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, kwargs) File ".../lib/python3.9/site-packages/requests/adapters.py", line 553, in send raise ConnectTimeout(e, request=request) requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='finder.creodias.eu', port=443): Max retries exceeded with url: /resto/api/collections/Sentinel2/search.json?startDate=2019-05-01T00:00:00&completionDate=2019-10-01T00:00:00&geometry=POLYGON%20((9.1010%2046.6489,%209.0945%2046.6490,%209.0946%2046.6534,%209.1011%2046.6534,%209.1010%2046.6489))&productType=L1C&maxRecords=2000&page=1 (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f0614fa07f0>, 'Connection to finder.creodias.eu timed out. (connect timeout=5)')) [2022-11-14 23:38:35,888][eodag.core][INFO] - No result from provider 'creodias' due to an error during search. Raise verbosity of log messages for details ``` ``` File ".../sentinel_api.py", line 49, in search_products all_products = session.search_all(search_criteria) File ".../lib/python3.9/site-packages/eodag/api/core.py", line 1099, in search_all for page_results in self.search_iter_page( File ".../lib/python3.9/site-packages/eodag/api/core.py", line 953, in search_iter_page products, _ = self._do_search( File ".../lib/python3.9/site-packages/eodag/api/core.py", line 1339, in _do_search res, nb_res = search_plugin.query(count=count, kwargs) File ".../lib/python3.9/site-packages/eodag/plugins/search/qssearch.py", line 341, in query eo_products = self.normalize_results(provider_results, kwargs) File ".../lib/python3.9/site-packages/eodag/plugins/search/qssearch.py", line 677, in normalize_results normalize_remaining_count = len(results) TypeError: object of type 'NoneType' has no len() ``` Environment: - Python version: 3.9 - EODAG version: 2.6.0 Additional context** The above error appeared in a large-scale search for 50k different searches. The error appeared on a specific search, after 10s of thousands of successful searches.	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index f23c149a..47f4b0f7 100644 --- a/tests/context.py +++ b/tests/context.py @@ -82,6 +82,7 @@ from eodag.utils.exceptions import ( NoMatchingProductType, NotAvailableError, PluginImplementationError, + RequestError, UnsupportedDatasetAddressScheme, UnsupportedProvider, ValidationError, diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 7b45f284..7bf4ca52 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -40,6 +40,7 @@ from tests.context import ( EOProduct, NoMatchingProductType, PluginImplementationError, + RequestError, SearchResult, UnsupportedProvider, get_geometry_from_various, @@ -1704,6 +1705,32 @@ class TestCoreSearch(TestCoreBase): dag.search_all(productType="S2_MSI_L1C", items_per_page=7) self.assertEqual(mocked_search_iter_page.call_args[1]["items_per_page"], 7) + @mock.patch( + "eodag.plugins.search.qssearch.QueryStringSearch._request", autospec=True + ) + def test_search_all_request_error(self, mocked_request): + """search_all must stop iteration if a request exception is raised""" + dag = EODataAccessGateway() + dummy_provider_config = """ + dummy_provider: + search: + type: QueryStringSearch + api_endpoint: https://api.my_new_provider/search + pagination: + next_page_url_tpl: 'dummy_next_page_url_tpl' + next_page_url_key_path: '$.links[?(@.rel="next")].href' + metadata_mapping: + dummy: 'dummy' + products: + S2_MSI_L1C: + productType: '{productType}' + """ + mocked_request.side_effect = RequestError() + dag.update_providers_config(dummy_provider_config) + dag.set_preferred_provider("dummy_provider") + results = dag.search_all(productType="S2_MSI_L1C") + self.assertEqual(len(results), 0) + class TestCoreDownload(TestCoreBase): @classmethod	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_issue_reference" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 1 }	2.6	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[tutorials]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-mock" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	affine==2.4.0 anyio==4.9.0 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 arrow==1.3.0 asttokens==3.0.0 async-lru==2.0.5 attrs==25.3.0 babel==2.17.0 beautifulsoup4==4.13.3 bleach==6.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 branca==0.8.1 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cftime==1.6.4.post1 charset-normalizer==3.4.1 click==8.1.8 click-plugins==1.1.1 cligj==0.7.2 comm==0.2.2 contourpy==1.3.0 coverage==7.8.0 cycler==0.12.1 datapi==0.3.0 debugpy==1.8.13 decorator==5.2.1 defusedxml==0.7.1 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@12e3cdc83bb46a2384b2821e28833b0fdb15c799#egg=eodag eodag-cube==0.3.1 exceptiongroup==1.2.2 executing==2.2.0 fastjsonschema==2.21.1 flasgger==0.9.7.1 Flask==3.1.0 folium==0.19.5 fonttools==4.56.0 fqdn==1.5.1 geojson==3.2.0 grpcio==1.71.0 h11==0.14.0 httpcore==1.0.7 httpx==0.28.1 idna==3.10 imageio==2.37.0 importlib_metadata==8.6.1 importlib_resources==6.5.2 iniconfig==2.1.0 ipykernel==6.29.5 ipyleaflet==0.19.2 ipython==8.18.1 ipywidgets==8.1.5 isoduration==20.11.0 itsdangerous==2.2.0 jedi==0.19.2 Jinja2==3.1.6 jmespath==1.0.1 json5==0.10.0 jsonpath-ng==1.7.0 jsonpointer==3.0.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 jupyter==1.1.1 jupyter-console==6.6.3 jupyter-events==0.12.0 jupyter-leaflet==0.19.2 jupyter-lsp==2.2.5 jupyter_client==8.6.3 jupyter_core==5.7.2 jupyter_server==2.15.0 jupyter_server_terminals==0.5.3 jupyterlab==4.3.6 jupyterlab_pygments==0.3.0 jupyterlab_server==2.27.3 jupyterlab_widgets==3.0.13 kiwisolver==1.4.7 lxml==5.3.1 MarkupSafe==3.0.2 matplotlib==3.9.4 matplotlib-inline==0.1.7 mistune==3.1.3 multiurl==0.3.5 nbclient==0.10.2 nbconvert==7.16.6 nbformat==5.10.4 nest-asyncio==1.6.0 netCDF4==1.7.2 notebook==7.3.3 notebook_shim==0.2.4 numpy==2.0.2 overrides==7.7.0 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 pandocfilters==1.5.1 parso==0.8.4 pexpect==4.9.0 pillow==11.1.0 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 prometheus_client==0.21.1 prompt_toolkit==3.0.50 protobuf==3.20.0 psutil==7.0.0 ptyprocess==0.7.0 pure_eval==0.2.3 pycparser==2.22 Pygments==2.19.1 pyparsing==3.2.3 pyproj==3.6.1 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-mock==3.14.0 python-dateutil==2.9.0.post0 python-json-logger==3.3.0 pytz==2025.2 PyYAML==6.0.2 pyzmq==26.3.0 rasterio==1.4.3 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 rioxarray==0.15.0 rpds-py==0.24.0 s3transfer==0.11.4 Send2Trash==1.8.3 shapely==2.0.7 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 stack-data==0.6.3 terminado==0.18.1 tinycss2==1.4.0 tomli==2.2.1 tornado==6.4.2 tqdm==4.67.1 traitlets==5.14.3 traittypes==0.2.1 types-python-dateutil==2.9.0.20241206 typing_extensions==4.13.0 tzdata==2025.2 uri-template==1.3.0 urllib3==1.26.20 usgs==0.3.5 wcwidth==0.2.13 webcolors==24.11.1 webencodings==0.5.1 websocket-client==1.8.0 Werkzeug==3.1.3 Whoosh==2.7.4 widgetsnbextension==4.0.13 xarray==2024.7.0 xyzservices==2025.1.0 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - affine==2.4.0 - anyio==4.9.0 - argon2-cffi==23.1.0 - argon2-cffi-bindings==21.2.0 - arrow==1.3.0 - asttokens==3.0.0 - async-lru==2.0.5 - attrs==25.3.0 - babel==2.17.0 - beautifulsoup4==4.13.3 - bleach==6.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - branca==0.8.1 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cftime==1.6.4.post1 - charset-normalizer==3.4.1 - click==8.1.8 - click-plugins==1.1.1 - cligj==0.7.2 - comm==0.2.2 - contourpy==1.3.0 - coverage==7.8.0 - cycler==0.12.1 - datapi==0.3.0 - debugpy==1.8.13 - decorator==5.2.1 - defusedxml==0.7.1 - ecmwf-api-client==1.6.5 - eodag==2.6.2.post2.dev1+g12e3cdc8 - eodag-cube==0.3.1 - exceptiongroup==1.2.2 - executing==2.2.0 - fastjsonschema==2.21.1 - flasgger==0.9.7.1 - flask==3.1.0 - folium==0.19.5 - fonttools==4.56.0 - fqdn==1.5.1 - geojson==3.2.0 - grpcio==1.71.0 - h11==0.14.0 - httpcore==1.0.7 - httpx==0.28.1 - idna==3.10 - imageio==2.37.0 - importlib-metadata==8.6.1 - importlib-resources==6.5.2 - iniconfig==2.1.0 - ipykernel==6.29.5 - ipyleaflet==0.19.2 - ipython==8.18.1 - ipywidgets==8.1.5 - isoduration==20.11.0 - itsdangerous==2.2.0 - jedi==0.19.2 - jinja2==3.1.6 - jmespath==1.0.1 - json5==0.10.0 - jsonpath-ng==1.7.0 - jsonpointer==3.0.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - jupyter==1.1.1 - jupyter-client==8.6.3 - jupyter-console==6.6.3 - jupyter-core==5.7.2 - jupyter-events==0.12.0 - jupyter-leaflet==0.19.2 - jupyter-lsp==2.2.5 - jupyter-server==2.15.0 - jupyter-server-terminals==0.5.3 - jupyterlab==4.3.6 - jupyterlab-pygments==0.3.0 - jupyterlab-server==2.27.3 - jupyterlab-widgets==3.0.13 - kiwisolver==1.4.7 - lxml==5.3.1 - markupsafe==3.0.2 - matplotlib==3.9.4 - matplotlib-inline==0.1.7 - mistune==3.1.3 - multiurl==0.3.5 - nbclient==0.10.2 - nbconvert==7.16.6 - nbformat==5.10.4 - nest-asyncio==1.6.0 - netcdf4==1.7.2 - notebook==7.3.3 - notebook-shim==0.2.4 - numpy==2.0.2 - overrides==7.7.0 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - pandocfilters==1.5.1 - parso==0.8.4 - pexpect==4.9.0 - pillow==11.1.0 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - prometheus-client==0.21.1 - prompt-toolkit==3.0.50 - protobuf==3.20.0 - psutil==7.0.0 - ptyprocess==0.7.0 - pure-eval==0.2.3 - pycparser==2.22 - pygments==2.19.1 - pyparsing==3.2.3 - pyproj==3.6.1 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-mock==3.14.0 - python-dateutil==2.9.0.post0 - python-json-logger==3.3.0 - pytz==2025.2 - pyyaml==6.0.2 - pyzmq==26.3.0 - rasterio==1.4.3 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - rfc3339-validator==0.1.4 - rfc3986-validator==0.1.1 - rioxarray==0.15.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - send2trash==1.8.3 - shapely==2.0.7 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - stack-data==0.6.3 - terminado==0.18.1 - tinycss2==1.4.0 - tomli==2.2.1 - tornado==6.4.2 - tqdm==4.67.1 - traitlets==5.14.3 - traittypes==0.2.1 - types-python-dateutil==2.9.0.20241206 - typing-extensions==4.13.0 - tzdata==2025.2 - uri-template==1.3.0 - urllib3==1.26.20 - usgs==0.3.5 - wcwidth==0.2.13 - webcolors==24.11.1 - webencodings==0.5.1 - websocket-client==1.8.0 - werkzeug==3.1.3 - whoosh==2.7.4 - widgetsnbextension==4.0.13 - xarray==2024.7.0 - xyzservices==2025.1.0 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCoreSearch::test_search_all_request_error" ]	[]	[ "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreDownload::test_download_local_product" ]	[]	Apache License 2.0	null
CS-SI__eodag-566	9af685574aa465456bca01f245b830b324db2c5c	2022-12-01 17:01:11	6fee322d19ecc2805f7ab44139b3c555b8cad40f	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 10m 24s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") - 2m 9s 325 tests +1 322 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +1 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 650 runs +2 644 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit e0d2a16e. ± Comparison against base commit 9af68557. [test-results]:data:application/gzip;base64,H4sIAGHgiGMC/03MSw7CIBSF4a00jB3ABS7VzRjCIyG2xfAYGfcurYId/t9Jzov4sLhMbhNcJpJrKCNsTbqEuLVEEA3aVPaRg+x1z9WYg+BPj/DcaYDXYWlAB7iUYvpJqtv+ibJHv0Qhhnwfsffp8Ojzn4nrGkoL4qgFzdChsbMygs2cM5DWe6ql5E6iUlfLKSfvD2TBVqgFAQAA github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/plugins/download/http.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_download\_plugins.py \| `96%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/plugins/download/http.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_download\_plugins.py \| `96%` \| :white_check_mark: \| \| eodag/plugins/download/http.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_download\_plugins.py \| `96%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against e0d2a16e6cd87c41833125dff0a553e56779d303 </p>	diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 58fe8a89..9b9b66ba 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -252,6 +252,9 @@ class HTTPDownload(Download): assets_urls = [ a["href"] for a in getattr(product, "assets", {}).values() if "href" in a ] + assets_values = [ + a for a in getattr(product, "assets", {}).values() if "href" in a + ] if not assets_urls: raise NotAvailableError("No assets available for %s" % product) @@ -271,55 +274,77 @@ class HTTPDownload(Download): "flatten_top_dirs", getattr(self.config, "flatten_top_dirs", False) ) - total_size = sum( - [ - # get total size using header[Content-length] of each asset - int( - requests.head( - asset_url, auth=auth, timeout=HTTP_REQ_TIMEOUT - ).headers.get("Content-length", 0) - ) - or int( # alternative: get total size using header[content-disposition][size] of each asset - parse_header( - requests.head( - asset_url, auth=auth, timeout=HTTP_REQ_TIMEOUT - ).headers.get("content-disposition", "") - )[-1].get("size", 0) - ) - for asset_url in assets_urls - if not asset_url.startswith("file:") - ] + # get extra parameters to pass to the query + params = kwargs.pop("dl_url_params", None) or getattr( + self.config, "dl_url_params", {} ) + + total_size = 0 + # loop for assets size & filename + for asset in assets_values: + if not asset["href"].startswith("file:"): + # HEAD request for size & filename + asset_headers = requests.head( + asset["href"], auth=auth, timeout=HTTP_REQ_TIMEOUT + ).headers + header_content_disposition_dict = {} + + if not asset.get("size", 0): + # size from HEAD header / Content-length + asset["size"] = int(asset_headers.get("Content-length", 0)) + + if not asset.get("size", 0) or not asset.get("filename", 0): + # header content-disposition + header_content_disposition_dict = parse_header( + asset_headers.get("content-disposition", "") + )[-1] + if not asset.get("size", 0): + # size from HEAD header / content-disposition / size + asset["size"] = int(header_content_disposition_dict.get("size", 0)) + if not asset.get("filename", 0): + # filename from HEAD header / content-disposition / size + asset["filename"] = header_content_disposition_dict.get( + "filename", None + ) + + if not asset.get("size", 0): + # GET request for size + with requests.get( + asset["href"], + stream=True, + auth=auth, + params=params, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) as stream: + # size from GET header / Content-length + asset["size"] = int(stream.headers.get("Content-length", 0)) + if not asset.get("size", 0): + # size from GET header / content-disposition / size + asset["size"] = int( + parse_header( + stream.headers.get("content-disposition", "") + )[-1].get("size", 0) + ) + + total_size += asset["size"] + progress_callback.reset(total=total_size) error_messages = set() local_assets_count = 0 - for asset_url in assets_urls: + # loop for assets download + for asset in assets_values: - if asset_url.startswith("file:"): - logger.info(f"Local asset detected. Download skipped for {asset_url}") + if asset["href"].startswith("file:"): + logger.info( + f"Local asset detected. Download skipped for {asset['href']}" + ) local_assets_count += 1 continue - # get asset filename from header - asset_content_disposition = requests.head( - asset_url, auth=auth, timeout=HTTP_REQ_TIMEOUT - ).headers.get("content-disposition", None) - if asset_content_disposition: - asset_filename = parse_header(asset_content_disposition)[-1].get( - "filename", None - ) - else: - asset_filename = None - - # get extra parameters to pass to the query - params = kwargs.pop("dl_url_params", None) or getattr( - self.config, "dl_url_params", {} - ) - with requests.get( - asset_url, + asset["href"], stream=True, auth=auth, params=params, @@ -343,28 +368,28 @@ class HTTPDownload(Download): ) else: logger.warning("Unexpected error: %s" % e) - logger.warning("Skipping %s" % asset_url) + logger.warning("Skipping %s" % asset["href"]) error_messages.add(str(e)) else: - asset_rel_path = urlparse(asset_url).path.strip("/") + asset_rel_path = urlparse(asset["href"]).path.strip("/") asset_rel_dir = os.path.dirname(asset_rel_path) - if asset_filename is None: - # try getting content-disposition header from GET if not in HEAD result - asset_content_disposition = stream.headers.get( + if not asset.get("filename", None): + # try getting filename in GET header if was not found in HEAD result + asset_content_disposition_dict = stream.headers.get( "content-disposition", None ) - if asset_content_disposition: - asset_filename = parse_header(asset_content_disposition)[ - -1 - ].get("filename", None) + if asset_content_disposition_dict: + asset["filename"] = parse_header( + asset_content_disposition_dict + )[-1].get("filename", None) - if asset_filename is None: + if not asset.get("filename", None): # default filename extracted from path - asset_filename = os.path.basename(asset_rel_path) + asset["filename"] = os.path.basename(asset_rel_path) asset_abs_path = os.path.join( - fs_dir_path, asset_rel_dir, asset_filename + fs_dir_path, asset_rel_dir, asset["filename"] ) asset_abs_path_dir = os.path.dirname(asset_abs_path) if not os.path.isdir(asset_abs_path_dir):	http asset filesize using GET method Following #542, in `HTTPDownload` try getting asset filesize from header while downloading with GET method if the filesize could not be obtained with HEAD method (`total_size == 0`). Implement something like: ```py if total_size == 0: asset_size = parse_header(asset_content_disposition)[-1].get("size", 0) progress_callback.total += asset_size ```	CS-SI/eodag	diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index d31a976b..f37a1b01 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -239,6 +239,73 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): ) ) + @mock.patch("eodag.utils.ProgressCallback.reset", autospec=True) + @mock.patch("eodag.plugins.download.http.requests.head", autospec=True) + @mock.patch("eodag.plugins.download.http.requests.get", autospec=True) + def test_plugins_download_http_assets_size( + self, mock_requests_get, mock_requests_head, mock_progress_callback_reset + ): + """HTTPDownload.download() must get assets sizes""" + + plugin = self.get_download_plugin(self.product) + self.product.location = self.product.remote_location = "http://somewhere" + self.product.assets = { + "foo": {"href": "http://somewhere/a"}, + "bar": {"href": "http://somewhere/b"}, + } + + mock_requests_head.return_value.headers = { + "Content-length": "1", + "content-disposition": '; size = "2"', + } + mock_requests_get.return_value.__enter__.return_value.headers = { + "Content-length": "3", + "content-disposition": '; size = "4"', + } + + # size from HEAD / Content-length + with TemporaryDirectory() as temp_dir: + plugin.download(self.product, outputs_prefix=temp_dir) + mock_progress_callback_reset.assert_called_once_with(mock.ANY, total=1 + 1) + + # size from HEAD / content-disposition + mock_requests_head.return_value.headers.pop("Content-length") + mock_progress_callback_reset.reset_mock() + self.product.location = "http://somewhere" + self.product.assets = { + "foo": {"href": "http://somewhere/a"}, + "bar": {"href": "http://somewhere/b"}, + } + with TemporaryDirectory() as temp_dir: + plugin.download(self.product, outputs_prefix=temp_dir) + mock_progress_callback_reset.assert_called_once_with(mock.ANY, total=2 + 2) + + # size from GET / Content-length + mock_requests_head.return_value.headers.pop("content-disposition") + mock_progress_callback_reset.reset_mock() + self.product.location = "http://somewhere" + self.product.assets = { + "foo": {"href": "http://somewhere/a"}, + "bar": {"href": "http://somewhere/b"}, + } + with TemporaryDirectory() as temp_dir: + plugin.download(self.product, outputs_prefix=temp_dir) + mock_progress_callback_reset.assert_called_once_with(mock.ANY, total=3 + 3) + + # size from GET / content-disposition + mock_requests_get.return_value.__enter__.return_value.headers.pop( + "Content-length" + ) + mock_progress_callback_reset.reset_mock() + self.product.location = "http://somewhere" + self.product.assets = { + "foo": {"href": "http://somewhere/a"}, + "bar": {"href": "http://somewhere/b"}, + } + with TemporaryDirectory() as temp_dir: + plugin.download(self.product, outputs_prefix=temp_dir) + mock_progress_callback_reset.assert_called_once_with(mock.ANY, total=4 + 4) + def test_plugins_download_http_one_local_asset( self, ):	{ "commit_name": "merge_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 0, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 1 }	2.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@9af685574aa465456bca01f245b830b324db2c5c#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.7.1.dev1+g9af68557 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix" ]	[]	Apache License 2.0	null
CS-SI__eodag-576	73e95cb6407329faf860cb775e1dd61838e6a2c4	2022-12-09 16:26:20	6fee322d19ecc2805f7ab44139b3c555b8cad40f	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 9m 54s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") -5s 328 tests +2 325 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 656 runs +4 650 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +4 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit e16293d9. ± Comparison against base commit 73e95cb6. [test-results]:data:application/gzip;base64,H4sIALpjk2MC/03Myw7CIBCF4VdpWLvgOjq+jKEUEmJbDJeV8d2F2mKX/zeT8ybOzzaR+8AvA0nF5x5TiTr7sNZUKCvUU25HwW9HPVIxZiP1p6d/NergtJ8r0A42xhB3iWVtm6Bgj2MSFO3yW+wfp8Gtz3smLIvPNYhlwFFMKMAiF9YZpoRmV8kMpwxQCxylpHIkny91Dvp5BQEAAA== github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/plugins/download/aws.py \| `49%` \| :x: \| \| eodag/plugins/download/http.py \| `83%` \| :white_check_mark: \| \| eodag/utils/\_\_init\_\_.py \| `89%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `95%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/plugins/download/aws.py \| `49%` \| :x: \| \| eodag/plugins/download/http.py \| `83%` \| :white_check_mark: \| \| eodag/utils/\_\_init\_\_.py \| `89%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `95%` \| :white_check_mark: \| \| eodag/plugins/download/aws.py \| `49%` \| :x: \| \| eodag/plugins/download/http.py \| `82%` \| :white_check_mark: \| \| eodag/utils/\_\_init\_\_.py \| `89%` \| :white_check_mark: \| \| tests/units/test\_utils.py \| `95%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against e16293d936e923efc153a1741c20169a39b4404b </p>	diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index e5d9d2a9..8c28dcce 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -19,7 +19,6 @@ import logging import os import re -import shutil from pathlib import Path import boto3 @@ -36,6 +35,7 @@ from eodag.api.product.metadata_mapping import ( from eodag.plugins.download.base import Download from eodag.utils import ( ProgressCallback, + flatten_top_directories, get_bucket_name_and_prefix, path_to_uri, rename_subfolder, @@ -421,13 +421,7 @@ class AwsDownload(Download): self.finalize_s2_safe_product(product_local_path) # flatten directory structure elif flatten_top_dirs: - tmp_product_local_path = "%s-tmp" % product_local_path - for d, dirs, files in os.walk(product_local_path): - if len(files) != 0: - shutil.copytree(d, tmp_product_local_path) - shutil.rmtree(product_local_path) - os.rename(tmp_product_local_path, product_local_path) - break + flatten_top_directories(product_local_path, common_prefix) if build_safe: self.check_manifest_file_list(product_local_path) diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 9b9b66ba..e7061532 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -33,7 +33,12 @@ from eodag.plugins.download.base import ( DEFAULT_STREAM_REQUESTS_TIMEOUT, Download, ) -from eodag.utils import ProgressCallback, path_to_uri, uri_to_path +from eodag.utils import ( + ProgressCallback, + flatten_top_directories, + path_to_uri, + uri_to_path, +) from eodag.utils.exceptions import ( AuthenticationError, MisconfiguredError, @@ -421,13 +426,7 @@ class HTTPDownload(Download): # flatten directory structure if flatten_top_dirs: - tmp_product_local_path = "%s-tmp" % fs_dir_path - for d, dirs, files in os.walk(fs_dir_path): - if len(files) != 0: - shutil.copytree(d, tmp_product_local_path) - shutil.rmtree(fs_dir_path) - os.rename(tmp_product_local_path, fs_dir_path) - break + flatten_top_directories(fs_dir_path) # save hash/record file with open(record_filename, "w") as fh: diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 40234376..b51b5f1e 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -31,6 +31,7 @@ import json import logging as py_logging import os import re +import shutil import string import types import unicodedata @@ -39,6 +40,7 @@ from collections import defaultdict from glob import glob from itertools import repeat, starmap from pathlib import Path +from tempfile import mkdtemp # All modules using these should import them from utils package from urllib.parse import ( # noqa; noqa @@ -1058,3 +1060,25 @@ def get_bucket_name_and_prefix(url=None, bucket_path_level=None): ) return bucket, prefix + + +def flatten_top_directories(nested_dir_root, common_subdirs_path=None): + """Flatten directory structure, removing common empty sub-directories + + :param nested_dir_root: Absolute path of the directory structure to flatten + :type nested_dir_root: str + :param common_subdirs_path: (optional) Absolute path of the desired subdirectory to remove + :type common_subdirs_path: str + """ + if not common_subdirs_path: + subpaths_list = [p for p in Path(nested_dir_root).glob("*/") if p.is_file()] + common_subdirs_path = os.path.commonpath(subpaths_list) + + if nested_dir_root != common_subdirs_path: + logger.debug(f"Flatten {common_subdirs_path} to {nested_dir_root}") + tmp_path = mkdtemp() + # need to delete tmp_path to prevent FileExistsError with copytree. Use dirs_exist_ok with py > 3.7 + shutil.rmtree(tmp_path) + shutil.copytree(common_subdirs_path, tmp_path) + shutil.rmtree(nested_dir_root) + shutil.move(tmp_path, nested_dir_root)	NITF files are not downloaded Describe the bug I don't know if its intended, but some STAC catalog have.... .ntf files 🙄, and eodag doesn't download them For example, all [SICD Capella products](https://www.stacindex.org/catalogs/capella-space-open-data#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?si=0&t=2#12/62.954289/-150.694400). ![2022-12-08_17h29_41](https://user-images.githubusercontent.com/67311115/206507457-84322f0c-45ca-485e-b375-1baa2e384d17.png) Feel free to close it if this is a feature and not a bug 😉 Code To Reproduce ```py from eodag import setup_logging from eodag.api.core import EODataAccessGateway if __name__ == "__main__": # Create an EODAG custom STAC provider setup_logging(verbose=3) dag = EODataAccessGateway() # Add the custom STAC provider + output + login an password catalog_path = 'https://capella-open-data.s3.us-west-2.amazonaws.com/stac/capella-open-data-by-datetime/capella-open-data-2022/capella-open-data-2022-3/capella-open-data-2022-3-20/catalog.json' outputs_prefix = r"D:\capella_eodag" dag.update_providers_config(r""" capella: search: type: StaticStacSearch api_endpoint: %s metadata_mapping: assets: '{$.assets#recursive_sub_str(r"https([^.])\.[^/]\/(.)",r"s3\1/\2")}' discover_product_types: fetch_url: products: GENERIC_PRODUCT_TYPE: productType: '{productType}' download: type: AwsDownload flatten_top_dirs: True outputs_prefix: %s """ % (catalog_path, outputs_prefix)) # Set the custom STAC provider as preferred dag.set_preferred_provider("capella") # Query every product from inside the catalog capella_prod, _ = dag.search({"sensorMode":"spotlight", "sar:product_type": "SICD"}) paths = dag.download_all(capella_prod) ``` Output* ``` 2022-12-08 17:26:27,628 eodag.config [INFO ] (config ) Loading user configuration from: C:\Users\rbraun\.config\eodag\eodag.yml 2022-12-08 17:26:27,745 eodag.core [INFO ] (core ) usgs: provider needing auth for search has been pruned because no crendentials could be found 2022-12-08 17:26:27,745 eodag.core [INFO ] (core ) aws_eos: provider needing auth for search has been pruned because no crendentials could be found 2022-12-08 17:26:27,841 eodag.core [DEBUG ] (core ) Opening product types index in C:\Users\rbraun\.config\eodag\.index 2022-12-08 17:26:27,849 eodag.core [DEBUG ] (core ) Out-of-date product types index removed from C:\Users\rbraun\.config\eodag\.index 2022-12-08 17:26:27,849 eodag.core [DEBUG ] (core ) Creating product types index in C:\Users\rbraun\.config\eodag\.index 2022-12-08 17:26:27,976 eodag.core [INFO ] (core ) Locations configuration loaded from C:\Users\rbraun\.config\eodag\locations.yml 2022-12-08 17:26:27,978 eodag.config [INFO ] (config ) capella: unknown provider found in user conf, trying to use provided configuration 2022-12-08 17:26:27,990 eodag.core [INFO ] (core ) No product type could be guessed with provided arguments 2022-12-08 17:26:27,990 eodag.core [DEBUG ] (core ) Fetching external product types sources to find None product type 2022-12-08 17:26:27,991 eodag.config [INFO ] (config ) Fetching external product types from https://cs-si.github.io/eodag/eodag/resources/ext_product_types.json 2022-12-08 17:26:28,217 eodag.core [DEBUG ] (core ) Added product type mod11a1 for astraea_eod 2022-12-08 17:26:28,218 eodag.core [DEBUG ] (core ) Added product type mod13a1 for astraea_eod 2022-12-08 17:26:28,218 eodag.core [DEBUG ] (core ) Added product type myd11a1 for astraea_eod 2022-12-08 17:26:28,218 eodag.core [DEBUG ] (core ) Added product type myd13a1 for astraea_eod 2022-12-08 17:26:28,219 eodag.core [DEBUG ] (core ) Added product type maxar_open_data for astraea_eod 2022-12-08 17:26:28,219 eodag.core [DEBUG ] (core ) Added product type spacenet7 for astraea_eod 2022-12-08 17:26:28,220 eodag.core [DEBUG ] (core ) Added product type Sentinel1RTC for creodias 2022-12-08 17:26:28,220 eodag.core [DEBUG ] (core ) Added product type Sentinel5P for creodias 2022-12-08 17:26:28,220 eodag.core [DEBUG ] (core ) Added product type Sentinel6 for creodias 2022-12-08 17:26:28,221 eodag.core [DEBUG ] (core ) Added product type Landsat5 for creodias 2022-12-08 17:26:28,221 eodag.core [DEBUG ] (core ) Added product type Landsat7 for creodias 2022-12-08 17:26:28,222 eodag.core [DEBUG ] (core ) Added product type Landsat8 for creodias 2022-12-08 17:26:28,222 eodag.core [DEBUG ] (core ) Added product type Envisat for creodias 2022-12-08 17:26:28,222 eodag.core [DEBUG ] (core ) Added product type SMOS for creodias 2022-12-08 17:26:28,223 eodag.core [DEBUG ] (core ) Added product type S2GLC for creodias 2022-12-08 17:26:28,223 eodag.core [DEBUG ] (core ) Added product type CopDem for creodias 2022-12-08 17:26:28,224 eodag.core [DEBUG ] (core ) Added product type landsat-c2ard-st for usgs_satapi_aws 2022-12-08 17:26:28,224 eodag.core [DEBUG ] (core ) Added product type landsat-c2l3-fsca for usgs_satapi_aws 2022-12-08 17:26:28,224 eodag.core [DEBUG ] (core ) Added product type landsat-c2ard-bt for usgs_satapi_aws 2022-12-08 17:26:28,225 eodag.core [DEBUG ] (core ) Added product type landsat-c2l3-ba for usgs_satapi_aws 2022-12-08 17:26:28,225 eodag.core [DEBUG ] (core ) Added product type landsat-c2ard-sr for usgs_satapi_aws 2022-12-08 17:26:28,226 eodag.core [DEBUG ] (core ) Added product type landsat-c2l3-dswe for usgs_satapi_aws 2022-12-08 17:26:28,226 eodag.core [DEBUG ] (core ) Added product type landsat-c2ard-ta for usgs_satapi_aws 2022-12-08 17:26:28,229 eodag.core [DEBUG ] (core ) Out-of-date product types index removed from C:\Users\rbraun\.config\eodag\.index 2022-12-08 17:26:28,229 eodag.core [DEBUG ] (core ) Creating product types index in C:\Users\rbraun\.config\eodag\.index 2022-12-08 17:26:29,158 eodag.core [INFO ] (core ) Searching product type 'None' on provider: capella 2022-12-08 17:26:29,158 eodag.core [DEBUG ] (core ) Using plugin class for search: StaticStacSearch 2022-12-08 17:26:30,004 eodag.utils.stac_reader [DEBUG ] (stac_reader ) Fetching 8 items 2022-12-08 17:26:30,004 eodag.utils.stac_reader [DEBUG ] (stac_reader ) read_local_json is not the right STAC opener 2022-12-08 17:26:31,008 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Getting genric provider product type definition parameters for None 2022-12-08 17:26:31,009 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Building the query string that will be used for search 2022-12-08 17:26:31,009 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Retrieving queryable metadata from metadata_mapping 2022-12-08 17:26:31,044 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Next page URL could not be collected 2022-12-08 17:26:31,061 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Next page Query-object could not be collected 2022-12-08 17:26:31,078 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Next page merge could not be collected 2022-12-08 17:26:31,078 eodag.plugins.search.qssearch [DEBUG ] (qssearch ) Adapting 8 plugin results to eodag product representation 2022-12-08 17:26:31,106 eodag.plugins.crunch.filter_overlap [DEBUG ] (filter_overlap ) Start filtering for overlapping products 2022-12-08 17:26:31,106 eodag.plugins.crunch.filter_overlap [WARNING ] (filter_overlap ) geometry not found in cruncher arguments, filtering disabled. 2022-12-08 17:26:31,106 eodag.plugins.crunch.filter_property [DEBUG ] (filter_property ) Start filtering for products matching operator.eq(product.properties['sensorMode'], spotlight) 2022-12-08 17:26:31,106 eodag.plugins.crunch.filter_property [INFO ] (filter_property ) Finished filtering products. 4 resulting products 2022-12-08 17:26:31,106 eodag.plugins.crunch.filter_property [DEBUG ] (filter_property ) Start filtering for products matching operator.eq(product.properties['sar:product_type'], SICD) 2022-12-08 17:26:31,106 eodag.plugins.crunch.filter_property [INFO ] (filter_property ) Finished filtering products. 1 resulting products 2022-12-08 17:26:31,109 eodag.core [INFO ] (core ) Found 1 result(s) on provider 'capella' 2022-12-08 17:26:31,109 eodag.core [INFO ] (core ) Downloading 1 products Downloaded products: 0%\| \| 0/1 [00:00<?, ?product/s] 0.00B [00:00, ?B/s] CAPELLA_C05_SP_SICD_HH_20220320114010_20220320114013: 0.00B [00:00, ?B/s]2022-12-08 17:26:31,110 eodag.plugins.download.base [INFO ] (base ) Download url: Not Available 2022-12-08 17:26:32,718 eodag.plugins.download.aws [DEBUG ] (aws ) Auth using _get_authenticated_objects_unsigned succeeded CAPELLA_C05_SP_SICD_HH_20220320114010_20220320114013: 100%\|██████████\| 699M/699M [01:20<00:00, 8.73MB/s] 2022-12-08 17:27:53,399 eodag.api.product [DEBUG ] (_product ) Product location updated from 'Not Available' to 'file:///D:/capella_eodag/CAPELLA_C05_SP_SICD_HH_20220320114010_20220320114013' 2022-12-08 17:27:53,399 eodag.api.product [INFO ] (_product ) Remote location of the product is still available through its 'remote_location' property: Not Available Downloaded products: 100%\|██████████\| 1/1 [01:22<00:00, 82.29s/product] ``` Environment: - Python version: `Python 3.9.15` - EODAG version: `eodag (Earth Observation Data Access Gateway): version 2.7.0`	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index dd8a54a7..dec2c197 100644 --- a/tests/context.py +++ b/tests/context.py @@ -73,6 +73,7 @@ from eodag.utils import ( parse_qsl, urlsplit, GENERIC_PRODUCT_TYPE, + flatten_top_directories, ) from eodag.utils.exceptions import ( AddressNotFound, diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 8616d6c2..04b4ad9b 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -16,15 +16,19 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os import sys import unittest from contextlib import closing from datetime import datetime from io import StringIO +from pathlib import Path +from tempfile import TemporaryDirectory from tests.context import ( DownloadedCallback, ProgressCallback, + flatten_top_directories, get_bucket_name_and_prefix, get_timestamp, merge_mappings, @@ -215,3 +219,46 @@ class TestUtils(unittest.TestCase): ), ("bucket", "path/to/product"), ) + + def test_flatten_top_dirs(self): + """flatten_top_dirs must flatten directory structure""" + with TemporaryDirectory() as nested_dir_root: + os.makedirs(os.path.join(nested_dir_root, "a", "b", "c1")) + os.makedirs(os.path.join(nested_dir_root, "a", "b", "c2")) + # create empty files + open(os.path.join(nested_dir_root, "a", "b", "c1", "foo"), "a").close() + open(os.path.join(nested_dir_root, "a", "b", "c2", "bar"), "a").close() + open(os.path.join(nested_dir_root, "a", "b", "c2", "baz"), "a").close() + + flatten_top_directories(nested_dir_root) + + dir_content = list(Path(nested_dir_root).glob("*/")) + + self.assertEqual(len(dir_content), 5) + self.assertIn(Path(nested_dir_root) / "c1", dir_content) + self.assertIn(Path(nested_dir_root) / "c1" / "foo", dir_content) + self.assertIn(Path(nested_dir_root) / "c2", dir_content) + self.assertIn(Path(nested_dir_root) / "c2" / "bar", dir_content) + self.assertIn(Path(nested_dir_root) / "c2" / "baz", dir_content) + + def test_flatten_top_dirs_given_subdir(self): + """flatten_top_dirs must flatten directory structure using given subdirectory""" + with TemporaryDirectory() as nested_dir_root: + os.makedirs(os.path.join(nested_dir_root, "a", "b", "c1")) + os.makedirs(os.path.join(nested_dir_root, "a", "b", "c2")) + # create empty files + open(os.path.join(nested_dir_root, "a", "b", "c1", "foo"), "a").close() + open(os.path.join(nested_dir_root, "a", "b", "c2", "bar"), "a").close() + open(os.path.join(nested_dir_root, "a", "b", "c2", "baz"), "a").close() + + flatten_top_directories(nested_dir_root, os.path.join(nested_dir_root, "a")) + + dir_content = list(Path(nested_dir_root).glob("*/")) + + self.assertEqual(len(dir_content), 6) + self.assertIn(Path(nested_dir_root) / "b", dir_content) + self.assertIn(Path(nested_dir_root) / "b" / "c1", dir_content) + self.assertIn(Path(nested_dir_root) / "b" / "c1" / "foo", dir_content) + self.assertIn(Path(nested_dir_root) / "b" / "c2", dir_content) + self.assertIn(Path(nested_dir_root) / "b" / "c2" / "bar", dir_content) + self.assertIn(Path(nested_dir_root) / "b" / "c2" / "baz", dir_content)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_media", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 0 }, "num_modified_files": 3 }	2.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@73e95cb6407329faf860cb775e1dd61838e6a2c4#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.7.1.dev4+g73e95cb6 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_utils.py::TestUtils::test_downloaded_callback", "tests/units/test_utils.py::TestUtils::test_flatten_top_dirs", "tests/units/test_utils.py::TestUtils::test_flatten_top_dirs_given_subdir", "tests/units/test_utils.py::TestUtils::test_get_bucket_name_and_prefix", "tests/units/test_utils.py::TestUtils::test_merge_mappings", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_progresscallback_copy", "tests/units/test_utils.py::TestUtils::test_progresscallback_disable", "tests/units/test_utils.py::TestUtils::test_progresscallback_init", "tests/units/test_utils.py::TestUtils::test_progresscallback_init_customize", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[]	[]	[]	Apache License 2.0	null
CS-SI__eodag-586	3465afe00a492e73bfd517f19c409ba21196f85f	2022-12-20 10:39:22	6fee322d19ecc2805f7ab44139b3c555b8cad40f	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 11m 15s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") -17s 331 tests +3 329 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +4 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") - 1 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 662 runs +6 657 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +7 5 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") - 1 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit a6388a0a. ± Comparison against base commit 3465afe0. [test-results]:data:application/gzip;base64,H4sIABuToWMC/02MSw7CIBQAr9KwdsEn/LyMeSIkxLYYPivj3QUL2OXMJPNGzq82oetCLwtKxecJjxIh+7BXFJJXUVNukTEy6JaKMU1R/VdP/+qLQzjwaxV4ChtjiN3EsrenELTDWAoupzmOfPBp+OPzz4Rt87kCAsGUAgzgjNSEKKExBXsHMMQ5x2QtGLC06PMFBJYnbAUBAAA= github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/api/product/\_product.py \| `83%` \| :white_check_mark: \| \| tests/units/test\_eoproduct.py \| `99%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/api/product/\_product.py \| `83%` \| :white_check_mark: \| \| tests/units/test\_eoproduct.py \| `99%` \| :white_check_mark: \| \| eodag/api/product/\_product.py \| `83%` \| :white_check_mark: \| \| tests/units/test\_eoproduct.py \| `99%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against a6388a0aafc791186902aebaac1fff370aa0a07e </p>	diff --git a/eodag/api/product/_product.py b/eodag/api/product/_product.py index 777b3026..a0a3d78c 100644 --- a/eodag/api/product/_product.py +++ b/eodag/api/product/_product.py @@ -221,15 +221,30 @@ class EOProduct(object): """ self.downloader = downloader self.downloader_auth = authenticator + # resolve locations and properties if needed with downloader configuration - self.location = self.location % vars(self.downloader.config) - self.remote_location = self.remote_location % vars(self.downloader.config) + location_attrs = ("location", "remote_location") + for location_attr in location_attrs: + try: + setattr( + self, + location_attr, + getattr(self, location_attr) % vars(self.downloader.config), + ) + except ValueError as e: + logger.debug( + f"Could not resolve product.{location_attr} ({getattr(self, location_attr)})" + f" in register_downloader: {str(e)}" + ) + for k, v in self.properties.items(): if isinstance(v, str): try: self.properties[k] = v % vars(self.downloader.config) - except TypeError: - pass + except (TypeError, ValueError) as e: + logger.debug( + f"Could not resolve {k} property ({v}) in register_downloader: {str(e)}" + ) def download( self,	percent variables parsing error in product location If product location contains some percent variables, it may conflict with `register_downloader()` internal parsing mechanism and raise: ``` Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/eodag/api/core.py", line 1474, in _do_search eo_product.register_downloader( File "/usr/local/lib/python3.8/dist-packages/eodag/api/product/_product.py", line 224, in register_downloader self.location = self.location % vars(self.downloader.config) ValueError: unsupported format character 'B' (0x42) at index 72 2022-12-19 16:27:20,206 - eodag.core - ERROR - Error while searching on provider foo (ignored): Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/eodag/api/core.py", line 1474, in _do_search eo_product.register_downloader( File "/usr/local/lib/python3.8/dist-packages/eodag/api/product/_product.py", line 224, in register_downloader self.location = self.location % vars(self.downloader.config) ValueError: unsupported format character 'B' (0x42) at index 72 ``` Reproductible as: ```py "https://example.com/%257B" % ({}) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In [26], line 1 ----> 1 "https://example.com/%257B" % ({}) ValueError: unsupported format character 'B' (0x42) at index 24 ``` - [ ] Intercept error, log a warning and ignore this step if it occurs	CS-SI/eodag	diff --git a/tests/units/test_eoproduct.py b/tests/units/test_eoproduct.py index ed716125..81bdd00f 100644 --- a/tests/units/test_eoproduct.py +++ b/tests/units/test_eoproduct.py @@ -453,3 +453,84 @@ class TestEOProduct(EODagTestCase): # progress bar finished self.assertEqual(progress_callback.n, progress_callback.total) self.assertGreater(progress_callback.total, 0) + + def test_eoproduct_register_downloader(self): + """eoproduct.register_donwloader must set download and auth plugins""" + product = self._dummy_product() + + self.assertIsNone(product.downloader) + self.assertIsNone(product.downloader_auth) + + downloader = mock.MagicMock() + downloader_auth = mock.MagicMock() + + product.register_downloader(downloader, downloader_auth) + + self.assertEqual(product.downloader, downloader) + self.assertEqual(product.downloader_auth, downloader_auth) + + def test_eoproduct_register_downloader_resolve_ok(self): + """eoproduct.register_donwloader must resolve locations and properties""" + downloadable_product = self._dummy_downloadable_product( + product=self._dummy_product( + properties=dict( + self.eoproduct_props, + { + "downloadLink": "%(base_uri)s/is/resolved", + "otherProperty": "%(outputs_prefix)s/also/resolved", + }, + ) + ) + ) + self.assertEqual( + downloadable_product.location, + f"{downloadable_product.downloader.config.base_uri}/is/resolved", + ) + self.assertEqual( + downloadable_product.remote_location, + f"{downloadable_product.downloader.config.base_uri}/is/resolved", + ) + self.assertEqual( + downloadable_product.properties["downloadLink"], + f"{downloadable_product.downloader.config.base_uri}/is/resolved", + ) + self.assertEqual( + downloadable_product.properties["otherProperty"], + f"{downloadable_product.downloader.config.outputs_prefix}/also/resolved", + ) + + def test_eoproduct_register_downloader_resolve_ignored(self): + """eoproduct.register_donwloader must ignore unresolvable locations and properties""" + with self.assertLogs(level="DEBUG") as cm: + downloadable_product = self._dummy_downloadable_product( + product=self._dummy_product( + properties=dict( + self.eoproduct_props, + { + "downloadLink": "%257B/cannot/be/resolved", + "otherProperty": "%/%s/neither/resolved", + }, + ) + ) + ) + self.assertEqual(downloadable_product.location, "%257B/cannot/be/resolved") + self.assertEqual( + downloadable_product.remote_location, "%257B/cannot/be/resolved" + ) + self.assertEqual( + downloadable_product.properties["downloadLink"], + "%257B/cannot/be/resolved", + ) + self.assertEqual( + downloadable_product.properties["otherProperty"], + "%/%s/neither/resolved", + ) + + needed_logs = [ + f"Could not resolve product.location ({downloadable_product.location})", + f"Could not resolve product.remote_location ({downloadable_product.remote_location})", + f"Could not resolve downloadLink property ({downloadable_product.properties['downloadLink']})", + f"Could not resolve otherProperty property ({downloadable_product.properties['otherProperty']})", + ] + for needed_log in needed_logs: + self.assertTrue(any(needed_log in log for log in cm.output))	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 1 }	2.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@3465afe00a492e73bfd517f19c409ba21196f85f#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.7.1.dev9+g3465afe0 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_register_downloader_resolve_ignored" ]	[]	[ "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_default_driver_unsupported_product_type", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_default", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_delete_archive", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_dynamic_options", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_http_extract", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_download_progress_bar", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_from_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_geointerface", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_http_error", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_no_quicklook_url", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_get_quicklook_ok_existing", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_register_downloader", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_register_downloader_resolve_ok", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_geom", "tests/units/test_eoproduct.py::TestEOProduct::test_eoproduct_search_intersection_none" ]	[]	Apache License 2.0	null
CS-SI__eodag-588	c91cba779c1913985640b99e2c019c9c000bb4a0	2022-12-20 15:07:48	6fee322d19ecc2805f7ab44139b3c555b8cad40f	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 10m 32s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") - 1m 38s 332 tests +1 329 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +1 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 664 runs +2 658 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit 5341847d. ± Comparison against base commit c91cba77. [test-results]:data:application/gzip;base64,H4sIACnSoWMC/03MSw6DIBSF4a0Yxh0AFy7azTTKIyFVaRBGTfdeaBUd/t9Jzps4P9uN3Dt+68iWfWphchyTD2tJhAplSnWEsx5b1roSH056+lelBm70cwHawMYY4i4xr/UTUexxXKLsm/wf8ejL4a+vfzosi08liATBeqGMFZJqbq1RCMoNVDpAwxTIiUmhJiCfL2RcS88FAQAA github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/plugins/authentication/header.py \| `84%` \| :white_check_mark: \| \| eodag/plugins/authentication/openid\_connect.py \| `27%` \| :x: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/plugins/authentication/header.py \| `84%` \| :white_check_mark: \| \| eodag/plugins/authentication/openid\_connect.py \| `27%` \| :x: \| \| eodag/plugins/authentication/header.py \| `84%` \| :white_check_mark: \| \| eodag/plugins/authentication/openid\_connect.py \| `26%` \| :x: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 5341847de450c2eed7637f905f36d1735b1547b3 </p>	diff --git a/eodag/plugins/authentication/header.py b/eodag/plugins/authentication/header.py index 48d27cf9..0913296d 100644 --- a/eodag/plugins/authentication/header.py +++ b/eodag/plugins/authentication/header.py @@ -22,7 +22,7 @@ from eodag.plugins.authentication import Authentication class HTTPHeaderAuth(Authentication): - """A Generic Authentication plugin. + """HTTPHeaderAuth Authentication plugin. This plugin enables implementation of custom HTTP authentication scheme (other than Basic, Digest, Token negotiation et al.) using HTTP headers. @@ -65,7 +65,7 @@ class HTTPHeaderAuth(Authentication): class HeaderAuth(requests.auth.AuthBase): - """HeaderAuth authentication plugin""" + """HeaderAuth custom authentication class to be used with requests module""" def __init__(self, authentication_headers): self.auth_headers = authentication_headers diff --git a/eodag/plugins/authentication/openid_connect.py b/eodag/plugins/authentication/openid_connect.py index 73f53c8a..effbea6e 100644 --- a/eodag/plugins/authentication/openid_connect.py +++ b/eodag/plugins/authentication/openid_connect.py @@ -279,7 +279,7 @@ class OIDCAuthorizationCodeFlowAuth(Authentication): class CodeAuthorizedAuth(AuthBase): - """CodeAuthorizedAuth authentication plugin""" + """CodeAuthorizedAuth custom authentication class to be used with requests module""" def __init__(self, token, where, key=None): self.token = token diff --git a/eodag/plugins/authentication/token.py b/eodag/plugins/authentication/token.py index a9e8f1cd..9419f85c 100644 --- a/eodag/plugins/authentication/token.py +++ b/eodag/plugins/authentication/token.py @@ -21,7 +21,7 @@ from requests import RequestException from eodag.plugins.authentication.base import Authentication from eodag.utils import RequestsTokenAuth -from eodag.utils.exceptions import MisconfiguredError +from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT @@ -49,18 +49,17 @@ class TokenAuth(Authentication): req_kwargs = ( {"headers": self.config.headers} if hasattr(self.config, "headers") else {} ) - - # First get the token - response = requests.post( - self.config.auth_uri, - data=self.config.credentials, - timeout=HTTP_REQ_TIMEOUT, - req_kwargs, - ) try: + # First get the token + response = requests.post( + self.config.auth_uri, + data=self.config.credentials, + timeout=HTTP_REQ_TIMEOUT, + req_kwargs, + ) response.raise_for_status() except RequestException as e: - raise e + raise AuthenticationError(f"Could no get authentication token: {str(e)}") else: if getattr(self.config, "token_type", "text") == "json": token = response.json()[self.config.token_key]	TokenAuth should raise AuthenticationError In `TokenAuth`, catch `RequestException` the same way as it was done for https://github.com/CS-SI/eodag/pull/555 and redirect to an `AuthenticationError`	CS-SI/eodag	diff --git a/tests/units/test_auth_plugins.py b/tests/units/test_auth_plugins.py index a1ad599b..60c093b1 100644 --- a/tests/units/test_auth_plugins.py +++ b/tests/units/test_auth_plugins.py @@ -20,9 +20,10 @@ import unittest from unittest import mock from requests.auth import AuthBase +from requests.exceptions import RequestException from eodag.config import override_config_from_mapping -from tests.context import MisconfiguredError, PluginManager +from tests.context import AuthenticationError, MisconfiguredError, PluginManager class BaseAuthPluginTest(unittest.TestCase): @@ -167,3 +168,18 @@ class TestAuthPluginTokenAuth(BaseAuthPluginTest): req = mock.Mock(headers={}) auth(req) assert req.headers["Authorization"] == "Bearer this_is_test_token" + + @mock.patch("eodag.plugins.authentication.token.requests.post", autospec=True) + def test_plugins_auth_tokenauth_request_error(self, mock_requests_post): + """TokenAuth.authenticate must raise an AuthenticationError if a request error occurs""" + auth_plugin = self.get_auth_plugin("provider_json_token_simple_url") + + auth_plugin.config.credentials = {"foo": "bar"} + + # mock token post request response + mock_requests_post.side_effect = RequestException + + self.assertRaises( + AuthenticationError, + auth_plugin.authenticate, + )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 3 }	2.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[tutorials]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	affine==2.4.0 anyio==4.9.0 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 arrow==1.3.0 asttokens==3.0.0 async-lru==2.0.5 attrs==25.3.0 babel==2.17.0 beautifulsoup4==4.13.3 bleach==6.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 branca==0.8.1 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cftime==1.6.4.post1 charset-normalizer==3.4.1 click==8.1.8 click-plugins==1.1.1 cligj==0.7.2 comm==0.2.2 contourpy==1.3.0 coverage==7.8.0 cycler==0.12.1 datapi==0.3.0 debugpy==1.8.13 decorator==5.2.1 defusedxml==0.7.1 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@c91cba779c1913985640b99e2c019c9c000bb4a0#egg=eodag eodag-cube==0.3.1 exceptiongroup==1.2.2 execnet==2.1.1 executing==2.2.0 fastjsonschema==2.21.1 flasgger==0.9.7.1 Flask==3.1.0 folium==0.19.5 fonttools==4.56.0 fqdn==1.5.1 geojson==3.2.0 grpcio==1.71.0 h11==0.14.0 httpcore==1.0.7 httpx==0.28.1 idna==3.10 imageio==2.37.0 importlib_metadata==8.6.1 importlib_resources==6.5.2 iniconfig==2.1.0 ipykernel==6.29.5 ipyleaflet==0.19.2 ipython==8.18.1 ipywidgets==8.1.5 isoduration==20.11.0 itsdangerous==2.2.0 jedi==0.19.2 Jinja2==3.1.6 jmespath==1.0.1 json5==0.10.0 jsonpath-ng==1.7.0 jsonpointer==3.0.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 jupyter==1.1.1 jupyter-console==6.6.3 jupyter-events==0.12.0 jupyter-leaflet==0.19.2 jupyter-lsp==2.2.5 jupyter_client==8.6.3 jupyter_core==5.7.2 jupyter_server==2.15.0 jupyter_server_terminals==0.5.3 jupyterlab==4.3.6 jupyterlab_pygments==0.3.0 jupyterlab_server==2.27.3 jupyterlab_widgets==3.0.13 kiwisolver==1.4.7 lxml==5.3.1 MarkupSafe==3.0.2 matplotlib==3.9.4 matplotlib-inline==0.1.7 mistune==3.1.3 multiurl==0.3.5 nbclient==0.10.2 nbconvert==7.16.6 nbformat==5.10.4 nest-asyncio==1.6.0 netCDF4==1.7.2 notebook==7.3.3 notebook_shim==0.2.4 numpy==2.0.2 overrides==7.7.0 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 pandocfilters==1.5.1 parso==0.8.4 pexpect==4.9.0 pillow==11.1.0 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 prometheus_client==0.21.1 prompt_toolkit==3.0.50 protobuf==3.20.0 psutil==7.0.0 ptyprocess==0.7.0 pure_eval==0.2.3 pycparser==2.22 Pygments==2.19.1 pyparsing==3.2.3 pyproj==3.6.1 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-json-logger==3.3.0 pytz==2025.2 PyYAML==6.0.2 pyzmq==26.3.0 rasterio==1.4.3 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 rioxarray==0.15.0 rpds-py==0.24.0 s3transfer==0.11.4 Send2Trash==1.8.3 shapely==2.0.7 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 stack-data==0.6.3 terminado==0.18.1 tinycss2==1.4.0 tomli==2.2.1 tornado==6.4.2 tqdm==4.67.1 traitlets==5.14.3 traittypes==0.2.1 types-python-dateutil==2.9.0.20241206 typing_extensions==4.13.0 tzdata==2025.2 uri-template==1.3.0 urllib3==1.26.20 usgs==0.3.5 wcwidth==0.2.13 webcolors==24.11.1 webencodings==0.5.1 websocket-client==1.8.0 Werkzeug==3.1.3 Whoosh==2.7.4 widgetsnbextension==4.0.13 xarray==2024.7.0 xyzservices==2025.1.0 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - affine==2.4.0 - anyio==4.9.0 - argon2-cffi==23.1.0 - argon2-cffi-bindings==21.2.0 - arrow==1.3.0 - asttokens==3.0.0 - async-lru==2.0.5 - attrs==25.3.0 - babel==2.17.0 - beautifulsoup4==4.13.3 - bleach==6.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - branca==0.8.1 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cftime==1.6.4.post1 - charset-normalizer==3.4.1 - click==8.1.8 - click-plugins==1.1.1 - cligj==0.7.2 - comm==0.2.2 - contourpy==1.3.0 - coverage==7.8.0 - cycler==0.12.1 - datapi==0.3.0 - debugpy==1.8.13 - decorator==5.2.1 - defusedxml==0.7.1 - ecmwf-api-client==1.6.5 - eodag==2.7.1.dev10+gc91cba77 - eodag-cube==0.3.1 - exceptiongroup==1.2.2 - execnet==2.1.1 - executing==2.2.0 - fastjsonschema==2.21.1 - flasgger==0.9.7.1 - flask==3.1.0 - folium==0.19.5 - fonttools==4.56.0 - fqdn==1.5.1 - geojson==3.2.0 - grpcio==1.71.0 - h11==0.14.0 - httpcore==1.0.7 - httpx==0.28.1 - idna==3.10 - imageio==2.37.0 - importlib-metadata==8.6.1 - importlib-resources==6.5.2 - iniconfig==2.1.0 - ipykernel==6.29.5 - ipyleaflet==0.19.2 - ipython==8.18.1 - ipywidgets==8.1.5 - isoduration==20.11.0 - itsdangerous==2.2.0 - jedi==0.19.2 - jinja2==3.1.6 - jmespath==1.0.1 - json5==0.10.0 - jsonpath-ng==1.7.0 - jsonpointer==3.0.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - jupyter==1.1.1 - jupyter-client==8.6.3 - jupyter-console==6.6.3 - jupyter-core==5.7.2 - jupyter-events==0.12.0 - jupyter-leaflet==0.19.2 - jupyter-lsp==2.2.5 - jupyter-server==2.15.0 - jupyter-server-terminals==0.5.3 - jupyterlab==4.3.6 - jupyterlab-pygments==0.3.0 - jupyterlab-server==2.27.3 - jupyterlab-widgets==3.0.13 - kiwisolver==1.4.7 - lxml==5.3.1 - markupsafe==3.0.2 - matplotlib==3.9.4 - matplotlib-inline==0.1.7 - mistune==3.1.3 - multiurl==0.3.5 - nbclient==0.10.2 - nbconvert==7.16.6 - nbformat==5.10.4 - nest-asyncio==1.6.0 - netcdf4==1.7.2 - notebook==7.3.3 - notebook-shim==0.2.4 - numpy==2.0.2 - overrides==7.7.0 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - pandocfilters==1.5.1 - parso==0.8.4 - pexpect==4.9.0 - pillow==11.1.0 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - prometheus-client==0.21.1 - prompt-toolkit==3.0.50 - protobuf==3.20.0 - psutil==7.0.0 - ptyprocess==0.7.0 - pure-eval==0.2.3 - pycparser==2.22 - pygments==2.19.1 - pyparsing==3.2.3 - pyproj==3.6.1 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-json-logger==3.3.0 - pytz==2025.2 - pyyaml==6.0.2 - pyzmq==26.3.0 - rasterio==1.4.3 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - rfc3339-validator==0.1.4 - rfc3986-validator==0.1.1 - rioxarray==0.15.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - send2trash==1.8.3 - shapely==2.0.7 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - stack-data==0.6.3 - terminado==0.18.1 - tinycss2==1.4.0 - tomli==2.2.1 - tornado==6.4.2 - tqdm==4.67.1 - traitlets==5.14.3 - traittypes==0.2.1 - types-python-dateutil==2.9.0.20241206 - typing-extensions==4.13.0 - tzdata==2025.2 - uri-template==1.3.0 - urllib3==1.26.20 - usgs==0.3.5 - wcwidth==0.2.13 - webcolors==24.11.1 - webencodings==0.5.1 - websocket-client==1.8.0 - werkzeug==3.1.3 - whoosh==2.7.4 - widgetsnbextension==4.0.13 - xarray==2024.7.0 - xyzservices==2025.1.0 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_request_error" ]	[]	[ "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_json_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_text_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_missing", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_ok", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_ok" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-588
CS-SI__eodag-592	870580e4565e533838a1b3c1b2acd2edd335de11	2022-12-22 15:41:21	6fee322d19ecc2805f7ab44139b3c555b8cad40f	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 11m 46s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") + 2m 0s 333 tests +1 330 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +1 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 666 runs +2 660 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit b9d4eacc. ± Comparison against base commit 870580e4. [test-results]:data:application/gzip;base64,H4sIAB99pGMC/02OQQ6DIBBFr2JYd4FCx9rLNDAMCalKg7AyvXvBKrp8b35eZmXWjbSwZ9PdGrYkFyuYFFR0fs7Yc8gin2I5CiEOei0JcVP8VG/3KaoKq9yYxbmgEHzYTUhzaQLADkcSgFfzL9bFJbjxtYd+mlzMwPRgJClEKfPDWraiax9IHAm4JDsYbam3d+Ds+wPf2302BQEAAA== github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/api/product/metadata\_mapping.py \| `80%` \| :white_check_mark: \| \| eodag/plugins/search/qssearch.py \| `77%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `85%` \| :white_check_mark: \| \| eodag/api/product/metadata\_mapping.py \| `80%` \| :white_check_mark: \| \| eodag/plugins/search/qssearch.py \| `77%` \| :white_check_mark: \| \| eodag/api/product/metadata\_mapping.py \| `79%` \| :white_check_mark: \| \| eodag/plugins/search/qssearch.py \| `77%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against b9d4eacc44333b413218ce0ce604ef9dbfe7f560 </p>	diff --git a/eodag/api/product/metadata_mapping.py b/eodag/api/product/metadata_mapping.py index e449a643..3f014290 100644 --- a/eodag/api/product/metadata_mapping.py +++ b/eodag/api/product/metadata_mapping.py @@ -302,7 +302,10 @@ def format_metadata(search_param, args, kwargs): @staticmethod def convert_csv_list(values_list): - return ",".join([str(x) for x in values_list]) + if isinstance(values_list, list): + return ",".join([str(x) for x in values_list]) + else: + return values_list @staticmethod def convert_remove_extension(string): diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index 6595d429..dda336da 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -33,6 +33,7 @@ from eodag.api.product.metadata_mapping import ( get_metadata_path, get_metadata_path_value, get_search_param, + mtd_cfg_as_jsonpath, properties_from_json, properties_from_xml, ) @@ -214,19 +215,19 @@ class QueryStringSearch(Search): for product_type_result in result: # providers_config extraction - mapping_config = { - k: (None, cached_parse(v)) - for k, v in self.config.discover_product_types[ - "generic_product_type_parsable_properties" - ].items() - } - mapping_config["generic_product_type_id"] = ( - None, - cached_parse( + mapping_config = {} + mapping_config = mtd_cfg_as_jsonpath( + dict( self.config.discover_product_types[ - "generic_product_type_id" - ] + "generic_product_type_parsable_properties" + ], + *{ + "generic_product_type_id": self.config.discover_product_types[ + "generic_product_type_id" + ] + }, ), + mapping_config, ) extracted_mapping = properties_from_json( @@ -244,15 +245,52 @@ class QueryStringSearch(Search): ), ) # product_types_config extraction - mapping_config = { - k: (None, cached_parse(v)) - for k, v in self.config.discover_product_types[ + mapping_config = {} + mapping_config = mtd_cfg_as_jsonpath( + self.config.discover_product_types[ "generic_product_type_parsable_metadata" - ].items() - } + ], + mapping_config, + ) conf_update_dict["product_types_config"][ generic_product_type_id ] = properties_from_json(product_type_result, mapping_config) + + # update keywords + keywords_fields = [ + "instrument", + "platform", + "platformSerialIdentifier", + "processingLevel", + "keywords", + ] + keywords_values_str = ",".join( + [generic_product_type_id] + + [ + str( + conf_update_dict["product_types_config"][ + generic_product_type_id + ][kf] + ) + for kf in keywords_fields + if conf_update_dict["product_types_config"][ + generic_product_type_id + ][kf] + != NOT_AVAILABLE + ] + ) + keywords_values_str = keywords_values_str.replace( + ", ", "," + ).replace(" ", "-") + keywords_values_str = re.sub( + r"[\[\]'\"]", "", keywords_values_str + ) + keywords_values_str = ",".join( + set(keywords_values_str.split(",")) + ) + conf_update_dict["product_types_config"][ + generic_product_type_id + ]["keywords"] = keywords_values_str except KeyError as e: logger.warning( "Incomplete %s discover_product_types configuration: %s", diff --git a/eodag/resources/stac_provider.yml b/eodag/resources/stac_provider.yml index 3018f52c..22981b82 100644 --- a/eodag/resources/stac_provider.yml +++ b/eodag/resources/stac_provider.yml @@ -38,11 +38,11 @@ search: productType: '$.id' generic_product_type_parsable_metadata: abstract: '$.description' - instrument: '$.summaries.instruments' - platform: '$.summaries.constellation' - platformSerialIdentifier: '$.summaries.platform' + instrument: '{$.summaries.instruments#csv_list}' + platform: '{$.summaries.constellation#csv_list}' + platformSerialIdentifier: '{$.summaries.platform#csv_list}' processingLevel: '$.summaries."processing:level"' - keywords: '$.keywords' + keywords: '{$.keywords#csv_list}' license: '$.license' title: '$.title' missionStartDate: '$.extent.temporal.interval[0][0]'	Add keywords to discovered product types Automatically add default keywords to discovered product types to help finding them using [guess_product_type()](https://eodag.readthedocs.io/en/stable/api_reference/core.html#eodag.api.core.EODataAccessGateway.guess_product_type). See also [Guess a product type documentation](https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/4_search.html#Guess-a-product-type)	CS-SI/eodag	diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index ca6c2456..b25b7ea7 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -187,6 +187,50 @@ class TestSearchPluginQueryStringSearch(BaseSearchPluginTest): "The FOO collection", ) + @mock.patch( + "eodag.plugins.search.qssearch.QueryStringSearch._request", autospec=True + ) + def test_plugins_search_querystringseach_discover_product_types_keywords( + self, mock__request + ): + """QueryStringSearch.discover_product_types must return a dict with well formatted keywords""" + # One of the providers that has a QueryStringSearch Search plugin and keywords configured + provider = "astraea_eod" + search_plugin = self.get_search_plugin(self.product_type, provider) + + mock__request.return_value = mock.Mock() + mock__request.return_value.json.return_value = { + "collections": [ + { + "id": "foo_collection", + "keywords": ["foo", "bar"], + "summaries": { + "instruments": ["baz"], + "constellation": "qux,foo", + "platform": ["quux", "corge", "bar"], + "processing:level": "grault", + }, + }, + ] + } + conf_update_dict = search_plugin.discover_product_types() + keywords_list = conf_update_dict["product_types_config"]["foo_collection"][ + "keywords" + ].split(",") + + self.assertEqual(len(keywords_list), 8) + for k in [ + "foo_collection", + "foo", + "bar", + "baz", + "qux", + "quux", + "corge", + "grault", + ]: + self.assertIn(k, keywords_list) + class TestSearchPluginPostJsonSearch(BaseSearchPluginTest): def setUp(self):	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 3 }	2.7	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@870580e4565e533838a1b3c1b2acd2edd335de11#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.7.1.dev13+g870580e4 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_discover_product_types_keywords" ]	[]	[ "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_discover_product_types", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_sobloo", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_default_geometry", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_mapping_earthsearch" ]	[]	Apache License 2.0	null
CS-SI__eodag-616	4a0d68987de913040e0ba16f9fe1d00cd10fe6e5	2023-01-24 16:32:13	216edc2a7e22676ecfcbc77b71795fc319cb3dc4	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 14m 36s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") + 2m 4s 350 tests ±0 347 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") ±0 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 700 runs ±0 694 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") ±0 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 Results for commit 4204d020. ± Comparison against base commit 4a0d6898. [test-results]:data:application/gzip;base64,H4sIAOkK0GMC/02Myw7CIBBFf6Vh7QJ5Fn/GwBQSYlsMj5Xx3wUV2s0k59zMeSHnV5vQbSKXCaXi84ClRJ192CvOUlRRp9xGynGneyoATTF5qId/NjWE036t4niyMYb4N7HsrSlxh54Uig3zK4rOp+CXzz0I2+ZzBcQIZgsm2AAAE1RRa+drPY4bAwqDYlISril6fwBu/Us7BQEAAA== github-actions[bot]: <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `86%` \| :white_check_mark: \| \| eodag/api/product/metadata\_mapping.py \| `79%` \| :white_check_mark: \| \| eodag/plugins/search/build\_search\_result.py \| `90%` \| :white_check_mark: \| \| eodag/plugins/search/qssearch.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_search\_plugins.py \| `99%` \| :white_check_mark: \| <strong> </strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `86%` \| :white_check_mark: \| \| eodag/api/product/metadata\_mapping.py \| `79%` \| :white_check_mark: \| \| eodag/plugins/search/build\_search\_result.py \| `90%` \| :white_check_mark: \| \| eodag/plugins/search/qssearch.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_search\_plugins.py \| `99%` \| :white_check_mark: \| \| eodag/api/product/metadata\_mapping.py \| `78%` \| :white_check_mark: \| \| eodag/plugins/search/build\_search\_result.py \| `90%` \| :white_check_mark: \| \| eodag/plugins/search/qssearch.py \| `80%` \| :white_check_mark: \| \| tests/units/test\_search\_plugins.py \| `99%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 4204d020bccc46393ee813eef5bbc90c947725a3 </p>	diff --git a/docs/_static/params_mapping_opensearch.csv b/docs/_static/params_mapping_opensearch.csv index 3e98c49f..053fe5dc 100644 --- a/docs/_static/params_mapping_opensearch.csv +++ b/docs/_static/params_mapping_opensearch.csv @@ -8,7 +8,7 @@ antennaLookDirection,,,,,,,,,metadata only,,,,metadata only, archivingCenter,,,,,,,,,metadata only,,,,metadata only, availabilityTime,metadata only,,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,,,,metadata only,:green:`queryable metadata` :abbr:`classification ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Name of the handling restrictions on the resource or metadata (String ))`,,,,,,,,,metadata only,,,,, -cloudCover,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` +cloudCover,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` completionTimeFromAscendingNode,:green:`queryable metadata`,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` :abbr:`compositeType ([OpenSearch Parameters for Collection Search] Type of composite product expressed as time period that the composite product covers (e.g. P10D for a 10 day composite) (String))`,,,,,,,,,metadata only,,,,, creationDate,metadata only,,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,metadata only,metadata only,metadata only,metadata only,:green:`queryable metadata` @@ -22,42 +22,42 @@ illuminationAzimuthAngle,metadata only,,,,:green:`queryable metadata`,:green:`qu illuminationElevationAngle,metadata only,,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,,,,metadata only,:green:`queryable metadata` illuminationZenithAngle,,,,,,,,,metadata only,,,,metadata only, incidenceAngleVariation,,,,,,,,,metadata only,,,,metadata only, -":abbr:`instrument ([OpenSearch Parameters for Collection Search] A string identifying the instrument (e.g. MERIS, AATSR, ASAR, HRVIR. SAR). (String))`",metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` +":abbr:`instrument ([OpenSearch Parameters for Collection Search] A string identifying the instrument (e.g. MERIS, AATSR, ASAR, HRVIR. SAR). (String))`",metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` :abbr:`keyword ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Commonly used word(s) or formalised word(s) or phrase(s) used to describe the subject. (String))`,,,,metadata only,,,,,metadata only,,metadata only,metadata only,metadata only, :abbr:`language ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Language of the intellectual content of the metadata record (String ))`,,,,,,,,,metadata only,,,,, -:abbr:`lineage ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] General explanation of the data producer’s knowledge about the lineage of a dataset. (String))`,,,,,,,,,metadata only,,,,, +:abbr:`lineage ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] General explanation of the data producer’s knowledge about the lineage of a dataset. (String))`,,,,,,,,,metadata only,:green:`queryable metadata`,,,, lowestLocation,,,,,,,,,metadata only,,,,, maximumIncidenceAngle,,,,,,,,,metadata only,,,,metadata only, minimumIncidenceAngle,,,,,,,,,metadata only,,,,metadata only, modificationDate,metadata only,,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,,metadata only,metadata only,metadata only,:green:`queryable metadata` -orbitDirection,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata` -orbitNumber,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` +orbitDirection,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata` +orbitNumber,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` ":abbr:`orbitType ([OpenSearch Parameters for Collection Search] A string identifying the platform orbit type (e.g. LEO, GEO) (String))`",,,,,,,,,metadata only,,,,, :abbr:`organisationName ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] A string identifying the name of the organization responsible for the resource (String))`,,,,:green:`queryable metadata`,,,,,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only, :abbr:`organisationRole ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] The function performed by the responsible party (String ))`,,,,,,,,,metadata only,,,,, :abbr:`otherConstraint ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Other restrictions and legal prerequisites for accessing and using the resource or metadata. (String))`,,,,,,,,,metadata only,,,,, parentIdentifier,,,,:green:`queryable metadata`,,,,,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only, :abbr:`platform ([OpenSearch Parameters for Collection Search] A string with the platform short name (e.g. Sentinel-1) (String))`,metadata only,,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,:green:`queryable metadata`,metadata only,metadata only,:green:`queryable metadata`,:green:`queryable metadata` -:abbr:`platformSerialIdentifier ([OpenSearch Parameters for Collection Search] A string with the Platform serial identifier (String))`,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,,metadata only,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` +:abbr:`platformSerialIdentifier ([OpenSearch Parameters for Collection Search] A string with the Platform serial identifier (String))`,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` processingCenter,,,,,,,,,metadata only,,metadata only,metadata only,, processingDate,,,,,,,,,metadata only,metadata only,,,metadata only, -:abbr:`processingLevel ([OpenSearch Parameters for Collection Search] A string identifying the processing level applied to the entry (String))`,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` +:abbr:`processingLevel ([OpenSearch Parameters for Collection Search] A string identifying the processing level applied to the entry (String))`,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` processingMode,,,,,,,,,metadata only,,,,metadata only, processorName,,,,,,,,,metadata only,,metadata only,metadata only,, productQualityDegradationTag,,,,,,,,,metadata only,,,,, -productQualityStatus,,,,,,,,,metadata only,metadata only,metadata only,metadata only,, +productQualityStatus,,,,,,,,,metadata only,:green:`queryable metadata`,metadata only,metadata only,, ":abbr:`productType ([OpenSearch Parameters for Collection Search] A string identifying the entry type (e.g. ER02_SAR_IM__0P, MER_RR__1P, SM_SLC__1S, GES_DISC_AIRH3STD_V005) (String ))`",:green:`queryable metadata`,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata` -productVersion,metadata only,,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,metadata only,metadata only,metadata only,metadata only,:green:`queryable metadata` +productVersion,metadata only,,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,:green:`queryable metadata`,metadata only,metadata only,metadata only,:green:`queryable metadata` :abbr:`publicationDate ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] The date when the resource was issued (Date time))`,metadata only,,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,,metadata only,metadata only,metadata only,:green:`queryable metadata` resolution,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` -sensorMode,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` -":abbr:`sensorType ([OpenSearch Parameters for Collection Search] A string identifying the sensor type. Suggested values are: OPTICAL, RADAR, ALTIMETRIC, ATMOSPHERIC, LIMB (String))`",,,,,,,,,metadata only,,,,, +sensorMode,metadata only,,,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only,:green:`queryable metadata` +":abbr:`sensorType ([OpenSearch Parameters for Collection Search] A string identifying the sensor type. Suggested values are: OPTICAL, RADAR, ALTIMETRIC, ATMOSPHERIC, LIMB (String))`",,,,,,,,,metadata only,:green:`queryable metadata`,,,, snowCover,,,,:green:`queryable metadata`,,,,,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,metadata only, ":abbr:`spectralRange ([OpenSearch Parameters for Collection Search] A string identifying the sensor spectral range (e.g. INFRARED, NEAR-INFRARED, UV, VISIBLE) (String))`",,,,,,,,,metadata only,,,,, startTimeFromAscendingNode,metadata only,,,:green:`queryable metadata`,metadata only,metadata only,metadata only,,:green:`queryable metadata`,metadata only,:green:`queryable metadata`,:green:`queryable metadata`,:green:`queryable metadata`,metadata only swathIdentifier,,,,,,,,,metadata only,,:green:`queryable metadata`,:green:`queryable metadata`,, :abbr:`title ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] A name given to the resource (String ))`,metadata only,,,metadata only,metadata only,metadata only,metadata only,,metadata only,metadata only,metadata only,metadata only,metadata only,metadata only -:abbr:`topicCategory ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Main theme(s) of the dataset (String ))`,,,,,,,,,metadata only,,metadata only,metadata only,, +:abbr:`topicCategory ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] Main theme(s) of the dataset (String ))`,,,,,,,,,metadata only,:green:`queryable metadata`,metadata only,metadata only,, track,,,,,,,,,metadata only,,,,, :abbr:`useLimitation ([Additional INSPIRE obligated OpenSearch Parameters for Collection Search] A string identifying informing if the resource has usage limitations (String))`,,,,,,,,,metadata only,,,,, wavelengths,,,,,,,,,metadata only,,,,, diff --git a/eodag/api/product/metadata_mapping.py b/eodag/api/product/metadata_mapping.py index 570213d6..cdb0c2ce 100644 --- a/eodag/api/product/metadata_mapping.py +++ b/eodag/api/product/metadata_mapping.py @@ -25,6 +25,7 @@ from string import Formatter import geojson from dateutil.parser import isoparse from dateutil.tz import UTC, tzutc +from jsonpath_ng.jsonpath import Child from lxml import etree from lxml.etree import XPathEvalError from shapely import wkt @@ -407,7 +408,7 @@ def format_metadata(search_param, args, kwargs): return MetadataFormatter().vformat(search_param, args, kwargs) -def properties_from_json(json, mapping, discovery_pattern=None, discovery_path=None): +def properties_from_json(json, mapping, discovery_config=None): """Extract properties from a provider json result. :param json: The representation of a provider result as a json object @@ -442,7 +443,7 @@ def properties_from_json(json, mapping, discovery_pattern=None, discovery_path=N match = path_or_text.find(json) if len(match) == 1: extracted_value = match[0].value - used_jsonpaths.append(match[0].path) + used_jsonpaths.append(match[0].full_path) else: extracted_value = NOT_AVAILABLE if extracted_value is None: @@ -483,16 +484,43 @@ def properties_from_json(json, mapping, discovery_pattern=None, discovery_path=N properties[metadata] = template.format(properties) # adds missing discovered properties + if not discovery_config: + discovery_config = {} + discovery_pattern = discovery_config.get("metadata_pattern", None) + discovery_path = discovery_config.get("metadata_path", None) if discovery_pattern and discovery_path: discovered_properties = cached_parse(discovery_path).find(json) for found_jsonpath in discovered_properties: - found_key = found_jsonpath.path.fields[-1] + if "metadata_path_id" in discovery_config.keys(): + found_key_paths = cached_parse( + discovery_config["metadata_path_id"] + ).find(found_jsonpath.value) + if not found_key_paths: + continue + found_key = found_key_paths[0].value + used_jsonpath = Child( + found_jsonpath.full_path, + cached_parse(discovery_config["metadata_path_value"]), + ) + else: + # default key got from metadata_path + found_key = found_jsonpath.path.fields[-1] + used_jsonpath = found_jsonpath.full_path if ( re.compile(discovery_pattern).match(found_key) and found_key not in properties.keys() - and found_jsonpath.path not in used_jsonpaths + and used_jsonpath not in used_jsonpaths ): - properties[found_key] = found_jsonpath.value + if "metadata_path_value" in discovery_config.keys(): + found_value_path = cached_parse( + discovery_config["metadata_path_value"] + ).find(found_jsonpath.value) + properties[found_key] = ( + found_value_path[0].value if found_value_path else NOT_AVAILABLE + ) + else: + # default value got from metadata_path + properties[found_key] = found_jsonpath.value return properties diff --git a/eodag/plugins/search/build_search_result.py b/eodag/plugins/search/build_search_result.py index 84b5b742..d978f99f 100644 --- a/eodag/plugins/search/build_search_result.py +++ b/eodag/plugins/search/build_search_result.py @@ -121,12 +121,7 @@ class BuildPostSearchResult(PostJsonSearch): parsed_properties = properties_from_json( result, self.config.metadata_mapping, - discovery_pattern=getattr(self.config, "discover_metadata", {}).get( - "metadata_pattern", None - ), - discovery_path=getattr(self.config, "discover_metadata", {}).get( - "metadata_path", "null" - ), + discovery_config=getattr(self.config, "discover_metadata", {}), ) if not product_type: diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index ec911bb7..9a48a2ac 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -732,12 +732,7 @@ class QueryStringSearch(Search): QueryStringSearch.extract_properties[self.config.result_type]( result, self.config.metadata_mapping, - discovery_pattern=getattr(self.config, "discover_metadata", {}).get( - "metadata_pattern", None - ), - discovery_path=getattr(self.config, "discover_metadata", {}).get( - "metadata_path", "null" - ), + discovery_config=getattr(self.config, "discover_metadata", {}), ), kwargs, ) @@ -956,29 +951,31 @@ class ODataV4Search(QueryStringSearch): all products metadata""" def do_search(self, args, *kwargs): - """Do a two step search, as the metadata are not given into the search result""" - # TODO: This plugin is still very specific to the ONDA provider. - # Be careful to generalize it if needed when the chance to do so arrives - final_result = [] - # Query the products entity set for basic metadata about the product - for entity in super(ODataV4Search, self).do_search(args, *kwargs): - metadata_url = self.get_metadata_search_url(entity) - try: - logger.debug("Sending metadata request: %s", metadata_url) - response = requests.get(metadata_url, timeout=HTTP_REQ_TIMEOUT) - response.raise_for_status() - except requests.RequestException: - logger.exception( - "Skipping error while searching for %s %s instance:", - self.provider, - self.__class__.__name__, - ) - else: - entity.update( - {item["id"]: item["value"] for item in response.json()["value"]} - ) - final_result.append(entity) - return final_result + """A two step search can be performed if the metadata are not given into the search result""" + + if getattr(self.config, "per_product_metadata_query", False): + final_result = [] + # Query the products entity set for basic metadata about the product + for entity in super(ODataV4Search, self).do_search(args, *kwargs): + metadata_url = self.get_metadata_search_url(entity) + try: + logger.debug("Sending metadata request: %s", metadata_url) + response = requests.get(metadata_url, timeout=HTTP_REQ_TIMEOUT) + response.raise_for_status() + except requests.RequestException: + logger.exception( + "Skipping error while searching for %s %s instance:", + self.provider, + self.__class__.__name__, + ) + else: + entity.update( + {item["id"]: item["value"] for item in response.json()["value"]} + ) + final_result.append(entity) + return final_result + else: + return super(ODataV4Search, self).do_search(args, *kwargs) def get_metadata_search_url(self, entity): """Build the metadata link for the given entity""" diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 6ac69347..5f0d4436 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1315,7 +1315,7 @@ - ':' pagination: count_endpoint: 'https://catalogue.onda-dias.eu/dias-catalogue/Products/$count' - next_page_url_tpl: '{url}?{search}&$top={items_per_page}&$skip={skip}' + next_page_url_tpl: '{url}?{search}&$top={items_per_page}&$skip={skip}&$expand=Metadata' # 2021/03/19: 2000 is the max, if greater 200 response but contains an error message max_items_per_page: 2_000 results_entry: 'value' @@ -1334,6 +1334,17 @@ - 'beginPosition:[{startTimeFromAscendingNode#to_iso_utc_datetime} TO ]' - 'endPosition:[* TO {completionTimeFromAscendingNode#to_iso_utc_datetime}]' - '{id#remove_extension}' + - 'platformSerialIdentifier:{platformSerialIdentifier}' + - 'instrumentShortName:{instrument}' + - 'processingLevel:{processingLevel}' + - 'sensorType:{sensorType}' + - 'topicCategory:{topicCategory}' + - 'lineage:{lineage}' + - 'orbitNumber:{orbitNumber}' + - 'orbitDirection:{orbitDirection}' + - 'processingBaseline:{productVersion}' + - 'generalQualityFlag:{productQualityStatus}' + - 'sensorOperationalMode:{sensorMode}' discover_metadata: auto_discovery: true metadata_pattern: '^[a-zA-Z0-9]+$' @@ -1343,42 +1354,74 @@ operations: AND: - '{metadata}:{{{metadata}}}' - metadata_path: '$.' + metadata_path: '$.Metadata[]' + metadata_path_id: 'id' + metadata_path_value: 'value' + per_product_metadata_query: false metadata_mapping: # Opensearch resource identifier within the search engine context (in our case # within the context of the data provider) + # Queryable parameters are set with null as 1st configuration list value to mark them as queryable, + # but `free_text_search_operations.$search.operations.AND` entries are then used instead. uid: '$.id' # OpenSearch Parameters for Collection Search (Table 3) productType: - - 'productType' - - '$.productType' + - null + - '$.Metadata[?(@.id="productType")].value' platform: - - platform - - '$.platformName' - platformSerialIdentifier: '$.platformSerialIdentifier' - instrument: '$.instrumentName' - processingLevel: '$.processingLevel' + - null + - '$.Metadata[?(@.id="platformName")].value' + platformSerialIdentifier: + - null + - '$.Metadata[?(@.id="platformSerialIdentifier")].value' + instrument: + - null + - '$.Metadata[?(@.id="instrumentShortName")].value' + processingLevel: + - null + - '$.Metadata[?(@.id="processingLevel")].value' + sensorType: + - null + - '$.Metadata[?(@.id="sensorType")].value' # INSPIRE obligated OpenSearch Parameters for Collection Search (Table 4) - title: '$.filename' + title: '$.Metadata[?(@.id="filename")].value' + topicCategory: + - null + - '$.Metadata[?(@.id="topicCategory")].value' + lineage: + - null + - '$.Metadata[?(@.id="lineage")].value' # OpenSearch Parameters for Product Search (Table 5) - orbitNumber: '$.orbitNumber' - orbitDirection: '$.orbitDirection' - cloudCover: '$.cloudCoverPercentage' - productVersion: '$.processingBaseline' - productQualityStatus: '$.generalQualityFlag' - creationDate: '$.creationDate' - processingDate: '$.processingDate' - sensorMode: '$.sensorOperationalMode' + orbitNumber: + - null + - '$.Metadata[?(@.id="orbitNumber")].value' + orbitDirection: + - null + - '$.Metadata[?(@.id="orbitDirection")].value' + cloudCover: + - null + - '$.Metadata[?(@.id="cloudCoverPercentage")].value' + productVersion: + - null + - '$.Metadata[?(@.id="processingBaseline")].value' + productQualityStatus: + - null + - '$.Metadata[?(@.id="generalQualityFlag")].value' + creationDate: '$.Metadata[?(@.id="creationDate")].value' + processingDate: '$.Metadata[?(@.id="processingDate")].value' + sensorMode: + - null + - '$.Metadata[?(@.id="sensorOperationalMode")].value' # OpenSearch Parameters for Acquistion Parameters Search (Table 6) startTimeFromAscendingNode: '$.beginPosition' completionTimeFromAscendingNode: '$.endPosition' - polarizationChannels: '{$.polarisationChannels#replace_str(","," ")}' + polarizationChannels: '{$.Metadata[?(@.id="polarisationChannels")].value#replace_str(","," ")}' # Custom parameters (not defined in the base document referenced above) - id: '{$.filename#remove_extension}' + id: '{$.Metadata[?(@.id="filename")].value#remove_extension}' # The geographic extent of the product geometry: '$.footprint' # The url of the quicklook	onda search optimize with expand=Metadata Avoid per-product metadata query using https://catalogue.onda-dias.eu/dias-catalogue/Products?$expand=Metadata link documented in https://www.onda-dias.eu/cms/knowledge-base/odata-querying-products-showing-all-metadata-together-with-their-properties/. Use a new `ODataV4Search` plugin configuration option, `per_product_metadata_query` (bool) to specify that per-product metadata query is no more needed. See also https://github.com/CS-SI/eodag/issues/606#issuecomment-1399962814	CS-SI/eodag	diff --git a/tests/context.py b/tests/context.py index dec2c197..2ca8dd32 100644 --- a/tests/context.py +++ b/tests/context.py @@ -33,6 +33,8 @@ from eodag.api.product.metadata_mapping import ( format_metadata, OFFLINE_STATUS, ONLINE_STATUS, + properties_from_json, + NOT_AVAILABLE, ) from eodag.api.search_result import SearchResult from eodag.cli import download, eodag, list_pt, search_crunch diff --git a/tests/units/test_metadata_mapping.py b/tests/units/test_metadata_mapping.py index ba8e15e5..4089601a 100644 --- a/tests/units/test_metadata_mapping.py +++ b/tests/units/test_metadata_mapping.py @@ -17,7 +17,14 @@ # limitations under the License. import unittest -from tests.context import format_metadata, get_geometry_from_various +from jsonpath_ng.ext import parse + +from tests.context import ( + NOT_AVAILABLE, + format_metadata, + get_geometry_from_various, + properties_from_json, +) class TestMetadataFormatter(unittest.TestCase): @@ -195,3 +202,67 @@ class TestMetadataFormatter(unittest.TestCase): format_metadata(to_format, *{"some_extension:a_parameter": "value"}), "value", ) + + def test_properties_from_json_discovery_config(self): + """properties_from_json must extract and discover metadata""" + json = { + "foo": "foo-val", + "bar": "bar-val", + "baz": {"baaz": "baz-val"}, + "qux": [ + {"somekey": "a", "someval": "a-val"}, + {"somekey": "b", "someval": "b-val", "some": "thing"}, + {"somekey": "c"}, + {"someval": "d-val"}, + ], + "ignored": "ignored-val", + } + mapping = { + "fooProperty": (None, parse("$.foo")), + "missingProperty": (None, parse("$.missing")), + } + # basic discovery + discovery_config = { + "auto_discovery": True, + "metadata_pattern": r"^(?!ignored)[a-zA-Z0-9_]+$", + "metadata_path": "$.", + } + properties = properties_from_json( + json=json, mapping=mapping, discovery_config=discovery_config + ) + self.assertDictEqual( + properties, + { + "fooProperty": "foo-val", + "bar": "bar-val", + "baz": {"baaz": "baz-val"}, + "missingProperty": NOT_AVAILABLE, + "qux": [ + {"somekey": "a", "someval": "a-val"}, + {"somekey": "b", "someval": "b-val", "some": "thing"}, + {"somekey": "c"}, + {"someval": "d-val"}, + ], + }, + ) + # advanced discovery + discovery_config = { + "auto_discovery": True, + "metadata_pattern": r"^(?!ignored)[a-zA-Z0-9_]+$", + "metadata_path": "$.qux[]", + "metadata_path_id": "somekey", + "metadata_path_value": "someval", + } + properties = properties_from_json( + json=json, mapping=mapping, discovery_config=discovery_config + ) + self.assertDictEqual( + properties, + { + "fooProperty": "foo-val", + "missingProperty": NOT_AVAILABLE, + "a": "a-val", + "b": "b-val", + "c": NOT_AVAILABLE, + }, + ) diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 16ec3d41..f02accbf 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -385,7 +385,13 @@ class TestSearchPluginODataV4Search(BaseSearchPluginTest): # Some expected results with open(self.provider_resp_dir / "onda_count.json") as f: self.onda_resp_count = json.load(f) - self.onda_url_count = 'https://catalogue.onda-dias.eu/dias-catalogue/Products/$count?productType=S2MSI1C&$search="footprint:"Intersects(POLYGON ((137.7729 13.1342, 137.7729 23.8860, 153.7491 23.8860, 153.7491 13.1342, 137.7729 13.1342)))" AND productType:S2MSI1C AND beginPosition:[2020-08-08T00:00:00.000Z TO ] AND endPosition:[* TO 2020-08-16T00:00:00.000Z]"' # noqa + self.onda_url_count = ( + 'https://catalogue.onda-dias.eu/dias-catalogue/Products/$count?$search="footprint:"' + "Intersects(POLYGON ((137.7729 13.1342, 137.7729 23.8860, 153.7491 23.8860, 153.7491 13.1342, " + '137.7729 13.1342)))" AND productType:S2MSI1C AND beginPosition:[2020-08-08T00:00:00.000Z TO ] ' + 'AND endPosition:[ TO 2020-08-16T00:00:00.000Z] AND foo:bar"' + ) + self.onda_products_count = 47 @mock.patch("eodag.plugins.search.qssearch.requests.get", autospec=True) @@ -414,12 +420,19 @@ class TestSearchPluginODataV4Search(BaseSearchPluginTest): page=1, items_per_page=2, auth=self.onda_auth_plugin, + # custom query argument that must be mapped using discovery_metata.search_param + foo="bar", *self.search_criteria_s2_msi_l1c ) # Specific expected results number_of_products = 2 - onda_url_search = 'https://catalogue.onda-dias.eu/dias-catalogue/Products?productType=S2MSI1C&$format=json&$search="footprint:"Intersects(POLYGON ((137.7729 13.1342, 137.7729 23.8860, 153.7491 23.8860, 153.7491 13.1342, 137.7729 13.1342)))" AND productType:S2MSI1C AND beginPosition:[2020-08-08T00:00:00.000Z TO ] AND endPosition:[* TO 2020-08-16T00:00:00.000Z]"&$top=2&$skip=0' # noqa + onda_url_search = ( + 'https://catalogue.onda-dias.eu/dias-catalogue/Products?$format=json&$search="' + 'footprint:"Intersects(POLYGON ((137.7729 13.1342, 137.7729 23.8860, 153.7491 23.8860, 153.7491 13.1342, ' + '137.7729 13.1342)))" AND productType:S2MSI1C AND beginPosition:[2020-08-08T00:00:00.000Z TO ] ' + 'AND endPosition:[ TO 2020-08-16T00:00:00.000Z] AND foo:bar"&$top=2&$skip=0&$expand=Metadata' + ) mock__request.assert_any_call( mock.ANY,	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 3, "issue_text_score": 2, "test_score": 1 }, "num_modified_files": 5 }	2.8	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@4a0d68987de913040e0ba16f9fe1d00cd10fe6e5#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.8.1.dev2+g4a0d6898 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_properties_from_json_discovery_config", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda" ]	[]	[ "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_csv_list", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_datetime_to_timestamp_milliseconds", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_dict_update", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_fake_l2a_title_from_l1c", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_get_group_name", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_recursive_sub_str", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_remove_extension", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_replace_str", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_s2msil2a_title_to_aws_productinfo", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_slice_str", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_bounds_lists", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_geojson", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_date", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_utc_datetime", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_iso_utc_datetime_from_milliseconds", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_convert_to_rounded_wkt", "tests/units/test_metadata_mapping.py::TestMetadataFormatter::test_format_stac_extension_parameter", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_peps", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_discover_product_types", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_discover_product_types_keywords", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_peps", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_request_auth_error", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_request_error", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_default_geometry", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_mapping_earthsearch", "tests/units/test_search_plugins.py::TestSearchPluginBuildPostSearchResult::test_plugins_search_buildpostsearchresult_count_and_search" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-616
CS-SI__eodag-634	69d24e856d04abbf44a81786b67cc26e98d43ee5	2023-02-07 15:52:38	216edc2a7e22676ecfcbc77b71795fc319cb3dc4	github-actions[bot]: ## Unit Test Results     2 files ±0     2 suites ±0 4m 22s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") - 1m 53s 356 tests +1 353 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +1 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") - 1 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") +1 712 runs +2 706 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") +2 5 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") - 1 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") +1 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/11170982115). Results for commit 976a5454. ± Comparison against base commit 69d24e85. [test-results]:data:application/gzip;base64,H4sIAEF14mMC/03MSQ7CIACF4as0rF0AZRAvYxgTYlsMw8p4d6Et2OX/veR9gPOLTeAx4dsEUvF5hClRZh+2lqxBnXIbZ8p6PVPReqf5Ty//Pi8OcNIvFdAAG2OIVWCVWLb2yRE+o19yyIYcj7T35XDv658O6+pzDSA4k5RQgpQwVhljBMUEWSOVtNRBKGcG745j8P0BpV/4CgUBAAA=	diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 35841aaf..d968c733 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -1148,6 +1148,9 @@ def flatten_top_directories(nested_dir_root, common_subdirs_path=None): subpaths_list = [p for p in Path(nested_dir_root).glob("*/") if p.is_file()] common_subdirs_path = os.path.commonpath(subpaths_list) + if Path(common_subdirs_path).is_file(): + common_subdirs_path = os.path.dirname(common_subdirs_path) + if nested_dir_root != common_subdirs_path: logger.debug(f"Flatten {common_subdirs_path} to {nested_dir_root}") tmp_path = mkdtemp()	flatten_top_directories on single file The following error is raised when trying to use `flatten_top_directories()` on a directory containing a single file or when downloading a product having a single asset and the download plugin configured with `flatten_top_dirs: true`: ``` Traceback (most recent call last): File "/home/sylvain/workspace/eodag-debug/20230207_download_issue.py", line 78, in <module> downloaded_path = dag.download(search_results[0]) File "/home/sylvain/workspace/eodag/eodag/api/core.py", line 1753, in download path = product.download( File "/home/sylvain/workspace/eodag/eodag/api/product/_product.py", line 313, in download fs_path = self.downloader.download( File "/home/sylvain/workspace/eodag/eodag/plugins/download/http.py", line 260, in download fs_path = self._download_assets( File "/home/sylvain/workspace/eodag/eodag/plugins/download/http.py", line 607, in _download_assets flatten_top_directories(fs_dir_path) File "/home/sylvain/workspace/eodag/eodag/utils/__init__.py", line 1159, in flatten_top_directories shutil.copytree(common_subdirs_path, tmp_path) File "/usr/lib/python3.8/shutil.py", line 555, in copytree with os.scandir(src) as itr: NotADirectoryError: [Errno 20] Not a directory: '/tmp/foo_path' ```	CS-SI/eodag	diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index b3514aec..e15a565b 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -487,7 +487,7 @@ class TestDownloadPluginHttpRetry(BaseDownloadPluginTest): self.plugin.download( self.product, outputs_prefix=self.output_dir, - wait=0.1 / 60, + wait=0.01 / 60, timeout=0.2 / 60, ) diff --git a/tests/units/test_utils.py b/tests/units/test_utils.py index 04b4ad9b..90697d31 100644 --- a/tests/units/test_utils.py +++ b/tests/units/test_utils.py @@ -241,6 +241,20 @@ class TestUtils(unittest.TestCase): self.assertIn(Path(nested_dir_root) / "c2" / "bar", dir_content) self.assertIn(Path(nested_dir_root) / "c2" / "baz", dir_content) + def test_flatten_top_dirs_single_file(self): + """flatten_top_dirs must flatten directory structure containing a single file""" + with TemporaryDirectory() as nested_dir_root: + os.makedirs(os.path.join(nested_dir_root, "a", "b", "c1")) + # create empty file + open(os.path.join(nested_dir_root, "a", "b", "c1", "foo"), "a").close() + + flatten_top_directories(nested_dir_root) + + dir_content = list(Path(nested_dir_root).glob("*/")) + + self.assertEqual(len(dir_content), 1) + self.assertIn(Path(nested_dir_root) / "foo", dir_content) + def test_flatten_top_dirs_given_subdir(self): """flatten_top_dirs must flatten directory structure using given subdirectory""" with TemporaryDirectory() as nested_dir_root:	{ "commit_name": "merge_commit", "failed_lite_validators": [], "has_test_patch": true, "is_lite": true, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 1 }	2.8	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@69d24e856d04abbf44a81786b67cc26e98d43ee5#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.8.1.dev14+g69d24e85 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_utils.py::TestUtils::test_flatten_top_dirs_single_file" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets", "tests/units/test_utils.py::TestUtils::test_downloaded_callback", "tests/units/test_utils.py::TestUtils::test_flatten_top_dirs", "tests/units/test_utils.py::TestUtils::test_flatten_top_dirs_given_subdir", "tests/units/test_utils.py::TestUtils::test_get_bucket_name_and_prefix", "tests/units/test_utils.py::TestUtils::test_merge_mappings", "tests/units/test_utils.py::TestUtils::test_path_to_uri", "tests/units/test_utils.py::TestUtils::test_progresscallback_copy", "tests/units/test_utils.py::TestUtils::test_progresscallback_disable", "tests/units/test_utils.py::TestUtils::test_progresscallback_init", "tests/units/test_utils.py::TestUtils::test_progresscallback_init_customize", "tests/units/test_utils.py::TestUtils::test_uri_to_path", "tests/units/test_utils.py::TestUtils::test_utils_get_timestamp" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-634
CS-SI__eodag-645	216edc2a7e22676ecfcbc77b71795fc319cb3dc4	2023-02-15 14:33:03	216edc2a7e22676ecfcbc77b71795fc319cb3dc4	github-actions[bot]: ## Unit Test Results     2 files ±    0     2 suites ±0 2m 2s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "duration of all tests") - 3m 37s 247 tests - 112 244 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") - 112 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") - 1 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 1 [:fire:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "test errors") +1 326 runs - 392 323 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "passed tests") - 389 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "skipped / disabled tests") - 4 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "failed tests") ±0 1 [:fire:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v1.20/README.md#the-symbols "test errors") +1 For more details on these errors, see [this check](https://github.com/CS-SI/eodag/runs/11361437367). Results for commit ccfa991a. ± Comparison against base commit 3099b2a8. <details> <summary>This pull request <b>removes</b> 116 and <b>adds</b> 4 tests. <i>Note that renamed tests count towards both.</i></summary> ``` tests.units.test_core.TestCore ‑ test_update_product_types_list tests.units.test_core.TestCore ‑ test_update_product_types_list_with_api_plugin tests.units.test_core.TestCore ‑ test_update_product_types_list_without_plugin tests.units.test_core.TestCore ‑ test_update_providers_config tests.units.test_http_server.RequestTestCase ‑ test_catalog_browse tests.units.test_http_server.RequestTestCase ‑ test_cloud_cover_search tests.units.test_http_server.RequestTestCase ‑ test_conformance tests.units.test_http_server.RequestTestCase ‑ test_date_search tests.units.test_http_server.RequestTestCase ‑ test_date_search_from_catalog_items tests.units.test_http_server.RequestTestCase ‑ test_date_search_from_items … ``` ``` pytest ‑ internal tests.units.test_download_plugins.TestDownloadPluginHttp ‑ test_plugins_download_http_order_status_search_again tests.units.test_download_plugins.TestDownloadPluginS3Rest ‑ test_plugins_download_s3rest_offline tests.units.test_download_plugins.TestDownloadPluginS3Rest ‑ test_plugins_download_s3rest_online ``` </details> [test-results]:data:application/gzip;base64,H4sIAAru7GMC/02Myw6DIBAAf8Vw7oFdqHb7Mw1BSDb10SCcTP+9SBU9zkwyq/A8uEU8G7w1YkkcK/QpmMjzlBFwEznFEnV30GtJ1halT/Xmz774C294yEJW4UKYw7bNJqRpeypsdziWClU157HwZVj4+rPzOHLMIKz1hgiMhB5c1yIY8NLdSYP22KGhhyJqyYnvD09KXSkFAQAA	diff --git a/eodag/plugins/authentication/token.py b/eodag/plugins/authentication/token.py index 9419f85c..75151f22 100644 --- a/eodag/plugins/authentication/token.py +++ b/eodag/plugins/authentication/token.py @@ -36,6 +36,11 @@ class TokenAuth(Authentication): self.config.auth_uri = self.config.auth_uri.format( self.config.credentials ) + # format headers if needed + self.config.headers = { + header: value.format(self.config.credentials) + for header, value in getattr(self.config, "headers", {}).items() + } except KeyError as e: raise MisconfiguredError( f"Missing credentials inputs for provider {self.provider}: {e}" @@ -66,4 +71,4 @@ class TokenAuth(Authentication): else: token = response.text # Return auth class set with obtained token - return RequestsTokenAuth(token, "header") + return RequestsTokenAuth(token, "header", headers=self.config.headers) diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 5eb666f9..fd4031cf 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -25,6 +25,7 @@ from urllib.parse import parse_qs, urlparse import geojson import requests +from lxml import etree from requests import HTTPError, RequestException from eodag.api.product.metadata_mapping import ( @@ -32,6 +33,7 @@ from eodag.api.product.metadata_mapping import ( ONLINE_STATUS, mtd_cfg_as_jsonpath, properties_from_json, + properties_from_xml, ) from eodag.plugins.download.base import ( DEFAULT_DOWNLOAD_TIMEOUT, @@ -47,6 +49,7 @@ from eodag.utils import ( ) from eodag.utils.exceptions import ( AuthenticationError, + DownloadError, MisconfiguredError, NotAvailableError, ) @@ -126,7 +129,7 @@ class HTTPDownload(Download): logger.warning( "%s could not be ordered, request returned %s", product.properties["title"], - e, + f"{e.response.content} - {e}", ) order_metadata_mapping = getattr(self.config, "order_on_response", {}).get( @@ -206,8 +209,11 @@ class HTTPDownload(Download): self.config, "order_status_percent", None ) if order_status_percent_key and order_status_percent_key in status_dict: + order_status_value = str(status_dict[order_status_percent_key]) + if order_status_value.isdigit(): + order_status_value += "%" logger.info( - f"{product.properties['title']} order status: {status_dict[order_status_percent_key]}%" + f"{product.properties['title']} order status: {order_status_value}" ) # display error if any order_status_error_dict = getattr(self.config, "order_status_error", {}) @@ -219,6 +225,79 @@ class HTTPDownload(Download): logger.warning(status_message) else: logger.debug(status_message) + # check if succeeds and need search again + order_status_success_dict = getattr( + self.config, "order_status_success", {} + ) + if ( + order_status_success_dict + and order_status_success_dict.items() <= status_dict.items() + and getattr(self.config, "order_status_on_success", {}).get( + "need_search" + ) + ): + logger.debug( + f"Search for new location: {product.properties['searchLink']}" + ) + # search again + response = requests.get(product.properties["searchLink"]) + response.raise_for_status() + if ( + self.config.order_status_on_success.get("result_type", "json") + == "xml" + ): + root_node = etree.fromstring(response.content) + namespaces = {k or "ns": v for k, v in root_node.nsmap.items()} + results = [ + etree.tostring(entry) + for entry in root_node.xpath( + self.config.order_status_on_success["results_entry"], + namespaces=namespaces, + ) + ] + if isinstance(results, list) and len(results) != 1: + raise DownloadError( + "Could not get a single result after order success for " + f"{product.properties['searchLink']} request. " + f"Please search and download {product} again" + ) + return + try: + assert isinstance( + results, list + ), "results must be in a list" + # single result + result = results[0] + # parse result + new_search_metadata_mapping = ( + self.config.order_status_on_success["metadata_mapping"] + ) + order_metadata_mapping_jsonpath = {} + order_metadata_mapping_jsonpath = mtd_cfg_as_jsonpath( + new_search_metadata_mapping, + order_metadata_mapping_jsonpath, + ) + properties_update = properties_from_xml( + result, + order_metadata_mapping_jsonpath, + ) + except Exception as e: + logger.debug(e) + raise DownloadError( + f"Could not parse result after order success for {product.properties['searchLink']} " + f"request. Please search and download {product} again" + ) + # update product + product.properties.update(properties_update) + product.location = product.remote_location = product.properties[ + "downloadLink" + ] + else: + logger.warning( + "JSON response parsing is not implemented yet for new searches " + f"after order success. Please search and download {product} again" + ) + except RequestException as e: logger.warning( "%s order status could not be checked, request returned %s", diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index fa2d0f65..d71c39b0 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -26,8 +26,13 @@ from xml.parsers.expat import ExpatError import requests from requests import RequestException -from eodag.api.product.metadata_mapping import OFFLINE_STATUS -from eodag.plugins.download.base import DEFAULT_STREAM_REQUESTS_TIMEOUT, Download +from eodag.api.product.metadata_mapping import OFFLINE_STATUS, ONLINE_STATUS +from eodag.plugins.download.base import ( + DEFAULT_DOWNLOAD_TIMEOUT, + DEFAULT_DOWNLOAD_WAIT, + DEFAULT_STREAM_REQUESTS_TIMEOUT, + Download, +) from eodag.plugins.download.http import HTTPDownload from eodag.utils import ( ProgressCallback, @@ -55,7 +60,19 @@ class S3RestDownload(Download): Re-use AwsDownload bucket some handling methods """ - def download(self, product, auth=None, progress_callback=None, kwargs): + def __init__(self, provider, config): + super(S3RestDownload, self).__init__(provider, config) + self.http_download_plugin = HTTPDownload(self.provider, self.config) + + def download( + self, + product, + auth=None, + progress_callback=None, + wait=DEFAULT_DOWNLOAD_WAIT, + timeout=DEFAULT_DOWNLOAD_TIMEOUT, + kwargs, + ): """Download method for S3 REST API. :param product: The EO product to download @@ -82,162 +99,210 @@ class S3RestDownload(Download): ) progress_callback = ProgressCallback(disable=True) - # get bucket urls - bucket_name, prefix = get_bucket_name_and_prefix( - url=product.location, bucket_path_level=self.config.bucket_path_level - ) - + # order product if it is offline + ordered_message = "" if ( - bucket_name is None + "orderLink" in product.properties and "storageStatus" in product.properties - and product.properties["storageStatus"] == OFFLINE_STATUS + and product.properties["storageStatus"] != ONLINE_STATUS + ): + self.http_download_plugin.orderDownload(product=product, auth=auth) + + @self._download_retry(product, wait, timeout) + def download_request( + product, + auth, + progress_callback, + ordered_message, + kwargs, ): - raise NotAvailableError( - "%s is not available for download on %s (status = %s)" - % ( - product.properties["title"], - self.provider, - product.properties["storageStatus"], + # check order status + if product.properties.get("orderStatusLink", None): + self.http_download_plugin.orderDownloadStatus( + product=product, auth=auth ) - ) - bucket_url = urljoin( - product.downloader.config.base_uri.strip("/") + "/", bucket_name - ) - nodes_list_url = bucket_url + "?prefix=" + prefix.strip("/") + # get bucket urls + bucket_name, prefix = get_bucket_name_and_prefix( + url=product.location, bucket_path_level=self.config.bucket_path_level + ) - # get nodes/files list contained in the bucket - logger.debug("Retrieving product content from %s", nodes_list_url) - bucket_contents = requests.get( - nodes_list_url, auth=auth, timeout=HTTP_REQ_TIMEOUT - ) - try: - bucket_contents.raise_for_status() - except requests.RequestException as err: - # check if error is identified as auth_error in provider conf - auth_errors = getattr(self.config, "auth_error_code", [None]) - if not isinstance(auth_errors, list): - auth_errors = [auth_errors] - if err.response.status_code in auth_errors: - raise AuthenticationError( - "HTTP Error %s returned, %s\nPlease check your credentials for %s" + if ( + bucket_name is None + and "storageStatus" in product.properties + and product.properties["storageStatus"] == OFFLINE_STATUS + ): + raise NotAvailableError( + "%s is not available for download on %s (status = %s)" % ( - err.response.status_code, - err.response.text.strip(), + product.properties["title"], self.provider, + product.properties["storageStatus"], ) ) - # other error - else: - logger.exception( - "Could not get content from %s (provider:%s, plugin:%s)\n%s", - nodes_list_url, - self.provider, - self.__class__.__name__, - bucket_contents.text, - ) - raise RequestError(str(err)) - try: - xmldoc = minidom.parseString(bucket_contents.text) - except ExpatError as err: - logger.exception("Could not parse xml data from %s", bucket_contents) - raise DownloadError(str(err)) - nodes_xml_list = xmldoc.getElementsByTagName("Contents") - - if len(nodes_xml_list) == 0: - logger.warning("Could not load any content from %s", nodes_list_url) - elif len(nodes_xml_list) == 1: - # single file download - product.remote_location = urljoin( - bucket_url.strip("/") + "/", prefix.strip("/") + + bucket_url = urljoin( + product.downloader.config.base_uri.strip("/") + "/", bucket_name ) - return HTTPDownload(self.provider, self.config).download( - product=product, - auth=auth, - progress_callback=progress_callback, - kwargs + nodes_list_url = bucket_url + "?prefix=" + prefix.strip("/") + + # get nodes/files list contained in the bucket + logger.debug("Retrieving product content from %s", nodes_list_url) + bucket_contents = requests.get( + nodes_list_url, auth=auth, timeout=HTTP_REQ_TIMEOUT ) + try: + bucket_contents.raise_for_status() + except requests.RequestException as err: + # check if error is identified as auth_error in provider conf + auth_errors = getattr(self.config, "auth_error_code", [None]) + if not isinstance(auth_errors, list): + auth_errors = [auth_errors] + if err.response.status_code in auth_errors: + raise AuthenticationError( + "HTTP Error %s returned, %s\nPlease check your credentials for %s" + % ( + err.response.status_code, + err.response.text.strip(), + self.provider, + ) + ) + # product not available + elif ( + product.properties.get("storageStatus", ONLINE_STATUS) + != ONLINE_STATUS + ): + msg = ( + ordered_message + if ordered_message and not err.response.text.strip() + else err.response.text.strip() + ) + raise NotAvailableError( + "%s(initially %s) requested, returned: %s" + % ( + product.properties["title"], + product.properties["storageStatus"], + msg, + ) + ) + # other error + else: + logger.exception( + "Could not get content from %s (provider:%s, plugin:%s)\n%s", + nodes_list_url, + self.provider, + self.__class__.__name__, + bucket_contents.text, + ) + raise RequestError(str(err)) + try: + xmldoc = minidom.parseString(bucket_contents.text) + except ExpatError as err: + logger.exception("Could not parse xml data from %s", bucket_contents) + raise DownloadError(str(err)) + nodes_xml_list = xmldoc.getElementsByTagName("Contents") - # destination product path - outputs_prefix = kwargs.pop("ouputs_prefix", None) or self.config.outputs_prefix - abs_outputs_prefix = os.path.abspath(outputs_prefix) - product_local_path = os.path.join(abs_outputs_prefix, prefix.split("/")[-1]) + if len(nodes_xml_list) == 0: + logger.warning("Could not load any content from %s", nodes_list_url) - # .downloaded cache record directory - download_records_dir = os.path.join(abs_outputs_prefix, ".downloaded") - try: - os.makedirs(download_records_dir) - except OSError as exc: - import errno + # destination product path + outputs_prefix = ( + kwargs.pop("outputs_prefix", None) or self.config.outputs_prefix + ) + abs_outputs_prefix = os.path.abspath(outputs_prefix) + product_local_path = os.path.join(abs_outputs_prefix, prefix.split("/")[-1]) + + # .downloaded cache record directory + download_records_dir = os.path.join(abs_outputs_prefix, ".downloaded") + try: + os.makedirs(download_records_dir) + except OSError as exc: + import errno - if exc.errno != errno.EEXIST: # Skip error if dir exists - import traceback as tb + if exc.errno != errno.EEXIST: # Skip error if dir exists + import traceback as tb - logger.warning( - "Unable to create records directory. Got:\n%s", tb.format_exc() + logger.warning( + "Unable to create records directory. Got:\n%s", tb.format_exc() + ) + # check if product has already been downloaded + url_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() + record_filename = os.path.join(download_records_dir, url_hash) + if os.path.isfile(record_filename) and os.path.exists(product_local_path): + product.location = path_to_uri(product_local_path) + return product_local_path + # Remove the record file if product_local_path is absent (e.g. it was deleted while record wasn't) + elif os.path.isfile(record_filename): + logger.debug( + "Record file found (%s) but not the actual file", record_filename ) - # check if product has already been downloaded - url_hash = hashlib.md5(product.remote_location.encode("utf-8")).hexdigest() - record_filename = os.path.join(download_records_dir, url_hash) - if os.path.isfile(record_filename) and os.path.exists(product_local_path): - product.location = path_to_uri(product_local_path) - return product_local_path - # Remove the record file if product_local_path is absent (e.g. it was deleted while record wasn't) - elif os.path.isfile(record_filename): - logger.debug( - "Record file found (%s) but not the actual file", record_filename - ) - logger.debug("Removing record file : %s", record_filename) - os.remove(record_filename) - - # total size for progress_callback - total_size = sum( - [ - int(node.firstChild.nodeValue) - for node in xmldoc.getElementsByTagName("Size") - ] - ) - progress_callback.reset(total=total_size) + logger.debug("Removing record file : %s", record_filename) + os.remove(record_filename) - # download each node key - for node_xml in nodes_xml_list: - node_key = unquote( - node_xml.getElementsByTagName("Key")[0].firstChild.nodeValue - ) - # As "Key", "Size" and "ETag" (md5 hash) can also be retrieved from node_xml - node_url = urljoin(bucket_url.strip("/") + "/", node_key.strip("/")) - # output file location - local_filename = os.path.join( - self.config.outputs_prefix, "/".join(node_key.split("/")[6:]) + # total size for progress_callback + total_size = sum( + [ + int(node.firstChild.nodeValue) + for node in xmldoc.getElementsByTagName("Size") + ] ) - local_filename_dir = os.path.dirname(os.path.realpath(local_filename)) - if not os.path.isdir(local_filename_dir): - os.makedirs(local_filename_dir) - - with requests.get( - node_url, - stream=True, - auth=auth, - timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, - ) as stream: - try: - stream.raise_for_status() - except RequestException: - import traceback as tb + progress_callback.reset(total=total_size) - logger.error("Error while getting resource :\n%s", tb.format_exc()) - else: - with open(local_filename, "wb") as fhandle: - for chunk in stream.iter_content(chunk_size=64 * 1024): - if chunk: - fhandle.write(chunk) - progress_callback(len(chunk)) + # download each node key + for node_xml in nodes_xml_list: + node_key = unquote( + node_xml.getElementsByTagName("Key")[0].firstChild.nodeValue + ) + # As "Key", "Size" and "ETag" (md5 hash) can also be retrieved from node_xml + node_url = urljoin(bucket_url.strip("/") + "/", node_key.strip("/")) + # output file location + local_filename_suffix_list = node_key.split("/")[6:] + if local_filename_suffix_list[0] == os.path.basename( + product_local_path + ): + local_filename_suffix_list.pop(0) + # single file: remove nested sub dirs + if len(nodes_xml_list) == 1: + local_filename_suffix_list = [local_filename_suffix_list[-1]] + local_filename = os.path.join( + product_local_path, local_filename_suffix_list + ) + local_filename_dir = os.path.dirname(os.path.realpath(local_filename)) + if not os.path.isdir(local_filename_dir): + os.makedirs(local_filename_dir) + + with requests.get( + node_url, + stream=True, + auth=auth, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) as stream: + try: + stream.raise_for_status() + except RequestException: + import traceback as tb - # TODO: check md5 hash ? + logger.error( + "Error while getting resource :\n%s", tb.format_exc() + ) + else: + with open(local_filename, "wb") as fhandle: + for chunk in stream.iter_content(chunk_size=64 1024): + if chunk: + fhandle.write(chunk) + progress_callback(len(chunk)) - with open(record_filename, "w") as fh: - fh.write(product.remote_location) - logger.debug("Download recorded in %s", record_filename) + with open(record_filename, "w") as fh: + fh.write(product.remote_location) + logger.debug("Download recorded in %s", record_filename) - product.location = path_to_uri(product_local_path) - return product_local_path + product.location = path_to_uri(product_local_path) + return product_local_path + + return download_request( + product, + auth, + progress_callback, + ordered_message, + *kwargs, + ) diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 71c2efd1..81a0b99b 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1275,7 +1275,9 @@ downloadLink: 'ns:link[@rel="enclosure"]/@href' # storageStatus: must be one of ONLINE, STAGING, OFFLINE storageStatus: 'DIAS:onlineStatus/text()' - + # order link formated for POST request usage + orderLink: 'https://apis.mundiwebservices.com/odrapi/0.1/request?{{"productId":"{id}","collectionId":"{platform}"}}' + searchLink: 'https://{platform}.browse.catalog.mundiwebservices.com/opensearch?uid={id}' # Additional metadata provided by the providers but that don't appear in the reference spec thumbnail: 'media:group/media:content[media:category="THUMBNAIL"]/@url' download: !plugin @@ -1284,9 +1286,38 @@ extract: true auth_error_code: 401 bucket_path_level: 0 + # order mechanism + order_enabled: true + order_method: 'POST' + order_headers: + accept: application/json + Content-Type: application/json + order_on_response: + metadata_mapping: + order_id: '{$.requestId#replace_str("Not Available","")}' + reorder_id: '{$.message.`sub(/.requestId: ([a-z0-9]+)/, \\1)`#replace_str("Not Available","")}' + orderStatusLink: 'https://apis.mundiwebservices.com/odrapi/0.1/request/{order_id}{reorder_id}' + order_status_method: 'GET' + order_status_percent: status + order_status_success: + status: Success + order_status_on_success: + need_search: true + result_type: 'xml' + results_entry: '//ns:entry' + metadata_mapping: + downloadLink: 'ns:link[@rel="enclosure"]/@href' + storageStatus: 'DIAS:onlineStatus/text()' auth: !plugin - type: HTTPHeaderAuth + # Mixed HTTPHeaderAuth and TokenAuth + type: TokenAuth + auth_uri: 'https://apis.mundiwebservices.com/token' + token_type: json + token_key: access_token + credentials: + grant_type: password headers: + Authorization: Basic cXJic1pTVjBUWTdkZzlNRGQ0Q3ZQVXkyQV9NYToxMU5oNGtFQjFva2NvZlBlRkRYemhudWxGR3dh Cookie: "seeedtoken={apikey}" products: S1_SAR_GRD: diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 7be96aec..c6f002e6 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -118,13 +118,17 @@ def _deprecated(reason="", version=None): class RequestsTokenAuth(AuthBase): """A custom authentication class to be used with requests module""" - def __init__(self, token, where, qs_key=None): + def __init__(self, token, where, qs_key=None, headers=None): self.token = token self.where = where self.qs_key = qs_key + self.headers = headers def __call__(self, request): """Perform the actual authentication""" + if self.headers and isinstance(self.headers, dict): + for k, v in self.headers.items(): + request.headers[k] = v if self.where == "qs": parts = urlparse(request.url) qs = parse_qs(parts.query)	404 error when trying to download mundi OFFLINE product ```py from eodag import EODataAccessGateway dag = EODataAccessGateway() prods, _ = dag.search(productType="S2_MSI_L1C", start="2019-01-01", end="2019-02-01") print("storageStatus: %s" % prods[0].properties["storageStatus"]) dag.download(prods[0]) ``` outputs: ``` storageStatus: OFFLINE [...] RequestError: 404 Client Error: Not Found for url: https://mundiwebservices.com/dp/?prefix=Not%20Available ``` related to #441	CS-SI/eodag	diff --git a/tests/units/test_auth_plugins.py b/tests/units/test_auth_plugins.py index 99ec9a81..569e916a 100644 --- a/tests/units/test_auth_plugins.py +++ b/tests/units/test_auth_plugins.py @@ -73,6 +73,7 @@ class TestAuthPluginTokenAuth(BaseAuthPluginTest): "headers": { "Content-Type": "application/json;charset=UTF-8", "Accept": "application/json", + "foo": "{foo}", }, }, }, @@ -146,6 +147,7 @@ class TestAuthPluginTokenAuth(BaseAuthPluginTest): req = mock.Mock(headers={}) auth(req) assert req.headers["Authorization"] == "Bearer this_is_test_token" + assert req.headers["foo"] == "bar" @mock.patch("eodag.plugins.authentication.token.requests.post", autospec=True) def test_plugins_auth_tokenauth_json_token_authenticate(self, mock_requests_post): diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index e15a565b..1556a210 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -16,17 +16,17 @@ # See the License for the specific language governing permissions and # limitations under the License. import os -import shutil import stat import unittest from pathlib import Path -from tempfile import NamedTemporaryFile, TemporaryDirectory, gettempdir, mkdtemp +from tempfile import NamedTemporaryFile, TemporaryDirectory, gettempdir from unittest import mock import responses from tests.context import ( OFFLINE_STATUS, + ONLINE_STATUS, EOProduct, NotAvailableError, PluginManager, @@ -71,12 +71,12 @@ class BaseDownloadPluginTest(unittest.TestCase): id="dummy", ), ) - self.output_dir = mkdtemp() + self.tmp_dir = TemporaryDirectory() + self.output_dir = self.tmp_dir.name def tearDown(self): super(BaseDownloadPluginTest, self).tearDown() - if os.path.isdir(self.output_dir): - shutil.rmtree(self.output_dir) + self.tmp_dir.cleanup() def get_download_plugin(self, product): return self.plugins_manager.get_download_plugin(product) @@ -451,6 +451,54 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): run() + def test_plugins_download_http_order_status_search_again(self): + """HTTPDownload.orderDownloadStatus() must search again after success if needed""" + plugin = self.get_download_plugin(self.product) + plugin.config.order_status_success = {"status": "great-success"} + plugin.config.order_status_on_success = { + "need_search": True, + "result_type": "xml", + "results_entry": "//entry", + "metadata_mapping": { + "downloadLink": "foo/text()", + }, + } + self.product.properties["orderStatusLink"] = "http://somewhere/order-status" + self.product.properties["searchLink"] = "http://somewhere/search-gain" + + auth_plugin = self.get_auth_plugin(self.product.provider) + auth_plugin.config.credentials = {"username": "foo", "password": "bar"} + auth = auth_plugin.authenticate() + + @responses.activate(registry=responses.registries.OrderedRegistry) + def run(): + responses.add( + responses.GET, + "http://somewhere/order-status", + status=200, + json={"status": "great-success"}, + ) + responses.add( + responses.GET, + "http://somewhere/search-gain", + status=200, + body=( + b"<?xml version='1.0' encoding='UTF-8' standalone='yes'?>" + b"<feed>" + b"<entry><foo>http://new-download-link</foo><bar>something else</bar></entry>" + b"</feed>" + ), + ) + + plugin.orderDownloadStatus(self.product, auth=auth) + + self.assertEqual( + self.product.properties["downloadLink"], "http://new-download-link" + ) + self.assertEqual(len(responses.calls), 2) + + run() + class TestDownloadPluginHttpRetry(BaseDownloadPluginTest): def setUp(self): @@ -870,3 +918,195 @@ class TestDownloadPluginAws(BaseDownloadPluginTest): self.assertEqual(mock_flatten_top_directories.call_count, 0) self.assertEqual(path, execpected_output) + + +class TestDownloadPluginS3Rest(BaseDownloadPluginTest): + def setUp(self): + super(TestDownloadPluginS3Rest, self).setUp() + + self.product = EOProduct( + "mundi", + dict( + geometry="POINT (0 0)", + title="dummy_product", + id="dummy", + downloadLink="http://somewhere/some-bucket/path/to/the/product", + ), + productType="S2_MSI_L1C", + ) + + @mock.patch( + "eodag.plugins.download.http.HTTPDownload.orderDownloadStatus", autospec=True + ) + @mock.patch("eodag.plugins.download.http.HTTPDownload.orderDownload", autospec=True) + def test_plugins_download_s3rest_online(self, mock_order, mock_order_status): + """S3RestDownload.download() must create outputfiles""" + + self.product.properties["storageStatus"] = ONLINE_STATUS + + plugin = self.get_download_plugin(self.product) + auth = self.get_auth_plugin(self.product.provider) + self.product.register_downloader(plugin, auth) + + @responses.activate(registry=responses.registries.OrderedRegistry) + def run(): + # List bucket content + responses.add( + responses.GET, + f"{plugin.config.base_uri}/some-bucket?prefix=path/to/the/product", + status=200, + body=( + b"<?xml version='1.0' encoding='UTF-8' standalone='yes'?>" + b"<ListBucketResult>" + b"<Contents><Key>0/1/2/3/4/5/path/to/some.file</Key><Size>2</Size></Contents>" + b"<Contents><Key>0/1/2/3/4/5/path/to/another.file</Key><Size>5</Size></Contents>" + b"</ListBucketResult>" + ), + ) + # 1st file download response + responses.add( + responses.GET, + f"{plugin.config.base_uri}/some-bucket/0/1/2/3/4/5/path/to/some.file", + status=200, + content_type="application/octet-stream", + body=b"something", + auto_calculate_content_length=True, + ) + # 2nd file download response + responses.add( + responses.GET, + f"{plugin.config.base_uri}/some-bucket/0/1/2/3/4/5/path/to/another.file", + status=200, + content_type="application/octet-stream", + body=b"something else", + auto_calculate_content_length=True, + ) + path = plugin.download(self.product, outputs_prefix=self.output_dir) + + # there must have been 3 calls (list, 1st download, 2nd download) + self.assertEqual(len(responses.calls), 3) + + self.assertEqual(path, os.path.join(self.output_dir, "product")) + self.assertTrue( + os.path.isfile( + os.path.join(self.output_dir, "product", "path", "to", "some.file") + ) + ) + self.assertTrue( + os.path.isfile( + os.path.join( + self.output_dir, "product", "path", "to", "another.file" + ) + ) + ) + + run() + + mock_order.assert_not_called() + mock_order_status.assert_not_called() + + @mock.patch( + "eodag.plugins.download.http.HTTPDownload.orderDownloadStatus", autospec=True + ) + @mock.patch("eodag.plugins.download.http.HTTPDownload.orderDownload", autospec=True) + def test_plugins_download_s3rest_offline(self, mock_order, mock_order_status): + """S3RestDownload.download() must order offline products""" + + self.product.properties["storageStatus"] = OFFLINE_STATUS + self.product.properties["orderLink"] = "https://some/order/api" + self.product.properties["orderStatusLink"] = "https://some/order/status/api" + + valid_remote_location = self.product.location + # unvalid location + self.product.location = self.product.remote_location = "somewhere" + + plugin = self.get_download_plugin(self.product) + auth = self.get_auth_plugin(self.product.provider) + self.product.register_downloader(plugin, auth) + + # no retry + @responses.activate(registry=responses.registries.OrderedRegistry) + def run(): + # bucket list request + responses.add( + responses.GET, + "https://mundiwebservices.com/dp/somewhere?prefix=", + status=403, + ) + with self.assertRaises(NotAvailableError): + plugin.download( + self.product, + outputs_prefix=self.output_dir, + wait=-1, + timeout=-1, + ) + # there must have been 1 try + self.assertEqual(len(responses.calls), 1) + + run() + mock_order.assert_called_once_with(mock.ANY, self.product, auth=None) + mock_order_status.assert_called_once_with(mock.ANY, self.product, auth=None) + + mock_order.reset_mock() + mock_order_status.reset_mock() + responses.calls.reset() + + # retry and success + self.product.retries = 0 + + def order_status_function(args, *kwargs): + if kwargs["product"].retries >= 1: + kwargs["product"].properties["storageStatus"] = ONLINE_STATUS + kwargs["product"].properties["downloadLink"] = kwargs[ + "product" + ].location = kwargs["product"].remote_location = valid_remote_location + kwargs["product"].retries += 1 + + mock_order_status.side_effect = order_status_function + + @responses.activate(registry=responses.registries.OrderedRegistry) + def run(): + # 1st bucket list request + responses.add( + responses.GET, + "https://mundiwebservices.com/dp/somewhere?prefix=", + status=403, + ) + # 2nd bucket list request + responses.add( + responses.GET, + f"{plugin.config.base_uri}/some-bucket?prefix=path/to/the/product", + status=200, + body=( + b"<?xml version='1.0' encoding='UTF-8' standalone='yes'?>" + b"<ListBucketResult>" + b"<Contents><Key>0/1/2/3/4/5/path/to/some.file</Key><Size>2</Size></Contents>" + b"</ListBucketResult>" + ), + ) + # file download response + responses.add( + responses.GET, + f"{plugin.config.base_uri}/some-bucket/0/1/2/3/4/5/path/to/some.file", + status=200, + content_type="application/octet-stream", + body=b"something", + auto_calculate_content_length=True, + ) + path = plugin.download( + self.product, + outputs_prefix=self.output_dir, + wait=0.001 / 60, + timeout=0.2 / 60, + ) + # there must have been 2 tries and 1 download + self.assertEqual(len(responses.calls), 3) + + self.assertEqual(path, os.path.join(self.output_dir, "product")) + self.assertTrue( + os.path.isfile(os.path.join(self.output_dir, "product", "some.file")) + ) + + run() + mock_order.assert_called_once_with(mock.ANY, self.product, auth=None) + self.assertEqual(mock_order_status.call_count, 2)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_issue_reference", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 1 }, "num_modified_files": 5 }	2.8	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	attrs==25.3.0 backports.tarfile==1.2.0 blinker==1.9.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 docutils==0.21.2 ecmwf-api-client==1.6.5 -e git+https://github.com/CS-SI/eodag.git@216edc2a7e22676ecfcbc77b71795fc319cb3dc4#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 filelock==3.18.0 flake8==7.2.0 flasgger==0.9.7.1 Flask==3.1.0 geojson==3.2.0 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 mistune==3.1.3 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 referencing==0.36.2 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rpds-py==0.24.0 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 six==1.17.0 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typing_extensions==4.13.0 tzdata==2025.2 urllib3==1.26.20 usgs==0.3.5 virtualenv==20.29.3 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - attrs==25.3.0 - backports-tarfile==1.2.0 - blinker==1.9.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - eodag==2.8.1.dev27+g216edc2a - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - filelock==3.18.0 - flake8==7.2.0 - flasgger==0.9.7.1 - flask==3.1.0 - geojson==3.2.0 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - jsonschema==4.23.0 - jsonschema-specifications==2024.10.1 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - mistune==3.1.3 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - referencing==0.36.2 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rpds-py==0.24.0 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - six==1.17.0 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typing-extensions==4.13.0 - tzdata==2025.2 - urllib3==1.26.20 - usgs==0.3.5 - virtualenv==20.29.3 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_text_token_authenticate", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status_search_again", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_offline", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_online" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_json_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_missing", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_ok", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_ok", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets" ]	[]	Apache License 2.0	null
CS-SI__eodag-749	50d6e60396cff60f6373ab64af5a0229a77e6906	2023-06-27 13:06:12	856121900f6d973eca9d2ad3c0de61bbd706ae2c	github-actions[bot]: ## Test Results       4 files ±0       4 suites ±0 4m 38s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "duration of all tests") +32s   388 tests ±0   386 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0   2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 1 552 runs ±0 1 496 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0 56 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 Results for commit babc42c6. ± Comparison against base commit 50d6e603. [test-results]:data:application/gzip;base64,H4sIAG7gmmQC/03MTQ6DIBCG4asY1l0AAkIv08AICalKw8/K9O4Fa6nL95nMtyPnF5vQfWC3AaXic4+5RJ192GrSSVaop9yOo+z1SAXgIPGnp3+1nw5O+6UC7mBjDPGUWLa2STinZ/02CVOi03eTd7hsHn2dhLCuPtdARhtgFMQsFVWSWADNqREEJhhrO3AYc6Ewen8A/fuizwgBAAA= github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `77%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against babc42c6d892981ecca52b61c7c3981fcf005690 </p>	diff --git a/.gitignore b/.gitignore index 6c8d9419..0e6aad37 100644 --- a/.gitignore +++ b/.gitignore @@ -116,3 +116,6 @@ ext_product_types.json .project .pydevproject .settings/ + +# Helm chart dependencies +charts/*/.tgz diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bb0201ac..54e5e823 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,6 +11,7 @@ repos: - id: check-json - id: check-yaml args: [--allow-multiple-documents, --unsafe] + exclude: ^charts/eodag-server/templates/ - id: check-xml - id: check-added-large-files args: ['--maxkb=1600'] diff --git a/charts/eodag-server/Chart.lock b/charts/eodag-server/Chart.lock new file mode 100644 index 00000000..d8f06a13 --- /dev/null +++ b/charts/eodag-server/Chart.lock @@ -0,0 +1,6 @@ +dependencies: +- name: common + repository: oci://registry-1.docker.io/bitnamicharts + version: 2.4.0 +digest: sha256:b371e6f7f1449fa3abdcb97a04b0bbb2b5d36a4facb8e79041ac36a455b02bb0 +generated: "2023-06-19T12:39:44.271254606+02:00" diff --git a/charts/eodag-server/Chart.yaml b/charts/eodag-server/Chart.yaml new file mode 100644 index 00000000..4333ee6b --- /dev/null +++ b/charts/eodag-server/Chart.yaml @@ -0,0 +1,18 @@ +apiVersion: v2 +appVersion: 2.10.0 +dependencies: +- name: common + repository: oci://registry-1.docker.io/bitnamicharts + tags: + - bitnami-common + version: 2.x +description: EODAG (Earth Observation Data Access Gateway) is a tool for searching, + aggregating results and downloading remote sensed images offering a unified API + for data access regardless of the data provider. +home: https://github.com/CS-SI/eodag +icon: https://raw.githubusercontent.com/CS-SI/eodag/master/docs/_static/eodag_bycs.png +name: eodag-server +sources: +- https://github.com/CS-SI/eodag +type: application +version: 2.10.0 diff --git a/charts/eodag-server/README.md b/charts/eodag-server/README.md new file mode 100644 index 00000000..646193d2 --- /dev/null +++ b/charts/eodag-server/README.md @@ -0,0 +1,206 @@ +# EODAG Server + +This chart bootstraps a [EODAG Server](https://github.com/CS-SI/eodag) deployment on a [Kubernetes](http://kubernetes.io) cluster using the [Helm](https://helm.sh) package manager. + +## TL;DR + +```console +helm install my-release oci://registry-1.docker.io/csspace/eodag-server +``` + +## Prerequisites + +- Kubernetes 1.19+ +- Helm 3.2.0+ + +## Installing the Chart + +To install the chart with the release name `my-release`: + +```console +helm install my-release oci://registry-1.docker.io/csspace/eodag-server +``` + +These commands deploy EODAG Server on the Kubernetes cluster in the default configuration. + +> Tip: List all releases using `helm list` + +## Uninstalling the Chart + + +To uninstall the `my-release` deployment: + +```bash +helm uninstall my-release +``` + +The command removes all the Kubernetes components associated with the chart and deletes the release. + +## Parameters + +### Global parameters + +\| Name \| Description \| Value \| +\| ------------------------- \| ----------------------------------------------- \| ----- \| +\| `global.imageRegistry` \| Global Docker image registry \| `""` \| +\| `global.imagePullSecrets` \| Global Docker registry secret names as an array \| `[]` \| +\| `global.storageClass` \| Global StorageClass for Persistent Volume(s) \| `""` \| + + +### Common parameters + +\| Name \| Description \| Value \| +\| ------------------- \| -------------------------------------------------------------------- \| --------------- \| +\| `kubeVersion` \| Force target Kubernetes version (using Helm capabilities if not set) \| `""` \| +\| `nameOverride` \| String to partially override common.names.fullname \| `""` \| +\| `fullnameOverride` \| String to fully override common.names.fullname \| `""` \| +\| `namespaceOverride` \| String to fully override common.names.namespaceapi \| `""` \| +\| `commonLabels` \| Labels to add to all deployed objects \| `{}` \| +\| `commonAnnotations` \| Annotations to add to all deployed objects \| `{}` \| +\| `clusterDomain` \| Kubernetes cluster domain name \| `cluster.local` \| +\| `extraDeploy` \| Array of extra objects to deploy with the release \| `[]` \| + + +### EODAG Server parameters + +\| Name \| Description \| Value \| +\| --------------------------------------------------- \| ------------------------------------------------------------------------------------------------------------------------- \| ------------------------ \| +\| `image.registry` \| EODAG Server image registry \| `docker.io` \| +\| `image.repository` \| EODAG Server image repository \| `csspace/eodag-server` \| +\| `image.tag` \| Overrides the EODAG Server image tag whose default is the chart appVersion (immutable tags are recommended) \| `""` \| +\| `image.digest` \| EODAG Server image digest in the way sha256:aa.... Please note this parameter, if set, will override the tag \| `""` \| +\| `image.pullPolicy` \| EODAG Server image pull policy \| `IfNotPresent` \| +\| `image.pullSecrets` \| Specify docker-registry secret names as an array \| `[]` \| +\| `replicaCount` \| Number of EODAG Server replicas \| `1` \| +\| `startupProbe.enabled` \| Enable startupProbe on EODAG Server containers \| `false` \| +\| `startupProbe.initialDelaySeconds` \| Initial delay seconds for startupProbe \| `10` \| +\| `startupProbe.periodSeconds` \| Period seconds for startupProbe \| `10` \| +\| `startupProbe.timeoutSeconds` \| Timeout seconds for startupProbe \| `1` \| +\| `startupProbe.failureThreshold` \| Failure threshold for startupProbe \| `3` \| +\| `startupProbe.successThreshold` \| Success threshold for startupProbe \| `1` \| +\| `livenessProbe.enabled` \| Enable livenessProbe on EODAG Server containers \| `false` \| +\| `livenessProbe.initialDelaySeconds` \| Initial delay seconds for livenessProbe \| `3` \| +\| `livenessProbe.periodSeconds` \| Period seconds for livenessProbe \| `10` \| +\| `livenessProbe.timeoutSeconds` \| Timeout seconds for livenessProbe \| `1` \| +\| `livenessProbe.failureThreshold` \| Failure threshold for livenessProbe \| `3` \| +\| `livenessProbe.successThreshold` \| Success threshold for livenessProbe \| `1` \| +\| `readinessProbe.enabled` \| Enable readinessProbe on EODAG Server containers \| `false` \| +\| `readinessProbe.initialDelaySeconds` \| Initial delay seconds for readinessProbe \| `3` \| +\| `readinessProbe.periodSeconds` \| Period seconds for readinessProbe \| `10` \| +\| `readinessProbe.timeoutSeconds` \| Timeout seconds for readinessProbe \| `1` \| +\| `readinessProbe.failureThreshold` \| Failure threshold for readinessProbe \| `3` \| +\| `readinessProbe.successThreshold` \| Success threshold for readinessProbe \| `1` \| +\| `customLivenessProbe` \| Custom livenessProbe that overrides the default one \| `{}` \| +\| `customReadinessProbe` \| Custom readinessProbe that overrides the default one \| `{}` \| +\| `resources.limits` \| The resources limits for the EODAG Server containers \| `{}` \| +\| `resources.requests` \| The requested resources for the EODAG Server containers \| `{}` \| +\| `podSecurityContext.enabled` \| Enabled EODAG Server pods' Security Context \| `false` \| +\| `podSecurityContext.fsGroup` \| Set EODAG Server pod's Security Context fsGroup \| `1001` \| +\| `containerSecurityContext.enabled` \| Enabled EODAG Server containers' Security Context \| `false` \| +\| `containerSecurityContext.runAsUser` \| Set EODAG Server containers' Security Context runAsUser \| `1001` \| +\| `containerSecurityContext.allowPrivilegeEscalation` \| Set EODAG Server containers' Security Context allowPrivilegeEscalation \| `false` \| +\| `containerSecurityContext.capabilities.drop` \| Set EODAG Server containers' Security Context capabilities to be dropped \| `["all"]` \| +\| `containerSecurityContext.readOnlyRootFilesystem` \| Set EODAG Server containers' Security Context readOnlyRootFilesystem \| `false` \| +\| `containerSecurityContext.runAsNonRoot` \| Set EODAG Server container's Security Context runAsNonRoot \| `true` \| +\| `command` \| Override default container command (useful when using custom images) \| `[]` \| +\| `args` \| Override default container args (useful when using custom images). Overrides the defaultArgs. \| `[]` \| +\| `containerPorts.http` \| EODAG Server application HTTP port number \| `5000` \| +\| `persistence.enabled` \| Enable persistence using PVC \| `false` \| +\| `persistence.medium` \| Provide a medium for `emptyDir` volumes. \| `""` \| +\| `persistence.sizeLimit` \| Set this to enable a size limit for `emptyDir` volumes. \| `8Gi` \| +\| `persistence.storageClass` \| PVC Storage Class for Matomo volume \| `""` \| +\| `persistence.accessModes` \| PVC Access Mode for Matomo volume \| `["ReadWriteOnce"]` \| +\| `persistence.size` \| PVC Storage Request for Matomo volume \| `8Gi` \| +\| `persistence.dataSource` \| Custom PVC data source \| `{}` \| +\| `persistence.existingClaim` \| A manually managed Persistent Volume Claim \| `""` \| +\| `persistence.hostPath` \| If defined, the matomo-data volume will mount to the specified hostPath. \| `""` \| +\| `persistence.annotations` \| Persistent Volume Claim annotations \| `{}` \| +\| `persistence.labels` \| Additional custom labels for the PVC \| `{}` \| +\| `persistence.selector` \| Selector to match an existing Persistent Volume for Matomo data PVC \| `{}` \| +\| `hostAliases` \| EODAG Server pods host aliases \| `[]` \| +\| `podLabels` \| Extra labels for EODAG Server pods \| `{}` \| +\| `podAnnotations` \| Annotations for EODAG Server pods \| `{}` \| +\| `podAffinityPreset` \| Pod affinity preset. Ignored if `affinity` is set. Allowed values: `soft` or `hard` \| `""` \| +\| `podAntiAffinityPreset` \| Pod anti-affinity preset. Ignored if `affinity` is set. Allowed values: `soft` or `hard` \| `soft` \| +\| `nodeAffinityPreset.type` \| Node affinity preset type. Ignored if `affinity` is set. Allowed values: `soft` or `hard` \| `""` \| +\| `nodeAffinityPreset.key` \| Node label key to match. Ignored if `affinity` is set \| `""` \| +\| `nodeAffinityPreset.values` \| Node label values to match. Ignored if `affinity` is set \| `[]` \| +\| `affinity` \| Affinity for EODAG Server pods assignment \| `{}` \| +\| `nodeSelector` \| Node labels for EODAG Server pods assignment \| `{}` \| +\| `tolerations` \| Tolerations for EODAG Server pods assignment \| `[]` \| +\| `schedulerName` \| Name of the k8s scheduler (other than default) \| `""` \| +\| `shareProcessNamespace` \| Enable shared process namespace in a pod. \| `false` \| +\| `topologySpreadConstraints` \| Topology Spread Constraints for pod assignment \| `[]` \| +\| `updateStrategy.type` \| EODAG Server statefulset strategy type \| `RollingUpdate` \| +\| `priorityClassName` \| EODAG Server pods' priorityClassName \| `""` \| +\| `runtimeClassName` \| Name of the runtime class to be used by pod(s) \| `""` \| +\| `lifecycleHooks` \| for the EODAG Server container(s) to automate configuration before or after startup \| `{}` \| +\| `extraEnvVars` \| Array with extra environment variables to add to EODAG Server nodes \| `[]` \| +\| `extraEnvVarsCM` \| Name of existing ConfigMap containing extra env vars for EODAG Server nodes \| `""` \| +\| `extraEnvVarsSecret` \| Name of existing Secret containing extra env vars for EODAG Server nodes \| `""` \| +\| `extraVolumes` \| Optionally specify extra list of additional volumes for the EODAG Server pod(s) \| `[]` \| +\| `extraVolumeMounts` \| Optionally specify extra list of additional volumeMounts for the EODAG Server container(s) \| `[]` \| +\| `sidecars` \| Add additional sidecar containers to the EODAG Server pod(s) \| `[]` \| +\| `initContainers` \| Add additional init containers to the EODAG Server pod(s) \| `[]` \| +\| `logLevel` \| Supported values are 0 (no log), 1 (no logging but progress bar), 2 (INFO) or 3 (DEBUG). \| `2` \| +\| `productTypes` \| Optional overwrite of product types default configuration \| `""` \| +\| `providers` \| Optional overwrite of providers default configuration \| `""` \| +\| `config` \| EODAG configuration \| `{}` \| +\| `configExistingSecret.name` \| Existing secret name for EODAG config. If this is set, value config will be ignored \| `nil` \| +\| `configExistingSecret.key` \| Existing secret key for EODAG config. If this is set, value config will be ignored \| `nil` \| +\| `service.type` \| Kubernetes service type \| `ClusterIP` \| +\| `service.http.enabled` \| Enable http port on service \| `true` \| +\| `service.ports.http` \| EODAG Server service HTTP port \| `8080` \| +\| `service.nodePorts` \| Specify the nodePort values for the LoadBalancer and NodePort service types. \| `{}` \| +\| `service.sessionAffinity` \| Control where client requests go, to the same pod or round-robin \| `None` \| +\| `service.sessionAffinityConfig` \| Additional settings for the sessionAffinity \| `{}` \| +\| `service.clusterIP` \| EODAG Server service clusterIP IP \| `""` \| +\| `service.loadBalancerIP` \| loadBalancerIP for the SuiteCRM Service (optional, cloud specific) \| `""` \| +\| `service.loadBalancerSourceRanges` \| Address that are allowed when service is LoadBalancer \| `[]` \| +\| `service.externalTrafficPolicy` \| Enable client source IP preservation \| `Cluster` \| +\| `service.annotations` \| Additional custom annotations for EODAG Server service \| `{}` \| +\| `service.extraPorts` \| Extra port to expose on EODAG Server service \| `[]` \| +\| `ingress.enabled` \| Enable the creation of an ingress for the EODAG Server \| `false` \| +\| `ingress.pathType` \| Path type for the EODAG Server ingress \| `ImplementationSpecific` \| +\| `ingress.apiVersion` \| Ingress API version for the EODAG Server ingress \| `""` \| +\| `ingress.hostname` \| Ingress hostname for the EODAG Server ingress \| `eodag.local` \| +\| `ingress.annotations` \| Annotations for the EODAG Server ingress. To enable certificate autogeneration, place here your cert-manager annotations. \| `{}` \| +\| `ingress.tls` \| Enable TLS for the EODAG Server ingress \| `false` \| +\| `ingress.extraHosts` \| Extra hosts array for the EODAG Server ingress \| `[]` \| +\| `ingress.path` \| Path array for the EODAG Server ingress \| `/` \| +\| `ingress.extraPaths` \| Extra paths for the EODAG Server ingress \| `[]` \| +\| `ingress.extraTls` \| Extra TLS configuration for the EODAG Server ingress \| `[]` \| +\| `ingress.secrets` \| Secrets array to mount into the Ingress \| `[]` \| +\| `ingress.ingressClassName` \| IngressClass that will be be used to implement the Ingress (Kubernetes 1.18+) \| `""` \| +\| `ingress.selfSigned` \| Create a TLS secret for this ingress record using self-signed certificates generated by Helm \| `false` \| +\| `ingress.servicePort` \| Backend service port to use \| `http` \| +\| `ingress.extraRules` \| Additional rules to be covered with this ingress record \| `[]` \| +\| `serviceAccount.create` \| Specifies whether a ServiceAccount should be created \| `true` \| +\| `serviceAccount.name` \| The name of the ServiceAccount to use. \| `""` \| +\| `serviceAccount.annotations` \| Additional custom annotations for the ServiceAccount \| `{}` \| +\| `serviceAccount.automountServiceAccountToken` \| Automount service account token for the server service account \| `true` \| + + +```console +$ helm install my-release \ + --set image.pullPolicy=Always \ + my-repo/eodag-server +``` + +The above command sets the `image.pullPolicy` to `Always`. + +Alternatively, a YAML file that specifies the values for the parameters can be provided while installing the chart. For example, + +```console +helm install my-release -f values.yaml my-repo/eodag-server +``` + +> Tip: You can use the default [values.yaml](values.yaml) + +## Configuration and installation details + +### [Rolling VS Immutable tags](https://docs.bitnami.com/containers/how-to/understand-rolling-tags-containers/) + +It is strongly recommended to use immutable tags in a production environment. This ensures your deployment does not change automatically if the same tag is updated with a different image. + +CS Group will release a new chart updating its containers if a new version of the main container, significant changes, or critical vulnerabilities exist. diff --git a/charts/eodag-server/templates/NOTES.txt b/charts/eodag-server/templates/NOTES.txt new file mode 100644 index 00000000..ea633f7d --- /dev/null +++ b/charts/eodag-server/templates/NOTES.txt @@ -0,0 +1,41 @@ +CHART NAME: {{ .Chart.Name }} +CHART VERSION: {{ .Chart.Version }} +APP VERSION: {{ .Chart.AppVersion }} + + Please be patient while the chart is being deployed + +1. Access your EODAG Server installation: + +{{- if .Values.ingress.enabled }} + Connect to one of the following hosts: + {{ if .Values.ingress.tls }} + https://{{ .Values.ingress.hostname }} + {{- else }} + http://{{ .Values.ingress.hostname }} + {{- end }} +{{- else }} + Execute the following commands: +{{- if contains "NodePort" .Values.service.type }} + export NODE_PORT=$(kubectl get --namespace {{ include "common.names.namespace" . }} -o jsonpath="{.spec.ports[0].nodePort}" services {{ include "common.names.fullname" . }}) + export NODE_IP=$(kubectl get nodes --namespace {{ include "common.names.namespace" . }} -o jsonpath="{.items[0].status.addresses[0].address}") + export URL="http://${NODE_IP}:${NODE_PORT}/" + echo "EODAG Server URL: http://$NODE_IP:$NODE_PORT/" + +{{- else if contains "LoadBalancer" .Values.service.type }} + + Please ensure an external IP is associated to the {{ include "common.names.fullname" . }} service before proceeding + Watch the status using: kubectl get svc --namespace {{ include "common.names.namespace" . }} -w {{ include "common.names.fullname" . }} + + export SERVICE_IP=$(kubectl get svc --namespace {{ include "common.names.namespace" . }} {{ include "common.names.fullname" . }} --template "{{ "{{ range (index .status.loadBalancer.ingress 0) }}{{ . }}{{ end }}" }}") + +{{- $port:=.Values.service.ports.http \| toString }} + export URL="http://${SERVICE_IP}{{- if ne $port "80" }}:{{ .Values.service.ports.http }}{{ end }}" + echo "EODAG Server URL: http://$SERVICE_IP{{- if ne $port "80" }}:{{ .Values.service.ports.http }}{{ end }}/" + +{{- else if contains "ClusterIP" .Values.service.type }} + + kubectl port-forward --namespace {{ include "common.names.namespace" . }} svc/{{ include "common.names.fullname" . }} 8080:{{ .Values.service.ports.http }} & + export URL=http://127.0.0.1:8080/ + echo "EODAG Server URL: http://127.0.0.1:8080/" +{{- end }} +{{- end }} diff --git a/charts/eodag-server/templates/_helpers.tpl b/charts/eodag-server/templates/_helpers.tpl new file mode 100644 index 00000000..06772571 --- /dev/null +++ b/charts/eodag-server/templates/_helpers.tpl @@ -0,0 +1,77 @@ +{{/* +Create the name of the service account to use +/}} +{{- define "eodag-server.serviceAccountName" -}} +{{- if .Values.serviceAccount.create -}} + {{ default (include "common.names.fullname" .) .Values.serviceAccount.name \| trunc 63 \| trimSuffix "-" }} +{{- else -}} + {{ default "default" .Values.serviceAccount.name }} +{{- end -}} +{{- end -}} + +{{/ +Return the proper Docker Image Registry Secret Names +/}} +{{- define "eodag-server.imagePullSecrets" -}} +{{- include "common.images.pullSecrets" (dict "images" (list .Values.image) "global" .Values.global) -}} +{{- end -}} + +{{/ +Get the config secret. +/}} +{{- define "eodag-server.configSecretName" -}} +{{- if .Values.configExistingSecret.name }} + {{- printf "%s" (tpl .Values.configExistingSecret.name $) -}} +{{- else -}} + {{- printf "%s-config" (include "common.names.fullname" .) -}} +{{- end -}} +{{- end -}} + +{{/ +Get the client secret key. +/}} +{{- define "eodag-server.configSecretKey" -}} +{{- if .Values.configExistingSecret.key }} + {{- printf "%s" (tpl .Values.configExistingSecret.key $) -}} +{{- else -}} + {{- "eodag.yml" -}} +{{- end -}} +{{- end -}} + +{{/ +Return the proper Storage Class +/}} +{{- define "eodag-server.storageClass" -}} +{{- include "common.storage.class" (dict "persistence" .Values.persistence "global" .Values.global) -}} +{{- end -}} + +{{/ +Create EODAG Server app version +/}} +{{- define "eodag-server.defaultTag" -}} +{{- default .Chart.AppVersion .Values.image.tag }} +{{- end -}} + +{{/ +Return the proper image name +/}} +{{- define "eodag-server.image" -}} +{{- $registryName := .Values.image.registry -}} +{{- $repositoryName := .Values.image.repository -}} +{{- $separator := ":" -}} +{{- $termination := .Values.image.tag \| default .Chart.AppVersion \| toString -}} +{{- if .Values.global }} + {{- if .Values.global.imageRegistry }} + {{- $registryName = .Values.global.imageRegistry -}} + {{- end -}} +{{- end -}} +{{- if .Values.image.digest }} + {{- $separator = "@" -}} + {{- $termination = .Values.image.digest \| toString -}} +{{- end -}} +{{- if $registryName }} + {{- printf "%s/%s%s%s" $registryName $repositoryName $separator $termination -}} +{{- else -}} + {{- printf "%s%s%s" $repositoryName $separator $termination -}} +{{- end -}} +{{- end -}} diff --git a/charts/eodag-server/templates/configmap.yaml b/charts/eodag-server/templates/configmap.yaml new file mode 100644 index 00000000..bd76e7c5 --- /dev/null +++ b/charts/eodag-server/templates/configmap.yaml @@ -0,0 +1,23 @@ +{{- if or .Values.productTypes .Values.providers }} +apiVersion: v1 +kind: ConfigMap +metadata: + name: {{ printf "%s-config" (include "common.names.fullname" .) }} + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonLabels "context" $) \| nindent 4 }} + {{- end }} + {{- if .Values.commonAnnotations }} + annotations: {{- include "common.tplvalues.render" (dict "value" .Values.commonAnnotations "context" $) \| nindent 4 }} + {{- end }} +data: + {{- if .Values.productTypes }} + product_types.yml: \|- + {{- .Values.productTypes \| nindent 4 }} + {{- end }} + {{- if .Values.providers }} + providers.yml: \|- + {{- .Values.providers \| nindent 4 }} + {{- end }} +{{- end }} diff --git a/charts/eodag-server/templates/deployment.yaml b/charts/eodag-server/templates/deployment.yaml new file mode 100644 index 00000000..33efeeab --- /dev/null +++ b/charts/eodag-server/templates/deployment.yaml @@ -0,0 +1,219 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ include "common.names.fullname" . }} + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonLabels "context" $) \| nindent 4 }} + {{- end }} + {{- if .Values.commonAnnotations }} + annotations: {{- include "common.tplvalues.render" (dict "value" .Values.commonAnnotations "context" $) \| nindent 4 }} + {{- end }} +spec: + replicas: {{ .Values.replicaCount }} + {{- if .Values.updateStrategy }} + strategy: {{- toYaml .Values.updateStrategy \| nindent 4 }} + {{- end }} + selector: + matchLabels: {{- include "common.labels.matchLabels" . \| nindent 6 }} + template: + metadata: + labels: {{- include "common.labels.standard" . \| nindent 8 }} + {{- if .Values.podLabels }} + {{- include "common.tplvalues.render" (dict "value" .Values.podLabels "context" $) \| nindent 8 }} + {{- end }} + annotations: + checksum/config: {{ include (print $.Template.BasePath "/configmap.yaml") . \| sha256sum }} + {{- if .Values.podAnnotations }} + {{- include "common.tplvalues.render" (dict "value" .Values.podAnnotations "context" $) \| nindent 8 }} + {{- end }} + spec: + serviceAccountName: {{ include "eodag-server.serviceAccountName" . }} + {{- include "eodag-server.imagePullSecrets" . \| nindent 6 }} + {{- if .Values.hostAliases }} + hostAliases: {{- include "common.tplvalues.render" (dict "value" .Values.hostAliases "context" $) \| nindent 8 }} + {{- end }} + {{- if .Values.affinity }} + affinity: {{- include "common.tplvalues.render" (dict "value" .Values.affinity "context" $) \| nindent 8 }} + {{- else if or .Values.podAffinityPreset .Values.podAntiAffinityPreset .Values.nodeAffinityPreset }} + affinity: + podAffinity: {{- include "common.affinities.pods" (dict "type" .Values.podAffinityPreset "instance" .Chart.Name "context" $) \| nindent 10 }} + podAntiAffinity: {{- include "common.affinities.pods" (dict "type" .Values.podAntiAffinityPreset "instance" .Chart.Name "context" $) \| nindent 10 }} + nodeAffinity: {{- include "common.affinities.nodes" (dict "type" .Values.nodeAffinityPreset.type "key" .Values.nodeAffinityPreset.key "values" .Values.nodeAffinityPreset.values) \| nindent 10 }} + {{- end }} + {{- if .Values.nodeSelector }} + nodeSelector: {{- include "common.tplvalues.render" (dict "value" .Values.nodeSelector "context" $) \| nindent 8 }} + {{- end }} + {{- if .Values.tolerations }} + tolerations: {{- include "common.tplvalues.render" (dict "value" .Values.tolerations "context" .) \| nindent 8 }} + {{- end }} + {{- if .Values.schedulerName }} + schedulerName: {{ .Values.schedulerName }} + {{- end }} + {{- if .Values.shareProcessNamespace }} + shareProcessNamespace: {{ .Values.shareProcessNamespace }} + {{- end }} + {{- if .Values.topologySpreadConstraints }} + topologySpreadConstraints: {{- include "common.tplvalues.render" (dict "value" .Values.topologySpreadConstraints "context" .) \| nindent 8 }} + {{- end }} + {{- if .Values.priorityClassName }} + priorityClassName: {{ .Values.priorityClassName \| quote }} + {{- end }} + {{- if .Values.runtimeClassName }} + runtimeClassName: {{ .Values.runtimeClassName }} + {{- end }} + {{- if .Values.podSecurityContext.enabled }} + securityContext: {{- omit .Values.podSecurityContext "enabled" \| toYaml \| nindent 8 }} + {{- end }} + {{- with .Values.imagePullSecrets }} + imagePullSecrets: + {{- toYaml . \| nindent 8 }} + {{- end }} + {{- if .Values.initContainers }} + initContainers: {{- include "common.tplvalues.render" (dict "value" .Values.initContainers "context" $) \| nindent 8 }} + {{- end }} + containers: + - name: eodag-server + image: {{ include "eodag-server.image" . }} + imagePullPolicy: {{ .Values.image.pullPolicy }} + {{- if .Values.lifecycleHooks }} + lifecycle: {{- include "common.tplvalues.render" (dict "value" .Values.lifecycleHooks "context" $) \| nindent 12 }} + {{- end }} + {{- if .Values.containerSecurityContext.enabled }} + securityContext: {{- omit .Values.containerSecurityContext "enabled" \| toYaml \| nindent 12 }} + {{- end }} + {{- if .Values.command }} + command: {{- include "common.tplvalues.render" (dict "value" .Values.command "context" $) \| nindent 12 }} + {{- end }} + {{- if .Values.args }} + args: {{- include "common.tplvalues.render" (dict "value" .Values.args "context" $) \| nindent 12 }} + {{- end }} + ports: + - name: http + containerPort: {{ .Values.containerPorts.http }} + protocol: TCP + {{- if .Values.customStartupProbe }} + startupProbe: {{- include "common.tplvalues.render" (dict "value" .Values.customStartupProbe "context" $) \| nindent 12 }} + {{- else if .Values.startupProbe.enabled }} + startupProbe: + httpGet: + path: / + port: {{ .Values.containerPorts.http }} + initialDelaySeconds: {{ .Values.startupProbe.initialDelaySeconds }} + periodSeconds: {{ .Values.startupProbe.periodSeconds }} + timeoutSeconds: {{ .Values.startupProbe.timeoutSeconds }} + successThreshold: {{ .Values.startupProbe.successThreshold }} + failureThreshold: {{ .Values.startupProbe.failureThreshold }} + {{- end }} + {{- if .Values.customLivenessProbe }} + livenessProbe: {{- include "common.tplvalues.render" (dict "value" .Values.customLivenessProbe "context" $) \| nindent 12 }} + {{- else if .Values.livenessProbe.enabled }} + livenessProbe: + httpGet: + path: / + port: {{ .Values.containerPorts.http }} + initialDelaySeconds: {{ .Values.livenessProbe.initialDelaySeconds }} + periodSeconds: {{ .Values.livenessProbe.periodSeconds }} + timeoutSeconds: {{ .Values.livenessProbe.timeoutSeconds }} + successThreshold: {{ .Values.livenessProbe.successThreshold }} + failureThreshold: {{ .Values.livenessProbe.failureThreshold }} + {{- end }} + {{- if .Values.customReadinessProbe }} + readinessProbe: {{- include "common.tplvalues.render" (dict "value" .Values.customReadinessProbe "context" $) \| nindent 12 }} + {{- else if .Values.readinessProbe.enabled }} + readinessProbe: + httpGet: + path: / + port: {{ .Values.containerPorts.http }} + initialDelaySeconds: {{ .Values.readinessProbe.initialDelaySeconds }} + periodSeconds: {{ .Values.readinessProbe.periodSeconds }} + timeoutSeconds: {{ .Values.readinessProbe.timeoutSeconds }} + successThreshold: {{ .Values.readinessProbe.successThreshold }} + failureThreshold: {{ .Values.readinessProbe.failureThreshold }} + {{- end }} + env: + {{- if or .Values.extraEnvVars }} + {{- include "common.tplvalues.render" (dict "value" .Values.extraEnvVars "context" $) \| nindent 12 }} + {{- end }} + {{- if or .Values.config .Values.configExistingSecret.name }} + - name: EODAG_CFG_FILE + value: /eodag/eodag.yml + {{- end }} + {{- if gt (.Values.logLevel \| int) 0 }} + - name: EODAG_LOGGING + value: {{ .Values.logLevel \| quote }} + {{- end }} + {{- if or .Values.extraEnvVarsCM .Values.extraEnvVarsSecret }} + envFrom: + {{- if .Values.extraEnvVarsCM }} + - configMapRef: + name: {{ include "common.tplvalues.render" (dict "value" .Values.extraEnvVarsCM "context" $) }} + {{- end }} + {{- if .Values.extraEnvVarsSecret }} + - secretRef: + name: {{ include "common.tplvalues.render" (dict "value" .Values.extraEnvVarsSecret "context" $) }} + {{- end }} + {{- end }} + {{- if .Values.resources }} + resources: {{- toYaml .Values.resources \| nindent 12 }} + {{- end }} + volumeMounts: + {{- if .Values.productTypes }} + - name: config + mountPath: /eodag/eodag/resources/product_types.yml + subPath: product_types.yml + {{- end }} + {{- if .Values.providers }} + - name: config + mountPath: /eodag/eodag/resources/providers.yml + subPath: providers.yml + {{- end }} + {{- if or .Values.config .Values.configExistingSecret.name }} + - name: eodag-config + mountPath: /eodag/eodag.yml + subPath: eodag.yml + {{- end }} + - name: download-data + mountPath: /tmp + {{- if .Values.extraVolumeMounts }} + {{- toYaml .Values.extraVolumeMounts \| nindent 12 }} + {{- end }} + {{- if .Values.sidecars }} + {{- include "common.tplvalues.render" (dict "value" .Values.sidecars "context" $) \| nindent 8 }} + {{- end }} + volumes: + {{- if or .Values.productTypes .Values.providers }} + - name: config + configMap: + name: {{ printf "%s-config" (include "common.names.fullname" .) }} + {{- end }} + {{- if or .Values.config .Values.configExistingSecret.name }} + - name: eodag-config + secret: + secretName: {{ include "eodag-server.configSecretName" . }} + items: + - key: {{ include "eodag-server.configSecretKey" . }} + path: {{ include "eodag-server.configSecretKey" . }} + {{- end }} + {{- if .Values.persistence.enabled }} + - name: download-data + persistentVolumeClaim: + claimName: {{ .Values.persistence.existingClaim \| default (printf "%s-download-data" (include "common.names.fullname" .)) }} + {{- else }} + - name: download-data + {{- if or .Values.persistence.medium .Values.persistence.sizeLimit }} + emptyDir: + {{- if .Values.persistence.medium }} + medium: {{ .Values.persistence.medium \| quote }} + {{- end }} + {{- if .Values.persistence.sizeLimit }} + sizeLimit: {{ .Values.persistence.sizeLimit \| quote }} + {{- end }} + {{- else }} + emptyDir: {} + {{- end }} + {{- end }} + {{- if .Values.extraVolumes }} + {{- toYaml .Values.extraVolumes \| nindent 8 }} + {{- end }} diff --git a/charts/eodag-server/templates/extra-list.yaml b/charts/eodag-server/templates/extra-list.yaml new file mode 100644 index 00000000..9ac65f9e --- /dev/null +++ b/charts/eodag-server/templates/extra-list.yaml @@ -0,0 +1,4 @@ +{{- range .Values.extraDeploy }} +--- +{{ include "common.tplvalues.render" (dict "value" . "context" $) }} +{{- end }} diff --git a/charts/eodag-server/templates/ingress.yaml b/charts/eodag-server/templates/ingress.yaml new file mode 100644 index 00000000..9e070a11 --- /dev/null +++ b/charts/eodag-server/templates/ingress.yaml @@ -0,0 +1,62 @@ +{{- if .Values.ingress.enabled -}} +apiVersion: {{ include "common.capabilities.ingress.apiVersion" . }} +kind: Ingress +metadata: + name: {{ include "common.names.fullname" . }} + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonLabels "context" $) \| nindent 4 }} + {{- end }} + {{- if or .Values.ingress.annotations .Values.commonAnnotations }} + annotations: + {{- if .Values.ingress.annotations }} + {{- include "common.tplvalues.render" (dict "value" .Values.ingress.annotations "context" $) \| nindent 4 }} + {{- end }} + {{- if .Values.commonAnnotations }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonAnnotations "context" $) \| nindent 4 }} + {{- end }} + {{- end }} +spec: + {{- if and .Values.ingress.ingressClassName (eq "true" (include "common.ingress.supportsIngressClassname" .)) }} + ingressClassName: {{ .Values.ingress.ingressClassName \| quote }} + {{- end }} + rules: + {{- if .Values.ingress.hostname }} + - host: {{ .Values.ingress.hostname }} + http: + paths: + {{- if .Values.ingress.extraPaths }} + {{- toYaml .Values.ingress.extraPaths \| nindent 10 }} + {{- end }} + - path: {{ .Values.ingress.path }} + {{- if eq "true" (include "common.ingress.supportsPathType" .) }} + pathType: {{ .Values.ingress.pathType }} + {{- end }} + backend: {{- include "common.ingress.backend" (dict "serviceName" (include "common.names.fullname" .) "servicePort" .Values.ingress.servicePort "context" $) \| nindent 14 }} + {{- end }} + {{- range .Values.ingress.extraHosts }} + - host: {{ .name \| quote }} + http: + paths: + - path: {{ default "/" .path }} + {{- if eq "true" (include "common.ingress.supportsPathType" $) }} + pathType: {{ default "ImplementationSpecific" .pathType }} + {{- end }} + backend: {{- include "common.ingress.backend" (dict "serviceName" (include "common.names.fullname" $) "servicePort" .Values.ingress.servicePort "context" $) \| nindent 14 }} + {{- end }} + {{- if .Values.ingress.extraRules }} + {{- include "common.tplvalues.render" (dict "value" .Values.ingress.extraRules "context" $) \| nindent 4 }} + {{- end }} + {{- if or .Values.ingress.tls .Values.ingress.extraTls }} + tls: + {{- if .Values.ingress.tls }} + - hosts: + - {{ .Values.ingress.hostname }} + secretName: {{ printf "%s-tls" .Values.ingress.hostname }} + {{- end }} + {{- if .Values.ingress.extraTls }} + {{- include "common.tplvalues.render" (dict "value" .Values.ingress.extraTls "context" $) \| nindent 4 }} + {{- end }} + {{- end }} +{{- end -}} diff --git a/charts/eodag-server/templates/pv.yaml b/charts/eodag-server/templates/pv.yaml new file mode 100644 index 00000000..d51081cc --- /dev/null +++ b/charts/eodag-server/templates/pv.yaml @@ -0,0 +1,25 @@ +{{- if and .Values.persistence.enabled .Values.persistence.hostPath (not .Values.persistence.existingClaim) -}} +apiVersion: v1 +kind: PersistentVolume +metadata: + name: {{ include "common.names.fullname" . }}-matomo + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" ( dict "value" .Values.commonLabels "context" $ ) \| nindent 4 }} + {{- end }} + {{- if .Values.commonAnnotations }} + annotations: {{- include "common.tplvalues.render" ( dict "value" .Values.commonAnnotations "context" $ ) \| nindent 4 }} + {{- end }} +spec: + {{- if not (empty .Values.persistence.accessModes) }} + accessModes: + {{- range .Values.persistence.accessModes }} + - {{ . \| quote }} + {{- end }} + {{- end }} + capacity: + storage: {{ .Values.persistence.size \| quote }} + hostPath: + path: {{ .Values.persistence.hostPath \| quote }} +{{- end -}} diff --git a/charts/eodag-server/templates/pvc.yaml b/charts/eodag-server/templates/pvc.yaml new file mode 100644 index 00000000..6c145bdc --- /dev/null +++ b/charts/eodag-server/templates/pvc.yaml @@ -0,0 +1,44 @@ +{{- if and .Values.persistence.enabled (not .Values.persistence.existingClaim) -}} +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: {{ include "common.names.fullname" . }}-download-data + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" ( dict "value" .Values.commonLabels "context" $ ) \| nindent 4 }} + {{- end }} + {{- if .Values.persistence.labels }} + {{- toYaml .Values.persistence.labels \| nindent 4 }} + {{- end }} + {{- if or .Values.persistence.annotations .Values.commonAnnotations }} + annotations: + {{- if .Values.commonAnnotations }} + {{- include "common.tplvalues.render" ( dict "value" .Values.commonAnnotations "context" $ ) \| nindent 4 }} + {{- end }} + {{- if .Values.persistence.annotations }} + {{- include "common.tplvalues.render" ( dict "value" .Values.persistence.annotations "context" $ ) \| nindent 4 }} + {{- end }} + {{- end }} +spec: + {{- if .Values.persistence.hostPath }} + storageClassName: "" + {{- else }} + {{- include "eodag-server.storageClass" . \| nindent 2 }} + {{- end }} + {{- if not (empty .Values.persistence.accessModes) }} + accessModes: + {{- range .Values.persistence.accessModes }} + - {{ . \| quote }} + {{- end }} + {{- end }} + resources: + requests: + storage: {{ .Values.persistence.size \| quote }} + {{- if .Values.persistence.selector }} + selector: {{- include "common.tplvalues.render" (dict "value" .Values.persistence.selector "context" $) \| nindent 4 }} + {{- end -}} + {{- if .Values.persistence.dataSource }} + dataSource: {{- include "common.tplvalues.render" (dict "value" .Values.persistence.dataSource "context" $) \| nindent 4 }} + {{- end }} +{{- end -}} diff --git a/charts/eodag-server/templates/secret.yaml b/charts/eodag-server/templates/secret.yaml new file mode 100644 index 00000000..4c11aaf9 --- /dev/null +++ b/charts/eodag-server/templates/secret.yaml @@ -0,0 +1,17 @@ +{{- if and .Values.config (not .Values.configExistingSecret.name) }} +apiVersion: v1 +kind: Secret +metadata: + name: {{ printf "%s-config" (include "common.names.fullname" .) }} + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonLabels "context" $) \| nindent 4 }} + {{- end }} + {{- if .Values.commonAnnotations }} + annotations: {{- include "common.tplvalues.render" (dict "value" .Values.commonAnnotations "context" $) \| nindent 4 }} + {{- end }} +type: Opaque +data: + eodag.yml: {{ include "common.tplvalues.render" (dict "value" .Values.config "context" $) \| b64enc }} +{{- end }} diff --git a/charts/eodag-server/templates/service.yaml b/charts/eodag-server/templates/service.yaml new file mode 100644 index 00000000..4cbcbd0a --- /dev/null +++ b/charts/eodag-server/templates/service.yaml @@ -0,0 +1,52 @@ +apiVersion: v1 +kind: Service +metadata: + name: {{ include "common.names.fullname" . }} + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonLabels "context" $) \| nindent 4 }} + {{- end }} + {{- if or .Values.commonAnnotations .Values.service.annotations }} + annotations: + {{- if .Values.commonAnnotations }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonAnnotations "context" $) \| nindent 4 }} + {{- end }} + {{- if .Values.service.annotations }} + {{- include "common.tplvalues.render" (dict "value" .Values.service.annotations "context" $) \| nindent 4 }} + {{- end }} + {{- end }} +spec: + type: {{ .Values.service.type }} + {{- if and .Values.service.clusterIP (eq .Values.service.type "ClusterIP") }} + clusterIP: {{ .Values.service.clusterIP }} + {{- end }} + {{- if (or (eq .Values.service.type "LoadBalancer") (eq .Values.service.type "NodePort")) }} + externalTrafficPolicy: {{ .Values.service.externalTrafficPolicy \| quote }} + {{- end }} + {{- if eq .Values.service.type "LoadBalancer" }} + loadBalancerSourceRanges: {{ .Values.service.loadBalancerSourceRanges }} + {{- end }} + {{- if (and (eq .Values.service.type "LoadBalancer") (not (empty .Values.service.loadBalancerIP))) }} + loadBalancerIP: {{ .Values.service.loadBalancerIP }} + {{- end }} + {{- if .Values.service.sessionAffinity }} + sessionAffinity: {{ .Values.service.sessionAffinity }} + {{- end }} + {{- if .Values.service.sessionAffinityConfig }} + sessionAffinityConfig: {{- include "common.tplvalues.render" (dict "value" .Values.service.sessionAffinityConfig "context" $) \| nindent 4 }} + {{- end }} + ports: + - port: {{ .Values.service.ports.http }} + targetPort: http + protocol: TCP + name: http + {{- if (and (or (eq .Values.service.type "NodePort") (eq .Values.service.type "LoadBalancer")) (not (empty .Values.service.nodePort))) }} + nodePort: {{ .Values.service.nodePorts.http }} + {{- else if eq .Values.service.type "ClusterIP" }} + nodePort: null + {{- end }} + {{- if .Values.service.extraPorts }} + {{- include "common.tplvalues.render" (dict "value" .Values.service.extraPorts "context" $) \| nindent 4 }} + {{- end }} + selector: {{- include "common.labels.matchLabels" . \| nindent 4 }} diff --git a/charts/eodag-server/templates/serviceaccount.yaml b/charts/eodag-server/templates/serviceaccount.yaml new file mode 100644 index 00000000..f4bde06d --- /dev/null +++ b/charts/eodag-server/templates/serviceaccount.yaml @@ -0,0 +1,21 @@ +{{- if .Values.serviceAccount.create -}} +apiVersion: v1 +kind: ServiceAccount +metadata: + name: {{ include "eodag-server.serviceAccountName" . }} + namespace: {{ include "common.names.namespace" . \| quote }} + labels: {{- include "common.labels.standard" . \| nindent 4 }} + {{- if .Values.commonLabels }} + {{- include "common.tplvalues.render" ( dict "value" .Values.commonLabels "context" $ ) \| nindent 4 }} + {{- end }} + {{- if or .Values.serviceAccount.annotations .Values.commonAnnotations }} + annotations: + {{- if .Values.commonAnnotations }} + {{- include "common.tplvalues.render" (dict "value" .Values.commonAnnotations "context" $) \| nindent 4 }} + {{- end }} + {{- if .Values.serviceAccount.annotations }} + {{- include "common.tplvalues.render" (dict "value" .Values.serviceAccount.annotations "context" $) \| nindent 4 }} + {{- end }} + {{- end }} +automountServiceAccountToken: {{ .Values.serviceAccount.automountServiceAccountToken }} +{{- end }} diff --git a/charts/eodag-server/values.yaml b/charts/eodag-server/values.yaml new file mode 100644 index 00000000..c1acef6b --- /dev/null +++ b/charts/eodag-server/values.yaml @@ -0,0 +1,692 @@ +## @section Global parameters +## Global Docker image parameters +## Please, note that this will override the image parameters, including dependencies, configured to use the global value +## Current available global Docker image parameters: imageRegistry, imagePullSecrets and storageClass +## + +## @param global.imageRegistry Global Docker image registry +## @param global.imagePullSecrets Global Docker registry secret names as an array +## @param global.storageClass Global StorageClass for Persistent Volume(s) +## +global: + imageRegistry: "" + ## E.g. + ## imagePullSecrets: + ## - myRegistryKeySecretName + ## + imagePullSecrets: [] + storageClass: "" + +## @section Common parameters +## + +## @param kubeVersion Force target Kubernetes version (using Helm capabilities if not set) +## +kubeVersion: "" +## @param nameOverride String to partially override common.names.fullname +## +nameOverride: "" +## @param fullnameOverride String to fully override common.names.fullname +## +fullnameOverride: "" +## @param namespaceOverride String to fully override common.names.namespaceapi +## +namespaceOverride: "" +## @param commonLabels Labels to add to all deployed objects +## +commonLabels: {} +## @param commonAnnotations Annotations to add to all deployed objects +## +commonAnnotations: {} +## @param clusterDomain Kubernetes cluster domain name +## +clusterDomain: cluster.local +## @param extraDeploy Array of extra objects to deploy with the release +## +extraDeploy: [] + +## @section EODAG Server parameters + +## CS Group EODAG Server image version +## ref: https://hub.docker.com/r/csspace/eodag-server +## @param image.registry EODAG Server image registry +## @param image.repository EODAG Server image repository +## @param image.tag Overrides the EODAG Server image tag whose default is the chart appVersion (immutable tags are recommended) +## @param image.digest EODAG Server image digest in the way sha256:aa.... Please note this parameter, if set, will override the tag +## @param image.pullPolicy EODAG Server image pull policy +## @param image.pullSecrets Specify docker-registry secret names as an array +## Number of replicas to deploy +## +image: + registry: docker.io + repository: csspace/eodag-server + tag: "" + digest: "" + ## Specify a imagePullPolicy + ## Defaults to 'Always' if image tag is 'latest', else set to 'IfNotPresent' + ## ref: https://kubernetes.io/docs/user-guide/images/#pre-pulling-images + ## + pullPolicy: IfNotPresent + ## Optionally specify an array of imagePullSecrets. + ## Secrets must be manually created in the namespace. + ## ref: https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/ + ## Example: + ## pullSecrets: + ## - myRegistryKeySecretName + ## + pullSecrets: [] + +## @param replicaCount Number of EODAG Server replicas +## +replicaCount: 1 + +## Configure extra options for EODAG Server containers' liveness and readiness probes +## ref: https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-probes/#configure-probes +## @param startupProbe.enabled Enable startupProbe on EODAG Server containers +## @param startupProbe.initialDelaySeconds Initial delay seconds for startupProbe +## @param startupProbe.periodSeconds Period seconds for startupProbe +## @param startupProbe.timeoutSeconds Timeout seconds for startupProbe +## @param startupProbe.failureThreshold Failure threshold for startupProbe +## @param startupProbe.successThreshold Success threshold for startupProbe +## +startupProbe: + enabled: false + initialDelaySeconds: 10 + periodSeconds: 10 + timeoutSeconds: 1 + failureThreshold: 3 + successThreshold: 1 +## @param livenessProbe.enabled Enable livenessProbe on EODAG Server containers +## @param livenessProbe.initialDelaySeconds Initial delay seconds for livenessProbe +## @param livenessProbe.periodSeconds Period seconds for livenessProbe +## @param livenessProbe.timeoutSeconds Timeout seconds for livenessProbe +## @param livenessProbe.failureThreshold Failure threshold for livenessProbe +## @param livenessProbe.successThreshold Success threshold for livenessProbe +## +livenessProbe: + enabled: false + initialDelaySeconds: 3 + periodSeconds: 10 + timeoutSeconds: 1 + failureThreshold: 3 + successThreshold: 1 +## @param readinessProbe.enabled Enable readinessProbe on EODAG Server containers +## @param readinessProbe.initialDelaySeconds Initial delay seconds for readinessProbe +## @param readinessProbe.periodSeconds Period seconds for readinessProbe +## @param readinessProbe.timeoutSeconds Timeout seconds for readinessProbe +## @param readinessProbe.failureThreshold Failure threshold for readinessProbe +## @param readinessProbe.successThreshold Success threshold for readinessProbe +## +readinessProbe: + enabled: false + initialDelaySeconds: 3 + periodSeconds: 10 + timeoutSeconds: 1 + failureThreshold: 3 + successThreshold: 1 + +## @param customLivenessProbe Custom livenessProbe that overrides the default one +## +customLivenessProbe: {} + +## @param customReadinessProbe Custom readinessProbe that overrides the default one +## +customReadinessProbe: {} + +## EODAG Server resource requests and limits +## ref: https://kubernetes.io/docs/user-guide/compute-resources/ +## @param resources.limits The resources limits for the EODAG Server containers +## @param resources.requests The requested resources for the EODAG Server containers +## +resources: + limits: {} + requests: {} + +## Configure Pods Security Context +## ref: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-the-security-context-for-a-pod +## @param podSecurityContext.enabled Enabled EODAG Server pods' Security Context +## @param podSecurityContext.fsGroup Set EODAG Server pod's Security Context fsGroup +## +podSecurityContext: + enabled: false + fsGroup: 1001 + +## Configure Container Security Context +## ref: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-the-security-context-for-a-pod +## @param containerSecurityContext.enabled Enabled EODAG Server containers' Security Context +## @param containerSecurityContext.runAsUser Set EODAG Server containers' Security Context runAsUser +## @param containerSecurityContext.allowPrivilegeEscalation Set EODAG Server containers' Security Context allowPrivilegeEscalation +## @param containerSecurityContext.capabilities.drop Set EODAG Server containers' Security Context capabilities to be dropped +## @param containerSecurityContext.readOnlyRootFilesystem Set EODAG Server containers' Security Context readOnlyRootFilesystem +## @param containerSecurityContext.runAsNonRoot Set EODAG Server container's Security Context runAsNonRoot +## +containerSecurityContext: + enabled: false + runAsUser: 1001 + allowPrivilegeEscalation: false + capabilities: + drop: + - all + readOnlyRootFilesystem: false + runAsNonRoot: true + +## @param command Override default container command (useful when using custom images) +## +command: [] + +## @param args Override default container args (useful when using custom images). Overrides the defaultArgs. +## +args: [] + +## EODAG Server application ports +## @param containerPorts.http EODAG Server application HTTP port number +containerPorts: + http: 5000 + +## Enable persistence using Persistent Volume Claims +## ref: https://kubernetes.io/docs/user-guide/persistent-volumes/ +## +persistence: + ## @param persistence.enabled Enable persistence using PVC + ## + enabled: false + ## @param persistence.medium Provide a medium for `emptyDir` volumes. + ## + medium: "" + ## @param persistence.sizeLimit Set this to enable a size limit for `emptyDir` volumes. + ## + sizeLimit: 8Gi + ## @param persistence.storageClass PVC Storage Class for Matomo volume + ## If defined, storageClassName: <storageClass> + ## If set to "-", storageClassName: "", which disables dynamic provisioning + ## If undefined (the default) or set to null, no storageClassName spec is + ## set, choosing the default provisioner. (gp2 on AWS, standard on + ## GKE, AWS & OpenStack) + ## + storageClass: "" + ## @param persistence.accessModes PVC Access Mode for Matomo volume + ## Requires persistence.enabled: true + ## If defined, PVC must be created manually before volume will be bound + ## + accessModes: + - ReadWriteOnce + ## @param persistence.size PVC Storage Request for Matomo volume + ## + size: 8Gi + ## @param persistence.dataSource Custom PVC data source + ## + dataSource: {} + ## @param persistence.existingClaim A manually managed Persistent Volume Claim + ## Requires persistence.enabled: true + ## If defined, PVC must be created manually before volume will be bound + ## + existingClaim: "" + ## @param persistence.hostPath If defined, the matomo-data volume will mount to the specified hostPath. + ## Requires persistence.enabled: true + ## Requires persistence.existingClaim: nil\|false + ## Default: nil. + ## + hostPath: "" + ## @param persistence.annotations Persistent Volume Claim annotations + ## + annotations: {} + ## @param persistence.labels Additional custom labels for the PVC + ## + labels: {} + ## @param persistence.selector Selector to match an existing Persistent Volume for Matomo data PVC + ## If set, the PVC can't have a PV dynamically provisioned for it + ## E.g. + ## selector: + ## matchLabels: + ## app: my-app + ## + selector: {} + +## @param hostAliases EODAG Server pods host aliases +## https://kubernetes.io/docs/concepts/services-networking/add-entries-to-pod-etc-hosts-with-host-aliases/ +## +hostAliases: [] + +## @param podLabels Extra labels for EODAG Server pods +## ref: https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/ +## +podLabels: {} + +## @param podAnnotations Annotations for EODAG Server pods +## ref: https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/ +## +podAnnotations: {} + +## @param podAffinityPreset Pod affinity preset. Ignored if `affinity` is set. Allowed values: `soft` or `hard` +## ref: https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#inter-pod-affinity-and-anti-affinity +## +podAffinityPreset: "" + +## @param podAntiAffinityPreset Pod anti-affinity preset. Ignored if `affinity` is set. Allowed values: `soft` or `hard` +## ref: https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#inter-pod-affinity-and-anti-affinity +## +podAntiAffinityPreset: soft + +## Node affinity preset +## ref: https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#node-affinity +## +nodeAffinityPreset: + ## @param nodeAffinityPreset.type Node affinity preset type. Ignored if `affinity` is set. Allowed values: `soft` or `hard` + ## + type: "" + ## @param nodeAffinityPreset.key Node label key to match. Ignored if `affinity` is set + ## + key: "" + ## @param nodeAffinityPreset.values Node label values to match. Ignored if `affinity` is set + ## E.g. + ## values: + ## - e2e-az1 + ## - e2e-az2 + ## + values: [] + +## @param affinity Affinity for EODAG Server pods assignment +## ref: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity +## NOTE: `podAffinityPreset`, `podAntiAffinityPreset`, and `nodeAffinityPreset` will be ignored when it's set +## +affinity: {} + +## @param nodeSelector Node labels for EODAG Server pods assignment +## ref: https://kubernetes.io/docs/user-guide/node-selection/ +## +nodeSelector: {} + +## @param tolerations Tolerations for EODAG Server pods assignment +## ref: https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/ +## +tolerations: [] + +## @param schedulerName Name of the k8s scheduler (other than default) +## ref: https://kubernetes.io/docs/tasks/administer-cluster/configure-multiple-schedulers/ +## +schedulerName: "" + +## @param shareProcessNamespace Enable shared process namespace in a pod. +## If set to false (default), each container will run in separate namespace, will have PID=1. +## If set to true, the /pause will run as init process and will reap any zombie PIDs, +## for example, generated by a custom exec probe running longer than a probe timeoutSeconds. +## Enable this only if customLivenessProbe or customReadinessProbe is used and zombie PIDs are accumulating. +## Ref: https://kubernetes.io/docs/tasks/configure-pod-container/share-process-namespace/ +## +shareProcessNamespace: false + +## @param topologySpreadConstraints Topology Spread Constraints for pod assignment +## https://kubernetes.io/docs/concepts/workloads/pods/pod-topology-spread-constraints/ +## The value is evaluated as a template +## +topologySpreadConstraints: [] + +## @param updateStrategy.type EODAG Server statefulset strategy type +## ref: https://kubernetes.io/docs/concepts/workloads/s/statefulset/#update-strategies +## +updateStrategy: + ## StrategyType + ## Can be set to RollingUpdate or OnDelete + ## + type: RollingUpdate + +## @param priorityClassName EODAG Server pods' priorityClassName +## +priorityClassName: "" + +## @param runtimeClassName Name of the runtime class to be used by pod(s) +## ref: https://kubernetes.io/docs/concepts/containers/runtime-class/ +## +runtimeClassName: "" + +## @param lifecycleHooks for the EODAG Server container(s) to automate configuration before or after startup +## +lifecycleHooks: {} + +## @param extraEnvVars Array with extra environment variables to add to EODAG Server nodes +## e.g: +## extraEnvVars: +## - name: FOO +## value: "bar" +## +extraEnvVars: [] + +## @param extraEnvVarsCM Name of existing ConfigMap containing extra env vars for EODAG Server nodes +## +extraEnvVarsCM: "" + +## @param extraEnvVarsSecret Name of existing Secret containing extra env vars for EODAG Server nodes +## +extraEnvVarsSecret: "" + +## @param extraVolumes Optionally specify extra list of additional volumes for the EODAG Server pod(s) +## +extraVolumes: [] + +## @param extraVolumeMounts Optionally specify extra list of additional volumeMounts for the EODAG Server container(s) +## +extraVolumeMounts: [] + +## @param sidecars Add additional sidecar containers to the EODAG Server pod(s) +## e.g: +## sidecars: +## - name: your-image-name +## image: your-image +## imagePullPolicy: Always +## ports: +## - name: portname +## containerPort: 1234 +## +sidecars: [] + +## @param initContainers Add additional init containers to the EODAG Server pod(s) +## ref: https://kubernetes.io/docs/concepts/workloads/pods/init-containers/ +## e.g: +## initContainers: +## - name: your-image-name +## image: your-image +## imagePullPolicy: Always +## command: ['sh', '-c', 'echo "hello world"'] +## +initContainers: [] + +## @param logLevel Supported values are 0 (no log), 1 (no logging but progress bar), 2 (INFO) or 3 (DEBUG). +logLevel: 2 + +## @param productTypes Optional overwrite of product types default configuration +## ref: https://github.com/CS-SI/eodag/blob/masster/eodag/resources/product_types.yml +productTypes: "" +# productTypes: \|- +# # CBERS 4 --------------------------------------------------------------------- +# CBERS4_MUX_L2: +# abstract: \| +# China-Brazil Earth Resources Satellite, CBERS-4 MUX camera Level-2 product. System corrected images, expect some +# translation error. +# instrument: MUX +# platform: CBERS +# platformSerialIdentifier: CBERS-4 +# processingLevel: L2 +# keywords: MUX,CBERS,CBERS-4,L2 +# sensorType: OPTICAL +# license: proprietary +# missionStartDate: "2014-12-07T00:00:00Z" +# title: CBERS-4 MUX Level-2 + +## @param providers Optional overwrite of providers default configuration +## ref: https://github.com/CS-SI/eodag/blob/master/eodag/resources/providers.yml +providers: "" +# providers: \|- +# !provider +# name: usgs +# priority: 0 +# description: U.S geological survey catalog for Landsat products +# roles: +# - host +# url: https://earthexplorer.usgs.gov/ +# api: !plugin +# type: UsgsApi +# need_auth: true +# google_base_url: 'http://storage.googleapis.com/earthengine-public/landsat/' +# pagination: +# max_items_per_page: 5000 +# total_items_nb_key_path: '$.totalHits' +# common_metadata_mapping_path: '$' +# metadata_mapping: +# id: '$.displayId' +# geometry: '$.spatialBounds' +# productType: '$.productType' +# title: '$.displayId' +# abstract: '$.summary' +# cloudCover: '$.cloudCover' +# startTimeFromAscendingNode: '$.temporalCoverage.startDate' +# completionTimeFromAscendingNode: '$.temporalCoverage.endDate' +# publicationDate: '$.publishDate' +# thumbnail: '$.browse[0].thumbnailPath' +# quicklook: '$.browse[0].browsePath' +# storageStatus: '{$.available#get_group_name((?P<ONLINE>True)\|(?P<OFFLINE>False))}' +# downloadLink: 'https://earthexplorer.usgs.gov/download/external/options/{productType}/{entityId}/M2M/' +# # metadata needed for download +# entityId: '$.entityId' +# productId: '$.id' +# extract: True +# order_enabled: true +# products: +# # datasets list http://kapadia.github.io/usgs/_sources/reference/catalog/ee.txt may be outdated +# # see also https://dds.cr.usgs.gov/ee-data/coveragemaps/shp/ee/ +# LANDSAT_C2L1: +# dataset: landsat_ot_c2_l1 +# outputs_extension: .tar.gz +# LANDSAT_C2L2: +# dataset: landsat_ot_c2_l2 +# outputs_extension: .tar.gz +# S2_MSI_L1C: +# dataset: SENTINEL_2A +# outputs_extension: .zip +# GENERIC_PRODUCT_TYPE: +# dataset: '{productType}' + +## @param config [object] EODAG configuration +## doc: https://eodag.readthedocs.io/en/stable/getting_started_guide/configure.html +## ref: https://github.com/CS-SI/eodag/blob/master/eodag/resources/user_conf_template.yml +config: + # peps: + # priority: # Lower value means lower priority (Default: 1) + # search: # Search parameters configuration + # download: + # extract: # whether to extract the downloaded products, only applies to archived products (true or false, Default: true). + # outputs_prefix: # where to store downloaded products, as an absolute file path (Default: local temporary directory) + # dl_url_params: # additional parameters to pass over to the download url as an url parameter + # delete_archive: # whether to delete the downloaded archives (true or false, Default: true). + # auth: + # credentials: + # username: + # password: + # cop_cds: + # priority: # Lower value means lower priority (Default: 0) + # api: + # outputs_prefix: + # credentials: + # username: + # password: + # cop_dataspace: + # priority: # Lower value means lower priority (Default: 0) + # search: # Search parameters configuration + # download: + # extract: + # outputs_prefix: + # auth: + # credentials: + # username: + # password: + +## @param configExistingSecret.name Existing secret name for EODAG config. If this is set, value config will be ignored +## @param configExistingSecret.key Existing secret key for EODAG config. If this is set, value config will be ignored +configExistingSecret: + name: + key: + +## Service configuration +## +service: + ## @param service.type Kubernetes service type + ## + type: ClusterIP + ## @param service.http.enabled Enable http port on service + ## + http: + enabled: true + ## @param service.ports.http EODAG Server service HTTP port + ## + ports: + http: 8080 + ## @param service.nodePorts [object] Specify the nodePort values for the LoadBalancer and NodePort service types. + ## ref: https://kubernetes.io/docs/concepts/services-networking/service/#type-nodeport + ## + nodePorts: + http: "" + ## @param service.sessionAffinity Control where client requests go, to the same pod or round-robin + ## Values: ClientIP or None + ## ref: https://kubernetes.io/docs/user-guide/services/ + ## + sessionAffinity: None + ## @param service.sessionAffinityConfig Additional settings for the sessionAffinity + ## sessionAffinityConfig: + ## clientIP: + ## timeoutSeconds: 300 + ## + sessionAffinityConfig: {} + ## @param service.clusterIP EODAG Server service clusterIP IP + ## e.g: + ## clusterIP: None + ## + clusterIP: "" + ## @param service.loadBalancerIP loadBalancerIP for the SuiteCRM Service (optional, cloud specific) + ## ref: https://kubernetes.io/docs/user-guide/services/#type-loadbalancer + ## + loadBalancerIP: "" + ## @param service.loadBalancerSourceRanges Address that are allowed when service is LoadBalancer + ## https://kubernetes.io/docs/tasks/access-application-cluster/configure-cloud-provider-firewall/#restrict-access-for-loadbalancer-service + ## Example: + ## loadBalancerSourceRanges: + ## - 10.10.10.0/24 + ## + loadBalancerSourceRanges: [] + ## @param service.externalTrafficPolicy Enable client source IP preservation + ## ref https://kubernetes.io/docs/tasks/access-application-cluster/create-external-load-balancer/#preserving-the-client-source-ip + ## + externalTrafficPolicy: Cluster + ## @param service.annotations Additional custom annotations for EODAG Server service + ## + annotations: {} + ## @param service.extraPorts Extra port to expose on EODAG Server service + ## + extraPorts: [] + +## Configure the ingress for the EODAG Server +## Ref: https://kubernetes.io/docs/user-guide/ingress/ +## @param ingress.enabled Enable the creation of an ingress for the EODAG Server +## @param ingress.pathType Path type for the EODAG Server ingress +## @param ingress.apiVersion Ingress API version for the EODAG Server ingress +## @param ingress.hostname Ingress hostname for the EODAG Server ingress +## @param ingress.annotations Annotations for the EODAG Server ingress. To enable certificate autogeneration, place here your cert-manager annotations. +## @param ingress.tls Enable TLS for the EODAG Server ingress +## @param ingress.extraHosts Extra hosts array for the EODAG Server ingress +## @param ingress.path Path array for the EODAG Server ingress +## @param ingress.extraPaths Extra paths for the EODAG Server ingress +## @param ingress.extraTls Extra TLS configuration for the EODAG Server ingress +## @param ingress.secrets Secrets array to mount into the Ingress +## @param ingress.ingressClassName IngressClass that will be be used to implement the Ingress (Kubernetes 1.18+) +## +ingress: + ## Set to true to enable ingress record generation + ## + enabled: false + ## @param ingress.selfSigned Create a TLS secret for this ingress record using self-signed certificates generated by Helm + ## + selfSigned: false + ## Ingress Path type + ## + pathType: ImplementationSpecific + ## Override API Version (automatically detected if not set) + ## + apiVersion: "" + ## When the ingress is enabled, a host pointing to this will be created + ## + hostname: eodag.local + ## The Path to eodag. You may need to set this to '/' in order to use this + ## with ALB ingress s. + ## + path: / + ## @param ingress.servicePort Backend service port to use + ## Default is http. Alternative is https. + ## + servicePort: http + ## For a full list of possible ingress annotations, please see + ## ref: https://github.com/kubernetes/ingress-nginx/blob/master/docs/user-guide/nginx-configuration/annotations.md + ## Use this parameter to set the required annotations for cert-manager, see + ## ref: https://cert-manager.io/docs/usage/ingress/#supported-annotations + ## + ## e.g: + ## annotations: + ## kubernetes.io/ingress.class: nginx + ## cert-manager.io/cluster-issuer: cluster-issuer-name + ## + annotations: {} + ## Enable TLS configuration for the hostname defined at ingress.hostname parameter + ## TLS certificates will be retrieved from a TLS secret with name: {{- printf "%s-tls" .Values.ingress.hostname }} + ## You can use the ingress.secrets parameter to create this TLS secret or rely on cert-manager to create it + ## + tls: false + ## The list of additional hostnames to be covered with this ingress record. + ## Most likely the hostname above will be enough, but in the event more hosts are needed, this is an array + extraHosts: [] + ## - name: eodag.local + ## path: / + ## + ## Any additional arbitrary paths that may need to be added to the ingress under the main host. + ## For example: The ALB ingress requires a special rule for handling SSL redirection. + extraPaths: [] + ## - path: /* + ## backend: + ## serviceName: ssl-redirect + ## servicePort: use-annotation + ## + ## The tls configuration for additional hostnames to be covered with this ingress record. + ## see: https://kubernetes.io/docs/concepts/services-networking/ingress/#tls + extraTls: [] + ## - hosts: + ## - eodag.local + ## secretName: eodag.local-tls + ## + + ## If you're providing your own certificates, please use this to add the certificates as secrets + ## key and certificate should start with -----BEGIN CERTIFICATE----- or + ## -----BEGIN RSA PRIVATE KEY----- + ## + ## name should line up with a tlsSecret set further up + ## If you're using cert-manager, this is unneeded, as it will create the secret for you if it is not set + ## + ## It is also possible to create and manage the certificates outside of this helm chart + ## Please see README.md for more information + ## + secrets: [] + ## - name: eodag.local-tls + ## key: + ## certificate: + ## + + ## This is supported in Kubernetes 1.18+ and required if you have more than one IngressClass marked as the default for your cluster . + ## ref: https://kubernetes.io/blog/2020/04/02/improvements-to-the-ingress-api-in-kubernetes-1.18/ + ## + ingressClassName: "" + ## @param ingress.extraRules Additional rules to be covered with this ingress record + ## ref: https://kubernetes.io/docs/concepts/services-networking/ingress/#ingress-rules + ## e.g: + ## extraRules: + ## - host: example.local + ## http: + ## path: / + ## backend: + ## service: + ## name: example-svc + ## port: + ## name: http + ## + extraRules: [] + +## ServiceAccount configuration +## +serviceAccount: + ## @param serviceAccount.create Specifies whether a ServiceAccount should be created + ## + create: true + ## @param serviceAccount.name The name of the ServiceAccount to use. + ## If not set and create is true, a name is generated using the common.names.fullname template + ## + name: "" + ## @param serviceAccount.annotations Additional custom annotations for the ServiceAccount + ## + annotations: {} + ## @param serviceAccount.automountServiceAccountToken Automount service account token for the server service account + ## + automountServiceAccountToken: true diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index cfd19e15..437d11b0 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -2714,7 +2714,6 @@ stream: oper class: mc expver: '0001' - step: 0 variable: - dust_aerosol_0.03-0.55um_mixing_ratio - dust_aerosol_0.55-0.9um_mixing_ratio diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index de5d168a..e9955275 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -589,8 +589,6 @@ def download_stac_item_by_id(catalogs, item_id, provider=None): product = search_product_by_id(item_id, product_type=catalogs[0])[0] - eodag_api.providers_config[product.provider].download.extract = False - product_path = eodag_api.download(product, extract=False) if os.path.isdir(product_path):	Invalid keyword `step` in download requests for CAMS_EAC4 with cop_ads While trying to download the product type `CAMS_EAC4` with the provider `cop_ads`, the download request fails with an `Invalid key name` error for the key `step`.	CS-SI/eodag	diff --git a/tests/units/test_apis_plugins.py b/tests/units/test_apis_plugins.py index 8c4c6d3c..496db0ce 100644 --- a/tests/units/test_apis_plugins.py +++ b/tests/units/test_apis_plugins.py @@ -640,7 +640,6 @@ class TestApisPluginCdsApi(BaseApisPluginTest): "stream": "oper", "class": "mc", "expver": "0001", - "step": 0, "variable": [ "dust_aerosol_0.03-0.55um_mixing_ratio", "dust_aerosol_0.55-0.9um_mixing_ratio", diff --git a/tests/units/test_http_server.py b/tests/units/test_http_server.py index 72f97057..f302ef06 100644 --- a/tests/units/test_http_server.py +++ b/tests/units/test_http_server.py @@ -627,7 +627,7 @@ class RequestTestCase(unittest.TestCase): "eodag.rest.utils.eodag_api.download", autospec=True, ) - def test_download_item_from_collection(self, mock_download): + def test_download_item_from_collection_api_plugin(self, mock_download): """Download through eodag server catalog should return a valid response""" # download returns a file that must be returned as is tmp_dl_dir = TemporaryDirectory() @@ -635,6 +635,11 @@ class RequestTestCase(unittest.TestCase): Path(expected_file).touch() mock_download.return_value = expected_file + # use an external python API provider for this test + self._request_valid_raw.patchings[0].kwargs["return_value"][0][ + 0 + ].provider = "cop_cds" + response = self._request_valid_raw( f"collections/{self.tested_product_type}/items/foo/download" )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 4 }	2.10	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": null, "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@50d6e60396cff60f6373ab64af5a0229a77e6906#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.10.1.dev17+g50d6e603 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_collection_api_plugin" ]	[]	[ "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_download_all", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_dates_missing", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_custom_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_with_producttype", "tests/units/test_apis_plugins.py::TestApisPluginEcmwfApi::test_plugins_apis_ecmwf_query_without_producttype", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_download", "tests/units/test_apis_plugins.py::TestApisPluginUsgsApi::test_plugins_apis_usgs_query", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_authenticate", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_download", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_download_all", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_logging", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_query_dates_missing", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_query_with_custom_producttype", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_query_with_producttype", "tests/units/test_apis_plugins.py::TestApisPluginCdsApi::test_plugins_apis_cds_query_without_producttype", "tests/units/test_http_server.py::RequestTestCase::test_auth_error", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse", "tests/units/test_http_server.py::RequestTestCase::test_cloud_cover_post_search", "tests/units/test_http_server.py::RequestTestCase::test_collection", "tests/units/test_http_server.py::RequestTestCase::test_conformance", "tests/units/test_http_server.py::RequestTestCase::test_date_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_catalog_items", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_items", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_filter", "tests/units/test_http_server.py::RequestTestCase::test_forward", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_nok", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_ok", "tests/units/test_http_server.py::RequestTestCase::test_not_found", "tests/units/test_http_server.py::RequestTestCase::test_request_params", "tests/units/test_http_server.py::RequestTestCase::test_route", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_collection", "tests/units/test_http_server.py::RequestTestCase::test_search_response_contains_pagination_info", "tests/units/test_http_server.py::RequestTestCase::test_service_desc", "tests/units/test_http_server.py::RequestTestCase::test_service_doc", "tests/units/test_http_server.py::RequestTestCase::test_stac_extension_oseo" ]	[]	Apache License 2.0	null
CS-SI__eodag-751	92a537f7811a06f336712de6b8c07a9b80cf6de6	2023-06-27 16:20:06	856121900f6d973eca9d2ad3c0de61bbd706ae2c	github-actions[bot]: ## Test Results       4 files ±0       4 suites ±0 4m 0s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "duration of all tests") -6s   388 tests ±0   386 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0   2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 1 552 runs ±0 1 496 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0 56 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 Results for commit 45cae33d. ± Comparison against base commit 50d6e603. [test-results]:data:application/gzip;base64,H4sIAB0Nm2QC/02MzQ6DIBAGX8Vw7qELC2JfxiA/CalKg3Bq+u4Fq9S9zXzZeRPnZ7uRR4e3jmzZpwYmR5V8WAtSvBdRplRHJuVJ45a13pX4q6d/1Z8mnPJzES0x2hhDPEzMa20C5/Sgswk4iKZ+Td7EpbnzNanDsvhUgCDXyjJmeqDUcGdAAR0Q5VQOJmWgRy6cBfL5AqpVr/cIAQAA github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 45cae33d7122d5fd1a129448bbbb1bad17456fe1 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `77%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 45cae33d7122d5fd1a129448bbbb1bad17456fe1 </p>	diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index de5d168a..e9955275 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -589,8 +589,6 @@ def download_stac_item_by_id(catalogs, item_id, provider=None): product = search_product_by_id(item_id, product_type=catalogs[0])[0] - eodag_api.providers_config[product.provider].download.extract = False - product_path = eodag_api.download(product, extract=False) if os.path.isdir(product_path):	Impossible to download data with providers using api plugin in REST mode In REST mode, when a provider does not get the download plugin, data download fails with the following error : `AttributeError: 'ProviderConfig' object has no attribute 'download'`	CS-SI/eodag	diff --git a/tests/units/test_http_server.py b/tests/units/test_http_server.py index 72f97057..f302ef06 100644 --- a/tests/units/test_http_server.py +++ b/tests/units/test_http_server.py @@ -627,7 +627,7 @@ class RequestTestCase(unittest.TestCase): "eodag.rest.utils.eodag_api.download", autospec=True, ) - def test_download_item_from_collection(self, mock_download): + def test_download_item_from_collection_api_plugin(self, mock_download): """Download through eodag server catalog should return a valid response""" # download returns a file that must be returned as is tmp_dl_dir = TemporaryDirectory() @@ -635,6 +635,11 @@ class RequestTestCase(unittest.TestCase): Path(expected_file).touch() mock_download.return_value = expected_file + # use an external python API provider for this test + self._request_valid_raw.patchings[0].kwargs["return_value"][0][ + 0 + ].provider = "cop_cds" + response = self._request_valid_raw( f"collections/{self.tested_product_type}/items/foo/download" )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 1 }	2.10	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@92a537f7811a06f336712de6b8c07a9b80cf6de6#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.10.1.dev18+g92a537f7 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_collection_api_plugin" ]	[]	[ "tests/units/test_http_server.py::RequestTestCase::test_auth_error", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse", "tests/units/test_http_server.py::RequestTestCase::test_cloud_cover_post_search", "tests/units/test_http_server.py::RequestTestCase::test_collection", "tests/units/test_http_server.py::RequestTestCase::test_conformance", "tests/units/test_http_server.py::RequestTestCase::test_date_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_catalog_items", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_items", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_filter", "tests/units/test_http_server.py::RequestTestCase::test_forward", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_nok", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_ok", "tests/units/test_http_server.py::RequestTestCase::test_not_found", "tests/units/test_http_server.py::RequestTestCase::test_request_params", "tests/units/test_http_server.py::RequestTestCase::test_route", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_collection", "tests/units/test_http_server.py::RequestTestCase::test_search_response_contains_pagination_info", "tests/units/test_http_server.py::RequestTestCase::test_service_desc", "tests/units/test_http_server.py::RequestTestCase::test_service_doc", "tests/units/test_http_server.py::RequestTestCase::test_stac_extension_oseo" ]	[]	Apache License 2.0	null
CS-SI__eodag-756	c38d36ec0be2c490d04060030c10726ecf327972	2023-07-04 12:56:02	856121900f6d973eca9d2ad3c0de61bbd706ae2c	github-actions[bot]: ## Test Results       4 files ±0       4 suites ±0 4m 12s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "duration of all tests") +5s   388 tests ±0   386 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0   2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 1 552 runs ±0 1 496 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0 56 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 Results for commit 9fa9f9d6. ± Comparison against base commit c38d36ec. [test-results]:data:application/gzip;base64,H4sIAM0XpGQC/02NSw7DIAwFrxKx7iIkQHAvUyGDJdR8KgKrqHcv0IZmOfPk8cHIz25n907cOrYnHxvYFEz025pxkEMWeYplHLU+6bEnxKrUXz39q9w0QcbPWfRNuBC28DMhraXJZX1R6GxyAaqpb1M2cWlWviZxWxYfMzAgAwRWkZhGDoMA0oBSO2MsAtDYS+TcTZK9P6pWRjkIAQAA github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 9fa9f9d6f47319249f89c58eaadc99f305c11e75 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `77%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 9fa9f9d6f47319249f89c58eaadc99f305c11e75 </p>	diff --git a/docker/stac-server.dockerfile b/docker/stac-server.dockerfile index 543e1ea8..f7fb4b6c 100644 --- a/docker/stac-server.dockerfile +++ b/docker/stac-server.dockerfile @@ -65,7 +65,7 @@ RUN chmod +x /eodag/run-stac-server.sh # add user RUN addgroup --system user \ - && adduser --system --group user + && adduser --system --home /home/user --group user # switch to non-root user USER user diff --git a/docs/api_reference/core.rst b/docs/api_reference/core.rst index f9a57381..6a5f8551 100644 --- a/docs/api_reference/core.rst +++ b/docs/api_reference/core.rst @@ -79,6 +79,7 @@ Misc .. autosummary:: EODataAccessGateway.group_by_extent + EODataAccessGateway.guess_product_type .. autoclass:: eodag.api.core.EODataAccessGateway :members: set_preferred_provider, get_preferred_provider, update_providers_config, list_product_types, diff --git a/docs/cli_user_guide.rst b/docs/cli_user_guide.rst index 798b4839..d36035d7 100644 --- a/docs/cli_user_guide.rst +++ b/docs/cli_user_guide.rst @@ -106,6 +106,26 @@ string search sent to the provider. For instance, if you want to add foo=1 and b --cruncher-args FilterOverlap minimum_overlap 10 \ --query "foo=1&bar=2" +* If the product type is not known, it can also be guessed by EODAG during the search based on parameters in the search request. The possible parameters are: + + * `instrument` (e.g. MSI) + * `platform` (e.g. SENTINEL2) + * `platformSerialIdentifier` (e.g. S2A) + * `processingLevel` (e.g. L1) + * `sensorType` (e.g. OPTICAL) + * `keywords` (e.g. SENTINEL2 L1C SAFE), which is case insensitive and ignores `-` or `_` characters + +For example, the following search request will first search for a product type for platform SENTINEL2 and processingLevel L1 +(there are several product types matching these criteria, e.g., `S2_MSI_L1C`) and then use this product type to execute the actual search. + +.. code-block:: console + + eodag search \ + --platform SENTINEL2 \ + --processingLevel L1 \ + --box 1 43 2 44 \ + --start 2021-03-01 --end 2021-03-31 + * To download the result of a previous call to ``search``: .. code-block:: console diff --git a/docs/getting_started_guide/configure.rst b/docs/getting_started_guide/configure.rst index 7c2b22ad..c7814106 100644 --- a/docs/getting_started_guide/configure.rst +++ b/docs/getting_started_guide/configure.rst @@ -136,7 +136,7 @@ API: Dynamic configuration ^^^^^^^^^^^^^^^^^^^^^^^^^^ ``eodag`` 's configuration can be altered directly from using Python. See this -`dedicated page <../notebooks/api_uder_guide/3_configuration.ipynb>`_ in the Python API user guide. +`dedicated page <../notebooks/api_user_guide/3_configuration.ipynb>`_ in the Python API user guide. Priority setting ^^^^^^^^^^^^^^^^ @@ -246,3 +246,62 @@ Or with the CLI: -p S2_MSI_L1C \ --storage my_search.geojson eodag download -f my_config.yml --search-results my_search.geojson + +Authenticate using an OTP / One Time Password (Two-Factor authentication) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Use OTP through Python code +""""""""""""""""""""""""""" + +``creodias`` needs a temporary 6-digits code to authenticate in addition of the ``username`` and ``password``. Check +`Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`_ +to see how to get this code once you are registered. This OTP code will only be valid for a few seconds, so you will +better set it dynamically in your python code instead of storing it statically in your user configuration file. + +Set the OTP by updating ``creodias`` credentials for ``totp`` entry, using one of the two following configuration update +commands: + +.. code-block:: python + + dag.providers_config["creodias"].auth.credentials["totp"] = "PLEASE_CHANGE_ME" + + # OR + + dag.update_providers_config( + """ + creodias: + auth: + credentials: + totp: PLEASE_CHANGE_ME + """ + ) + +Then quickly authenticate as this OTP has a few seconds only lifetime. First authentication will retrieve a token that +will be stored and used if further authentication tries fail: + +.. code-block:: python + + dag._plugins_manager.get_auth_plugin("creodias").authenticate() + +Please note that authentication mechanism is already included in +`download methods <https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/7_download.html>`_ , so you could also directly execute a +download to retrieve the token while the OTP is still valid. + +Use OTP through CLI +""""""""""""""""""" + +To download through CLI a product on a provider that needs a One Time Password, e.g. ``creodias``, first search on this +provider (increase the provider priotity to make eodag search on it): + +.. code-block:: console + + EODAG__CREODIAS__PRIORITY=2 eodag search -b 1 43 2 44 -s 2018-01-01 -e 2018-01-31 -p S2_MSI_L1C --items 1 + +Then download using the OTP (``creodias`` needs it as ``totp`` parameter): + +.. code-block:: console + + EODAG__CREODIAS__AUTH__CREDENTIALS__TOTP=PLEASE_CHANGE_ME eodag download --search-results search_results.geojson + +If needed, check in the documentation how to +`use environment variables to configure EODAG <#environment-variable-configuration>`_. diff --git a/docs/getting_started_guide/register.rst b/docs/getting_started_guide/register.rst index bea5eecd..863b5455 100644 --- a/docs/getting_started_guide/register.rst +++ b/docs/getting_started_guide/register.rst @@ -15,7 +15,12 @@ to each provider supported by ``eodag``: * ``peps``: create an account `here <https://peps.cnes.fr/rocket/#/register>`__, then use your email as `username` in eodag credentials. -* ``creodias``: create an account `here <https://portal.creodias.eu/register.php>`__ +* ``creodias``: create an account `here <https://portal.creodias.eu/register.php>`__, then use your `username`, `password` in eodag credentials. You will also + need `totp` in credentials, a temporary 6-digits OTP (One Time Password, see + `Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`__) + to be able to authenticate and download. Check + `Authenticate using an OTP <https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication>`__ + to see how to proceed. * ``onda``: create an account `here: <https://www.onda-dias.eu/cms/>`__ @@ -54,7 +59,7 @@ to each provider supported by ``eodag``: * Create an account on `EOS <https://auth.eos.com>`__ - * Get your EOS api key from `here <https://console.eos.com>`__ + * Get your EOS api key from `here <https://api-connect.eos.com/user-dashboard/statistics>`__ * Create an account on `AWS <https://aws.amazon.com/>`__ @@ -97,3 +102,5 @@ to each provider supported by ``eodag``: none exists). * Add these credentials to the user configuration file. + +* ``earth_search_cog``: no authentication needed. diff --git a/docs/notebooks/api_user_guide/4_search.ipynb b/docs/notebooks/api_user_guide/4_search.ipynb index b7dffa31..fb306d9f 100644 --- a/docs/notebooks/api_user_guide/4_search.ipynb +++ b/docs/notebooks/api_user_guide/4_search.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -15,6 +17,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -45,6 +48,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -66,6 +70,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -73,6 +78,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -82,6 +88,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -89,6 +96,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -96,6 +104,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -103,6 +112,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -202,6 +212,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -218,6 +229,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -281,6 +293,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -322,6 +335,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -329,6 +343,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -379,6 +394,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -408,6 +424,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -415,6 +432,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -422,6 +440,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -480,6 +499,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -487,6 +507,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -515,6 +536,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -543,6 +565,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -564,9 +587,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"1.44996527605046 43.21336996886716 0.4785046887226798 1.0742476656826767\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,87.500987603417)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.021484953313653535\" opacity=\"0.6\" d=\"M 1.4897522266313,43.253156919448 L 1.8886830141923,43.259391910537 L 1.8702372867615,44.247830683969 L 1.8166844329605,44.246978608269 L 1.8115531485622,44.231531984313 L 1.7625666172723,44.084918382335 L 1.7143540280423,43.938123829876 L 1.6656909244209,43.791387494216 L 1.6175634104158,43.644454837603 L 1.5693024790492,43.497542376303 L 1.521405582652,43.350555612604 L 1.4897522266313,43.253156919448 z\" /></g></g></svg>" - ], + "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"1.44996527605046 43.21336996886716 0.4785046887226798 1.0742476656826767\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,87.500987603417)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.021484953313653535\" opacity=\"0.6\" d=\"M 1.4897522266313,43.253156919448 L 1.8886830141923,43.259391910537 L 1.8702372867615,44.247830683969 L 1.8166844329605,44.246978608269 L 1.8115531485622,44.231531984313 L 1.7625666172723,44.084918382335 L 1.7143540280423,43.938123829876 L 1.6656909244209,43.791387494216 L 1.6175634104158,43.644454837603 L 1.5693024790492,43.497542376303 L 1.521405582652,43.350555612604 L 1.4897522266313,43.253156919448 z\" /></g></g></svg>", "text/plain": [ "<shapely.geometry.multipolygon.MultiPolygon at 0x7fa7ea210bb0>" ] @@ -666,6 +687,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -696,6 +718,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -727,6 +750,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -734,6 +758,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -756,6 +781,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -763,6 +789,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -777,6 +804,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -784,6 +812,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -791,6 +820,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -826,6 +856,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -833,6 +864,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -840,6 +872,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -847,6 +880,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -854,6 +888,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -867,6 +902,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -883,6 +919,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -890,6 +927,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -923,6 +961,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -951,6 +990,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1157,6 +1197,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1164,6 +1205,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1250,6 +1292,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1303,6 +1346,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1533,6 +1577,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1546,6 +1591,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1553,6 +1599,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1569,6 +1616,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1576,6 +1624,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1872,6 +1921,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1879,6 +1929,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1886,6 +1937,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1914,6 +1966,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1952,6 +2005,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1959,10 +2013,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "`eodag` has an internal product type catalog which stores a number of metadata for each product type (see [this page](../../getting_started_guide/product_types.rst) for more information). It is actually possible to search for products by using some of these metadata. In that case, `eodag` will query its own catalog and use the product type that best matches the query. The supported kwargs are:\n", + "`eodag` has an internal product type catalog which stores a number of metadata for each product type (see [this page](../../getting_started_guide/product_types.rst) for more information). It is actually possible to search for products by using some of these metadata. In that case, `eodag` will query its own catalog and use the product type that best matches the query among the available product types (i.e. all product types offered by providers where the necessary credentials are provided). The supported kwargs are:\n", "\n", "* `instrument` (e.g. MSI)\n", "* `platform` (e.g. SENTINEL2)\n", @@ -1972,6 +2027,14 @@ "* `keywords` (e.g. SENTINEL2 L1C SAFE), which is case insensitive and ignores `-` or `_` characters" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example if we call the method with platform=\"SENTINEL1\", all the product types offering products from this platform will be returned." + ] + }, { "cell_type": "code", "execution_count": 46, @@ -1992,6 +2055,14 @@ "dag.guess_product_type(platform=\"SENTINEL1\")" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By passing the following argument we can get all collections that contain the keyword collection2 and a keyword that starts with \"LANDSAT\"." + ] + }, { "cell_type": "code", "execution_count": 47, @@ -2024,10 +2095,142 @@ ] }, { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous request we made use of the whoosh query language which can be used to do complex text search. It supports the boolean operators AND, OR and NOT to combine the search terms. If a space is given between two words as in the example above, this corresponds to the operator AND. Brackets '()' can also be used. The example above also shows the use of the wildcard operator '' which can represent any numer of characters. The wildcard operator '?' always represents only one character. It is also possible to match a range of terms by using square brackets '[]' and TO, e.g. [A TO D] will match all words in the lexical range between A and D. Below you can find some examples for the different operators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dag.guess_product_type(platform=\"LANDSAT OR SENTINEL1\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "returns all product types where the platform is either LANDSAT or SENTINEL1:\n", + "\n", + "['L57_REFLECTANCE', 'LANDSAT_C2L1', 'LANDSAT_C2L2', 'LANDSAT_C2L2ALB_BT', 'LANDSAT_C2L2ALB_SR', 'LANDSAT_C2L2ALB_ST', 'LANDSAT_C2L2ALB_TA', 'LANDSAT_C2L2_SR', 'LANDSAT_C2L2_ST', 'LANDSAT_ETM_C1', 'LANDSAT_ETM_C2L1', 'LANDSAT_ETM_C2L2', 'LANDSAT_TM_C1', 'LANDSAT_TM_C2L1', 'LANDSAT_TM_C2L2', 'S1_SAR_GRD', 'S1_SAR_OCN', 'S1_SAR_RAW', 'S1_SAR_SLC']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dag.guess_product_type(keywords=\"(LANDSAT AND collection2) OR SAR\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "returns all product types which contain either the keywords LANDSAT and collection2 or the keyword SAR:\n", + "\n", + "['LANDSAT_C2L1', 'LANDSAT_C2L2', 'LANDSAT_C2L2ALB_BT', 'LANDSAT_C2L2ALB_SR', 'LANDSAT_C2L2ALB_ST', 'LANDSAT_C2L2ALB_TA', 'LANDSAT_C2L2_SR', 'LANDSAT_C2L2_ST', 'LANDSAT_ETM_C2L1', 'LANDSAT_ETM_C2L2', 'LANDSAT_TM_C2L1', 'LANDSAT_TM_C2L2', 'S1_SAR_GRD', 'S1_SAR_OCN', 'S1_SAR_RAW', 'S1_SAR_SLC']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dag.guess_product_type(platformSerialIdentifier=\"L?\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "returns all product types where the platformSerialIdentifier is composed of 'L' and one other character:\n", + "\n", + "['L57_REFLECTANCE', 'L8_OLI_TIRS_C1L1', 'L8_REFLECTANCE', 'LANDSAT_C2L1', 'LANDSAT_C2L2', 'LANDSAT_C2L2ALB_BT', 'LANDSAT_C2L2ALB_SR', 'LANDSAT_C2L2ALB_ST', 'LANDSAT_C2L2ALB_TA', 'LANDSAT_C2L2_SR', 'LANDSAT_C2L2_ST', 'LANDSAT_ETM_C1', 'LANDSAT_ETM_C2L1', 'LANDSAT_ETM_C2L2', 'LANDSAT_TM_C1', 'LANDSAT_TM_C2L1', 'LANDSAT_TM_C2L2']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dag.guess_product_type(platform=\"[SENTINEL1 TO SENTINEL3]\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "returns all product types where the platform is SENTINEL1, SENTINEL2 or SENTINEL3:\n", + "\n", + "['S1_SAR_GRD', 'S1_SAR_OCN', 'S1_SAR_RAW', 'S1_SAR_SLC', 'S2_MSI_L1C', 'S2_MSI_L2A', 'S2_MSI_L2A_COG', 'S2_MSI_L2A_MAJA', 'S2_MSI_L2B_MAJA_SNOW', 'S2_MSI_L2B_MAJA_WATER', 'S2_MSI_L3A_WASP', 'S3_EFR', 'S3_ERR', 'S3_LAN', 'S3_OLCI_L2LFR', 'S3_OLCI_L2LRR', 'S3_OLCI_L2WFR', 'S3_OLCI_L2WRR', 'S3_RAC', 'S3_SLSTR_L1RBT', 'S3_SLSTR_L2AOD', 'S3_SLSTR_L2FRP', 'S3_SLSTR_L2LST', 'S3_SLSTR_L2WST', 'S3_SRA', 'S3_SRA_A', 'S3_SRA_BS', 'S3_SY_AOD', 'S3_SY_SYN', 'S3_SY_V10', 'S3_SY_VG1', 'S3_SY_VGP', 'S3_WAT']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also pass several parameters at the same time, e.g.:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dag.guess_product_type(platform=\"LANDSAT\", platformSerialIdentifier=\"L1\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "['LANDSAT_C2L1', \n", + "'L57_REFLECTANCE', \n", + "'LANDSAT_C2L2', \n", + "'LANDSAT_C2L2ALB_BT', \n", + "'LANDSAT_C2L2ALB_SR', \n", + "'LANDSAT_C2L2ALB_ST', \n", + "'LANDSAT_C2L2ALB_TA', \n", + "'LANDSAT_C2L2_SR', \n", + "'LANDSAT_C2L2_ST', \n", + "'LANDSAT_ETM_C1', \n", + "'LANDSAT_ETM_C2L1', \n", + "'LANDSAT_ETM_C2L2', \n", + "'LANDSAT_TM_C1', \n", + "'LANDSAT_TM_C2L1', \n", + "'LANDSAT_TM_C2L2']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The product types in the result are ordered by how well they match the criteria. In the example above only the first product type (LANDSAT_C2L1) matches the second parameter (platformSerialIdentifier=\"L1\"), all other product types only match the first criterion. Therefore, it is usually best to use the first product type in the list as it will be the one that fits best." + ] + }, + { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "A search is made without specifying `productType`, however hints are passed with following supported kwargs. `guess_product_type()` will internally be used to get the best matching product type." + "If a search is made without specifying `productType` but hints are passed with following supported kwargs, `guess_product_type()` will internally be used to get the best matching product type." ] }, { @@ -2105,10 +2308,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "`eodag` searched through its metadata catalog and the product type that best matched the hints happend to be `S2_MSI_L1C`. Internally it used that product type to search for products." + "`eodag` searches through its metadata catalog and checks which product type corresponds to the given parameters. There is a product type called `S2_MSI_L1C`, for the platform SENTINEL2 with the platformSerialIdentifiers S2A and S2B using the instrument MSI and the sensorType OPTICAL. Thus, all parameters of the product type `S2_MSI_L1C` are matching to the given search criteria and it is chosen to be internally used to search for products. If no product type is matching all the criteria, the one with the most matches is chosen, if several product types match all the given criteria, the first one is chosen." ] }, { @@ -2132,6 +2336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2139,6 +2344,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2146,6 +2352,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2159,6 +2366,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2191,6 +2399,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2218,6 +2427,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2225,6 +2435,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2255,6 +2466,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ diff --git a/eodag/plugins/authentication/keycloak.py b/eodag/plugins/authentication/keycloak.py index 26b4d2e7..9266e721 100644 --- a/eodag/plugins/authentication/keycloak.py +++ b/eodag/plugins/authentication/keycloak.py @@ -21,43 +21,101 @@ import requests from eodag.plugins.authentication import Authentication from eodag.plugins.authentication.openid_connect import CodeAuthorizedAuth from eodag.utils import USER_AGENT -from eodag.utils.exceptions import AuthenticationError +from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT class KeycloakOIDCPasswordAuth(Authentication): - """Authentication plugin using Keycloak and OpenId Connect""" + """Authentication plugin using Keycloak and OpenId Connect. + + This plugin request a token and use it through a query-string or a header. + + Using :class:`~eodag.plugins.download.http.HTTPDownload` a download link + `http://example.com?foo=bar` will become + `http://example.com?foo=bar&my-token=obtained-token` if associated to the following + configuration:: + + provider: + ... + auth: + plugin: KeycloakOIDCPasswordAuth + auth_base_uri: 'https://somewhere/auth' + realm: 'the-realm' + client_id: 'SOME_ID' + client_secret: '01234-56789' + token_provision: qs + token_qs_key: 'my-token' + ... + ... + + If configured to send the token through the header, the download request header will + be updated with `Authorization: "Bearer obtained-token"` if associated to the + following configuration:: + + provider: + ... + auth: + plugin: KeycloakOIDCPasswordAuth + auth_base_uri: 'https://somewhere/auth' + realm: 'the-realm' + client_id: 'SOME_ID' + client_secret: '01234-56789' + token_provision: header + ... + ... + """ GRANT_TYPE = "password" TOKEN_URL_TEMPLATE = "{auth_base_uri}/realms/{realm}/protocol/openid-connect/token" + REQUIRED_PARAMS = ["auth_base_uri", "client_id", "client_secret", "token_provision"] + # already retrieved token store, to be used if authenticate() fails (OTP use-case) + retrieved_token = None def __init__(self, provider, config): super(KeycloakOIDCPasswordAuth, self).__init__(provider, config) self.session = requests.Session() + def validate_config_credentials(self): + """Validate configured credentials""" + super(KeycloakOIDCPasswordAuth, self).validate_config_credentials() + + for param in self.REQUIRED_PARAMS: + if not hasattr(self.config, param): + raise MisconfiguredError( + "The following authentication configuration is missing for provider ", + f"{self.provider}: {param}", + ) + def authenticate(self): """ Makes authentication request """ self.validate_config_credentials() + req_data = { + "client_id": self.config.client_id, + "client_secret": self.config.client_secret, + "grant_type": self.GRANT_TYPE, + } + credentials = {k: v for k, v in self.config.credentials.items()} try: response = self.session.post( self.TOKEN_URL_TEMPLATE.format( auth_base_uri=self.config.auth_base_uri.rstrip("/"), realm=self.config.realm, ), - data={ - "client_id": self.config.client_id, - "client_secret": self.config.client_secret, - "username": self.config.credentials["username"], - "password": self.config.credentials["password"], - "grant_type": self.GRANT_TYPE, - }, + data=dict(req_data, credentials), headers=USER_AGENT, timeout=HTTP_REQ_TIMEOUT, ) response.raise_for_status() except requests.RequestException as e: + if self.retrieved_token: + # try using already retrieved token if authenticate() fails (OTP use-case) + return CodeAuthorizedAuth( + self.retrieved_token, + self.config.token_provision, + key=getattr(self.config, "token_qs_key", None), + ) # check if error is identified as auth_error in provider conf auth_errors = getattr(self.config, "auth_error_code", [None]) if not isinstance(auth_errors, list): @@ -79,8 +137,10 @@ class KeycloakOIDCPasswordAuth(Authentication): tb.format_exc() ) ) + + self.retrieved_token = response.json()["access_token"] return CodeAuthorizedAuth( - response.json()["access_token"], + self.retrieved_token, self.config.token_provision, key=getattr(self.config, "token_qs_key", None), ) diff --git a/eodag/plugins/authentication/openid_connect.py b/eodag/plugins/authentication/openid_connect.py index 5531a1fd..b535ad66 100644 --- a/eodag/plugins/authentication/openid_connect.py +++ b/eodag/plugins/authentication/openid_connect.py @@ -25,14 +25,7 @@ from lxml import etree from requests.auth import AuthBase from eodag.plugins.authentication import Authentication -from eodag.utils import ( - USER_AGENT, - parse_qs, - repeatfunc, - urlencode, - urlparse, - urlunparse, -) +from eodag.utils import USER_AGENT, parse_qs, repeatfunc, urlparse from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT @@ -306,18 +299,12 @@ class CodeAuthorizedAuth(AuthBase): """Perform the actual authentication""" if self.where == "qs": parts = urlparse(request.url) - qs = parse_qs(parts.query) - qs[self.key] = self.token - request.url = urlunparse( - ( - parts.scheme, - parts.netloc, - parts.path, - parts.params, - urlencode(qs), - parts.fragment, - ) - ) + query_dict = parse_qs(parts.query) + query_dict.update({self.key: self.token}) + url_without_args = parts._replace(query=None).geturl() + + request.prepare_url(url_without_args, query_dict) + elif self.where == "header": request.headers["Authorization"] = "Bearer {}".format(self.token) return request diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 3e440a02..4f53085f 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -169,16 +169,15 @@ class AwsDownload(Download): :param provider: provider name :type provider: str - :param config: Download plugin configuration - config.requester_pays: - :param config: Download plugin configuration: - ``config.base_uri`` (str) - s3 endpoint url - * ``config.requester_pays`` (bool) - whether download is done from - a requester-pays bucket or not - * ``config.flatten_top_dirs`` (bool) - flatten directory structure - * ``config.products`` (dict) - product_type specific configuration + * ``config.base_uri`` (str) - s3 endpoint url + * ``config.requester_pays`` (bool) - (optional) whether download is done from a + requester-pays bucket or not + * ``config.flatten_top_dirs`` (bool) - (optional) flatten directory structure + * ``config.products`` (dict) - (optional) product_type specific configuration + * ``config.ignore_assets`` (bool) - (optional) ignore assets and download using downloadLink + :type config: :class:`~eodag.config.PluginConfig` """ diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 4a5a20ab..716e2176 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -61,7 +61,30 @@ logger = logging.getLogger("eodag.plugins.download.http") class HTTPDownload(Download): - """HTTPDownload plugin. Handles product download over HTTP protocol""" + """HTTPDownload plugin. Handles product download over HTTP protocol + + :param provider: provider name + :type provider: str + :param config: Download plugin configuration: + + * ``config.base_uri`` (str) - default endpoint url + * ``config.extract`` (bool) - (optional) extract downloaded archive or not + * ``config.auth_error_code`` (int) - (optional) authentication error code + * ``config.dl_url_params`` (dict) - (optional) attitional parameters to send in the request + * ``config.archive_depth`` (int) - (optional) level in extracted path tree where to find data + * ``config.flatten_top_dirs`` (bool) - (optional) flatten directory structure + * ``config.ignore_assets`` (bool) - (optional) ignore assets and download using downloadLink + * ``config.order_enabled`` (bool) - (optional) wether order is enabled or not if product is `OFFLINE` + * ``config.order_method`` (str) - (optional) HTTP request method, GET (default) or POST + * ``config.order_headers`` (dict) - (optional) order request headers + * ``config.order_on_response`` (dict) - (optional) edit or add new product properties + * ``config.order_status_method`` (str) - (optional) status HTTP request method, GET (default) or POST + * ``config.order_status_percent`` (str) - (optional) progress percentage key in obtained status response + * ``config.order_status_error`` (dict) - (optional) key/value identifying an error status + + :type config: :class:`~eodag.config.PluginConfig` + + """ def __init__(self, provider, config): super(HTTPDownload, self).__init__(provider, config) @@ -341,19 +364,22 @@ class HTTPDownload(Download): return fs_path # download assets if exist instead of remote_location - try: - fs_path = self._download_assets( - product, - fs_path.replace(".zip", ""), - record_filename, - auth, - progress_callback, - kwargs, - ) - product.location = path_to_uri(fs_path) - return fs_path - except NotAvailableError: - pass + if hasattr(product, "assets") and not getattr( + self.config, "ignore_assets", False + ): + try: + fs_path = self._download_assets( + product, + fs_path.replace(".zip", ""), + record_filename, + auth, + progress_callback, + kwargs, + ) + product.location = path_to_uri(fs_path) + return fs_path + except NotAvailableError: + pass # order product if it is offline ordered_message = "" diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index cdbe470c..2602df4c 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -59,6 +59,25 @@ class S3RestDownload(Download): https://mundiwebservices.com/keystoneapi/uploads/documents/CWS-DATA-MUT-087-EN-Mundi_Download_v1.1.pdf#page=13 Re-use AwsDownload bucket some handling methods + + :param provider: provider name + :type provider: str + :param config: Download plugin configuration: + + * ``config.base_uri`` (str) - default endpoint url + * ``config.extract`` (bool) - (optional) extract downloaded archive or not + * ``config.auth_error_code`` (int) - (optional) authentication error code + * ``config.bucket_path_level`` (int) - (optional) bucket location index in path.split('/') + * ``config.order_enabled`` (bool) - (optional) wether order is enabled or not if product is `OFFLINE` + * ``config.order_method`` (str) - (optional) HTTP request method, GET (default) or POST + * ``config.order_headers`` (dict) - (optional) order request headers + * ``config.order_on_response`` (dict) - (optional) edit or add new product properties + * ``config.order_status_method`` (str) - (optional) status HTTP request method, GET (default) or POST + * ``config.order_status_percent`` (str) - (optional) progress percentage key in obtained status response + * ``config.order_status_success`` (dict) - (optional) key/value identifying an error success + * ``config.order_status_on_success`` (dict) - (optional) edit or add new product properties + + :type config: :class:`~eodag.config.PluginConfig` """ def __init__(self, provider, config): diff --git a/eodag/resources/ext_product_types.json b/eodag/resources/ext_product_types.json index a5a900b7..7c26d424 100644 --- a/eodag/resources/ext_product_types.json +++ b/eodag/resources/ext_product_types.json @@ -1,1 +1,1 @@ -{"astraea_eod": {"providers_config": {"landsat8_c2l1t1": {"productType": "landsat8_c2l1t1"}, "mcd43a4": {"productType": "mcd43a4"}, "mod11a1": {"productType": "mod11a1"}, "mod13a1": {"productType": "mod13a1"}, "myd11a1": {"productType": "myd11a1"}, "myd13a1": {"productType": "myd13a1"}, "maxar_open_data": {"productType": "maxar_open_data"}, "naip": {"productType": "naip"}, "sentinel1_l1c_grd": {"productType": "sentinel1_l1c_grd"}, "sentinel2_l1c": {"productType": "sentinel2_l1c"}, "sentinel2_l2a": {"productType": "sentinel2_l2a"}, "spacenet7": {"productType": "spacenet7"}, "umbra_open_data": {"productType": "umbra_open_data"}}, "product_types_config": {"landsat8_c2l1t1": {"abstract": "Landsat 8 Collection 2 Tier 1 Precision Terrain from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat8-c2l1t1", "license": "PDDL-1.0", "title": "Landsat 8 - Level 1", "missionStartDate": "2013-03-18T15:59:02.333Z"}, "mcd43a4": {"abstract": "MCD43A4: MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mcd43a4", "license": "CC-PDDC", "title": "MCD43A4 NBAR", "missionStartDate": "2000-02-16T00:00:00.000Z"}, "mod11a1": {"abstract": "MOD11A1: MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod11a1", "license": "CC-PDDC", "title": "MOD11A1 LST", "missionStartDate": "2000-02-24T00:00:00.000Z"}, "mod13a1": {"abstract": "MOD13A1: MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod13a1", "license": "CC-PDDC", "title": "MOD13A1 VI", "missionStartDate": "2000-02-18T00:00:00.000Z"}, "myd11a1": {"abstract": "MYD11A1: MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd11a1", "license": "CC-PDDC", "title": "MYD11A1 LST", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "myd13a1": {"abstract": "MYD13A1: MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd13a1", "license": "CC-PDDC", "title": "MYD13A1 VI", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "maxar_open_data": {"abstract": "Maxar Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "maxar-open-data", "license": "CC-BY-NC-4.0", "title": "Maxar Open Data", "missionStartDate": "2008-01-15T00:00:00.000Z"}, "naip": {"abstract": "National Agriculture Imagery Program aerial imagery", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "naip", "license": "CC-PDDC", "title": "NAIP", "missionStartDate": "2012-04-23T12:00:00.000Z"}, "sentinel1_l1c_grd": {"abstract": "Sentinel-1 Level-1 Ground Range Detected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel1-l1c-grd", "license": "CC-BY-SA-3.0", "title": "Sentinel-1 L1C GRD", "missionStartDate": "2017-09-27T14:19:16.000"}, "sentinel2_l1c": {"abstract": "Sentinel-2 Level-1C top of atmosphere", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l1c", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L1C", "missionStartDate": "2015-06-27T10:25:31.456Z"}, "sentinel2_l2a": {"abstract": "Sentinel-2 Level-2A atmospherically corrected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l2a", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L2A", "missionStartDate": "2018-04-01T07:02:22.463Z"}, "spacenet7": {"abstract": "SpaceNet 7 Imagery and Labels", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "spacenet7", "license": "CC-BY-SA-4.0", "title": "SpaceNet 7", "missionStartDate": "2018-01-01T00:00:00.000Z"}, "umbra_open_data": {"abstract": "Umbra Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "umbra-open-data", "license": "proprietary", "title": "Umbra Open Data", "missionStartDate": null}}}, "creodias": {"providers_config": {"SENTINEL-3": {"collection": "SENTINEL-3"}, "LANDSAT-5": {"collection": "LANDSAT-5"}, "SENTINEL-1": {"collection": "SENTINEL-1"}, "SENTINEL-2": {"collection": "SENTINEL-2"}, "COP-DEM": {"collection": "COP-DEM"}, "ENVISAT": {"collection": "ENVISAT"}, "SENTINEL-5P": {"collection": "SENTINEL-5P"}, "TERRAAQUA": {"collection": "TERRAAQUA"}, "SENTINEL-1-RTC": {"collection": "SENTINEL-1-RTC"}, "SMOS": {"collection": "SMOS"}, "LANDSAT-8": {"collection": "LANDSAT-8"}, "S2GLC": {"collection": "S2GLC"}, "LANDSAT-7": {"collection": "LANDSAT-7"}, "SENTINEL-6": {"collection": "SENTINEL-6"}}, "product_types_config": {"SENTINEL-3": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-3", "license": null, "title": "SENTINEL-3", "missionStartDate": null}, "LANDSAT-5": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat-5", "license": null, "title": "LANDSAT-5", "missionStartDate": null}, "SENTINEL-1": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-1", "license": null, "title": "SENTINEL-1", "missionStartDate": null}, "SENTINEL-2": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-2", "license": null, "title": "SENTINEL-2", "missionStartDate": null}, "COP-DEM": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cop-dem", "license": null, "title": "COP-DEM", "missionStartDate": null}, "ENVISAT": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "envisat", "license": null, "title": "ENVISAT", "missionStartDate": null}, "SENTINEL-5P": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-5p", "license": null, "title": "SENTINEL-5P", "missionStartDate": null}, "TERRAAQUA": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "terraaqua", "license": null, "title": "TERRAAQUA", "missionStartDate": null}, "SENTINEL-1-RTC": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-1-rtc", "license": null, "title": "SENTINEL-1-RTC", "missionStartDate": null}, "SMOS": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "smos", "license": null, "title": "SMOS", "missionStartDate": null}, "LANDSAT-8": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat-8", "license": null, "title": "LANDSAT-8", "missionStartDate": null}, "S2GLC": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "s2glc", "license": null, "title": "S2GLC", "missionStartDate": null}, "LANDSAT-7": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat-7", "license": null, "title": "LANDSAT-7", "missionStartDate": null}, "SENTINEL-6": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-6", "license": null, "title": "SENTINEL-6", "missionStartDate": null}}}, "earth_search": {"providers_config": {"sentinel-s2-l2a": {"productType": "sentinel-s2-l2a"}, "sentinel-s2-l1c": {"productType": "sentinel-s2-l1c"}, "landsat-8-l1-c1": {"productType": "landsat-8-l1-c1"}}, "product_types_config": {"sentinel-s2-l2a": {"abstract": "Sentinel-2a and Sentinel-2b imagery, processed to Level 2A (Surface Reflectance)", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2a,sentinel-2b,sentinel-s2-l2a", "license": "proprietary", "title": "Sentinel 2 L2A", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "sentinel-s2-l1c": {"abstract": "Sentinel-2a and Sentinel-2b imagery, processed to Level 1C (Top-Of-Atmosphere Geometrically Corrected)", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2a,sentinel-2b,sentinel-s2-l1c", "license": "proprietary", "title": "Sentinel 2 L1C", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "landsat-8-l1-c1": {"abstract": "Landat-8 L1 Collection-1 imagery radiometrically calibrated and orthorectified using gound points and Digital Elevation Model (DEM) data to correct relief displacement.", "instrument": "oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-8", "processingLevel": null, "keywords": "earth-observation,landsat,landsat-8,landsat-8-l1-c1,oli,tirs,usgs", "license": "PDDL-1.0", "title": "Landsat-8 L1 Collection-1", "missionStartDate": "2013-06-01T00:00:00Z"}}}, "earth_search_cog": null, "earth_search_gcs": null, "planetary_computer": {"providers_config": {"daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "3dep-seamless": {"productType": "3dep-seamless"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "fia": {"productType": "fia"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "gridmet": {"productType": "gridmet"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "hgb": {"productType": "hgb"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "goes-cmi": {"productType": "goes-cmi"}, "terraclimate": {"productType": "terraclimate"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "mtbs": {"productType": "mtbs"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "mobi": {"productType": "mobi"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "era5-pds": {"productType": "era5-pds"}, "chloris-biomass": {"productType": "chloris-biomass"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "gap": {"productType": "gap"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "gbif": {"productType": "gbif"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "alos-dem": {"productType": "alos-dem"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "us-census": {"productType": "us-census"}, "jrc-gsw": {"productType": "jrc-gsw"}, "deltares-floods": {"productType": "deltares-floods"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "hrea": {"productType": "hrea"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "nasadem": {"productType": "nasadem"}, "io-lulc": {"productType": "io-lulc"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "drcog-lulc": {"productType": "drcog-lulc"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "goes-glm": {"productType": "goes-glm"}, "usda-cdl": {"productType": "usda-cdl"}, "eclipse": {"productType": "eclipse"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "fws-nwi": {"productType": "fws-nwi"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "aster-l1t": {"productType": "aster-l1t"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-biodiversity": {"productType": "io-biodiversity"}, "naip": {"productType": "naip"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "ms-buildings": {"productType": "ms-buildings"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}}, "product_types_config": {"daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Puerto Rico", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Hawaii", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "title": "USGS 3DEP Seamless DEMs", "missionStartDate": "1925-01-01T00:00:00Z"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Surface Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "title": "Forest Inventory and Analysis", "missionStartDate": "2020-06-01T00:00:00Z"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)", "missionStartDate": "2014-10-10T00:28:21Z"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "title": "gridMET", "missionStartDate": "1979-01-01T00:00:00Z"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual North America", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly North America", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Hawaii", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Hawaii", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Puerto Rico", "missionStartDate": "1980-01-16T12:00:00Z"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Tables", "missionStartDate": "2020-07-01T00:00:00Z"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "title": "HGB: Harmonized Global Biomass for 2010", "missionStartDate": "2010-12-31T00:00:00Z"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-30", "missionStartDate": "2021-04-22T00:00:00Z"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-90", "missionStartDate": "2021-04-22T00:00:00Z"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "title": "GOES-R Cloud & Moisture Imagery", "missionStartDate": "2017-02-28T00:16:52Z"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "title": "TerraClimate", "missionStartDate": "1958-01-01T00:00:00Z"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- model is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- scenario is one of \"historical\", \"ssp245\" or \"ssp585\".\n- variable is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- year depends on the value of scenario. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some experimental reference files as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)", "missionStartDate": "1950-01-01T00:00:00Z"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "title": "GPM IMERG", "missionStartDate": "2000-06-01T00:00:00Z"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Rasters", "missionStartDate": "2020-07-01T00:00:00Z"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Height above Ground", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Intensity", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Source", "missionStartDate": "2012-01-01T00:00:00Z"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "title": "MTBS: Monitoring Trends in Burn Severity", "missionStartDate": "1984-12-31T00:00:00Z"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "title": "C-CAP Regional Land Cover and Change", "missionStartDate": "1975-01-01T00:00:00Z"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Cloud", "missionStartDate": "2012-01-01T00:00:00Z"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Burned Area Monthly", "missionStartDate": "2000-11-01T00:00:00Z"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "title": "ALOS Forest/Non-Forest Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Returns", "missionStartDate": "2012-01-01T00:00:00Z"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "title": "MoBI: Map of Biodiversity Importance", "missionStartDate": "2020-04-14T00:00:00Z"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-2", "missionStartDate": "1982-08-22T00:00:00Z"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An analysis, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A forecast starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "title": "ERA5 - PDS", "missionStartDate": "1979-01-01T00:00:00Z"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "title": "Chloris Biomass", "missionStartDate": "2003-07-31T00:00:00Z"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "title": "HydroForecast - Kwando & Upper Zambezi Rivers", "missionStartDate": "2022-01-01T00:00:00Z"}, "planet-nicfi-analytic": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Analytic)", "missionStartDate": "2015-12-01T00:00:00Z"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Puerto Rico", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model (Native)", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Classification", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "title": "USGS Gap Land Cover", "missionStartDate": "1999-01-01T00:00:00Z"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "planet-nicfi-visual": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Visual)", "missionStartDate": "2015-12-01T00:00:00Z"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "title": "Global Biodiversity Information Facility (GBIF)", "missionStartDate": "2021-04-13T00:00:00Z"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Primary Production Yearly Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "title": "ALOS World 3D-30m", "missionStartDate": "2016-12-07T00:00:00Z"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "title": "ALOS PALSAR Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n\| Name \| Scale \| Period \| Number of basins \|\n\|--------\|--------------------------\|-----------\|------------------\|\n\| ERA5 \| Global \| 1967-2020 \| 3236 \|\n\| CHIRPS \| Global (+/- 50 latitude) \| 1981-2020 \| 2951 \|\n\| EOBS \| Europe/North Africa \| 1979-2020 \| 682 \|\n\| NLDAS \| USA \| 1979-2020 \| 1090 \|\n\| BOM \| Australia \| 1979-2020 \| 116 \|\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Water Availability", "missionStartDate": "1970-01-01T00:00:00Z"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Evapotranspiration Yearly Gap-Filled", "missionStartDate": "2001-01-01T00:00:00Z"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day", "missionStartDate": "2000-02-16T00:00:00Z"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "title": "US Census", "missionStartDate": "2021-08-01T00:00:00Z"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "title": "JRC Global Surface Water", "missionStartDate": "1984-03-01T00:00:00Z"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Flood Maps", "missionStartDate": "2018-01-01T00:00:00Z"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily", "missionStartDate": "2000-02-16T00:00:00Z"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire Daily", "missionStartDate": "2000-02-18T00:00:00Z"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. Access: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. Reliability: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. Usage: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. Nighttime Lights: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "title": "HREA: High Resolution Electricity Access", "missionStartDate": "2012-12-31T00:00:00Z"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level-2A", "missionStartDate": "2015-06-27T10:25:31Z"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 8-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 4-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily North America", "missionStartDate": "1980-01-01T12:00:00Z"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "title": "Land Cover of Canada", "missionStartDate": "2015-01-01T00:00:00Z"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover 8-day", "missionStartDate": "2000-02-18T00:00:00Z"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows forecast times in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* stream is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* type is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* reference time is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the reference time changes and `step` as the forecast time changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast was made, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "title": "ECMWF Open Data (real-time)", "missionStartDate": null}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 24-Hour Pass 2 sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 24-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)", "missionStartDate": "2014-10-10T00:28:21Z"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "title": "NASADEM HGT v001", "missionStartDate": "2000-02-20T00:00:00Z"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "Esri 10-Meter Land Cover (10-class)", "missionStartDate": "2017-01-01T00:00:00Z"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-1", "missionStartDate": "1972-07-25T00:00:00Z"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "title": "Denver Regional Council of Governments Land Use Land Cover", "missionStartDate": "2018-01-01T00:00:00Z"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (7-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (13-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "title": "Chesapeake Land Use", "missionStartDate": "2013-01-01T00:00:00Z"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 1 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 1", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 2 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains Monthly data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nNote: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)", "missionStartDate": "1895-01-01T00:00:00Z"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "title": "GOES-R Lightning Detection", "missionStartDate": "2018-02-13T16:10:00Z"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "title": "USDA Cropland Data Layers (CDLs)", "missionStartDate": "2008-01-01T00:00:00Z"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "title": "Urban Innovation Eclipse Sensor Data", "missionStartDate": "2021-01-01T00:00:00Z"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\nNOTE: Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "title": "FWS National Wetlands Inventory", "missionStartDate": "2022-10-01T00:00:00Z"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "title": "USGS LCMAP CONUS Collection 1.3", "missionStartDate": "1985-01-01T00:00:00Z"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "title": "USGS LCMAP Hawaii Collection 1.0", "missionStartDate": "2000-01-01T00:00:00Z"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "title": "NOAA US Tabular Climate Normals", "missionStartDate": "1981-01-01T00:00:00Z"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (NetCDF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n\| Abbreviation \| Description \|\n\| ------------ \| ---------------------------------------- \|\n\| prcp \| Precipitation over the time period \|\n\| tavg \| Mean temperature over the time period \|\n\| tmax \| Maximum temperature over the time period \|\n\| tmin \| Minimum temperature over the time period \|\n\nAnd an \"aggregation\":\n\n\| Abbreviation \| Description \|\n\| ------------ \| ------------------------------------------------------------------------------ \|\n\| max \| Maximum of the variable over the time period \|\n\| min \| Minimum of the variable over the time period \|\n\| std \| Standard deviation of the value over the time period \|\n\| flag \| An count of the number of inputs (months, years, etc.) to calculate the normal \|\n\| norm \| The normal for the variable over the time period \|\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "title": "ASTER L1T", "missionStartDate": "2000-03-04T12:00:00Z"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40] \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40] \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-SA-40] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "io-lulc-9-class": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class)", "missionStartDate": "2017-01-01T00:00:00Z"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "title": "Biodiversity Intactness", "missionStartDate": "2017-01-01T00:00:00Z"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program", "missionStartDate": "2010-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR", "missionStartDate": "1972-03-01T00:00:00Z"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR NetCDFs", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - Optimum Interpolation CDR", "missionStartDate": "1981-09-01T00:00:00Z"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "title": "Sentinel-5P Level-2", "missionStartDate": "2018-04-30T00:18:50Z"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "title": "Sentinel-3 Water (Full Resolution)", "missionStartDate": "2017-11-01T00:07:01.738487Z"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR NetCDFs", "missionStartDate": "1972-03-01T00:00:00Z"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Global Aerosol", "missionStartDate": "2020-04-16T19:36:28.012367Z"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-09-27T11:17:21Z"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "title": "Sentinel-3 Land (Full Resolution)", "missionStartDate": "2016-04-25T11:33:47.368562Z"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Land Radar Altimetry", "missionStartDate": "2016-03-01T14:07:51.632846Z"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Land Surface Temperature", "missionStartDate": "2016-04-19T01:35:17.188500Z"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Sea Surface Temperature", "missionStartDate": "2017-10-31T23:59:57.451604Z"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Ocean Radar Altimetry", "missionStartDate": "2017-01-28T00:59:14.149496Z"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\nStage 1: Semantic Segmentation\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\nStage 2: Polygonization\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are not spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "title": "Microsoft Building Footprints", "missionStartDate": "2014-01-01T00:00:00Z"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Fire Radiative Power", "missionStartDate": "2020-08-08T23:11:15.617203Z"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Land Surface Reflectance and Aerosol", "missionStartDate": "2018-09-22T16:51:00.001276Z"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)", "missionStartDate": "2018-10-08T08:09:40.491227Z"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-10-04T23:17:21Z"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "title": "ESA WorldCover", "missionStartDate": "2020-01-01T00:00:00Z"}}}, "usgs_satapi_aws": {"providers_config": {"landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}, "landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}}, "product_types_config": {"landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-1 Product", "missionStartDate": "1972-07-25T00:00:00.000Z"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}}}} +{"astraea_eod": {"providers_config": {"landsat8_c2l1t1": {"productType": "landsat8_c2l1t1"}, "mcd43a4": {"productType": "mcd43a4"}, "mod11a1": {"productType": "mod11a1"}, "mod13a1": {"productType": "mod13a1"}, "myd11a1": {"productType": "myd11a1"}, "myd13a1": {"productType": "myd13a1"}, "maxar_open_data": {"productType": "maxar_open_data"}, "naip": {"productType": "naip"}, "sentinel1_l1c_grd": {"productType": "sentinel1_l1c_grd"}, "sentinel2_l1c": {"productType": "sentinel2_l1c"}, "sentinel2_l2a": {"productType": "sentinel2_l2a"}, "spacenet7": {"productType": "spacenet7"}, "umbra_open_data": {"productType": "umbra_open_data"}}, "product_types_config": {"landsat8_c2l1t1": {"abstract": "Landsat 8 Collection 2 Tier 1 Precision Terrain from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat8-c2l1t1", "license": "PDDL-1.0", "title": "Landsat 8 - Level 1", "missionStartDate": "2013-03-18T15:59:02.333Z"}, "mcd43a4": {"abstract": "MCD43A4: MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mcd43a4", "license": "CC-PDDC", "title": "MCD43A4 NBAR", "missionStartDate": "2000-02-16T00:00:00.000Z"}, "mod11a1": {"abstract": "MOD11A1: MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod11a1", "license": "CC-PDDC", "title": "MOD11A1 LST", "missionStartDate": "2000-02-24T00:00:00.000Z"}, "mod13a1": {"abstract": "MOD13A1: MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "mod13a1", "license": "CC-PDDC", "title": "MOD13A1 VI", "missionStartDate": "2000-02-18T00:00:00.000Z"}, "myd11a1": {"abstract": "MYD11A1: MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd11a1", "license": "CC-PDDC", "title": "MYD11A1 LST", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "myd13a1": {"abstract": "MYD13A1: MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "myd13a1", "license": "CC-PDDC", "title": "MYD13A1 VI", "missionStartDate": "2002-07-04T00:00:00.000Z"}, "maxar_open_data": {"abstract": "Maxar Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "maxar-open-data", "license": "CC-BY-NC-4.0", "title": "Maxar Open Data", "missionStartDate": "2008-01-15T00:00:00.000Z"}, "naip": {"abstract": "National Agriculture Imagery Program aerial imagery", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "naip", "license": "CC-PDDC", "title": "NAIP", "missionStartDate": "2012-04-23T12:00:00.000Z"}, "sentinel1_l1c_grd": {"abstract": "Sentinel-1 Level-1 Ground Range Detected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel1-l1c-grd", "license": "CC-BY-SA-3.0", "title": "Sentinel-1 L1C GRD", "missionStartDate": "2017-09-27T14:19:16.000"}, "sentinel2_l1c": {"abstract": "Sentinel-2 Level-1C top of atmosphere", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l1c", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L1C", "missionStartDate": "2015-06-27T10:25:31.456Z"}, "sentinel2_l2a": {"abstract": "Sentinel-2 Level-2A atmospherically corrected data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel2-l2a", "license": "CC-BY-SA-3.0", "title": "Sentinel-2 L2A", "missionStartDate": "2018-04-01T07:02:22.463Z"}, "spacenet7": {"abstract": "SpaceNet 7 Imagery and Labels", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "spacenet7", "license": "CC-BY-SA-4.0", "title": "SpaceNet 7", "missionStartDate": "2018-01-01T00:00:00.000Z"}, "umbra_open_data": {"abstract": "Umbra Open Data", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "umbra-open-data", "license": "proprietary", "title": "Umbra Open Data", "missionStartDate": null}}}, "creodias": {"providers_config": {"SENTINEL-3": {"collection": "SENTINEL-3"}, "LANDSAT-5": {"collection": "LANDSAT-5"}, "SENTINEL-2": {"collection": "SENTINEL-2"}, "SENTINEL-1": {"collection": "SENTINEL-1"}, "COP-DEM": {"collection": "COP-DEM"}, "ENVISAT": {"collection": "ENVISAT"}, "SENTINEL-5P": {"collection": "SENTINEL-5P"}, "TERRAAQUA": {"collection": "TERRAAQUA"}, "SENTINEL-1-RTC": {"collection": "SENTINEL-1-RTC"}, "SMOS": {"collection": "SMOS"}, "LANDSAT-8": {"collection": "LANDSAT-8"}, "S2GLC": {"collection": "S2GLC"}, "LANDSAT-7": {"collection": "LANDSAT-7"}, "SENTINEL-6": {"collection": "SENTINEL-6"}}, "product_types_config": {"SENTINEL-3": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-3", "license": null, "title": "SENTINEL-3", "missionStartDate": null}, "LANDSAT-5": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat-5", "license": null, "title": "LANDSAT-5", "missionStartDate": null}, "SENTINEL-2": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-2", "license": null, "title": "SENTINEL-2", "missionStartDate": null}, "SENTINEL-1": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-1", "license": null, "title": "SENTINEL-1", "missionStartDate": null}, "COP-DEM": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cop-dem", "license": null, "title": "COP-DEM", "missionStartDate": null}, "ENVISAT": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "envisat", "license": null, "title": "ENVISAT", "missionStartDate": null}, "SENTINEL-5P": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-5p", "license": null, "title": "SENTINEL-5P", "missionStartDate": null}, "TERRAAQUA": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "terraaqua", "license": null, "title": "TERRAAQUA", "missionStartDate": null}, "SENTINEL-1-RTC": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-1-rtc", "license": null, "title": "SENTINEL-1-RTC", "missionStartDate": null}, "SMOS": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "smos", "license": null, "title": "SMOS", "missionStartDate": null}, "LANDSAT-8": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat-8", "license": null, "title": "LANDSAT-8", "missionStartDate": null}, "S2GLC": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "s2glc", "license": null, "title": "S2GLC", "missionStartDate": null}, "LANDSAT-7": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "landsat-7", "license": null, "title": "LANDSAT-7", "missionStartDate": null}, "SENTINEL-6": {"abstract": null, "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "sentinel-6", "license": null, "title": "SENTINEL-6", "missionStartDate": null}}}, "earth_search": {"providers_config": {"sentinel-s2-l2a": {"productType": "sentinel-s2-l2a"}, "sentinel-s2-l1c": {"productType": "sentinel-s2-l1c"}, "landsat-8-l1-c1": {"productType": "landsat-8-l1-c1"}}, "product_types_config": {"sentinel-s2-l2a": {"abstract": "Sentinel-2a and Sentinel-2b imagery, processed to Level 2A (Surface Reflectance)", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2a,sentinel-2b,sentinel-s2-l2a", "license": "proprietary", "title": "Sentinel 2 L2A", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "sentinel-s2-l1c": {"abstract": "Sentinel-2a and Sentinel-2b imagery, processed to Level 1C (Top-Of-Atmosphere Geometrically Corrected)", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2a,sentinel-2b,sentinel-s2-l1c", "license": "proprietary", "title": "Sentinel 2 L1C", "missionStartDate": "2015-06-27T10:25:31.456000Z"}, "landsat-8-l1-c1": {"abstract": "Landat-8 L1 Collection-1 imagery radiometrically calibrated and orthorectified using gound points and Digital Elevation Model (DEM) data to correct relief displacement.", "instrument": "oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-8", "processingLevel": null, "keywords": "earth-observation,landsat,landsat-8,landsat-8-l1-c1,oli,tirs,usgs", "license": "PDDL-1.0", "title": "Landsat-8 L1 Collection-1", "missionStartDate": "2013-06-01T00:00:00Z"}}}, "earth_search_cog": null, "earth_search_gcs": null, "planetary_computer": {"providers_config": {"daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "3dep-seamless": {"productType": "3dep-seamless"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "fia": {"productType": "fia"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "gridmet": {"productType": "gridmet"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "hgb": {"productType": "hgb"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "goes-cmi": {"productType": "goes-cmi"}, "terraclimate": {"productType": "terraclimate"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "mtbs": {"productType": "mtbs"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "mobi": {"productType": "mobi"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "era5-pds": {"productType": "era5-pds"}, "chloris-biomass": {"productType": "chloris-biomass"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "gap": {"productType": "gap"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "gbif": {"productType": "gbif"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "alos-dem": {"productType": "alos-dem"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "us-census": {"productType": "us-census"}, "jrc-gsw": {"productType": "jrc-gsw"}, "deltares-floods": {"productType": "deltares-floods"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "hrea": {"productType": "hrea"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "nasadem": {"productType": "nasadem"}, "io-lulc": {"productType": "io-lulc"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "drcog-lulc": {"productType": "drcog-lulc"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "goes-glm": {"productType": "goes-glm"}, "usda-cdl": {"productType": "usda-cdl"}, "eclipse": {"productType": "eclipse"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "fws-nwi": {"productType": "fws-nwi"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "aster-l1t": {"productType": "aster-l1t"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-biodiversity": {"productType": "io-biodiversity"}, "naip": {"productType": "naip"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "ms-buildings": {"productType": "ms-buildings"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}}, "product_types_config": {"daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Puerto Rico", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Hawaii", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "title": "USGS 3DEP Seamless DEMs", "missionStartDate": "1925-01-01T00:00:00Z"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Surface Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "title": "Forest Inventory and Analysis", "missionStartDate": "2020-06-01T00:00:00Z"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)", "missionStartDate": "2014-10-10T00:28:21Z"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "title": "gridMET", "missionStartDate": "1979-01-01T00:00:00Z"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual North America", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly North America", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Annual Hawaii", "missionStartDate": "1980-07-01T12:00:00Z"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Hawaii", "missionStartDate": "1980-01-16T12:00:00Z"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "title": "Daymet Monthly Puerto Rico", "missionStartDate": "1980-01-16T12:00:00Z"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Tables", "missionStartDate": "2020-07-01T00:00:00Z"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "title": "HGB: Harmonized Global Biomass for 2010", "missionStartDate": "2010-12-31T00:00:00Z"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-30", "missionStartDate": "2021-04-22T00:00:00Z"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "title": "Copernicus DEM GLO-90", "missionStartDate": "2021-04-22T00:00:00Z"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "title": "GOES-R Cloud & Moisture Imagery", "missionStartDate": "2017-02-28T00:16:52Z"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "title": "TerraClimate", "missionStartDate": "1958-01-01T00:00:00Z"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- model is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- scenario is one of \"historical\", \"ssp245\" or \"ssp585\".\n- variable is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- year depends on the value of scenario. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some experimental reference files as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)", "missionStartDate": "1950-01-01T00:00:00Z"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "title": "GPM IMERG", "missionStartDate": "2000-06-01T00:00:00Z"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "title": "gNATSGO Soil Database - Rasters", "missionStartDate": "2020-07-01T00:00:00Z"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Height above Ground", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Intensity", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Source", "missionStartDate": "2012-01-01T00:00:00Z"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "title": "MTBS: Monitoring Trends in Burn Severity", "missionStartDate": "1984-12-31T00:00:00Z"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "title": "C-CAP Regional Land Cover and Change", "missionStartDate": "1975-01-01T00:00:00Z"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Point Cloud", "missionStartDate": "2012-01-01T00:00:00Z"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Burned Area Monthly", "missionStartDate": "2000-11-01T00:00:00Z"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "title": "ALOS Forest/Non-Forest Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Returns", "missionStartDate": "2012-01-01T00:00:00Z"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "title": "MoBI: Map of Biodiversity Importance", "missionStartDate": "2020-04-14T00:00:00Z"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-2", "missionStartDate": "1982-08-22T00:00:00Z"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An analysis, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A forecast starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "title": "ERA5 - PDS", "missionStartDate": "1979-01-01T00:00:00Z"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "title": "Chloris Biomass", "missionStartDate": "2003-07-31T00:00:00Z"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "title": "HydroForecast - Kwando & Upper Zambezi Rivers", "missionStartDate": "2022-01-01T00:00:00Z"}, "planet-nicfi-analytic": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Analytic)", "missionStartDate": "2015-12-01T00:00:00Z"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily Puerto Rico", "missionStartDate": "1980-01-01T12:00:00Z"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model (Native)", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Classification", "missionStartDate": "2012-01-01T00:00:00Z"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "title": "USGS 3DEP Lidar Digital Terrain Model", "missionStartDate": "2012-01-01T00:00:00Z"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "title": "USGS Gap Land Cover", "missionStartDate": "1999-01-01T00:00:00Z"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "planet-nicfi-visual": {"abstract": "Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [[email protected]](mailto:[email protected]).\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "title": "Planet-NICFI Basemaps (Visual)", "missionStartDate": "2015-12-01T00:00:00Z"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "title": "Global Biodiversity Information Facility (GBIF)", "missionStartDate": "2021-04-13T00:00:00Z"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Primary Production Yearly Gap-Filled", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "title": "ALOS World 3D-30m", "missionStartDate": "2016-12-07T00:00:00Z"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "title": "ALOS PALSAR Annual Mosaic", "missionStartDate": "2015-01-01T00:00:00Z"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n\| Name \| Scale \| Period \| Number of basins \|\n\|--------\|--------------------------\|-----------\|------------------\|\n\| ERA5 \| Global \| 1967-2020 \| 3236 \|\n\| CHIRPS \| Global (+/- 50 latitude) \| 1981-2020 \| 2951 \|\n\| EOBS \| Europe/North Africa \| 1979-2020 \| 682 \|\n\| NLDAS \| USA \| 1979-2020 \| 1090 \|\n\| BOM \| Australia \| 1979-2020 \| 116 \|\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Water Availability", "missionStartDate": "1970-01-01T00:00:00Z"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Net Evapotranspiration Yearly Gap-Filled", "missionStartDate": "2001-01-01T00:00:00Z"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day", "missionStartDate": "2000-02-16T00:00:00Z"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "title": "US Census", "missionStartDate": "2021-08-01T00:00:00Z"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "title": "JRC Global Surface Water", "missionStartDate": "1984-03-01T00:00:00Z"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=deltares-floods%20question).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "title": "Deltares Global Flood Maps", "missionStartDate": "2018-01-01T00:00:00Z"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily", "missionStartDate": "2000-02-16T00:00:00Z"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "title": "MODIS Surface Reflectance 8-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire Daily", "missionStartDate": "2000-02-18T00:00:00Z"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. Access: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. Reliability: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. Usage: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. Nighttime Lights: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "title": "HREA: High Resolution Electricity Access", "missionStartDate": "2012-12-31T00:00:00Z"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (250m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "title": "MODIS Thermal Anomalies/Fire 8-Day", "missionStartDate": "2000-02-18T00:00:00Z"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "title": "Sentinel-2 Level-2A", "missionStartDate": "2015-06-27T10:25:31Z"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 8-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "title": "MODIS Land Surface Temperature/Emissivity Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Leaf Area Index/FPAR 4-Day", "missionStartDate": "2002-07-04T00:00:00Z"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "title": "MODIS Vegetation Indices 16-Day (500m)", "missionStartDate": "2000-02-18T00:00:00Z"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "title": "Daymet Daily North America", "missionStartDate": "1980-01-01T12:00:00Z"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "title": "Land Cover of Canada", "missionStartDate": "2015-01-01T00:00:00Z"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover 8-day", "missionStartDate": "2000-02-18T00:00:00Z"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows forecast times in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* stream is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* type is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* reference time is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the reference time changes and `step` as the forecast time changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast was made, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "title": "ECMWF Open Data (real-time)", "missionStartDate": null}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 24-Hour Pass 2 sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 24-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)", "missionStartDate": "2014-10-10T00:28:21Z"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "title": "NASADEM HGT v001", "missionStartDate": "2000-02-20T00:00:00Z"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "Esri 10-Meter Land Cover (10-class)", "missionStartDate": "2017-01-01T00:00:00Z"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "title": "Landsat Collection 2 Level-1", "missionStartDate": "1972-07-25T00:00:00Z"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "title": "Denver Regional Council of Governments Land Use Land Cover", "missionStartDate": "2018-01-01T00:00:00Z"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (7-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "title": "Chesapeake Land Cover (13-class)", "missionStartDate": "2013-01-01T00:00:00Z"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "title": "Chesapeake Land Use", "missionStartDate": "2013-01-01T00:00:00Z"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 1 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 1", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the 1-Hour Pass 2 sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "title": "NOAA MRMS QPE 1-Hour Pass 2", "missionStartDate": "2022-07-21T20:00:00Z"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains Monthly data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nNote: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)", "missionStartDate": "1895-01-01T00:00:00Z"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "title": "GOES-R Lightning Detection", "missionStartDate": "2018-02-13T16:10:00Z"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "title": "USDA Cropland Data Layers (CDLs)", "missionStartDate": "2008-01-01T00:00:00Z"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`[email protected]`](mailto:[email protected]?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "title": "Urban Innovation Eclipse Sensor Data", "missionStartDate": "2021-01-01T00:00:00Z"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)", "missionStartDate": "1992-01-01T00:00:00Z"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\nNOTE: Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "title": "FWS National Wetlands Inventory", "missionStartDate": "2022-10-01T00:00:00Z"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "title": "USGS LCMAP CONUS Collection 1.3", "missionStartDate": "1985-01-01T00:00:00Z"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "title": "USGS LCMAP Hawaii Collection 1.0", "missionStartDate": "2000-01-01T00:00:00Z"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "title": "NOAA US Tabular Climate Normals", "missionStartDate": "1981-01-01T00:00:00Z"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (NetCDF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n\| Abbreviation \| Description \|\n\| ------------ \| ---------------------------------------- \|\n\| prcp \| Precipitation over the time period \|\n\| tavg \| Mean temperature over the time period \|\n\| tmax \| Maximum temperature over the time period \|\n\| tmin \| Minimum temperature over the time period \|\n\nAnd an \"aggregation\":\n\n\| Abbreviation \| Description \|\n\| ------------ \| ------------------------------------------------------------------------------ \|\n\| max \| Maximum of the variable over the time period \|\n\| min \| Minimum of the variable over the time period \|\n\| std \| Standard deviation of the value over the time period \|\n\| flag \| An count of the number of inputs (months, years, etc.) to calculate the normal \|\n\| norm \| The normal for the variable over the time period \|\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)", "missionStartDate": "1901-01-01T00:00:00Z"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "title": "ASTER L1T", "missionStartDate": "2000-03-04T12:00:00Z"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40] \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40] \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40] \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40] \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40] \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40] \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-SA-40] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "io-lulc-9-class": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "title": "10m Annual Land Use Land Cover (9-class)", "missionStartDate": "2017-01-01T00:00:00Z"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "title": "Biodiversity Intactness", "missionStartDate": "2017-01-01T00:00:00Z"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "title": "NAIP: National Agriculture Imagery Program", "missionStartDate": "2010-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR", "missionStartDate": "1972-03-01T00:00:00Z"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n\| Modeling institution \| Source model \| Available experiments \| License collection \|\n\| -------------------- \| ----------------- \| ------------------------------------------ \| ---------------------- \|\n\| CAS \| FGOALS-g3 [^1] \| SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM4-8 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| INM \| INM-CM5-0 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| Public domain datasets \|\n\| BCC \| BCC-CSM2-MR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-CM2-SR5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CMCC \| CMCC-ESM2 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40 \|\n\| CSIRO-ARCCSS \| ACCESS-CM2 \| SSP2-4.5 and SSP3-7.0 \| CC-BY-40 \|\n\| CSIRO \| ACCESS-ESM1-5 \| SSP1-2.6, SSP2-4.5, and SSP3-7.0 \| CC-BY-40 \|\n\| MIROC \| MIROC-ES2L \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MIROC \| MIROC6 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| HadGEM3-GC31-LL \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| MOHC \| UKESM1-0-LL \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M \| MPI-ESM1-2-LR \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| MPI-M/DKRZ [^2] \| MPI-ESM1-2-HR \| SSP1-2.6 and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-LM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NCC \| NorESM2-MM \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-CM4 \| SSP2-4.5 and SSP5-8.5 \| CC-BY-40 \|\n\| NOAA-GFDL \| GFDL-ESM4 \| SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 \| CC-BY-40 \|\n\| NUIST \| NESM3 \| SSP1-2.6, SSP2-4.5, and SSP5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-AerChem \| ssp370 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-CC \| ssp245 and ssp585 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| EC-Earth-Consortium \| EC-Earth3-Veg-LR \| ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 \| CC-BY-40 \|\n\| CCCma \| CanESM5 \| ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \| CC-BY-40[^3] \|\n\nNotes:\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs requires citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* FGOALS-g3\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan (2019). CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* INM-CM4-8\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 CMIP. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* INM-CM5-0\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 CMIP. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana (2019). INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* ACCESS-CM2\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* ACCESS-ESM1-5\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* BCC-CSM2-MR\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min (2019). BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* CMCC-CM2-SR5\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* CMCC-ESM2\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme (2021). CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* EC-Earth3-AerChem\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* EC-Earth3-CC\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2021). EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* EC-Earth3-Veg-LR\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* EC-Earth3-Veg\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* EC-Earth3\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) (2019). EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* GFDL-CM4\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong (2018). NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* GFDL-ESM4\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* HadGEM3-GC31-LL\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter (2019). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* MIROC-ES2L\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru (2019). MIROC MIROC-ES2L model output prepared for CMIP6 CMIP. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio (2019). MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* MIROC6\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki (2019). MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* MPI-ESM1-2-HR\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* MPI-ESM1-2-LR\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich (2019). MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* NESM3\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin (2019). NUIST NESMv3 model output prepared for CMIP6 CMIP. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian (2019). NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* NorESM2-LM\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* NorESM2-MM\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 CMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael (2019). NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* UKESM1-0-LL\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* CanESM5\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 CMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)", "missionStartDate": "1950-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - WHOI CDR NetCDFs", "missionStartDate": "1988-01-01T00:00:00Z"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "title": "Sea Surface Temperature - Optimum Interpolation CDR", "missionStartDate": "1981-09-01T00:00:00Z"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "title": "MODIS Snow Cover Daily", "missionStartDate": "2000-02-24T00:00:00Z"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "title": "Sentinel-5P Level-2", "missionStartDate": "2018-04-30T00:18:50Z"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "title": "Sentinel-3 Water (Full Resolution)", "missionStartDate": "2017-11-01T00:07:01.738487Z"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "title": "Global Ocean Heat Content CDR NetCDFs", "missionStartDate": "1972-03-01T00:00:00Z"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Global Aerosol", "missionStartDate": "2020-04-16T19:36:28.012367Z"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-09-27T11:17:21Z"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "title": "Sentinel-3 Land (Full Resolution)", "missionStartDate": "2016-04-25T11:33:47.368562Z"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Land Radar Altimetry", "missionStartDate": "2016-03-01T14:07:51.632846Z"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Land Surface Temperature", "missionStartDate": "2016-04-19T01:35:17.188500Z"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Sea Surface Temperature", "missionStartDate": "2017-10-31T23:59:57.451604Z"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "title": "Sentinel-3 Ocean Radar Altimetry", "missionStartDate": "2017-01-28T00:59:14.149496Z"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\nStage 1: Semantic Segmentation\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\nStage 2: Polygonization\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are not spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "title": "Microsoft Building Footprints", "missionStartDate": "2014-01-01T00:00:00Z"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "title": "Sentinel-3 Fire Radiative Power", "missionStartDate": "2020-08-08T23:11:15.617203Z"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Land Surface Reflectance and Aerosol", "missionStartDate": "2018-09-22T16:51:00.001276Z"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)", "missionStartDate": "2018-10-08T08:09:40.491227Z"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)", "missionStartDate": "2018-10-04T23:17:21Z"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "title": "ESA WorldCover", "missionStartDate": "2020-01-01T00:00:00Z"}}}, "usgs_satapi_aws": {"providers_config": {"landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}, "landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}}, "product_types_config": {"landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-1 Product", "missionStartDate": "1972-07-25T00:00:00.000Z"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product", "missionStartDate": "1982-08-22T00:00:00.000Z"}}}} diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 437d11b0..74648ba1 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1032,7 +1032,7 @@ auth: !plugin type: KeycloakOIDCPasswordAuth auth_base_uri: 'https://identity.cloudferro.com/auth' - realm: 'dias' + realm: 'Creodias-new' client_id: 'CLOUDFERRO_PUBLIC' client_secret: 'dc0aca03-2dc6-4798-a5de-fc5aeb6c8ee1' token_provision: qs diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index 6d7dfcc4..ef6920fe 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -69,6 +69,7 @@ creodias: credentials: username: password: + totp: # set totp dynamically, see https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication mundi: priority: # Lower value means lower priority (Default: 0) search: # Search parameters configuration	better document how to guess a product type see https://github.com/CS-SI/eodag/issues/242#issuecomment-877068032 - [x] Add more examples showing how to guess the product type in https://eodag.readthedocs.io/en/latest/notebooks/api_user_guide/4_search.html#Guess-a-product-type - [x] Link the `guess_product_type()` method in the API reference documentation - [x] Document [whoosh query language](https://github.com/whoosh-community/whoosh/blob/master/docs/source/querylang.rst) available through `guess_product_type()` method (operators, wildcard) - [x] Show an example on how to guess the product type while performing a CLI search, for example: ```py eodag search --platform SENTINEL2 --processingLevel L1 --box 1 43 2 44 --start 2021-03-01 --end 2021-03-31 ```	CS-SI/eodag	diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index d86f2cd2..2793adb1 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -423,6 +423,9 @@ class TestEODagEndToEnd(EndToEndBase): def test_end_to_end_search_download_creodias(self): product = self.execute_search(*CREODIAS_SEARCH_ARGS) + self.eodag.providers_config["creodias"].auth.credentials[ + "totp" + ] = "PLEASE_CHANGE_ME" expected_filename = "{}.zip".format(product.properties["title"]) self.execute_download(product, expected_filename) diff --git a/tests/units/test_auth_plugins.py b/tests/units/test_auth_plugins.py index f064f056..93c8d71d 100644 --- a/tests/units/test_auth_plugins.py +++ b/tests/units/test_auth_plugins.py @@ -19,6 +19,7 @@ import unittest from unittest import mock +import responses from requests import Request from requests.auth import AuthBase from requests.exceptions import RequestException @@ -391,3 +392,183 @@ class TestAuthPluginSASAuth(BaseAuthPluginTest): req = mock.Mock(headers={}, url="url") with self.assertRaises(AuthenticationError): auth(req) + + +class TestAuthPluginKeycloakOIDCPasswordAuth(BaseAuthPluginTest): + @classmethod + def setUpClass(cls): + super(TestAuthPluginKeycloakOIDCPasswordAuth, cls).setUpClass() + override_config_from_mapping( + cls.providers_config, + { + "foo_provider": { + "products": {}, + "auth": { + "type": "KeycloakOIDCPasswordAuth", + "auth_base_uri": "http://foo.bar", + "client_id": "baz", + "realm": "qux", + "client_secret": "1234", + "token_provision": "qs", + "token_qs_key": "totoken", + }, + }, + }, + ) + cls.plugins_manager = PluginManager(cls.providers_config) + + def test_plugins_auth_keycloak_validate_credentials(self): + """KeycloakOIDCPasswordAuth.validate_credentials must raise exception if not well configured""" + auth_plugin = self.get_auth_plugin("foo_provider") + + # credentials missing + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + + auth_plugin.config.credentials = {"username": "john"} + + # no error + auth_plugin.validate_config_credentials() + + # auth_base_uri missing + auth_base_uri = auth_plugin.config.__dict__.pop("auth_base_uri") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.auth_base_uri = auth_base_uri + # client_id missing + client_id = auth_plugin.config.__dict__.pop("client_id") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.client_id = client_id + # client_secret missing + client_secret = auth_plugin.config.__dict__.pop("client_secret") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.client_secret = client_secret + # token_provision missing + token_provision = auth_plugin.config.__dict__.pop("token_provision") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.token_provision = token_provision + + # no error + auth_plugin.validate_config_credentials() + + def test_plugins_auth_keycloak_authenticate(self): + """KeycloakOIDCPasswordAuth.authenticate must query and store the token as expected""" + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + req_kwargs = { + "client_id": "baz", + "client_secret": "1234", + "grant_type": "password", + "username": "john", + } + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + match=[responses.matchers.urlencoded_params_matcher(req_kwargs)], + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + self.assertEqual(auth.key, "totoken") + self.assertEqual(auth.token, "obtained-token") + self.assertEqual(auth.where, "qs") + + # check that token has been stored + self.assertEqual(auth_plugin.retrieved_token, "obtained-token") + + # check that stored token is used if new auth request fails + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + req_kwargs = { + "client_id": "baz", + "client_secret": "1234", + "grant_type": "password", + "username": "john", + } + rsps.add( + responses.POST, + url, + status=401, + json={"error": "not allowed"}, + match=[responses.matchers.urlencoded_params_matcher(req_kwargs)], + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + self.assertEqual(auth.key, "totoken") + self.assertEqual(auth.token, "obtained-token") + self.assertEqual(auth.where, "qs") + + def test_plugins_auth_keycloak_authenticate_qs(self): + """KeycloakOIDCPasswordAuth.authenticate must return a AuthBase object that will inject the token in a query-string""" # noqa + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + + # check if query string is integrated to the request + req = Request("GET", "https://httpbin.org/get").prepare() + auth(req) + self.assertEqual(req.url, "https://httpbin.org/get?totoken=obtained-token") + + another_req = Request("GET", "https://httpbin.org/get?baz=qux").prepare() + auth(another_req) + self.assertEqual( + another_req.url, + "https://httpbin.org/get?baz=qux&totoken=obtained-token", + ) + + def test_plugins_auth_keycloak_authenticate_header(self): + """KeycloakOIDCPasswordAuth.authenticate must return a AuthBase object that will inject the token in the header""" # noqa + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + # backup token_provision and change it to header mode + token_provision_qs = auth_plugin.config.token_provision + auth_plugin.config.token_provision = "header" + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + + # check if token header is integrated to the request + req = Request("GET", "https://httpbin.org/get").prepare() + auth(req) + self.assertEqual(req.url, "https://httpbin.org/get") + self.assertEqual(req.headers, {"Authorization": "Bearer obtained-token"}) + + another_req = Request( + "GET", "https://httpbin.org/get", headers={"existing-header": "value"} + ).prepare() + auth(another_req) + self.assertEqual( + another_req.headers, + {"Authorization": "Bearer obtained-token", "existing-header": "value"}, + ) + + auth_plugin.config.token_provision = token_provision_qs diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index 0755ad55..6a94fa5d 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -16,6 +16,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import os +import shutil import stat import unittest from pathlib import Path @@ -171,6 +172,71 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, ) + @mock.patch("eodag.plugins.download.http.requests.request", autospec=True) + @mock.patch("eodag.plugins.download.http.requests.head", autospec=True) + @mock.patch("eodag.plugins.download.http.requests.get", autospec=True) + def test_plugins_download_http_ignore_assets( + self, mock_requests_get, mock_requests_head, mock_requests_request + ): + """HTTPDownload.download() must ignore assets if configured to""" + + plugin = self.get_download_plugin(self.product) + self.product.location = ( + self.product.remote_location + ) = "http://somewhere/dowload_from_location" + self.product.properties["id"] = "someproduct" + self.product.assets = {"foo": {"href": "http://somewhere/download_asset"}} + + # download asset if ignore_assets = False + plugin.config.ignore_assets = False + path = plugin.download(self.product, outputs_prefix=self.output_dir) + mock_requests_get.assert_called_once_with( + self.product.assets["foo"]["href"], + stream=True, + auth=None, + params=plugin.config.dl_url_params, + headers=USER_AGENT, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) + mock_requests_request.assert_not_called() + # re-enable product download + self.product.location = self.product.remote_location + shutil.rmtree(path) + mock_requests_get.reset_mock() + mock_requests_request.reset_mock() + + # download using remote_location if ignore_assets = True + plugin.config.ignore_assets = True + path = plugin.download(self.product, outputs_prefix=self.output_dir) + mock_requests_get.assert_not_called() + mock_requests_request.assert_called_once_with( + "get", + self.product.remote_location, + stream=True, + auth=None, + params=plugin.config.dl_url_params, + headers=USER_AGENT, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) + # re-enable product download + self.product.location = self.product.remote_location + os.remove(path) + mock_requests_get.reset_mock() + mock_requests_request.reset_mock() + + # download asset if ignore_assets unset + del plugin.config.ignore_assets + plugin.download(self.product, outputs_prefix=self.output_dir) + mock_requests_get.assert_called_once_with( + self.product.assets["foo"]["href"], + stream=True, + auth=None, + params=plugin.config.dl_url_params, + headers=USER_AGENT, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) + mock_requests_request.assert_not_called() + @mock.patch("eodag.plugins.download.http.requests.head", autospec=True) @mock.patch("eodag.plugins.download.http.requests.get", autospec=True) def test_plugins_download_http_assets_filename_from_href(	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": -1, "issue_text_score": 1, "test_score": -1 }, "num_modified_files": 14 }	2.10	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@c38d36ec0be2c490d04060030c10726ecf327972#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.10.1.dev20+gc38d36ec - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_header", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_qs", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_validate_credentials", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_ads", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_cds", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_dataspace", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_hydroweb_next", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_meteoblue", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sara", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_old", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_recent", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEnd::test_search_by_tile", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search_gcs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias_noresult", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_gcs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_planetary_computer", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_cop_dataspace", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_hydroweb_next", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_meteoblue", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_json_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_text_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_missing", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_ok", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_with_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_without_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_validate_credentials_ok", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status_search_again", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_offline", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_online" ]	[]	Apache License 2.0	null
CS-SI__eodag-757	c38d36ec0be2c490d04060030c10726ecf327972	2023-07-06 13:19:07	856121900f6d973eca9d2ad3c0de61bbd706ae2c	github-actions[bot]: ## Test Results       4 files ±0       4 suites ±0 4m 33s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "duration of all tests") +26s   388 tests ±0   386 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0   2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 1 552 runs ±0 1 496 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") ±0 56 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") ±0 Results for commit d0107e29. ± Comparison against base commit c38d36ec. [test-results]:data:application/gzip;base64,H4sIAFLApmQC/03Myw6DIBCF4VcxrLsYLl7oyzR0GBNSlQZhZfruBavU5f9N5mxsdBOt7N6oW8PW5GINm4KJzi85RS8z5FMsRzkMZz3WhLhT96eXe5efCqNxUwaoQCH4cEhIS9nkbSuOOje50l2l32Zb4bK593US/Ty7mINZ4NCT0GQ5igGlQtQ9KbASLAKS6ARZ8wT2+QKJbaxECAEAAA== github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against d0107e29ed1c28c34cc97e40d30dc0ce262edab0 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `77%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against d0107e29ed1c28c34cc97e40d30dc0ce262edab0 </p>	diff --git a/docker/stac-server.dockerfile b/docker/stac-server.dockerfile index 543e1ea8..f7fb4b6c 100644 --- a/docker/stac-server.dockerfile +++ b/docker/stac-server.dockerfile @@ -65,7 +65,7 @@ RUN chmod +x /eodag/run-stac-server.sh # add user RUN addgroup --system user \ - && adduser --system --group user + && adduser --system --home /home/user --group user # switch to non-root user USER user diff --git a/docs/getting_started_guide/configure.rst b/docs/getting_started_guide/configure.rst index 7c2b22ad..2193594a 100644 --- a/docs/getting_started_guide/configure.rst +++ b/docs/getting_started_guide/configure.rst @@ -246,3 +246,62 @@ Or with the CLI: -p S2_MSI_L1C \ --storage my_search.geojson eodag download -f my_config.yml --search-results my_search.geojson + +Authenticate using an OTP / One Time Password (Two-Factor authentication) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Use OTP through Python code +""""""""""""""""""""""""""" + +``creodias`` needs a temporary 6-digits code to authenticate in addition of the ``username`` and ``password``. Check +`Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`_ +to see how to get this code once you are registered. This OTP code will only be valid for a few seconds, so you will +better set it dynamically in your python code instead of storing it statically in your user configuration file. + +Set the OTP by updating ``creodias`` credentials for ``totp`` entry, using one of the two following configuration update +commands: + +.. code-block:: python + + dag.providers_config["creodias"].auth.credentials["totp"] = "PLEASE_CHANGE_ME" + + # OR + + dag.update_providers_config( + """ + creodias: + auth: + credentials: + totp: PLEASE_CHANGE_ME + """ + ) + +Then quickly authenticate as this OTP has a few seconds only lifetime. First authentication will retrieve a token that +will be stored and used if further authentication tries fail: + +.. code-block:: python + + dag._plugins_manager.get_auth_plugin("creodias").authenticate() + +Please note that authentication mechanism is already included in +`download methods <https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/7_download.html>`_ , so you could also directly execute a +download to retrieve the token while the OTP is still valid. + +Use OTP through CLI +""""""""""""""""""" + +To download through CLI a product on a provider that needs a One Time Password, e.g. ``creodias``, first search on this +provider (increase the provider priotity to make eodag search on it): + +.. code-block:: console + + EODAG__CREODIAS__PRIORITY=2 eodag search -b 1 43 2 44 -s 2018-01-01 -e 2018-01-31 -p S2_MSI_L1C --items 1 + +Then download using the OTP (``creodias`` needs it as ``totp`` parameter): + +.. code-block:: console + + EODAG__CREODIAS__AUTH__CREDENTIALS__TOTP=PLEASE_CHANGE_ME eodag download --search-results search_results.geojson + +If needed, check in the documentation how to +`use environment variables to configure EODAG <#environment-variable-configuration>`_. diff --git a/docs/getting_started_guide/register.rst b/docs/getting_started_guide/register.rst index bea5eecd..c2f7d15a 100644 --- a/docs/getting_started_guide/register.rst +++ b/docs/getting_started_guide/register.rst @@ -15,7 +15,12 @@ to each provider supported by ``eodag``: * ``peps``: create an account `here <https://peps.cnes.fr/rocket/#/register>`__, then use your email as `username` in eodag credentials. -* ``creodias``: create an account `here <https://portal.creodias.eu/register.php>`__ +* ``creodias``: create an account `here <https://portal.creodias.eu/register.php>`__, then use your `username`, `password` in eodag credentials. You will also + need `totp` in credentials, a temporary 6-digits OTP (One Time Password, see + `Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`__) + to be able to authenticate and download. Check + `Authenticate using an OTP <https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication>`__ + to see how to proceed. * ``onda``: create an account `here: <https://www.onda-dias.eu/cms/>`__ diff --git a/eodag/plugins/authentication/keycloak.py b/eodag/plugins/authentication/keycloak.py index 26b4d2e7..9266e721 100644 --- a/eodag/plugins/authentication/keycloak.py +++ b/eodag/plugins/authentication/keycloak.py @@ -21,43 +21,101 @@ import requests from eodag.plugins.authentication import Authentication from eodag.plugins.authentication.openid_connect import CodeAuthorizedAuth from eodag.utils import USER_AGENT -from eodag.utils.exceptions import AuthenticationError +from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT class KeycloakOIDCPasswordAuth(Authentication): - """Authentication plugin using Keycloak and OpenId Connect""" + """Authentication plugin using Keycloak and OpenId Connect. + + This plugin request a token and use it through a query-string or a header. + + Using :class:`~eodag.plugins.download.http.HTTPDownload` a download link + `http://example.com?foo=bar` will become + `http://example.com?foo=bar&my-token=obtained-token` if associated to the following + configuration:: + + provider: + ... + auth: + plugin: KeycloakOIDCPasswordAuth + auth_base_uri: 'https://somewhere/auth' + realm: 'the-realm' + client_id: 'SOME_ID' + client_secret: '01234-56789' + token_provision: qs + token_qs_key: 'my-token' + ... + ... + + If configured to send the token through the header, the download request header will + be updated with `Authorization: "Bearer obtained-token"` if associated to the + following configuration:: + + provider: + ... + auth: + plugin: KeycloakOIDCPasswordAuth + auth_base_uri: 'https://somewhere/auth' + realm: 'the-realm' + client_id: 'SOME_ID' + client_secret: '01234-56789' + token_provision: header + ... + ... + """ GRANT_TYPE = "password" TOKEN_URL_TEMPLATE = "{auth_base_uri}/realms/{realm}/protocol/openid-connect/token" + REQUIRED_PARAMS = ["auth_base_uri", "client_id", "client_secret", "token_provision"] + # already retrieved token store, to be used if authenticate() fails (OTP use-case) + retrieved_token = None def __init__(self, provider, config): super(KeycloakOIDCPasswordAuth, self).__init__(provider, config) self.session = requests.Session() + def validate_config_credentials(self): + """Validate configured credentials""" + super(KeycloakOIDCPasswordAuth, self).validate_config_credentials() + + for param in self.REQUIRED_PARAMS: + if not hasattr(self.config, param): + raise MisconfiguredError( + "The following authentication configuration is missing for provider ", + f"{self.provider}: {param}", + ) + def authenticate(self): """ Makes authentication request """ self.validate_config_credentials() + req_data = { + "client_id": self.config.client_id, + "client_secret": self.config.client_secret, + "grant_type": self.GRANT_TYPE, + } + credentials = {k: v for k, v in self.config.credentials.items()} try: response = self.session.post( self.TOKEN_URL_TEMPLATE.format( auth_base_uri=self.config.auth_base_uri.rstrip("/"), realm=self.config.realm, ), - data={ - "client_id": self.config.client_id, - "client_secret": self.config.client_secret, - "username": self.config.credentials["username"], - "password": self.config.credentials["password"], - "grant_type": self.GRANT_TYPE, - }, + data=dict(req_data, *credentials), headers=USER_AGENT, timeout=HTTP_REQ_TIMEOUT, ) response.raise_for_status() except requests.RequestException as e: + if self.retrieved_token: + # try using already retrieved token if authenticate() fails (OTP use-case) + return CodeAuthorizedAuth( + self.retrieved_token, + self.config.token_provision, + key=getattr(self.config, "token_qs_key", None), + ) # check if error is identified as auth_error in provider conf auth_errors = getattr(self.config, "auth_error_code", [None]) if not isinstance(auth_errors, list): @@ -79,8 +137,10 @@ class KeycloakOIDCPasswordAuth(Authentication): tb.format_exc() ) ) + + self.retrieved_token = response.json()["access_token"] return CodeAuthorizedAuth( - response.json()["access_token"], + self.retrieved_token, self.config.token_provision, key=getattr(self.config, "token_qs_key", None), ) diff --git a/eodag/plugins/authentication/openid_connect.py b/eodag/plugins/authentication/openid_connect.py index 5531a1fd..b535ad66 100644 --- a/eodag/plugins/authentication/openid_connect.py +++ b/eodag/plugins/authentication/openid_connect.py @@ -25,14 +25,7 @@ from lxml import etree from requests.auth import AuthBase from eodag.plugins.authentication import Authentication -from eodag.utils import ( - USER_AGENT, - parse_qs, - repeatfunc, - urlencode, - urlparse, - urlunparse, -) +from eodag.utils import USER_AGENT, parse_qs, repeatfunc, urlparse from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT @@ -306,18 +299,12 @@ class CodeAuthorizedAuth(AuthBase): """Perform the actual authentication""" if self.where == "qs": parts = urlparse(request.url) - qs = parse_qs(parts.query) - qs[self.key] = self.token - request.url = urlunparse( - ( - parts.scheme, - parts.netloc, - parts.path, - parts.params, - urlencode(qs), - parts.fragment, - ) - ) + query_dict = parse_qs(parts.query) + query_dict.update({self.key: self.token}) + url_without_args = parts._replace(query=None).geturl() + + request.prepare_url(url_without_args, query_dict) + elif self.where == "header": request.headers["Authorization"] = "Bearer {}".format(self.token) return request diff --git a/eodag/plugins/download/aws.py b/eodag/plugins/download/aws.py index 3e440a02..4f53085f 100644 --- a/eodag/plugins/download/aws.py +++ b/eodag/plugins/download/aws.py @@ -169,16 +169,15 @@ class AwsDownload(Download): :param provider: provider name :type provider: str - :param config: Download plugin configuration - config.requester_pays: - :param config: Download plugin configuration: - ``config.base_uri`` (str) - s3 endpoint url - * ``config.requester_pays`` (bool) - whether download is done from - a requester-pays bucket or not - * ``config.flatten_top_dirs`` (bool) - flatten directory structure - * ``config.products`` (dict) - product_type specific configuration + * ``config.base_uri`` (str) - s3 endpoint url + * ``config.requester_pays`` (bool) - (optional) whether download is done from a + requester-pays bucket or not + * ``config.flatten_top_dirs`` (bool) - (optional) flatten directory structure + * ``config.products`` (dict) - (optional) product_type specific configuration + * ``config.ignore_assets`` (bool) - (optional) ignore assets and download using downloadLink + :type config: :class:`~eodag.config.PluginConfig` """ diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 4a5a20ab..716e2176 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -61,7 +61,30 @@ logger = logging.getLogger("eodag.plugins.download.http") class HTTPDownload(Download): - """HTTPDownload plugin. Handles product download over HTTP protocol""" + """HTTPDownload plugin. Handles product download over HTTP protocol + + :param provider: provider name + :type provider: str + :param config: Download plugin configuration: + + * ``config.base_uri`` (str) - default endpoint url + * ``config.extract`` (bool) - (optional) extract downloaded archive or not + * ``config.auth_error_code`` (int) - (optional) authentication error code + * ``config.dl_url_params`` (dict) - (optional) attitional parameters to send in the request + * ``config.archive_depth`` (int) - (optional) level in extracted path tree where to find data + * ``config.flatten_top_dirs`` (bool) - (optional) flatten directory structure + * ``config.ignore_assets`` (bool) - (optional) ignore assets and download using downloadLink + * ``config.order_enabled`` (bool) - (optional) wether order is enabled or not if product is `OFFLINE` + * ``config.order_method`` (str) - (optional) HTTP request method, GET (default) or POST + * ``config.order_headers`` (dict) - (optional) order request headers + * ``config.order_on_response`` (dict) - (optional) edit or add new product properties + * ``config.order_status_method`` (str) - (optional) status HTTP request method, GET (default) or POST + * ``config.order_status_percent`` (str) - (optional) progress percentage key in obtained status response + * ``config.order_status_error`` (dict) - (optional) key/value identifying an error status + + :type config: :class:`~eodag.config.PluginConfig` + + """ def __init__(self, provider, config): super(HTTPDownload, self).__init__(provider, config) @@ -341,19 +364,22 @@ class HTTPDownload(Download): return fs_path # download assets if exist instead of remote_location - try: - fs_path = self._download_assets( - product, - fs_path.replace(".zip", ""), - record_filename, - auth, - progress_callback, - kwargs, - ) - product.location = path_to_uri(fs_path) - return fs_path - except NotAvailableError: - pass + if hasattr(product, "assets") and not getattr( + self.config, "ignore_assets", False + ): + try: + fs_path = self._download_assets( + product, + fs_path.replace(".zip", ""), + record_filename, + auth, + progress_callback, + kwargs, + ) + product.location = path_to_uri(fs_path) + return fs_path + except NotAvailableError: + pass # order product if it is offline ordered_message = "" diff --git a/eodag/plugins/download/s3rest.py b/eodag/plugins/download/s3rest.py index cdbe470c..2602df4c 100644 --- a/eodag/plugins/download/s3rest.py +++ b/eodag/plugins/download/s3rest.py @@ -59,6 +59,25 @@ class S3RestDownload(Download): https://mundiwebservices.com/keystoneapi/uploads/documents/CWS-DATA-MUT-087-EN-Mundi_Download_v1.1.pdf#page=13 Re-use AwsDownload bucket some handling methods + + :param provider: provider name + :type provider: str + :param config: Download plugin configuration: + + * ``config.base_uri`` (str) - default endpoint url + * ``config.extract`` (bool) - (optional) extract downloaded archive or not + * ``config.auth_error_code`` (int) - (optional) authentication error code + * ``config.bucket_path_level`` (int) - (optional) bucket location index in path.split('/') + * ``config.order_enabled`` (bool) - (optional) wether order is enabled or not if product is `OFFLINE` + * ``config.order_method`` (str) - (optional) HTTP request method, GET (default) or POST + * ``config.order_headers`` (dict) - (optional) order request headers + * ``config.order_on_response`` (dict) - (optional) edit or add new product properties + * ``config.order_status_method`` (str) - (optional) status HTTP request method, GET (default) or POST + * ``config.order_status_percent`` (str) - (optional) progress percentage key in obtained status response + * ``config.order_status_success`` (dict) - (optional) key/value identifying an error success + * ``config.order_status_on_success`` (dict) - (optional) edit or add new product properties + + :type config: :class:`~eodag.config.PluginConfig` """ def __init__(self, provider, config): diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 437d11b0..74648ba1 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1032,7 +1032,7 @@ auth: !plugin type: KeycloakOIDCPasswordAuth auth_base_uri: 'https://identity.cloudferro.com/auth' - realm: 'dias' + realm: 'Creodias-new' client_id: 'CLOUDFERRO_PUBLIC' client_secret: 'dc0aca03-2dc6-4798-a5de-fc5aeb6c8ee1' token_provision: qs diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index 6d7dfcc4..ef6920fe 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -69,6 +69,7 @@ creodias: credentials: username: password: + totp: # set totp dynamically, see https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication mundi: priority: # Lower value means lower priority (Default: 0) search: # Search parameters configuration	downloadLink usage for STAC providers Implement a way to use `downloadLink` if specified, to dowload a product coming from a STAC provider - [ ] remove or set `downloadLink` to `None` by default on STAC providers - [ ] if `downloadLink` exists, first try using it before downloading all assets `ignore_assets` can also be used as it is done in `AwsDownload`	CS-SI/eodag	diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index d86f2cd2..2793adb1 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -423,6 +423,9 @@ class TestEODagEndToEnd(EndToEndBase): def test_end_to_end_search_download_creodias(self): product = self.execute_search(*CREODIAS_SEARCH_ARGS) + self.eodag.providers_config["creodias"].auth.credentials[ + "totp" + ] = "PLEASE_CHANGE_ME" expected_filename = "{}.zip".format(product.properties["title"]) self.execute_download(product, expected_filename) diff --git a/tests/units/test_auth_plugins.py b/tests/units/test_auth_plugins.py index f064f056..93c8d71d 100644 --- a/tests/units/test_auth_plugins.py +++ b/tests/units/test_auth_plugins.py @@ -19,6 +19,7 @@ import unittest from unittest import mock +import responses from requests import Request from requests.auth import AuthBase from requests.exceptions import RequestException @@ -391,3 +392,183 @@ class TestAuthPluginSASAuth(BaseAuthPluginTest): req = mock.Mock(headers={}, url="url") with self.assertRaises(AuthenticationError): auth(req) + + +class TestAuthPluginKeycloakOIDCPasswordAuth(BaseAuthPluginTest): + @classmethod + def setUpClass(cls): + super(TestAuthPluginKeycloakOIDCPasswordAuth, cls).setUpClass() + override_config_from_mapping( + cls.providers_config, + { + "foo_provider": { + "products": {}, + "auth": { + "type": "KeycloakOIDCPasswordAuth", + "auth_base_uri": "http://foo.bar", + "client_id": "baz", + "realm": "qux", + "client_secret": "1234", + "token_provision": "qs", + "token_qs_key": "totoken", + }, + }, + }, + ) + cls.plugins_manager = PluginManager(cls.providers_config) + + def test_plugins_auth_keycloak_validate_credentials(self): + """KeycloakOIDCPasswordAuth.validate_credentials must raise exception if not well configured""" + auth_plugin = self.get_auth_plugin("foo_provider") + + # credentials missing + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + + auth_plugin.config.credentials = {"username": "john"} + + # no error + auth_plugin.validate_config_credentials() + + # auth_base_uri missing + auth_base_uri = auth_plugin.config.__dict__.pop("auth_base_uri") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.auth_base_uri = auth_base_uri + # client_id missing + client_id = auth_plugin.config.__dict__.pop("client_id") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.client_id = client_id + # client_secret missing + client_secret = auth_plugin.config.__dict__.pop("client_secret") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.client_secret = client_secret + # token_provision missing + token_provision = auth_plugin.config.__dict__.pop("token_provision") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.token_provision = token_provision + + # no error + auth_plugin.validate_config_credentials() + + def test_plugins_auth_keycloak_authenticate(self): + """KeycloakOIDCPasswordAuth.authenticate must query and store the token as expected""" + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + req_kwargs = { + "client_id": "baz", + "client_secret": "1234", + "grant_type": "password", + "username": "john", + } + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + match=[responses.matchers.urlencoded_params_matcher(req_kwargs)], + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + self.assertEqual(auth.key, "totoken") + self.assertEqual(auth.token, "obtained-token") + self.assertEqual(auth.where, "qs") + + # check that token has been stored + self.assertEqual(auth_plugin.retrieved_token, "obtained-token") + + # check that stored token is used if new auth request fails + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + req_kwargs = { + "client_id": "baz", + "client_secret": "1234", + "grant_type": "password", + "username": "john", + } + rsps.add( + responses.POST, + url, + status=401, + json={"error": "not allowed"}, + match=[responses.matchers.urlencoded_params_matcher(req_kwargs)], + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + self.assertEqual(auth.key, "totoken") + self.assertEqual(auth.token, "obtained-token") + self.assertEqual(auth.where, "qs") + + def test_plugins_auth_keycloak_authenticate_qs(self): + """KeycloakOIDCPasswordAuth.authenticate must return a AuthBase object that will inject the token in a query-string""" # noqa + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + + # check if query string is integrated to the request + req = Request("GET", "https://httpbin.org/get").prepare() + auth(req) + self.assertEqual(req.url, "https://httpbin.org/get?totoken=obtained-token") + + another_req = Request("GET", "https://httpbin.org/get?baz=qux").prepare() + auth(another_req) + self.assertEqual( + another_req.url, + "https://httpbin.org/get?baz=qux&totoken=obtained-token", + ) + + def test_plugins_auth_keycloak_authenticate_header(self): + """KeycloakOIDCPasswordAuth.authenticate must return a AuthBase object that will inject the token in the header""" # noqa + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + # backup token_provision and change it to header mode + token_provision_qs = auth_plugin.config.token_provision + auth_plugin.config.token_provision = "header" + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + + # check if token header is integrated to the request + req = Request("GET", "https://httpbin.org/get").prepare() + auth(req) + self.assertEqual(req.url, "https://httpbin.org/get") + self.assertEqual(req.headers, {"Authorization": "Bearer obtained-token"}) + + another_req = Request( + "GET", "https://httpbin.org/get", headers={"existing-header": "value"} + ).prepare() + auth(another_req) + self.assertEqual( + another_req.headers, + {"Authorization": "Bearer obtained-token", "existing-header": "value"}, + ) + + auth_plugin.config.token_provision = token_provision_qs diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index 0755ad55..6a94fa5d 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -16,6 +16,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import os +import shutil import stat import unittest from pathlib import Path @@ -171,6 +172,71 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, ) + @mock.patch("eodag.plugins.download.http.requests.request", autospec=True) + @mock.patch("eodag.plugins.download.http.requests.head", autospec=True) + @mock.patch("eodag.plugins.download.http.requests.get", autospec=True) + def test_plugins_download_http_ignore_assets( + self, mock_requests_get, mock_requests_head, mock_requests_request + ): + """HTTPDownload.download() must ignore assets if configured to""" + + plugin = self.get_download_plugin(self.product) + self.product.location = ( + self.product.remote_location + ) = "http://somewhere/dowload_from_location" + self.product.properties["id"] = "someproduct" + self.product.assets = {"foo": {"href": "http://somewhere/download_asset"}} + + # download asset if ignore_assets = False + plugin.config.ignore_assets = False + path = plugin.download(self.product, outputs_prefix=self.output_dir) + mock_requests_get.assert_called_once_with( + self.product.assets["foo"]["href"], + stream=True, + auth=None, + params=plugin.config.dl_url_params, + headers=USER_AGENT, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) + mock_requests_request.assert_not_called() + # re-enable product download + self.product.location = self.product.remote_location + shutil.rmtree(path) + mock_requests_get.reset_mock() + mock_requests_request.reset_mock() + + # download using remote_location if ignore_assets = True + plugin.config.ignore_assets = True + path = plugin.download(self.product, outputs_prefix=self.output_dir) + mock_requests_get.assert_not_called() + mock_requests_request.assert_called_once_with( + "get", + self.product.remote_location, + stream=True, + auth=None, + params=plugin.config.dl_url_params, + headers=USER_AGENT, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) + # re-enable product download + self.product.location = self.product.remote_location + os.remove(path) + mock_requests_get.reset_mock() + mock_requests_request.reset_mock() + + # download asset if ignore_assets unset + del plugin.config.ignore_assets + plugin.download(self.product, outputs_prefix=self.output_dir) + mock_requests_get.assert_called_once_with( + self.product.assets["foo"]["href"], + stream=True, + auth=None, + params=plugin.config.dl_url_params, + headers=USER_AGENT, + timeout=DEFAULT_STREAM_REQUESTS_TIMEOUT, + ) + mock_requests_request.assert_not_called() + @mock.patch("eodag.plugins.download.http.requests.head", autospec=True) @mock.patch("eodag.plugins.download.http.requests.get", autospec=True) def test_plugins_download_http_assets_filename_from_href(	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 10 }	2.10	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "sphinx", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	alabaster==0.7.16 annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 babel==2.17.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@c38d36ec0be2c490d04060030c10726ecf327972#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 imagesize==1.4.1 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 snowballstemmer==2.2.0 Sphinx==7.4.7 sphinxcontrib-applehelp==2.0.0 sphinxcontrib-devhelp==2.0.0 sphinxcontrib-htmlhelp==2.1.0 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==2.0.0 sphinxcontrib-serializinghtml==2.0.0 starlette==0.46.1 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - alabaster==0.7.16 - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - babel==2.17.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.10.1.dev20+gc38d36ec - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - imagesize==1.4.1 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - snowballstemmer==2.2.0 - sphinx==7.4.7 - sphinxcontrib-applehelp==2.0.0 - sphinxcontrib-devhelp==2.0.0 - sphinxcontrib-htmlhelp==2.1.0 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==2.0.0 - sphinxcontrib-serializinghtml==2.0.0 - starlette==0.46.1 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_header", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_qs", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_validate_credentials", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_ads", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_cds", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_dataspace", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_hydroweb_next", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_meteoblue", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sara", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_old", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_recent", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEnd::test_search_by_tile", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search_gcs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias_noresult", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_gcs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_planetary_computer", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_cop_dataspace", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_hydroweb_next", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_meteoblue", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_json_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_text_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_missing", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_ok", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_with_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_without_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_validate_credentials_ok", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status_search_again", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_offline", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_online" ]	[]	Apache License 2.0	null
CS-SI__eodag-763	c38d36ec0be2c490d04060030c10726ecf327972	2023-07-13 16:08:46	856121900f6d973eca9d2ad3c0de61bbd706ae2c	github-actions[bot]: ## Test Results     1 files -       3     1 suites - 3 1m 10s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "duration of all tests") - 3m 10s 392 tests +      4 107 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") -   279 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") ±  0 62 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") +62 221 [:fire:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "test errors") +221 392 runs - 1 160 107 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "passed tests") - 1 389 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "skipped / disabled tests") - 54 62 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "failed tests") +62 221 [:fire:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.8.0/README.md#the-symbols "test errors") +221 For more details on these failures and errors, see [this check](https://github.com/CS-SI/eodag/runs/15020308526). Results for commit 9ce88708. ± Comparison against base commit c38d36ec. [test-results]:data:application/gzip;base64,H4sIAG8isGQC/1WNyw7CIBBFf6Vh7QJKkcGfMeWVENtieKwa/92hRrHLc+bmzE58WFwmt4FdBpJrKD+wNc0lxA1RUmS8lHbjavzSPVdj2pzKrh7hiapv/BwWFNduXEoxtc3Y/qS69WyDc/VjevTg/+YhzkkT1zUURKKMA5AUuJ30pDj1VoMFYYQHnGrFBdMMqCevN+xw9mAKAQAA	diff --git a/docs/getting_started_guide/configure.rst b/docs/getting_started_guide/configure.rst index 7c2b22ad..2193594a 100644 --- a/docs/getting_started_guide/configure.rst +++ b/docs/getting_started_guide/configure.rst @@ -246,3 +246,62 @@ Or with the CLI: -p S2_MSI_L1C \ --storage my_search.geojson eodag download -f my_config.yml --search-results my_search.geojson + +Authenticate using an OTP / One Time Password (Two-Factor authentication) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Use OTP through Python code +""""""""""""""""""""""""""" + +``creodias`` needs a temporary 6-digits code to authenticate in addition of the ``username`` and ``password``. Check +`Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`_ +to see how to get this code once you are registered. This OTP code will only be valid for a few seconds, so you will +better set it dynamically in your python code instead of storing it statically in your user configuration file. + +Set the OTP by updating ``creodias`` credentials for ``totp`` entry, using one of the two following configuration update +commands: + +.. code-block:: python + + dag.providers_config["creodias"].auth.credentials["totp"] = "PLEASE_CHANGE_ME" + + # OR + + dag.update_providers_config( + """ + creodias: + auth: + credentials: + totp: PLEASE_CHANGE_ME + """ + ) + +Then quickly authenticate as this OTP has a few seconds only lifetime. First authentication will retrieve a token that +will be stored and used if further authentication tries fail: + +.. code-block:: python + + dag._plugins_manager.get_auth_plugin("creodias").authenticate() + +Please note that authentication mechanism is already included in +`download methods <https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/7_download.html>`_ , so you could also directly execute a +download to retrieve the token while the OTP is still valid. + +Use OTP through CLI +""""""""""""""""""" + +To download through CLI a product on a provider that needs a One Time Password, e.g. ``creodias``, first search on this +provider (increase the provider priotity to make eodag search on it): + +.. code-block:: console + + EODAG__CREODIAS__PRIORITY=2 eodag search -b 1 43 2 44 -s 2018-01-01 -e 2018-01-31 -p S2_MSI_L1C --items 1 + +Then download using the OTP (``creodias`` needs it as ``totp`` parameter): + +.. code-block:: console + + EODAG__CREODIAS__AUTH__CREDENTIALS__TOTP=PLEASE_CHANGE_ME eodag download --search-results search_results.geojson + +If needed, check in the documentation how to +`use environment variables to configure EODAG <#environment-variable-configuration>`_. diff --git a/docs/getting_started_guide/register.rst b/docs/getting_started_guide/register.rst index bea5eecd..c2f7d15a 100644 --- a/docs/getting_started_guide/register.rst +++ b/docs/getting_started_guide/register.rst @@ -15,7 +15,12 @@ to each provider supported by ``eodag``: * ``peps``: create an account `here <https://peps.cnes.fr/rocket/#/register>`__, then use your email as `username` in eodag credentials. -* ``creodias``: create an account `here <https://portal.creodias.eu/register.php>`__ +* ``creodias``: create an account `here <https://portal.creodias.eu/register.php>`__, then use your `username`, `password` in eodag credentials. You will also + need `totp` in credentials, a temporary 6-digits OTP (One Time Password, see + `Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`__) + to be able to authenticate and download. Check + `Authenticate using an OTP <https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication>`__ + to see how to proceed. * ``onda``: create an account `here: <https://www.onda-dias.eu/cms/>`__ diff --git a/eodag/plugins/authentication/keycloak.py b/eodag/plugins/authentication/keycloak.py index 26b4d2e7..9266e721 100644 --- a/eodag/plugins/authentication/keycloak.py +++ b/eodag/plugins/authentication/keycloak.py @@ -21,43 +21,101 @@ import requests from eodag.plugins.authentication import Authentication from eodag.plugins.authentication.openid_connect import CodeAuthorizedAuth from eodag.utils import USER_AGENT -from eodag.utils.exceptions import AuthenticationError +from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT class KeycloakOIDCPasswordAuth(Authentication): - """Authentication plugin using Keycloak and OpenId Connect""" + """Authentication plugin using Keycloak and OpenId Connect. + + This plugin request a token and use it through a query-string or a header. + + Using :class:`~eodag.plugins.download.http.HTTPDownload` a download link + `http://example.com?foo=bar` will become + `http://example.com?foo=bar&my-token=obtained-token` if associated to the following + configuration:: + + provider: + ... + auth: + plugin: KeycloakOIDCPasswordAuth + auth_base_uri: 'https://somewhere/auth' + realm: 'the-realm' + client_id: 'SOME_ID' + client_secret: '01234-56789' + token_provision: qs + token_qs_key: 'my-token' + ... + ... + + If configured to send the token through the header, the download request header will + be updated with `Authorization: "Bearer obtained-token"` if associated to the + following configuration:: + + provider: + ... + auth: + plugin: KeycloakOIDCPasswordAuth + auth_base_uri: 'https://somewhere/auth' + realm: 'the-realm' + client_id: 'SOME_ID' + client_secret: '01234-56789' + token_provision: header + ... + ... + """ GRANT_TYPE = "password" TOKEN_URL_TEMPLATE = "{auth_base_uri}/realms/{realm}/protocol/openid-connect/token" + REQUIRED_PARAMS = ["auth_base_uri", "client_id", "client_secret", "token_provision"] + # already retrieved token store, to be used if authenticate() fails (OTP use-case) + retrieved_token = None def __init__(self, provider, config): super(KeycloakOIDCPasswordAuth, self).__init__(provider, config) self.session = requests.Session() + def validate_config_credentials(self): + """Validate configured credentials""" + super(KeycloakOIDCPasswordAuth, self).validate_config_credentials() + + for param in self.REQUIRED_PARAMS: + if not hasattr(self.config, param): + raise MisconfiguredError( + "The following authentication configuration is missing for provider ", + f"{self.provider}: {param}", + ) + def authenticate(self): """ Makes authentication request """ self.validate_config_credentials() + req_data = { + "client_id": self.config.client_id, + "client_secret": self.config.client_secret, + "grant_type": self.GRANT_TYPE, + } + credentials = {k: v for k, v in self.config.credentials.items()} try: response = self.session.post( self.TOKEN_URL_TEMPLATE.format( auth_base_uri=self.config.auth_base_uri.rstrip("/"), realm=self.config.realm, ), - data={ - "client_id": self.config.client_id, - "client_secret": self.config.client_secret, - "username": self.config.credentials["username"], - "password": self.config.credentials["password"], - "grant_type": self.GRANT_TYPE, - }, + data=dict(req_data, **credentials), headers=USER_AGENT, timeout=HTTP_REQ_TIMEOUT, ) response.raise_for_status() except requests.RequestException as e: + if self.retrieved_token: + # try using already retrieved token if authenticate() fails (OTP use-case) + return CodeAuthorizedAuth( + self.retrieved_token, + self.config.token_provision, + key=getattr(self.config, "token_qs_key", None), + ) # check if error is identified as auth_error in provider conf auth_errors = getattr(self.config, "auth_error_code", [None]) if not isinstance(auth_errors, list): @@ -79,8 +137,10 @@ class KeycloakOIDCPasswordAuth(Authentication): tb.format_exc() ) ) + + self.retrieved_token = response.json()["access_token"] return CodeAuthorizedAuth( - response.json()["access_token"], + self.retrieved_token, self.config.token_provision, key=getattr(self.config, "token_qs_key", None), ) diff --git a/eodag/plugins/authentication/openid_connect.py b/eodag/plugins/authentication/openid_connect.py index 5531a1fd..b535ad66 100644 --- a/eodag/plugins/authentication/openid_connect.py +++ b/eodag/plugins/authentication/openid_connect.py @@ -25,14 +25,7 @@ from lxml import etree from requests.auth import AuthBase from eodag.plugins.authentication import Authentication -from eodag.utils import ( - USER_AGENT, - parse_qs, - repeatfunc, - urlencode, - urlparse, - urlunparse, -) +from eodag.utils import USER_AGENT, parse_qs, repeatfunc, urlparse from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT @@ -306,18 +299,12 @@ class CodeAuthorizedAuth(AuthBase): """Perform the actual authentication""" if self.where == "qs": parts = urlparse(request.url) - qs = parse_qs(parts.query) - qs[self.key] = self.token - request.url = urlunparse( - ( - parts.scheme, - parts.netloc, - parts.path, - parts.params, - urlencode(qs), - parts.fragment, - ) - ) + query_dict = parse_qs(parts.query) + query_dict.update({self.key: self.token}) + url_without_args = parts._replace(query=None).geturl() + + request.prepare_url(url_without_args, query_dict) + elif self.where == "header": request.headers["Authorization"] = "Bearer {}".format(self.token) return request diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 437d11b0..74648ba1 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1032,7 +1032,7 @@ auth: !plugin type: KeycloakOIDCPasswordAuth auth_base_uri: 'https://identity.cloudferro.com/auth' - realm: 'dias' + realm: 'Creodias-new' client_id: 'CLOUDFERRO_PUBLIC' client_secret: 'dc0aca03-2dc6-4798-a5de-fc5aeb6c8ee1' token_provision: qs diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index 6d7dfcc4..ef6920fe 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -69,6 +69,7 @@ creodias: credentials: username: password: + totp: # set totp dynamically, see https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html#authenticate-using-an-otp-one-time-password-two-factor-authentication mundi: priority: # Lower value means lower priority (Default: 0) search: # Search parameters configuration	[URGENT] Unable to download products from CreoDIAS due to authentification change Describe the bug When searching for Sentinel-1 GRD products with CreoDIAS as a provider, the `download` command returns an error. It seems than CreoDIAS has recently updated their website (see new website "[CreoDIAS 2.0](https://creodias.eu/)" that now requires MFA to sign in) and their authentification method through the API. Code To Reproduce With CreoDIAS as main provider configured, `search` will pass successfully but `download` will fail: ```sh eodag search --productType S1_SAR_GRD --start 2023-06-01 --end 2023-06-02 eodag -vvv download --search-results search_results.geojson ``` Output The `download` command will return an authentification error: ```sh eodag.utils.exceptions.AuthenticationError: Something went wrong while trying to get access token: Traceback (most recent call last): File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/authentication/keycloak.py", line 59, in authenticate response.raise_for_status() File "/home/username/.local/lib/python3.8/site-packages/requests/models.py", line 1022, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://identity.cloudferro.com/auth/realms/dias/protocol/openid-connect/token ``` <details open> <summary>See the full error below returned by the `download` command</summary> <br> ```sh 2023-06-28 14:37:35,395 eodag.config [INFO ] (config ) Loading user configuration from: /home/username/.config/eodag/eodag.yml 2023-06-28 14:37:35,468 eodag.core [INFO ] (core ) usgs: provider needing auth for search has been pruned because no crendentials could be found 2023-06-28 14:37:35,468 eodag.core [INFO ] (core ) aws_eos: provider needing auth for search has been pruned because no crendentials could be found 2023-06-28 14:37:35,468 eodag.core [INFO ] (core ) meteoblue: provider needing auth for search has been pruned because no crendentials could be found 2023-06-28 14:37:35,468 eodag.core [INFO ] (core ) hydroweb_next: provider needing auth for search has been pruned because no crendentials could be found 2023-06-28 14:37:35,540 eodag.core [DEBUG ] (core ) Opening product types index in /home/username/.config/eodag/.index 2023-06-28 14:37:35,546 eodag.core [INFO ] (core ) Locations configuration loaded from /home/username/.config/eodag/locations.yml 2023-06-28 14:37:35,549 eodag.core [INFO ] (core ) Downloading 20 products Downloaded products: 0%\| \| 0/20 [00:00<?, ?product/s]2023-06-28 14:37:35,686 eodag.plugins.download.base [ERROR ] (base ) Stopped because of credentials problems with provider creodias Traceback (most recent call last): File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/authentication/keycloak.py", line 59, in authenticate response.raise_for_status() File "/home/username/.local/lib/python3.8/site-packages/requests/models.py", line 1022, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://identity.cloudferro.com/auth/realms/dias/protocol/openid-connect/token During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/download/base.py", line 453, in download_all product.download( File "/home/username/.local/lib/python3.8/site-packages/eodag/api/product/_product.py", line 304, in download self.downloader_auth.authenticate() File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/authentication/keycloak.py", line 77, in authenticate raise AuthenticationError( eodag.utils.exceptions.AuthenticationError: Something went wrong while trying to get access token: Traceback (most recent call last): File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/authentication/keycloak.py", line 59, in authenticate response.raise_for_status() File "/home/username/.local/lib/python3.8/site-packages/requests/models.py", line 1022, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://identity.cloudferro.com/auth/realms/dias/protocol/openid-connect/token Downloaded products: 0%\| \| 0/20 [00:00<?, ?product/s] Traceback (most recent call last): File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/authentication/keycloak.py", line 59, in authenticate response.raise_for_status() File "/home/username/.local/lib/python3.8/site-packages/requests/models.py", line 1022, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://identity.cloudferro.com/auth/realms/dias/protocol/openid-connect/token During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/username/.local/bin/eodag", line 8, in <module> sys.exit(eodag()) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1130, in __call__ return self.main(args, kwargs) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1657, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, ctx.params) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 760, in invoke return __callback(args, *kwargs) File "/home/username/.local/lib/python3.8/site-packages/click/decorators.py", line 26, in new_func return f(get_current_context(), args, kwargs) File "/home/username/.local/lib/python3.8/site-packages/eodag/cli.py", line 573, in download downloaded_files = satim_api.download_all(search_results) File "/home/username/.local/lib/python3.8/site-packages/eodag/api/core.py", line 1612, in download_all paths = download_plugin.download_all( File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/download/http.py", line 718, in download_all return super(HTTPDownload, self).download_all( File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/download/base.py", line 453, in download_all product.download( File "/home/username/.local/lib/python3.8/site-packages/eodag/api/product/_product.py", line 304, in download self.downloader_auth.authenticate() File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/authentication/keycloak.py", line 77, in authenticate raise AuthenticationError( eodag.utils.exceptions.AuthenticationError: Something went wrong while trying to get access token: Traceback (most recent call last): File "/home/username/.local/lib/python3.8/site-packages/eodag/plugins/authentication/keycloak.py", line 59, in authenticate response.raise_for_status() File "/home/username/.local/lib/python3.8/site-packages/requests/models.py", line 1022, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://identity.cloudferro.com/auth/realms/dias/protocol/openid-connect/token ``` </details> Environment:** - Python version: `3.8.10` - EODAG version: `2.10.0`	CS-SI/eodag	diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index d86f2cd2..2793adb1 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -423,6 +423,9 @@ class TestEODagEndToEnd(EndToEndBase): def test_end_to_end_search_download_creodias(self): product = self.execute_search(*CREODIAS_SEARCH_ARGS) + self.eodag.providers_config["creodias"].auth.credentials[ + "totp" + ] = "PLEASE_CHANGE_ME" expected_filename = "{}.zip".format(product.properties["title"]) self.execute_download(product, expected_filename) diff --git a/tests/units/test_auth_plugins.py b/tests/units/test_auth_plugins.py index f064f056..93c8d71d 100644 --- a/tests/units/test_auth_plugins.py +++ b/tests/units/test_auth_plugins.py @@ -19,6 +19,7 @@ import unittest from unittest import mock +import responses from requests import Request from requests.auth import AuthBase from requests.exceptions import RequestException @@ -391,3 +392,183 @@ class TestAuthPluginSASAuth(BaseAuthPluginTest): req = mock.Mock(headers={}, url="url") with self.assertRaises(AuthenticationError): auth(req) + + +class TestAuthPluginKeycloakOIDCPasswordAuth(BaseAuthPluginTest): + @classmethod + def setUpClass(cls): + super(TestAuthPluginKeycloakOIDCPasswordAuth, cls).setUpClass() + override_config_from_mapping( + cls.providers_config, + { + "foo_provider": { + "products": {}, + "auth": { + "type": "KeycloakOIDCPasswordAuth", + "auth_base_uri": "http://foo.bar", + "client_id": "baz", + "realm": "qux", + "client_secret": "1234", + "token_provision": "qs", + "token_qs_key": "totoken", + }, + }, + }, + ) + cls.plugins_manager = PluginManager(cls.providers_config) + + def test_plugins_auth_keycloak_validate_credentials(self): + """KeycloakOIDCPasswordAuth.validate_credentials must raise exception if not well configured""" + auth_plugin = self.get_auth_plugin("foo_provider") + + # credentials missing + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + + auth_plugin.config.credentials = {"username": "john"} + + # no error + auth_plugin.validate_config_credentials() + + # auth_base_uri missing + auth_base_uri = auth_plugin.config.__dict__.pop("auth_base_uri") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.auth_base_uri = auth_base_uri + # client_id missing + client_id = auth_plugin.config.__dict__.pop("client_id") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.client_id = client_id + # client_secret missing + client_secret = auth_plugin.config.__dict__.pop("client_secret") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.client_secret = client_secret + # token_provision missing + token_provision = auth_plugin.config.__dict__.pop("token_provision") + self.assertRaises(MisconfiguredError, auth_plugin.validate_config_credentials) + auth_plugin.config.token_provision = token_provision + + # no error + auth_plugin.validate_config_credentials() + + def test_plugins_auth_keycloak_authenticate(self): + """KeycloakOIDCPasswordAuth.authenticate must query and store the token as expected""" + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + req_kwargs = { + "client_id": "baz", + "client_secret": "1234", + "grant_type": "password", + "username": "john", + } + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + match=[responses.matchers.urlencoded_params_matcher(req_kwargs)], + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + self.assertEqual(auth.key, "totoken") + self.assertEqual(auth.token, "obtained-token") + self.assertEqual(auth.where, "qs") + + # check that token has been stored + self.assertEqual(auth_plugin.retrieved_token, "obtained-token") + + # check that stored token is used if new auth request fails + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + req_kwargs = { + "client_id": "baz", + "client_secret": "1234", + "grant_type": "password", + "username": "john", + } + rsps.add( + responses.POST, + url, + status=401, + json={"error": "not allowed"}, + match=[responses.matchers.urlencoded_params_matcher(req_kwargs)], + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + self.assertEqual(auth.key, "totoken") + self.assertEqual(auth.token, "obtained-token") + self.assertEqual(auth.where, "qs") + + def test_plugins_auth_keycloak_authenticate_qs(self): + """KeycloakOIDCPasswordAuth.authenticate must return a AuthBase object that will inject the token in a query-string""" # noqa + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + + # check if query string is integrated to the request + req = Request("GET", "https://httpbin.org/get").prepare() + auth(req) + self.assertEqual(req.url, "https://httpbin.org/get?totoken=obtained-token") + + another_req = Request("GET", "https://httpbin.org/get?baz=qux").prepare() + auth(another_req) + self.assertEqual( + another_req.url, + "https://httpbin.org/get?baz=qux&totoken=obtained-token", + ) + + def test_plugins_auth_keycloak_authenticate_header(self): + """KeycloakOIDCPasswordAuth.authenticate must return a AuthBase object that will inject the token in the header""" # noqa + auth_plugin = self.get_auth_plugin("foo_provider") + auth_plugin.config.credentials = {"username": "john"} + + # backup token_provision and change it to header mode + token_provision_qs = auth_plugin.config.token_provision + auth_plugin.config.token_provision = "header" + + with responses.RequestsMock(assert_all_requests_are_fired=True) as rsps: + url = "http://foo.bar/realms/qux/protocol/openid-connect/token" + rsps.add( + responses.POST, + url, + status=200, + json={"access_token": "obtained-token"}, + ) + + # check if returned auth object is an instance of requests.AuthBase + auth = auth_plugin.authenticate() + assert isinstance(auth, AuthBase) + + # check if token header is integrated to the request + req = Request("GET", "https://httpbin.org/get").prepare() + auth(req) + self.assertEqual(req.url, "https://httpbin.org/get") + self.assertEqual(req.headers, {"Authorization": "Bearer obtained-token"}) + + another_req = Request( + "GET", "https://httpbin.org/get", headers={"existing-header": "value"} + ).prepare() + auth(another_req) + self.assertEqual( + another_req.headers, + {"Authorization": "Bearer obtained-token", "existing-header": "value"}, + ) + + auth_plugin.config.token_provision = token_provision_qs	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 2 }, "num_modified_files": 6 }	2.10	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "sphinx", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	alabaster==0.7.16 annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 babel==2.17.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@c38d36ec0be2c490d04060030c10726ecf327972#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 imagesize==1.4.1 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 snowballstemmer==2.2.0 Sphinx==7.4.7 sphinxcontrib-applehelp==2.0.0 sphinxcontrib-devhelp==2.0.0 sphinxcontrib-htmlhelp==2.1.0 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==2.0.0 sphinxcontrib-serializinghtml==2.0.0 starlette==0.46.1 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.30.0 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - alabaster==0.7.16 - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - babel==2.17.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.10.1.dev20+gc38d36ec - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - imagesize==1.4.1 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - snowballstemmer==2.2.0 - sphinx==7.4.7 - sphinxcontrib-applehelp==2.0.0 - sphinxcontrib-devhelp==2.0.0 - sphinxcontrib-htmlhelp==2.1.0 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==2.0.0 - sphinxcontrib-serializinghtml==2.0.0 - starlette==0.46.1 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.30.0 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_header", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_qs", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_validate_credentials" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test__search_by_id_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_astraea_eod_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_default", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_all_mundi_iterate", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_astraea_eod", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_ads", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_cds", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_cop_dataspace", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_hydroweb_next", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_meteoblue", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_mundi", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_onda", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_peps", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_sara", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_theia", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_old", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_recent", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_get_quicklook_peps", "tests/test_end_to_end.py::TestEODagEndToEnd::test_search_by_tile", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_mundi", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_onda" ]	[ "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_creodias", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_earth_search_gcs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_discover_product_types_usgs_satapi_aws", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_creodias_noresult", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_cog", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_earth_search_gcs", "tests/test_end_to_end.py::TestEODagEndToEnd::test_end_to_end_search_download_planetary_computer", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_apikey_search_aws_eos", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_cop_dataspace", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_creodias", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_peps", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_hydroweb_next", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_meteoblue", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_search_usgs", "tests/test_end_to_end.py::TestEODagEndToEndWrongCredentials::test_end_to_end_wrong_credentials_theia", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_json_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_text_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_missing", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_ok", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_with_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_without_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_validate_credentials_ok" ]	[]	Apache License 2.0	null
CS-SI__eodag-790	9acf017451f71c5488011f3add086a9936cfffc8	2023-08-22 14:16:26	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: ## Test Results       4 files ±0       4 suites ±0 5m 6s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "duration of all tests") -40s   402 tests +1   400 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "passed tests") +1   2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "failed tests") ±0 1 608 runs +4 1 548 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "passed tests") +4 60 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "failed tests") ±0 Results for commit 9756f25c. ± Comparison against base commit f2ac36ba. [test-results]:data:application/gzip;base64,H4sIAFXE5GQC/02MzQ6DIBAGX8Vw7gEUEPoyjcCSkKo0/JxM372USvE4s9/OgaxbIaL7QG8DitmlP5gcluT8XnDCvIhySvWIx0aPmLWuCnf1dK+i+sYubi2iLyAEH04T8v5tEo7FSa1JGO3q1+Tt49qsfE1qv20uFUByZtyOTIM1aqYMDEwKm4kKDERJIam2Aoxl6P0BLHLFFwgBAAA= github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `81%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 9756f25cefdb745ede3b0d3480e1b9894cf8edf5 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `76%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 9756f25cefdb745ede3b0d3480e1b9894cf8edf5 </p>	diff --git a/eodag/api/core.py b/eodag/api/core.py index 0d589122..9b394f4a 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -832,6 +832,7 @@ class EODataAccessGateway(object): end=None, geom=None, locations=None, + provider=None, kwargs, ): """Look for products matching criteria on known providers. @@ -877,6 +878,9 @@ class EODataAccessGateway(object): :type locations: dict :param kwargs: Some other criteria that will be used to do the search, using paramaters compatibles with the provider + :param provider: (optional) the provider to be used, if not set, the configured + default provider will be used + :type provider: str :type kwargs: Union[int, str, bool, dict] :returns: A collection of EO products matching the criteria and the total number of results found @@ -888,7 +892,12 @@ class EODataAccessGateway(object): enforced here. """ search_kwargs = self._prepare_search( - start=start, end=end, geom=geom, locations=locations, kwargs + start=start, + end=end, + geom=geom, + locations=locations, + provider=provider, + kwargs, ) search_plugins = search_kwargs.pop("search_plugins", []) if search_kwargs.get("id"): @@ -899,7 +908,9 @@ class EODataAccessGateway(object): ) # remove auth from search_kwargs as a loop over providers will be performed search_kwargs.pop("auth", None) - return self._search_by_id(search_kwargs.pop("id"), search_kwargs) + return self._search_by_id( + search_kwargs.pop("id"), provider=provider, search_kwargs + ) search_kwargs.update( page=page, items_per_page=items_per_page, @@ -1254,12 +1265,16 @@ class EODataAccessGateway(object): of EO products retrieved (0 or 1) :rtype: tuple(:class:`~eodag.api.search_result.SearchResult`, int) """ + if not provider: + provider = self.get_preferred_provider()[0] get_search_plugins_kwargs = dict( provider=provider, product_type=kwargs.get("productType", None) ) - for plugin in self._plugins_manager.get_search_plugins( + search_plugins = self._plugins_manager.get_search_plugins( get_search_plugins_kwargs - ): + ) + + for plugin in search_plugins: logger.info( "Searching product with id '%s' on provider: %s", uid, plugin.provider ) @@ -1294,7 +1309,7 @@ class EODataAccessGateway(object): return SearchResult([]), 0 def _prepare_search( - self, start=None, end=None, geom=None, locations=None, kwargs + self, start=None, end=None, geom=None, locations=None, provider=None, kwargs ): """Internal method to prepare the search kwargs and get the search and auth plugins. @@ -1323,6 +1338,9 @@ class EODataAccessGateway(object): :type geom: Union[str, dict, shapely.geometry.base.BaseGeometry] :param locations: (optional) Location filtering by name using locations configuration :type locations: dict + :param provider: provider to be used, if no provider is given or the product type + is not available for the provider, the preferred provider is used + :type provider: str :param kwargs: Some other criteria * id and/or a provider for a search by * search criteria to guess the product type @@ -1400,15 +1418,23 @@ class EODataAccessGateway(object): ): search_plugins.append(plugin) - if search_plugins[0].provider != self.get_preferred_provider()[0]: + if not provider: + provider = self.get_preferred_provider()[0] + providers = [plugin.provider for plugin in search_plugins] + if provider not in providers: logger.warning( "Product type '%s' is not available with provider '%s'. " "Searching it on provider '%s' instead.", product_type, - self.get_preferred_provider()[0], + provider, search_plugins[0].provider, ) else: + provider_plugin = list( + filter(lambda p: p.provider == provider, search_plugins) + )[0] + search_plugins.remove(provider_plugin) + search_plugins.insert(0, provider_plugin) logger.info( "Searching product type '%s' on provider: %s", product_type, @@ -1440,9 +1466,6 @@ class EODataAccessGateway(object): # Remove the ID since this is equal to productType. search_plugin.config.product_type_config.pop("ID", None) - logger.debug( - "Using plugin class for search: %s", search_plugin.__class__.__name__ - ) auth_plugin = self._plugins_manager.get_auth_plugin(search_plugin.provider) auth_plugins[search_plugin.provider] = auth_plugin @@ -1493,6 +1516,7 @@ class EODataAccessGateway(object): results = SearchResult([]) total_results = 0 + try: if "auth" in kwargs: if isinstance(kwargs["auth"], dict): @@ -1586,6 +1610,7 @@ class EODataAccessGateway(object): pass else: eo_product.product_type = guesses[0] + if eo_product.search_intersection is not None: download_plugin = self._plugins_manager.get_download_plugin( eo_product diff --git a/eodag/plugins/authentication/token.py b/eodag/plugins/authentication/token.py index b1032426..377268c7 100644 --- a/eodag/plugins/authentication/token.py +++ b/eodag/plugins/authentication/token.py @@ -15,19 +15,28 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import logging import requests from requests import RequestException +from requests.adapters import HTTPAdapter +from urllib3 import Retry from eodag.plugins.authentication.base import Authentication from eodag.utils import USER_AGENT, RequestsTokenAuth from eodag.utils.exceptions import AuthenticationError, MisconfiguredError from eodag.utils.stac_reader import HTTP_REQ_TIMEOUT +logger = logging.getLogger("eodag.authentication.token") + class TokenAuth(Authentication): """TokenAuth authentication plugin""" + def __init__(self, provider, config): + super(TokenAuth, self).__init__(provider, config) + self.token = "" + def validate_config_credentials(self): """Validate configured credentials""" super(TokenAuth, self).validate_config_credentials() @@ -56,10 +65,15 @@ class TokenAuth(Authentication): if hasattr(self.config, "headers") else {"headers": USER_AGENT} ) + s = requests.Session() + retries = Retry( + total=3, backoff_factor=2, status_forcelist=[401, 429, 500, 502, 503, 504] + ) + s.mount(self.config.auth_uri, HTTPAdapter(max_retries=retries)) try: # First get the token if getattr(self.config, "request_method", "POST") == "POST": - response = requests.post( + response = s.post( self.config.auth_uri, data=self.config.credentials, timeout=HTTP_REQ_TIMEOUT, @@ -67,7 +81,7 @@ class TokenAuth(Authentication): ) else: cred = self.config.credentials - response = requests.get( + response = s.get( self.config.auth_uri, auth=(cred["username"], cred["password"]), timeout=HTTP_REQ_TIMEOUT, @@ -84,11 +98,21 @@ class TokenAuth(Authentication): token = response.json()[self.config.token_key] else: token = response.text + headers = self._get_headers(token) + self.token = token # Return auth class set with obtained token - return RequestsTokenAuth(token, "header", headers=self._get_headers(token)) + return RequestsTokenAuth(token, "header", headers=headers) def _get_headers(self, token): headers = self.config.headers if "Authorization" in headers and "$" in headers["Authorization"]: headers["Authorization"] = headers["Authorization"].replace("$token", token) + if ( + self.token + and token != self.token + and self.token in headers["Authorization"] + ): + headers["Authorization"] = headers["Authorization"].replace( + self.token, token + ) return headers diff --git a/eodag/plugins/download/base.py b/eodag/plugins/download/base.py index dce4db23..1e040cef 100644 --- a/eodag/plugins/download/base.py +++ b/eodag/plugins/download/base.py @@ -373,7 +373,7 @@ class Download(PluginTopic): shutil.move(extraction_dir, outputs_dir) elif fs_path.endswith(".tar.gz"): - with tarfile.open(fs_path, "r:gz") as zfile: + with tarfile.open(fs_path, "r") as zfile: progress_callback.reset(total=1) zfile.extractall(path=extraction_dir) progress_callback(1) diff --git a/eodag/plugins/search/data_request_search.py b/eodag/plugins/search/data_request_search.py index 1f6c586e..0bf2b043 100644 --- a/eodag/plugins/search/data_request_search.py +++ b/eodag/plugins/search/data_request_search.py @@ -11,6 +11,7 @@ from eodag.api.product.metadata_mapping import ( ) from eodag.plugins.search.base import Search from eodag.utils import GENERIC_PRODUCT_TYPE, string_to_jsonpath +from eodag.utils.exceptions import RequestError logger = logging.getLogger("eodag.search.data_request_search") @@ -91,10 +92,9 @@ class DataRequestSearch(Search): metadata = requests.get(metadata_url, headers=headers) metadata.raise_for_status() except requests.RequestException: - logger.error( - "metadata for product_type %s could not be retrieved", product_type + raise RequestError( + f"metadata for product_type {product_type} could not be retrieved" ) - raise else: try: url = self.config.data_request_url @@ -107,11 +107,8 @@ class DataRequestSearch(Search): request_job = requests.post(url, json=request_body, headers=headers) request_job.raise_for_status() except requests.RequestException as e: - logger.error( - "search job for product_type %s could not be created: %s, %s", - product_type, - str(e), - request_job.text, + raise RequestError( + f"search job for product_type {product_type} could not be created: {str(e)}, {request_job.text}" ) else: logger.info("search job for product_type %s created", product_type) @@ -122,13 +119,11 @@ class DataRequestSearch(Search): status_url = self.config.status_url + data_request_id status_data = requests.get(status_url, headers=self.auth.headers).json() if "status_code" in status_data and status_data["status_code"] == 403: - logger.error("authentication token expired during request") - raise requests.RequestException + raise RequestError("authentication token expired during request") if status_data["status"] == "failed": - logger.error( - "data request job has failed, message: %s", status_data["message"] + raise RequestError( + f"data request job has failed, message: {status_data['message']}" ) - raise requests.RequestException return status_data["status"] == "completed" def _get_result_data(self, data_request_id): diff --git a/eodag/rest/server.py b/eodag/rest/server.py index 0c9ecbda..57cc4381 100755 --- a/eodag/rest/server.py +++ b/eodag/rest/server.py @@ -233,12 +233,13 @@ async def handle_resource_not_found(request: Request, error): @app.exception_handler(AuthenticationError) async def handle_auth_error(request: Request, error): - """Unauthorized [401] errors handle""" + """AuthenticationError should be sent as internal server error to the client""" + logger.error(f"{type(error).__name__}: {str(error)}") return await default_exception_handler( request, HTTPException( - status_code=401, - detail=f"{type(error).__name__}: {str(error)}", + status_code=500, + detail="Internal server error: please contact the administrator", ), ) @@ -409,12 +410,9 @@ def stac_collections_item_download(collection_id, item_id, request: Request): body = {} arguments = dict(request.query_params, body) provider = arguments.pop("provider", None) - zipped = "True" - if "zip" in arguments: - zipped = arguments["zip"] return download_stac_item_by_id_stream( - catalogs=[collection_id], item_id=item_id, provider=provider, zip=zipped + catalogs=[collection_id], item_id=item_id, provider=provider ) diff --git a/eodag/rest/stac.py b/eodag/rest/stac.py index 6e8fcbe5..afbcbce1 100644 --- a/eodag/rest/stac.py +++ b/eodag/rest/stac.py @@ -83,9 +83,6 @@ class StacCommon(object): self.data = {} - if self.provider and self.eodag_api.get_preferred_provider() != self.provider: - self.eodag_api.set_preferred_provider(self.provider) - def update_data(self, data): """Updates data using given input STAC dict data diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index afa872ae..8fbda850 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -393,6 +393,10 @@ def search_products(product_type, arguments, stac_formatted=True): try: arg_product_type = arguments.pop("product_type", None) + provider = arguments.pop("provider", None) + if not provider: + provider = eodag_api.get_preferred_provider()[0] + unserialized = arguments.pop("unserialized", None) page, items_per_page = get_pagination_info(arguments) @@ -407,6 +411,7 @@ def search_products(product_type, arguments, stac_formatted=True): "start": dtstart, "end": dtend, "geom": geom, + "provider": provider, } if stac_formatted: @@ -455,20 +460,24 @@ def search_products(product_type, arguments, stac_formatted=True): return response -def search_product_by_id(uid, product_type=None): +def search_product_by_id(uid, product_type=None, provider=None): """Search a product by its id :param uid: The uid of the EO product :type uid: str :param product_type: (optional) The product type :type product_type: str + :param provider: (optional) The provider to be used + :type provider: str :returns: A search result :rtype: :class:`~eodag.api.search_result.SearchResult` :raises: :class:`~eodag.utils.exceptions.ValidationError` :raises: RuntimeError """ try: - products, total = eodag_api.search(id=uid, productType=product_type) + products, total = eodag_api.search( + id=uid, productType=product_type, provider=provider + ) return products except ValidationError: raise @@ -573,7 +582,7 @@ def get_stac_item_by_id(url, item_id, catalogs, root="/", provider=None): return None -def download_stac_item_by_id_stream(catalogs, item_id, provider=None, zip="True"): +def download_stac_item_by_id_stream(catalogs, item_id, provider=None): """Download item :param catalogs: Catalogs list (only first is used as product_type) @@ -587,17 +596,16 @@ def download_stac_item_by_id_stream(catalogs, item_id, provider=None, zip="True" :returns: a stream of the downloaded data (either as a zip or the individual assets) :rtype: StreamingResponse """ - if provider: - eodag_api.set_preferred_provider(provider) - - product = search_product_by_id(item_id, product_type=catalogs[0])[0] - + product = search_product_by_id( + item_id, product_type=catalogs[0], provider=provider + )[0] if product.downloader is None: download_plugin = eodag_api._plugins_manager.get_download_plugin(product) auth_plugin = eodag_api._plugins_manager.get_auth_plugin( download_plugin.provider ) product.register_downloader(download_plugin, auth_plugin) + auth = ( product.downloader_auth.authenticate() if product.downloader_auth is not None @@ -664,7 +672,7 @@ def get_stac_catalogs(url, root="/", catalogs=[], provider=None, fetch_providers :param fetch_providers: (optional) Whether to fetch providers for new product types or not :type fetch_providers: bool - :returns: Catalog dictionnary + :returns: Catalog dictionary :rtype: dict """ return StacCatalog( @@ -734,10 +742,10 @@ def search_stac_items(url, arguments, root="/", catalogs=[], provider=None): ids = [ids] if ids: search_results = SearchResult([]) - if provider: - eodag_api.set_preferred_provider(provider) for item_id in ids: - found_products = search_product_by_id(item_id, product_type=collections[0]) + found_products = search_product_by_id( + item_id, product_type=collections[0], provider=provider + ) if len(found_products) == 1: search_results.extend(found_products) search_results.properties = { @@ -769,7 +777,9 @@ def search_stac_items(url, arguments, root="/", catalogs=[], provider=None): arguments.pop("datetime") search_products_arguments = dict( - arguments, result_catalog.search_args, {"unserialized": "true"} + arguments, + result_catalog.search_args, + {"unserialized": "true", "provider": provider}, ) # check if time filtering appears twice @@ -825,7 +835,6 @@ def search_stac_items(url, arguments, root="/", catalogs=[], provider=None): {"url": result_catalog.url, "root": result_catalog.root}, ), ) - search_results = search_products( product_type=result_catalog.search_args["product_type"], arguments=search_products_arguments,	[eodag-server] setting provider parameter in a request, default this provider as preferred for future requests Describe the bug A clear and concise description of what the bug is. You first request a product type from a specific provider to EODAG Server. This provider is set as the preferred one for all future requests to this EODAG Server instance. Setting the provider parameter in requests should not impact the preferred provider defined in the configuration. Code To Reproduce We assume the default provider is not set to WEkEO but another one like PEPS 1. Request using the default provider ```shell curl http://localhost:8000/search?collections=S1_SAR_GRD ``` 3. Request specifying a custom provider ```shell curl http://localhost:8000/search?collections=S1_SAR_GRD&provider=wekeo ``` 4. Request without specifying a provider ```shell curl http://localhost:8000/search?collections=S1_SAR_GRD ``` The 3rd request use the same provider as the 2nd (a.k.a WEkEO) Additional context This behavior is caused by ```python # https://github.com/CS-SI/eodag/blob/develop/eodag/rest/stac.py#L86-L87 if self.provider and self.eodag_api.get_preferred_provider() != self.provider: self.eodag_api.set_preferred_provider(self.provider) ``` It has been introduced in commit: https://github.com/CS-SI/eodag/commit/05b24fc692cc5b55400ed8339b638b1eafe4d0e9	CS-SI/eodag	diff --git a/tests/integration/test_core_search_results.py b/tests/integration/test_core_search_results.py index 8d94a465..1fff5fe7 100644 --- a/tests/integration/test_core_search_results.py +++ b/tests/integration/test_core_search_results.py @@ -264,3 +264,23 @@ class TestCoreSearchResults(EODagTestCase): for search_result in search_results: self.assertIsInstance(search_result.downloader, PluginTopic) self.assertIsInstance(search_result.downloader_auth, PluginTopic) + + @mock.patch( + "eodag.plugins.search.qssearch.QueryStringSearch.do_search", + autospec=True, + ) + def test_core_search_with_provider(self, mock_query): + """The core api must register search results downloaders""" + self.dag.set_preferred_provider("creodias") + search_results_file = os.path.join( + TEST_RESOURCES_PATH, "provider_responses/peps_search.json" + ) + with open(search_results_file, encoding="utf-8") as f: + search_results_peps = json.load(f) + + mock_query.return_value = search_results_peps["features"] + search_results, count = self.dag.search( + productType="S2_MSI_L1C", provider="peps" + ) + # use given provider and not preferred provider + self.assertEqual("peps", search_results[0].provider) diff --git a/tests/units/test_auth_plugins.py b/tests/units/test_auth_plugins.py index 93c8d71d..70cf6c2d 100644 --- a/tests/units/test_auth_plugins.py +++ b/tests/units/test_auth_plugins.py @@ -129,7 +129,9 @@ class TestAuthPluginTokenAuth(BaseAuthPluginTest): auth_plugin.config.credentials = {"foo": "bar", "username": "john"} auth_plugin.validate_config_credentials() - @mock.patch("eodag.plugins.authentication.token.requests.post", autospec=True) + @mock.patch( + "eodag.plugins.authentication.token.requests.Session.post", autospec=True + ) def test_plugins_auth_tokenauth_text_token_authenticate(self, mock_requests_post): """TokenAuth.authenticate must return a RequestsTokenAuth object using text token""" auth_plugin = self.get_auth_plugin("provider_text_token_header") @@ -146,7 +148,7 @@ class TestAuthPluginTokenAuth(BaseAuthPluginTest): # check token post request call arguments args, kwargs = mock_requests_post.call_args - assert args[0] == auth_plugin.config.auth_uri + assert args[1] == auth_plugin.config.auth_uri assert kwargs["data"] == auth_plugin.config.credentials assert kwargs["headers"] == dict(auth_plugin.config.headers, USER_AGENT) @@ -156,7 +158,9 @@ class TestAuthPluginTokenAuth(BaseAuthPluginTest): assert req.headers["Authorization"] == "Bearer this_is_test_token" assert req.headers["foo"] == "bar" - @mock.patch("eodag.plugins.authentication.token.requests.post", autospec=True) + @mock.patch( + "eodag.plugins.authentication.token.requests.Session.post", autospec=True + ) def test_plugins_auth_tokenauth_json_token_authenticate(self, mock_requests_post): """TokenAuth.authenticate must return a RequestsTokenAuth object using json token""" auth_plugin = self.get_auth_plugin("provider_json_token_simple_url") @@ -179,7 +183,9 @@ class TestAuthPluginTokenAuth(BaseAuthPluginTest): auth(req) assert req.headers["Authorization"] == "Bearer this_is_test_token" - @mock.patch("eodag.plugins.authentication.token.requests.post", autospec=True) + @mock.patch( + "eodag.plugins.authentication.token.requests.Session.post", autospec=True + ) def test_plugins_auth_tokenauth_request_error(self, mock_requests_post): """TokenAuth.authenticate must raise an AuthenticationError if a request error occurs""" auth_plugin = self.get_auth_plugin("provider_json_token_simple_url") diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 092beac5..c3b4347e 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -1292,7 +1292,7 @@ class TestCoreSearch(TestCoreBase): """_prepare_search must handle a search by id""" base = {"id": "dummy-id", "provider": "creodias"} prepared_search = self.dag._prepare_search(base) - expected = base + expected = {"id": "dummy-id"} self.assertDictEqual(expected, prepared_search) def test__prepare_search_preserve_additional_kwargs(self): diff --git a/tests/units/test_http_server.py b/tests/units/test_http_server.py index d1a8a9aa..9a144cd8 100644 --- a/tests/units/test_http_server.py +++ b/tests/units/test_http_server.py @@ -317,6 +317,7 @@ class RequestTestCase(unittest.TestCase): self._request_valid( f"search?collections={self.tested_product_type}", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -326,6 +327,7 @@ class RequestTestCase(unittest.TestCase): self._request_valid( f"search?collections={self.tested_product_type}&bbox=0,43,1,44", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -344,22 +346,26 @@ class RequestTestCase(unittest.TestCase): side_effect=AuthenticationError("you are no authorized"), ) def test_auth_error(self, mock_search): - """A request to eodag server raising a Authentication error must return a 401 HTTP error code""" - response = self.app.get( - f"search?collections={self.tested_product_type}", follow_redirects=True - ) - response_content = json.loads(response.content.decode("utf-8")) + """A request to eodag server raising a Authentication error must return a 500 HTTP error code""" - self.assertEqual(401, response.status_code) - self.assertIn("description", response_content) - self.assertIn("AuthenticationError", response_content["description"]) - self.assertIn("you are no authorized", response_content["description"]) + with self.assertLogs(level="ERROR") as cm_logs: + response = self.app.get( + f"search?collections={self.tested_product_type}", follow_redirects=True + ) + response_content = json.loads(response.content.decode("utf-8")) + + self.assertIn("description", response_content) + self.assertIn("AuthenticationError", str(cm_logs.output)) + self.assertIn("you are no authorized", str(cm_logs.output)) + + self.assertEqual(500, response.status_code) def test_filter(self): """latestIntersect filter should only keep the latest products once search area is fully covered""" result1 = self._request_valid( f"search?collections={self.tested_product_type}&bbox=89.65,2.65,89.7,2.7", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -371,6 +377,7 @@ class RequestTestCase(unittest.TestCase): result2 = self._request_valid( f"search?collections={self.tested_product_type}&bbox=89.65,2.65,89.7,2.7&filter=latestIntersect", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -386,6 +393,7 @@ class RequestTestCase(unittest.TestCase): self._request_valid( f"search?collections={self.tested_product_type}&bbox=0,43,1,44&datetime=2018-01-20/2018-01-25", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -401,6 +409,7 @@ class RequestTestCase(unittest.TestCase): self._request_valid( f"collections/{self.tested_product_type}/items?bbox=0,43,1,44", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -411,6 +420,7 @@ class RequestTestCase(unittest.TestCase): self._request_valid( f"collections/{self.tested_product_type}/items?bbox=0,43,1,44&datetime=2018-01-20/2018-01-25", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -426,6 +436,7 @@ class RequestTestCase(unittest.TestCase): results = self._request_valid( f"catalogs/{self.tested_product_type}/year/2018/month/01/items?bbox=0,43,1,44", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -441,6 +452,7 @@ class RequestTestCase(unittest.TestCase): f"catalogs/{self.tested_product_type}/year/2018/month/01/items" "?bbox=0,43,1,44&datetime=2018-01-20/2018-01-25", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -456,6 +468,7 @@ class RequestTestCase(unittest.TestCase): f"catalogs/{self.tested_product_type}/year/2018/month/01/items" "?bbox=0,43,1,44&datetime=2018-01-20/2019-01-01", expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, @@ -489,6 +502,7 @@ class RequestTestCase(unittest.TestCase): f"catalogs/{self.tested_product_type}/items/foo", expected_search_kwargs={ "id": "foo", + "provider": None, "productType": self.tested_product_type, }, ) @@ -499,6 +513,7 @@ class RequestTestCase(unittest.TestCase): f"collections/{self.tested_product_type}/items/foo", expected_search_kwargs={ "id": "foo", + "provider": None, "productType": self.tested_product_type, }, ) @@ -521,6 +536,7 @@ class RequestTestCase(unittest.TestCase): "query": {"eo:cloud_cover": {"lte": 10}}, }, expected_search_kwargs=dict( + provider="peps", productType=self.tested_product_type, page=1, items_per_page=DEFAULT_ITEMS_PER_PAGE, diff --git a/tests/units/test_search_plugins.py b/tests/units/test_search_plugins.py index 9e09dc6f..9d99ea9f 100644 --- a/tests/units/test_search_plugins.py +++ b/tests/units/test_search_plugins.py @@ -22,7 +22,6 @@ import unittest from pathlib import Path from unittest import mock -import requests import responses from requests import RequestException @@ -936,10 +935,11 @@ class MockResponse: class TestSearchPluginDataRequestSearch(BaseSearchPluginTest): - @mock.patch("eodag.plugins.authentication.token.requests.get", autospec=True) + @mock.patch( + "eodag.plugins.authentication.token.requests.Session.get", autospec=True + ) def setUp(self, mock_requests_get): - # One of the providers that has a BuildPostSearchResult Search plugin provider = "wekeo" self.search_plugin = self.get_search_plugin(self.product_type, provider) self.auth_plugin = self.get_auth_plugin(provider) @@ -973,7 +973,7 @@ class TestSearchPluginDataRequestSearch(BaseSearchPluginTest): mock_requests_get.return_value = MockResponse( {"status": "failed", "message": "failed"}, 500 ) - with self.assertRaises(requests.RequestException): + with self.assertRaises(RequestError): self.search_plugin._check_request_status("123") @mock.patch("eodag.plugins.search.data_request_search.requests.get", autospec=True) diff --git a/tests/units/test_stac_utils.py b/tests/units/test_stac_utils.py index 0c8f618d..50ea9dff 100644 --- a/tests/units/test_stac_utils.py +++ b/tests/units/test_stac_utils.py @@ -447,6 +447,7 @@ class TestStacUtils(unittest.TestCase): mock__request.return_value.json.return_value = ( self.earth_search_resp_search_json ) + self.rest_utils.eodag_api.set_preferred_provider("peps") response = self.rest_utils.search_stac_items( url="http://foo/search", @@ -467,6 +468,8 @@ class TestStacUtils(unittest.TestCase): "assets" ].items(): self.assertIn((k, v), response["features"][0]["assets"].items()) + # preferred provider should not be changed + self.assertEqual("peps", self.rest_utils.eodag_api.get_preferred_provider()[0]) @mock.patch( "eodag.plugins.search.qssearch.QueryStringSearch._request",	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 7 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "tox" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@9acf017451f71c5488011f3add086a9936cfffc8#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.0b2.dev4+g9acf0174 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_core_search_with_provider", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_json_token_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_text_token_authenticate", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_http_server.py::RequestTestCase::test_auth_error", "tests/units/test_http_server.py::RequestTestCase::test_cloud_cover_post_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_catalog_items", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_items", "tests/units/test_http_server.py::RequestTestCase::test_filter", "tests/units/test_http_server.py::RequestTestCase::test_request_params", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_collection", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_check_request_status", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_create_data_request", "tests/units/test_search_plugins.py::TestSearchPluginDataRequestSearch::test_plugins_get_result_data", "tests/units/test_stac_utils.py::TestStacUtils::test_search_stac_items_with_stac_providers" ]	[ "tests/units/test_stac_utils.py::TestStacUtils::test_get_arguments_query_paths", "tests/units/test_stac_utils.py::TestStacUtils::test_get_metadata_query_paths" ]	[ "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_core_deserialize_search_results", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_core_search_results_registered", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_core_serialize_search_results", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_download_all_callback", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_empty_search_result_return_empty_list", "tests/integration/test_core_search_results.py::TestCoreSearchResults::test_group_by_extent", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_missing", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_format_url_ok", "tests/units/test_auth_plugins.py::TestAuthPluginTokenAuth::test_plugins_auth_tokenauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_empty", "tests/units/test_auth_plugins.py::TestAuthPluginHttpQueryStringAuth::test_plugins_auth_qsauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_request_error", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_with_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_text_token_authenticate_without_credentials", "tests/units/test_auth_plugins.py::TestAuthPluginSASAuth::test_plugins_auth_sasauth_validate_credentials_ok", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_header", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_authenticate_qs", "tests/units/test_auth_plugins.py::TestAuthPluginKeycloakOIDCPasswordAuth::test_plugins_auth_keycloak_validate_credentials", "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_request_error", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreSearch::test_search_request_error", "tests/units/test_core.py::TestCoreDownload::test_download_local_product", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse", "tests/units/test_http_server.py::RequestTestCase::test_collection", "tests/units/test_http_server.py::RequestTestCase::test_conformance", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_collection_api_plugin", "tests/units/test_http_server.py::RequestTestCase::test_forward", "tests/units/test_http_server.py::RequestTestCase::test_ids_post_search", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_nok", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_ok", "tests/units/test_http_server.py::RequestTestCase::test_not_found", "tests/units/test_http_server.py::RequestTestCase::test_route", "tests/units/test_http_server.py::RequestTestCase::test_search_provider_in_downloadlink", "tests/units/test_http_server.py::RequestTestCase::test_search_response_contains_pagination_info", "tests/units/test_http_server.py::RequestTestCase::test_service_desc", "tests/units/test_http_server.py::RequestTestCase::test_service_doc", "tests/units/test_http_server.py::RequestTestCase::test_stac_extension_oseo", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearchXml::test_plugins_search_querystringseach_xml_count_and_search_mundi", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearchXml::test_plugins_search_querystringseach_xml_no_count_and_search_mundi", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_count_and_search_peps", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_discover_product_types", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_discover_product_types_keywords", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_no_count_and_search_peps", "tests/units/test_search_plugins.py::TestSearchPluginQueryStringSearch::test_plugins_search_querystringseach_search_cloudcover_peps", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_no_count_and_search_awseos", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_request_auth_error", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_request_error", "tests/units/test_search_plugins.py::TestSearchPluginPostJsonSearch::test_plugins_search_postjsonsearch_search_cloudcover_awseos", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda_per_product_metadata_query", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_count_and_search_onda_per_product_metadata_query_request_error", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_normalize_results_onda", "tests/units/test_search_plugins.py::TestSearchPluginODataV4Search::test_plugins_search_odatav4search_search_cloudcover_onda", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_default_geometry", "tests/units/test_search_plugins.py::TestSearchPluginStacSearch::test_plugins_search_stacsearch_mapping_earthsearch", "tests/units/test_search_plugins.py::TestSearchPluginBuildPostSearchResult::test_plugins_search_buildpostsearchresult_count_and_search", "tests/units/test_stac_utils.py::TestStacUtils::test_detailled_collections_list", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products_filter_misuse_raise_error", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products_missing_additional_parameters_raise_error", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products_unknown_cruncher_raise_error", "tests/units/test_stac_utils.py::TestStacUtils::test_format_product_types", "tests/units/test_stac_utils.py::TestStacUtils::test_get_criterias_from_metadata_mapping", "tests/units/test_stac_utils.py::TestStacUtils::test_get_date", "tests/units/test_stac_utils.py::TestStacUtils::test_get_datetime", "tests/units/test_stac_utils.py::TestStacUtils::test_get_geometry", "tests/units/test_stac_utils.py::TestStacUtils::test_get_int", "tests/units/test_stac_utils.py::TestStacUtils::test_get_pagination_info", "tests/units/test_stac_utils.py::TestStacUtils::test_get_product_types", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_catalogs", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_collection_by_id", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_collections", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_conformance", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_extension_oseo", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_item_by_id", "tests/units/test_stac_utils.py::TestStacUtils::test_get_templates_path", "tests/units/test_stac_utils.py::TestStacUtils::test_home_page_content", "tests/units/test_stac_utils.py::TestStacUtils::test_search_bbox", "tests/units/test_stac_utils.py::TestStacUtils::test_search_product_by_id", "tests/units/test_stac_utils.py::TestStacUtils::test_search_products", "tests/units/test_stac_utils.py::TestStacUtils::test_search_stac_items_with_non_stac_providers" ]	[]	Apache License 2.0	null
CS-SI__eodag-795	2eb52b8300e3859712c74412a97d15181865ce97	2023-08-25 08:21:36	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: ## Test Results     2 files -     2     2 suites - 2 2m 28s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "duration of all tests") - 3m 1s 406 tests +    3 348 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "passed tests") -   53 2 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "skipped / disabled tests") ±  0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "failed tests") ±0 56 [:fire:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "test errors") +56 812 runs - 800 752 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "passed tests") - 800 4 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "skipped / disabled tests") - 56 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "failed tests") ±0 56 [:fire:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.9.0/README.md#the-symbols "test errors") +56 For more details on these errors, see [this check](https://github.com/CS-SI/eodag/runs/16206301337). Results for commit a986c1a4. ± Comparison against base commit 90627560. [test-results]:data:application/gzip;base64,H4sIAGNl6GQC/1WMyw6DIBQFf8Ww7gIQEPszBi+QkKo0PFZN/73X+mqXM+dkXsSHyWVyb/itIbmGcoKtyZQQF0QmNAqcyjoKqg4acgVA1V6HIT/Cc09swpswoaCncCnFhEaunVSXNaoZ3+FodvIyW1Ic/FP88l8Q4jyHgkRMrxUwI2CUngJXVksYWWeV9Zb3VDNhKGu9I+8PpTUEkgcBAAA= github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against a986c1a4cb5f0c26d85cb17d6dfd290814a013fe </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `32%` \| :x: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against a986c1a4cb5f0c26d85cb17d6dfd290814a013fe </p>	diff --git a/eodag/rest/server.py b/eodag/rest/server.py index 57cc4381..541da3a8 100755 --- a/eodag/rest/server.py +++ b/eodag/rest/server.py @@ -18,11 +18,12 @@ import io import logging import os +import re import traceback from contextlib import asynccontextmanager from distutils import dist from json.decoder import JSONDecodeError -from typing import List, Union +from typing import List, Optional, Union import pkg_resources from fastapi import APIRouter as FastAPIRouter @@ -37,8 +38,11 @@ from starlette.exceptions import HTTPException as StarletteHTTPException from eodag.config import load_stac_api_config from eodag.rest.utils import ( + QueryableProperty, + Queryables, download_stac_item_by_id_stream, eodag_api_init, + fetch_collection_queryable_properties, get_detailled_collections_list, get_stac_api_version, get_stac_catalogs, @@ -61,6 +65,7 @@ from eodag.utils.exceptions import ( ) logger = logging.getLogger("eodag.rest.server") +CAMEL_TO_SPACE_TITLED = re.compile(r"[:_-]\|(?<=[a-z])(?=[A-Z])") class APIRouter(FastAPIRouter): @@ -595,4 +600,57 @@ async def stac_catalogs(catalogs, request: Request): return jsonable_encoder(response) [email protected]("/queryables", tags=["Capabilities"], response_model_exclude_none=True) +def list_queryables(request: Request) -> Queryables: + """Returns the list of terms available for use when writing filter expressions. + + This endpoint provides a list of terms that can be used as filters when querying + the data. These terms correspond to properties that can be filtered using comparison + operators. + + :param request: The incoming request object. + :type request: fastapi.Request + :returns: An object containing the list of available queryable terms. + :rtype: eodag.rest.utils.Queryables + """ + + return Queryables(q_id=request.state.url) + + [email protected]( + "/collections/{collection_id}/queryables", + tags=["Capabilities"], + response_model_exclude_none=True, +) +def list_collection_queryables( + request: Request, collection_id: str, provider: Optional[str] = None +) -> Queryables: + """Returns the list of queryable properties for a specific collection. + + This endpoint provides a list of properties that can be used as filters when querying + the specified collection. These properties correspond to characteristics of the data + that can be filtered using comparison operators. + + :param request: The incoming request object. + :type request: fastapi.Request + :param collection_id: The identifier of the collection for which to retrieve queryable properties. + :type collection_id: str + :param provider: (optional) The provider for which to retrieve additional properties. + :type provider: str + :returns: An object containing the list of available queryable properties for the specified collection. + :rtype: eodag.rest.utils.Queryables + """ + + queryables = Queryables(q_id=request.state.url, additional_properties=False) + conf_args = [collection_id, provider] if provider else [collection_id] + + provider_properties = set(fetch_collection_queryable_properties(conf_args)) + + for prop in provider_properties: + titled_name = re.sub(CAMEL_TO_SPACE_TITLED, " ", prop).title() + queryables[prop] = QueryableProperty(description=titled_name) + + return queryables + + app.include_router(router) diff --git a/eodag/rest/utils.py b/eodag/rest/utils.py index 8fbda850..76ab874c 100644 --- a/eodag/rest/utils.py +++ b/eodag/rest/utils.py @@ -9,10 +9,12 @@ import os import re from collections import namedtuple from shutil import make_archive, rmtree +from typing import Dict, Optional import dateutil.parser from dateutil import tz from fastapi.responses import StreamingResponse +from pydantic import BaseModel, Field from shapely.geometry import Polygon, shape import eodag @@ -145,7 +147,6 @@ def search_bbox(request_bbox): search_bbox_keys = ["lonmin", "latmin", "lonmax", "latmax"] if request_bbox: - try: request_bbox_list = [float(coord) for coord in request_bbox.split(",")] except ValueError as e: @@ -883,6 +884,135 @@ def get_stac_extension_oseo(url): ) +class QueryableProperty(BaseModel): + """A class representing a queryable property. + + :param description: The description of the queryables property + :type description: str + :param ref: (optional) A reference link to the schema of the property. + :type ref: str + """ + + description: str + ref: Optional[str] = Field(default=None, serialization_alias="$ref") + + +class Queryables(BaseModel): + """A class representing queryable properties for the STAC API. + + :param json_schema: The URL of the JSON schema. + :type json_schema: str + :param q_id: (optional) The identifier of the queryables. + :type q_id: str + :param q_type: The type of the object. + :type q_type: str + :param title: The title of the queryables. + :type title: str + :param description: The description of the queryables + :type description: str + :param properties: A dictionary of queryable properties. + :type properties: dict + :param additional_properties: Whether additional properties are allowed. + :type additional_properties: bool + """ + + json_schema: str = Field( + default="https://json-schema.org/draft/2019-09/schema", + serialization_alias="$schema", + ) + q_id: Optional[str] = Field(default=None, serialization_alias="$id") + q_type: str = Field(default="object", serialization_alias="type") + title: str = Field(default="Queryables for EODAG STAC API") + description: str = Field( + default="Queryable names for the EODAG STAC API Item Search filter." + ) + properties: Dict[str, QueryableProperty] = Field( + default={ + "id": QueryableProperty( + description="ID", + ref="https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json#/id", + ), + "collection": QueryableProperty( + description="Collection", + ref="https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json#/collection", + ), + "geometry": QueryableProperty( + description="Geometry", + ref="https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json#/geometry", + ), + "bbox": QueryableProperty( + description="Bbox", + ref="https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json#/bbox", + ), + "datetime": QueryableProperty( + description="Datetime", + ref="https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/datetime.json#/properties/datetime", + ), + "ids": QueryableProperty(description="IDs"), + } + ) + additional_properties: bool = Field( + default=True, serialization_alias="additionalProperties" + ) + + def get_properties(self) -> Dict[str, QueryableProperty]: + """Get the queryable properties. + + :returns: A dictionary containing queryable properties. + :rtype: typing.Dict[str, QueryableProperty] + """ + return self.properties + + def __contains__(self, name: str): + return name in self.properties + + def __setitem__(self, name: str, qprop: QueryableProperty): + self.properties[name] = qprop + + +def rename_to_stac_standard(key: str) -> str: + """Fetch the queryable properties for a collection. + + :param key: The camelCase key name obtained from a collection's metadata mapping. + :type key: str + :returns: The STAC-standardized property name if it exists, else the default camelCase queryable name + :rtype: str + """ + # Load the stac config properties for renaming the properties + # to their STAC standard + stac_config_properties = stac_config["item"]["properties"] + + for stac_property, value in stac_config_properties.items(): + if str(value).endswith(key): + return stac_property + return key + + +def fetch_collection_queryable_properties( + collection_id: str, provider: Optional[str] = None +) -> set: + """Fetch the queryable properties for a collection. + + :param collection_id: The ID of the collection. + :type collection_id: str + :param provider: (optional) The provider. + :type provider: str + :returns queryable_properties: A set containing the STAC standardized queryable properties for a collection. + :rtype queryable_properties: set + """ + # Fetch the metadata mapping for collection-specific queryables + args = [collection_id, provider] if provider else [collection_id] + search_plugin = next(eodag_api._plugins_manager.get_search_plugins(args)) + mapping = dict(search_plugin.config.metadata_mapping) + + # list of all the STAC standardized collection-specific queryables + queryable_properties = set() + for key, value in mapping.items(): + if isinstance(value, list) and "TimeFromAscendingNode" not in key: + queryable_properties.add(rename_to_stac_standard(key)) + return queryable_properties + + def eodag_api_init(): """Init EODataAccessGateway server instance, pre-running all time consuming tasks""" eodag_api.fetch_product_types_list()	server mode queryables Add queryables, available through `/queryables` and `/collections/<PRODUCT_TYPE>/queryables`. See https://github.com/radiantearth/stac-api-spec/blob/master/fragments/filter/README.md#queryables for documentation and https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a/queryables for an example. - `/queryables` should be minimal - `/collections/<PRODUCT_TYPE>/queryables` should be build using product type queryable parameters from `providers.yml`	CS-SI/eodag	diff --git a/tests/units/test_http_server.py b/tests/units/test_http_server.py index 9a144cd8..8cfe0926 100644 --- a/tests/units/test_http_server.py +++ b/tests/units/test_http_server.py @@ -760,3 +760,18 @@ class RequestTestCase(unittest.TestCase): response = self._request_valid("/extensions/oseo/json-schema/schema.json") self.assertEqual(response["title"], "OpenSearch for Earth Observation") self.assertEqual(response["allOf"][0]["$ref"], "#/definitions/oseo") + + def test_queryables(self): + """Request to /queryables should return a valid response.""" + self._request_valid("queryables") + + def test_product_type_queryables(self): + """Request to /collections/{collection_id}/queryables should return a valid response.""" + self._request_valid(f"collections/{self.tested_product_type}/queryables") + + def test_product_type_queryables_with_provider(self): + """Request a collection-specific list of queryables for a given provider.""" + + self._request_valid( + f"collections/{self.tested_product_type}/queryables?provider=peps" + )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 2 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@2eb52b8300e3859712c74412a97d15181865ce97#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.0b2.dev8+g2eb52b83 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_queryables" ]	[]	[ "tests/units/test_http_server.py::RequestTestCase::test_auth_error", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse", "tests/units/test_http_server.py::RequestTestCase::test_cloud_cover_post_search", "tests/units/test_http_server.py::RequestTestCase::test_collection", "tests/units/test_http_server.py::RequestTestCase::test_conformance", "tests/units/test_http_server.py::RequestTestCase::test_date_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_catalog_items", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_items", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_collection_api_plugin", "tests/units/test_http_server.py::RequestTestCase::test_filter", "tests/units/test_http_server.py::RequestTestCase::test_forward", "tests/units/test_http_server.py::RequestTestCase::test_ids_post_search", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_nok", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_ok", "tests/units/test_http_server.py::RequestTestCase::test_not_found", "tests/units/test_http_server.py::RequestTestCase::test_request_params", "tests/units/test_http_server.py::RequestTestCase::test_route", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_collection", "tests/units/test_http_server.py::RequestTestCase::test_search_provider_in_downloadlink", "tests/units/test_http_server.py::RequestTestCase::test_search_response_contains_pagination_info", "tests/units/test_http_server.py::RequestTestCase::test_service_desc", "tests/units/test_http_server.py::RequestTestCase::test_service_doc", "tests/units/test_http_server.py::RequestTestCase::test_stac_extension_oseo" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-795
CS-SI__eodag-802	4b1397bfdc79b0b9be0be3fc825df40949f4acae	2023-08-29 15:56:05	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `81%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against d8bb641da73773eea32741184e436b891d47b210 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `0%` \| :x: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against d8bb641da73773eea32741184e436b891d47b210 </p>	diff --git a/eodag/api/core.py b/eodag/api/core.py index 9b394f4a..bff4f2cc 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -20,6 +20,7 @@ import os import re import shutil from operator import itemgetter +from typing import List import geojson import pkg_resources @@ -45,6 +46,7 @@ from eodag.config import ( ) from eodag.plugins.download.base import DEFAULT_DOWNLOAD_TIMEOUT, DEFAULT_DOWNLOAD_WAIT from eodag.plugins.manager import PluginManager +from eodag.plugins.search.base import Search from eodag.utils import ( GENERIC_PRODUCT_TYPE, MockResponse, @@ -891,7 +893,7 @@ class EODataAccessGateway(object): return a list as a result of their processing. This requirement is enforced here. """ - search_kwargs = self._prepare_search( + search_plugins, search_kwargs = self._prepare_search( start=start, end=end, geom=geom, @@ -899,15 +901,13 @@ class EODataAccessGateway(object): provider=provider, kwargs, ) - search_plugins = search_kwargs.pop("search_plugins", []) + if search_kwargs.get("id"): # adds minimal pagination to be able to check only 1 product is returned search_kwargs.update( page=1, items_per_page=2, ) - # remove auth from search_kwargs as a loop over providers will be performed - search_kwargs.pop("auth", None) return self._search_by_id( search_kwargs.pop("id"), provider=provider, search_kwargs ) @@ -981,10 +981,9 @@ class EODataAccessGateway(object): matching the criteria :rtype: Iterator[:class:`~eodag.api.search_result.SearchResult`] """ - search_kwargs = self._prepare_search( + search_plugins, search_kwargs = self._prepare_search( start=start, end=end, geom=geom, locations=locations, kwargs ) - search_plugins = search_kwargs.pop("search_plugins") for i, search_plugin in enumerate(search_plugins): try: return self.search_iter_page_plugin( @@ -1198,10 +1197,9 @@ class EODataAccessGateway(object): ) self.fetch_product_types_list() - search_kwargs = self._prepare_search( + search_plugins, search_kwargs = self._prepare_search( start=start, end=end, geom=geom, locations=locations, kwargs ) - search_plugins = search_kwargs.get("search_plugins", []) for i, search_plugin in enumerate(search_plugins): if items_per_page is None: items_per_page = search_plugin.config.pagination.get( @@ -1279,13 +1277,8 @@ class EODataAccessGateway(object): "Searching product with id '%s' on provider: %s", uid, plugin.provider ) logger.debug("Using plugin class for search: %s", plugin.__class__.__name__) - auth_plugin = self._plugins_manager.get_auth_plugin(plugin.provider) - if getattr(plugin.config, "need_auth", False) and callable( - getattr(auth_plugin, "authenticate", None) - ): - plugin.auth = auth_plugin.authenticate() plugin.clear() - results, _ = self._do_search(plugin, auth=auth_plugin, id=uid, kwargs) + results, _ = self._do_search(plugin, id=uid, kwargs) if len(results) == 1: if not results[0].product_type: # guess product type from properties @@ -1311,8 +1304,7 @@ class EODataAccessGateway(object): def _prepare_search( self, start=None, end=None, geom=None, locations=None, provider=None, *kwargs ): - """Internal method to prepare the search kwargs and get the search - and auth plugins. + """Internal method to prepare the search kwargs and get the search plugins. Product query: By id (plus optional 'provider') @@ -1375,7 +1367,7 @@ class EODataAccessGateway(object): "No product type could be guessed with provided arguments" ) else: - return kwargs + return [], kwargs kwargs["productType"] = product_type if start is not None: @@ -1412,7 +1404,7 @@ class EODataAccessGateway(object): ) self.fetch_product_types_list() - search_plugins = [] + search_plugins: List[Search] = [] for plugin in self._plugins_manager.get_search_plugins( product_type=product_type ): @@ -1442,7 +1434,6 @@ class EODataAccessGateway(object): ) # Add product_types_config to plugin config. This dict contains product # type metadata that will also be stored in each product's properties. - auth_plugins = {} for search_plugin in search_plugins: try: search_plugin.config.product_type_config = dict( @@ -1466,23 +1457,7 @@ class EODataAccessGateway(object): # Remove the ID since this is equal to productType. search_plugin.config.product_type_config.pop("ID", None) - auth_plugin = self._plugins_manager.get_auth_plugin(search_plugin.provider) - auth_plugins[search_plugin.provider] = auth_plugin - - # append auth to search plugin if needed - if getattr(search_plugin.config, "need_auth", False) and callable( - getattr(auth_plugin, "authenticate", None) - ): - try: - search_plugin.auth = auth_plugin.authenticate() - except RuntimeError: - logger.error( - "could not authenticatate at provider %s", - search_plugin.provider, - ) - search_plugin.auth = None - - return dict(search_plugins=search_plugins, auth=auth_plugins, kwargs) + return search_plugins, kwargs def _do_search(self, search_plugin, count=True, raise_errors=False, kwargs): """Internal method that performs a search on a given provider. @@ -1514,22 +1489,18 @@ class EODataAccessGateway(object): max_items_per_page, ) + need_auth = getattr(search_plugin.config, "need_auth", False) + auth_plugin = self._plugins_manager.get_auth_plugin(search_plugin.provider) + can_authenticate = callable(getattr(auth_plugin, "authenticate", None)) + results = SearchResult([]) total_results = 0 try: - if "auth" in kwargs: - if isinstance(kwargs["auth"], dict): - auth = kwargs["auth"][search_plugin.provider] - else: - auth = kwargs["auth"] - kwargs.pop("auth") - else: - auth = None + if need_auth and auth_plugin and can_authenticate: + search_plugin.auth = auth_plugin.authenticate() - if "search_plugins" in kwargs: - kwargs.pop("search_plugins") - res, nb_res = search_plugin.query(count=count, auth=auth, kwargs) + res, nb_res = search_plugin.query(count=count, auth=auth_plugin, kwargs) # Only do the pagination computations when it makes sense. For example, # for a search by id, we can reasonably guess that the provider will return @@ -1615,7 +1586,7 @@ class EODataAccessGateway(object): download_plugin = self._plugins_manager.get_download_plugin( eo_product ) - eo_product.register_downloader(download_plugin, auth) + eo_product.register_downloader(download_plugin, auth_plugin) results.extend(res) total_results = None if nb_res is None else total_results + nb_res diff --git a/eodag/plugins/manager.py b/eodag/plugins/manager.py index 93884728..2adf341f 100644 --- a/eodag/plugins/manager.py +++ b/eodag/plugins/manager.py @@ -18,6 +18,7 @@ import logging from operator import attrgetter from pathlib import Path +from typing import Optional import pkg_resources @@ -200,7 +201,7 @@ class PluginManager(object): plugin = self._build_plugin(product.provider, plugin_conf.api, Api) return plugin - def get_auth_plugin(self, provider): + def get_auth_plugin(self, provider: str) -> Optional[Authentication]: """Build and return the authentication plugin for the given product_type and provider	prevent authentication during search preparation Following https://github.com/CS-SI/eodag/pull/753, authentication is performed for all providers needing auth for search in `_prepare_search()`. Only auth plugin should be passed as argument, and authentication should be performed before search for the given provider.	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index c3b4347e..03323865 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -1188,12 +1188,12 @@ class TestCoreSearch(TestCoreBase): ) def test__prepare_search_no_parameters(self, mock_fetch_product_types_list): """_prepare_search must create some kwargs even when no parameter has been provided""" - prepared_search = self.dag._prepare_search() + _, prepared_search = self.dag._prepare_search() expected = { "geometry": None, "productType": None, } - expected = set(["geometry", "productType", "auth", "search_plugins"]) + expected = set(["geometry", "productType"]) self.assertSetEqual(expected, set(prepared_search)) @mock.patch( @@ -1205,7 +1205,7 @@ class TestCoreSearch(TestCoreBase): "start": "2020-01-01", "end": "2020-02-01", } - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertEqual(prepared_search["startTimeFromAscendingNode"], base["start"]) self.assertEqual( prepared_search["completionTimeFromAscendingNode"], base["end"] @@ -1219,22 +1219,22 @@ class TestCoreSearch(TestCoreBase): # The default way to provide a geom is through the 'geom' argument. base = {"geom": (0, 50, 2, 52)} # "geom": "POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))" - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertIn("geometry", prepared_search) # 'box' and 'bbox' are supported for backwards compatibility, # The priority is geom > bbox > box base = {"box": (0, 50, 2, 52)} - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertIn("geometry", prepared_search) base = {"bbox": (0, 50, 2, 52)} - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertIn("geometry", prepared_search) base = { "geom": "POLYGON ((1 43, 1 44, 2 44, 2 43, 1 43))", "bbox": (0, 50, 2, 52), "box": (0, 50, 1, 51), } - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertIn("geometry", prepared_search) self.assertNotIn("bbox", prepared_search) self.assertNotIn("bbox", prepared_search) @@ -1249,19 +1249,19 @@ class TestCoreSearch(TestCoreBase): # as regular kwargs. The new and recommended way to provide # them is through the 'locations' parameter. base = {"locations": {"country": "FRA"}} - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertIn("geometry", prepared_search) # TODO: Remove this when support for locations kwarg is dropped. base = {"country": "FRA"} - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertIn("geometry", prepared_search) self.assertNotIn("country", prepared_search) def test__prepare_search_product_type_provided(self): """_prepare_search must handle when a product type is given""" base = {"productType": "S2_MSI_L1C"} - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertEqual(prepared_search["productType"], base["productType"]) def test__prepare_search_product_type_guess_it(self): @@ -1273,7 +1273,7 @@ class TestCoreSearch(TestCoreBase): platform="SENTINEL2", platformSerialIdentifier="S2A", ) - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertEqual(prepared_search["productType"], "S2_MSI_L1C") def test__prepare_search_remove_guess_kwargs(self): @@ -1285,13 +1285,13 @@ class TestCoreSearch(TestCoreBase): platform="SENTINEL2", platformSerialIdentifier="S2A", ) - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertEqual(len(base.keys() & prepared_search.keys()), 0) def test__prepare_search_with_id(self): """_prepare_search must handle a search by id""" base = {"id": "dummy-id", "provider": "creodias"} - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) expected = {"id": "dummy-id"} self.assertDictEqual(expected, prepared_search) @@ -1301,7 +1301,7 @@ class TestCoreSearch(TestCoreBase): "productType": "S2_MSI_L1C", "cloudCover": 10, } - prepared_search = self.dag._prepare_search(base) + _, prepared_search = self.dag._prepare_search(base) self.assertEqual(prepared_search["productType"], base["productType"]) self.assertEqual(prepared_search["cloudCover"], base["cloudCover"]) @@ -1311,14 +1311,12 @@ class TestCoreSearch(TestCoreBase): try: self.dag.set_preferred_provider("peps") base = {"productType": "S2_MSI_L1C"} - prepared_search = self.dag._prepare_search(base) + search_plugins, _ = self.dag._prepare_search(base) # Just check that the title has been set correctly. There are more (e.g. # abstract, platform, etc.) but this is sufficient to check that the # product_type_config dict has been created and populated. self.assertEqual( - prepared_search["search_plugins"][0].config.product_type_config[ - "title" - ], + search_plugins[0].config.product_type_config["title"], "SENTINEL2 Level-1C", ) finally: @@ -1335,38 +1333,35 @@ class TestCoreSearch(TestCoreBase): try: self.dag.set_preferred_provider("peps") base = {"productType": "product_unknown_to_eodag"} - prepared_search = self.dag._prepare_search(base) + search_plugins, _ = self.dag._prepare_search(base) # product_type_config is still created if the product is not known to eodag # however it contains no data. self.assertIsNone( - prepared_search["search_plugins"][0].config.product_type_config[ - "title" - ], + search_plugins[0].config.product_type_config["title"], ) finally: self.dag.set_preferred_provider(prev_fav_provider) def test__prepare_search_peps_plugins_product_available(self): - """_prepare_search must return the search and auth plugins when productType is defined""" + """_prepare_search must return the search plugins when productType is defined""" prev_fav_provider = self.dag.get_preferred_provider()[0] try: self.dag.set_preferred_provider("peps") base = {"productType": "S2_MSI_L1C"} - prepared_search = self.dag._prepare_search(base) - self.assertEqual(prepared_search["search_plugins"][0].provider, "peps") - self.assertEqual(prepared_search["auth"]["peps"].provider, "peps") + search_plugins, _ = self.dag._prepare_search(base) + self.assertEqual(search_plugins[0].provider, "peps") finally: self.dag.set_preferred_provider(prev_fav_provider) def test__prepare_search_no_plugins_when_search_by_id(self): """_prepare_search must not return the search and auth plugins for a search by id""" base = {"id": "some_id", "provider": "some_provider"} - prepared_search = self.dag._prepare_search(base) - self.assertNotIn("search_plugins", prepared_search) + search_plugins, prepared_search = self.dag._prepare_search(base) + self.assertListEqual(search_plugins, []) self.assertNotIn("auth", prepared_search) def test__prepare_search_peps_plugins_product_not_available(self): - """_prepare_search can use another set of search and auth plugins than the ones of the preferred provider""" + """_prepare_search can use another search plugin than the preferred one""" # Document a special behaviour whereby the search and auth plugins don't # correspond to the preferred one. This occurs whenever the searched product # isn't available for the preferred provider but is made available by another @@ -1376,9 +1371,8 @@ class TestCoreSearch(TestCoreBase): try: self.dag.set_preferred_provider("theia") base = {"productType": "S2_MSI_L1C"} - prepared_search = self.dag._prepare_search(base) - self.assertEqual(prepared_search["search_plugins"][0].provider, "peps") - self.assertEqual(prepared_search["auth"]["peps"].provider, "peps") + search_plugins, _ = self.dag._prepare_search(base) + self.assertEqual(search_plugins[0].provider, "peps") finally: self.dag.set_preferred_provider(prev_fav_provider) @@ -1420,7 +1414,6 @@ class TestCoreSearch(TestCoreBase): mock__do_search.assert_called_once_with( self.dag, mock_get_search_plugins.return_value[0], - auth=mock_get_auth_plugin.return_value, id="foo", productType="bar", ) @@ -1668,7 +1661,7 @@ class TestCoreSearch(TestCoreBase): pagination = {} search_plugin.config = DummyConfig() - prepare_seach.return_value = dict(search_plugin=search_plugin) + prepare_seach.return_value = ([search_plugin], {}) page_iterator = self.dag.search_iter_page_plugin( items_per_page=2, search_plugin=search_plugin ) @@ -1695,7 +1688,7 @@ class TestCoreSearch(TestCoreBase): pagination = {} search_plugin.config = DummyConfig() - prepare_seach.return_value = dict(search_plugin=search_plugin) + prepare_seach.return_value = ([search_plugin], {}) page_iterator = self.dag.search_iter_page_plugin( items_per_page=2, search_plugin=search_plugin ) @@ -1720,7 +1713,7 @@ class TestCoreSearch(TestCoreBase): pagination = {} search_plugin.config = DummyConfig() - prepare_seach.return_value = dict(search_plugin=search_plugin) + prepare_seach.return_value = ([search_plugin], {}) page_iterator = self.dag.search_iter_page_plugin( items_per_page=2, search_plugin=search_plugin ) @@ -1736,7 +1729,7 @@ class TestCoreSearch(TestCoreBase): """search_iter_page must propagate errors""" search_plugin.provider = "peps" search_plugin.query.side_effect = AttributeError() - prepare_seach.return_value = dict(search_plugin=search_plugin) + prepare_seach.return_value = ([search_plugin], {}) page_iterator = self.dag.search_iter_page_plugin() with self.assertRaises(AttributeError): next(page_iterator) @@ -1806,13 +1799,13 @@ class TestCoreSearch(TestCoreBase): search_plugin.config = DummyConfig() - # Infinite generator her because passing directly the dict to + # Infinite generator here because passing directly the dict to # prepare_search.return_value (or side_effect) didn't work. One function # would do a dict.pop("search_plugin") that would remove the item from the # mocked return value. Later calls would then break def yield_search_plugin(): while True: - yield {"search_plugins": [search_plugin]} + yield ([search_plugin], {}) prepare_seach.side_effect = yield_search_plugin() all_results = self.dag.search_all(items_per_page=2)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 2, "test_score": 0 }, "num_modified_files": 2 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@4b1397bfdc79b0b9be0be3fc825df40949f4acae#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.7.0 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.0b2.dev11+g4b1397bf - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.7.0 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all" ]	[]	[ "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_request_error", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreSearch::test_search_request_error", "tests/units/test_core.py::TestCoreDownload::test_download_local_product" ]	[]	Apache License 2.0	null
CS-SI__eodag-836	db336ebe9e3359bc3549e546948ef59c54b632ae	2023-09-19 13:47:39	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against df350c69e0a7099fc741626e47753f892b4301bc </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `76%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against df350c69e0a7099fc741626e47753f892b4301bc </p>	diff --git a/docs/getting_started_guide/configure.rst b/docs/getting_started_guide/configure.rst index c7814106..ac0559c5 100644 --- a/docs/getting_started_guide/configure.rst +++ b/docs/getting_started_guide/configure.rst @@ -20,10 +20,10 @@ for products in a provider's catalog, how to download products, etc. However, us to complete this configuration with additional settings, such as provider credentials. Users can also override pre-configured settings (e.g. the download folder). -YAML configuration file -^^^^^^^^^^^^^^^^^^^^^^^ +YAML user configuration file +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The first time ``eodag`` is used after an install a default YAML configuration file is saved +The first time ``eodag`` is used after an install, a default YAML user configuration file is saved in a local directory (``~/.config/eodag/eodag.yml`` on Linux). This YAML file contains a template that shows how to complete the configuration of one or @@ -86,8 +86,11 @@ to the configuration file. In that case it is also loaded automatically: Environment variable configuration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -``eodag`` can also be configured with environment variables, which have priority over -the YAML file configuration. +Providers configuration using environment variables +""""""""""""""""""""""""""""""""""""""""""""""""""" + +``eodag`` providers can also be configured with environment variables, which have priority over +the YAML user configuration file. The name of the environment variables understood by ``eodag`` must follow the pattern ``EODAG__KEY1__KEY2__[...]__KEYN`` (note the double underscore between the keys). @@ -116,13 +119,32 @@ Each configuration parameter can be set with an environment variable. .. note:: - Setting credentials must be done according to the `provider's plugin <https://eodag.readthedocs.io/en/stable/plugins.html#plugins-available>`_ (auth \| api): + Setting credentials must be done according to the + `provider's plugin <https://eodag.readthedocs.io/en/stable/plugins.html#plugins-available>`_ (auth \| api): * Authentication plugin: ``EODAG__<PROVIDER>__AUTH__CREDENTIALS__<KEY>`` * API plugin: ``EODAG__<PROVIDER>__API__CREDENTIALS__<KEY>`` - ``<KEY>`` should be replaced with the adapted credentials key (``USERNAME``, ``PASSWORD``, ``APIKEY``, ...) according to the provider configuration template in `the YAML configuration file <https://eodag.readthedocs.io/en/stable/getting_started_guide/configure.html#yaml-configuration-file>`_. + ``<KEY>`` should be replaced with the adapted credentials key (``USERNAME``, ``PASSWORD``, ``APIKEY``, ...) according + to the provider configuration template in + `the YAML user configuration file\ + <https://eodag.readthedocs.io/en/stable/getting_started_guide/configure.html#yaml-user-configuration-file>`_. + + +Core configuration using environment variables +"""""""""""""""""""""""""""""""""""""""""""""" + +Some EODAG core settings can be overriden using environment variables: + +* ``EODAG_CFG_FILE`` for defining the desired path to the `user configuration file\ + <https://eodag.readthedocs.io/en/stable/getting_started_guide/configure.html#yaml-user-configuration-file>`_ +* ``EODAG_LOCS_CFG_FILE`` for defining the desired path to the + `locations <https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/4_search.html#Locations-search>`_ + configuration file +* ``EODAG_PROVIDERS_CFG_FILE`` for defining the desired path to the providers configuration file +* ``EODAG_EXT_PRODUCT_TYPES_CFG_FILE`` for defining the desired path to the `external product types configuration file\ + <https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/2_providers_products_available.html#Product-types-discovery>`_ CLI configuration ^^^^^^^^^^^^^^^^^ @@ -254,7 +276,8 @@ Use OTP through Python code """"""""""""""""""""""""""" ``creodias`` needs a temporary 6-digits code to authenticate in addition of the ``username`` and ``password``. Check -`Creodias documentation <https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`_ +`Creodias documentation\ +<https://creodias.docs.cloudferro.com/en/latest/gettingstarted/Two-Factor-Authentication-for-Creodias-Site.html>`_ to see how to get this code once you are registered. This OTP code will only be valid for a few seconds, so you will better set it dynamically in your python code instead of storing it statically in your user configuration file. @@ -284,8 +307,8 @@ will be stored and used if further authentication tries fail: dag._plugins_manager.get_auth_plugin("creodias").authenticate() Please note that authentication mechanism is already included in -`download methods <https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/7_download.html>`_ , so you could also directly execute a -download to retrieve the token while the OTP is still valid. +`download methods <https://eodag.readthedocs.io/en/stable/notebooks/api_user_guide/7_download.html>`_ , so you could +also directly execute a download to retrieve the token while the OTP is still valid. Use OTP through CLI """"""""""""""""""" diff --git a/eodag/config.py b/eodag/config.py index 8523d9b7..0e5a5eb1 100644 --- a/eodag/config.py +++ b/eodag/config.py @@ -226,12 +226,18 @@ class PluginConfig(yaml.YAMLObject): def load_default_config(): - """Load the providers configuration into a dictionnary + """Load the providers configuration into a dictionnary. + + Load from eodag `resources/providers.yml` or `EODAG_PROVIDERS_CFG_FILE` environment + variable if exists. :returns: The default provider's configuration :rtype: dict """ - return load_config(resource_filename("eodag", "resources/providers.yml")) + eodag_providers_cfg_file = os.getenv( + "EODAG_PROVIDERS_CFG_FILE" + ) or resource_filename("eodag", "resources/providers.yml") + return load_config(eodag_providers_cfg_file) def load_config(config_path): @@ -242,6 +248,7 @@ def load_config(config_path): :returns: The default provider's configuration :rtype: dict """ + logger.debug(f"Loading configuration from {config_path}") config = {} try: # Providers configs are stored in this file as separated yaml documents	configure providers.yml path Implement a way to configure where `providers.yml` is located. - through a environment variable	CS-SI/eodag	diff --git a/tests/resources/file_providers_override.yml b/tests/resources/file_providers_override.yml new file mode 100644 index 00000000..fd3fea75 --- /dev/null +++ b/tests/resources/file_providers_override.yml @@ -0,0 +1,33 @@ +# -- coding: utf-8 -- +# Copyright 2023, CS GROUP - France, https://www.csgroup.eu/ +# +# This file is part of EODAG project +# https://www.github.com/CS-SI/EODAG +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +--- +!provider + name: foo_provider + priority: 2 + roles: + - host + description: provider for test + search: !plugin + type: StacSearch + api_endpoint: https://foo.bar/search + products: + GENERIC_PRODUCT_TYPE: + productType: '{productType}' + download: !plugin + type: HTTPDownload + base_uri: https://foo.bar diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 77b98952..430eb40c 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -900,7 +900,7 @@ class TestCoreConfWithEnvVar(TestCoreBase): cls.mock_os_environ.stop() def test_core_object_prioritize_locations_file_in_envvar(self): - """The core object must use the locations file pointed to by the EODAG_LOCS_CFG_FILE env var""" + """The core object must use the locations file pointed by the EODAG_LOCS_CFG_FILE env var""" try: os.environ["EODAG_LOCS_CFG_FILE"] = os.path.join( TEST_RESOURCES_PATH, "file_locations_override.yml" @@ -914,7 +914,7 @@ class TestCoreConfWithEnvVar(TestCoreBase): os.environ.pop("EODAG_LOCS_CFG_FILE", None) def test_core_object_prioritize_config_file_in_envvar(self): - """The core object must use the config file pointed to by the EODAG_CFG_FILE env var""" + """The core object must use the config file pointed by the EODAG_CFG_FILE env var""" try: os.environ["EODAG_CFG_FILE"] = os.path.join( TEST_RESOURCES_PATH, "file_config_override.yml" @@ -929,6 +929,22 @@ class TestCoreConfWithEnvVar(TestCoreBase): finally: os.environ.pop("EODAG_CFG_FILE", None) + def test_core_object_prioritize_providers_file_in_envvar(self): + """The core object must use the providers conf file pointed by the EODAG_PROVIDERS_CFG_FILE env var""" + try: + os.environ["EODAG_PROVIDERS_CFG_FILE"] = os.path.join( + TEST_RESOURCES_PATH, "file_providers_override.yml" + ) + dag = EODataAccessGateway() + # only foo_provider in conf + self.assertEqual(dag.available_providers(), ["foo_provider"]) + self.assertEqual( + dag.providers_config["foo_provider"].search.api_endpoint, + "https://foo.bar/search", + ) + finally: + os.environ.pop("EODAG_PROVIDERS_CFG_FILE", None) + class TestCoreInvolvingConfDir(unittest.TestCase): def setUp(self):	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 3 }, "num_modified_files": 2 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio", "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@db336ebe9e3359bc3549e546948ef59c54b632ae#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.0b2.dev30+gdb336ebe - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar" ]	[]	[ "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreDownload::test_download_local_product" ]	[]	Apache License 2.0	null
CS-SI__eodag-882	177d77120e1c5bda5c84af638bdaa49a07edf5f4	2023-10-13 09:00:08	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: ## Test Results     1 files -       3     1 suites - 3 1m 20s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "duration of all tests") - 4m 52s 431 tests ±      0 425 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") -       3 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±  0 3 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") +3 431 runs - 1 293 425 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") - 1 223 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") - 73 3 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") +3 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/17671383402). Results for commit 43eb0460. ± Comparison against base commit 177d7712. [test-results]:data:application/gzip;base64,H4sIAOUHKWUC/1WMSw6DIBQAr2JYd4HyKfQyDeAjIVVpEFamd+/DpqLLmUlmIz5MsJJH1986spaQDxhLMjnEBVFRZCy5Ns76Pz3X4lxVg2jqFd6o2CG8CdNFQEoxoanTVJb2rHBd/kw77nwa7nz+uTjPISMQzsBSLikzHhyT0oCxQgmtGb9rPjqpYFDUCvL5Ao3wUI0EAQAA github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 43eb04603afec366aeab5859934794dc68e280b5 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `76%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 51171f8933d81affe05fb50208cc7df414c8ecff </p>	diff --git a/docs/notebooks/tutos/tuto_cop_dem.ipynb b/docs/notebooks/tutos/tuto_cop_dem.ipynb index 6fd63b31..05f6d515 100644 --- a/docs/notebooks/tutos/tuto_cop_dem.ipynb +++ b/docs/notebooks/tutos/tuto_cop_dem.ipynb @@ -16,14 +16,7 @@ "outputs": [], "source": [ "import os\n", - "import boto3\n", - "import json\n", - "import zipfile\n", - "\n", - "from botocore.handlers import disable_signing\n", - "\n", - "from eodag import EODataAccessGateway\n", - "from eodag.utils import ProgressCallback" + "from eodag import EODataAccessGateway" ] }, { @@ -48,7 +41,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Download grid index from AWS:" + "## Search products" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Init EODAG and get available product types from Copernicus DEM" ] }, { @@ -58,88 +58,50 @@ "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0a905e212bbb486aa0c7624605f18b1e", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ - " 0%\| \| 0.00/2.24M [00:00<?, ?B/s]" + "['COP_DEM_GLO30_DGED',\n", + " 'COP_DEM_GLO30_DTED',\n", + " 'COP_DEM_GLO90_DGED',\n", + " 'COP_DEM_GLO90_DTED']" ] }, + "execution_count": 3, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "grid_zipfilename = \"grid.zip\"\n", - "progress_callback = ProgressCallback()\n", - "path_to_zipfile = os.path.join(os.path.abspath(workspace), grid_zipfilename)\n", - "\n", - "s3_resource = boto3.resource(\"s3\")\n", - "s3_resource.meta.client.meta.events.register(\n", - " \"choose-signer.s3.\", disable_signing\n", - ")\n", - "s3_bucket = s3_resource.Bucket(\"copernicus-dem-30m\")\n", - "\n", - "# file size\n", - "progress_callback.reset(total=sum([\n", - " p.size for p in s3_bucket.objects.filter(Prefix=grid_zipfilename)\n", - "]))\n", - "# download\n", - "objects = s3_bucket.download_file(\n", - " grid_zipfilename,\n", - " path_to_zipfile,\n", - " Callback=progress_callback,\n", - ")\n", - "progress_callback.close()\n", + "dag = EODataAccessGateway()\n", "\n", - "# unzip\n", - "with zipfile.ZipFile(path_to_zipfile, 'r') as zip_ref:\n", - " zip_ref.extractall(workspace)" + "dag.guess_product_type(keywords=\"DEM\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Init EODAG and add a custom provider for this local Copernicus DEM index:" + "Check which providers support `COP_DEM_GLO30_DGED`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [ - "dag = EODataAccessGateway()\n", - "dag.update_providers_config(\"\"\"\n", - "cop_dem_aws:\n", - " search:\n", - " type: StacSearch\n", - " api_endpoint: https://fake-endpoint\n", - " metadata_mapping:\n", - " id: '$.properties.id'\n", - " title: '$.properties.id'\n", - " downloadLink: 's3://copernicus-dem-30m/{id}'\n", - " products:\n", - " GENERIC_PRODUCT_TYPE:\n", - " productType: '{productType}'\n", - " download:\n", - " type: AwsDownload\n", - " base_uri: https://fake-endpoint\n", - " flatten_top_dirs: True\n", - " auth:\n", - " type: AwsAuth\n", - " credentials:\n", - "\"\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['creodias', 'wekeo']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Convert downloaded geojson to EODAG serialized search result" + "product_type = \"COP_DEM_GLO30_DGED\"\n", + "dag.available_providers(product_type)" ] }, { @@ -148,44 +110,36 @@ "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "64800 items written to /home/sylvain/workspace/eodag/examples/eodag_workspace_copdem/dem30mGrid_eodag.json\n", - "64800 items loaded as SearchResult\n" + "Product type 'COP_DEM_GLO30_DGED' is not available with provider 'peps'. Searching it on provider 'creodias' instead.\n" ] + }, + { + "data": { + "text/plain": [ + "SearchResult([EOProduct(id=DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP, provider=creodias),\n", + " EOProduct(id=DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx, provider=creodias),\n", + " EOProduct(id=DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX, provider=creodias)])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "dem_in = os.path.join(os.path.abspath(workspace), \"dem30mGrid.json\")\n", - "dem_out = os.path.join(os.path.abspath(workspace), \"dem30mGrid_eodag.json\")\n", - "with open(dem_in) as fin, open(dem_out, \"w\") as fout:\n", - " data = json.load(fin)\n", - " data[\"features\"] = [dict(feat, *{\n", - " \"id\": feat[\"properties\"][\"id\"],\n", - " \"properties\" : {\n", - " \"title\": feat[\"properties\"][\"id\"],\n", - " \"eodag_product_type\": \"COP_DEM\",\n", - " \"eodag_provider\": \"cop_dem_aws\",\n", - " \"eodag_search_intersection\": {\"type\": \"Polygon\",\"coordinates\": []},\n", - " \"downloadLink\": \"s3://copernicus-dem-30m/%s\" % feat[\"properties\"][\"id\"],\n", - " }\n", - " }) for feat in data[\"features\"]\n", - " ]\n", - " json.dump(data, fout)\n", - "print(\"%s items written to %s\" % (len(data[\"features\"]), dem_out))\n", - "\n", - "items = dag.deserialize_and_register(dem_out)\n", - "print(\"%s items loaded as SearchResult\" % len(items))" + "search_geom = [0.25, 43.2, 2.8, 43.9]\n", + "search_result, count = dag.search(productType=product_type, geom=search_geom)\n", + "search_result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Filter on a specific area\n", - "\n", - "Draw geometry on the map:" + "Draw results geometries on a map:" ] }, { @@ -195,116 +149,222 @@ "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8fafcf737b164e8688e07dfb22bdbddb", - "version_major": 2, - "version_minor": 0 - }, + "text/html": [ + "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"<!DOCTYPE html>\n", + "<html>\n", + "<head>\n", + " \n", + " <meta http-equiv="content-type" content="text/html; charset=UTF-8" />\n", + " \n", + " <script>\n", + " L_NO_TOUCH = false;\n", + " L_DISABLE_3D = false;\n", + " </script>\n", + " \n", + " <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n", + " <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n", + " <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.js"></script>\n", + " <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n", + " <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>\n", + " <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n", + " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.css"/>\n", + " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"/>\n", + " <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>\n", + " <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/css/all.min.css"/>\n", + " <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>\n", + " <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>\n", + " \n", + " <meta name="viewport" content="width=device-width,\n", + " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", + " <style>\n", + " #map_bb529a40614b71994d4f3b84c24ff3c6 {\n", + " position: relative;\n", + " width: 100.0%;\n", + " height: 100.0%;\n", + " left: 0.0%;\n", + " top: 0.0%;\n", + " }\n", + " .leaflet-container { font-size: 1rem; }\n", + " </style>\n", + " \n", + " \n", + " <style>\n", + " .foliumtooltip {\n", + " \n", + " }\n", + " .foliumtooltip table{\n", + " margin: auto;\n", + " }\n", + " .foliumtooltip tr{\n", + " text-align: left;\n", + " }\n", + " .foliumtooltip th{\n", + " padding: 2px; padding-right: 8px;\n", + " }\n", + " </style>\n", + " \n", + "</head>\n", + "<body>\n", + " \n", + " \n", + " <div class="folium-map" id="map_bb529a40614b71994d4f3b84c24ff3c6" ></div>\n", + " \n", + "</body>\n", + "<script>\n", + " \n", + " \n", + " var map_bb529a40614b71994d4f3b84c24ff3c6 = L.map(\n", + " "map_bb529a40614b71994d4f3b84c24ff3c6",\n", + " {\n", + " center: [43.5, 1.5],\n", + " crs: L.CRS.EPSG3857,\n", + " zoom: 8,\n", + " zoomControl: true,\n", + " preferCanvas: false,\n", + " }\n", + " );\n", + "\n", + " \n", + "\n", + " \n", + " \n", + " var tile_layer_f202a5440eb73c205c8aeab90cc160cf = L.tileLayer(\n", + " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", + " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", + " ).addTo(map_bb529a40614b71994d4f3b84c24ff3c6);\n", + " \n", + " \n", + " var rectangle_91153f824feb5444e8e5b34d6f066e06 = L.rectangle(\n", + " [[43.2, 0.25], [43.9, 2.8]],\n", + " {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "noClip": false, "opacity": 1.0, "smoothFactor": 1.0, "stroke": true, "weight": 3}\n", + " ).addTo(map_bb529a40614b71994d4f3b84c24ff3c6);\n", + " \n", + " \n", + " rectangle_91153f824feb5444e8e5b34d6f066e06.bindTooltip(\n", + " `<div>\n", + " Search extent\n", + " </div>`,\n", + " {"sticky": true}\n", + " );\n", + " \n", + " \n", + "\n", + " function geo_json_a1a84a7e944c3478f83d093c02ad9c6e_onEachFeature(feature, layer) {\n", + " layer.on({\n", + " });\n", + " };\n", + " var geo_json_a1a84a7e944c3478f83d093c02ad9c6e = L.geoJson(null, {\n", + " onEachFeature: geo_json_a1a84a7e944c3478f83d093c02ad9c6e_onEachFeature,\n", + " \n", + " });\n", + "\n", + " function geo_json_a1a84a7e944c3478f83d093c02ad9c6e_add (data) {\n", + " geo_json_a1a84a7e944c3478f83d093c02ad9c6e\n", + " .addData(data)\n", + " .addTo(map_bb529a40614b71994d4f3b84c24ff3c6);\n", + " }\n", + " geo_json_a1a84a7e944c3478f83d093c02ad9c6e_add({"features": [{"geometry": {"coordinates": [[[2.0, 43.0], [3.0, 43.0], [3.0, 44.0], [2.0, 44.0], [2.0, 43.0]]], "type": "Polygon"}, "id": "DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP", "properties": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. Data were acquired through the TanDEM-X mission between 2011 and 2015 [https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model].", "accessConstraint": {"description": {"shortName": "No license"}, "grantedCountries": null, "grantedFlags": null, "grantedOrganizationCountries": null, "hasToBeSigned": "never", "licenseId": "unlicensed", "signatureQuota": -1, "viewService": "public"}, "centroid": {"coordinates": [2.5, 43.5], "type": "Point"}, "completionTimeFromAscendingNode": "2014-09-06T17:42:15.000Z", "downloadLink": "https://zipper.creodias.eu/download/e2d2e6dd-198f-53b0-9b93-f898ab28cf8c", "eodag_product_type": "COP_DEM_GLO30_DGED", "eodag_provider": "creodias", "eodag_search_intersection": {"coordinates": [[[2.0, 43.9], [2.8, 43.9], [2.8, 43.2], [2.0, 43.2], [2.0, 43.9]]], "type": "Polygon"}, "gmlgeometry": "\\u003cgml:Polygon srsName=\\"EPSG:4326\\"\\u003e\\u003cgml:outerBoundaryIs\\u003e\\u003cgml:LinearRing\\u003e\\u003cgml:coordinates\\u003e2,43 3,43 3,44 2,44 2,43\\u003c/gml:coordinates\\u003e\\u003c/gml:LinearRing\\u003e\\u003c/gml:outerBoundaryIs\\u003e\\u003c/gml:Polygon\\u003e", "instrument": null, "keywords": "TerraSAR,TanDEM-X,COP-DEM,DEM,surface,GLO-30,DSM,GDGED", "license": "proprietary", "links": [{"href": "https://datahub.creodias.eu/resto/collections/COP-DEM/e2d2e6dd-198f-53b0-9b93-f898ab28cf8c.json", "rel": "self", "title": "GeoJSON link for e2d2e6dd-198f-53b0-9b93-f898ab28cf8c", "type": "application/json"}], "missionStartDate": "2010-06-21T00:00:00Z", "modificationDate": "2021-06-21T22:01:44.918Z", "orbitNumber": 0, "organisationName": "ESA", "parentIdentifier": null, "platform": "COP-DEM", "platformSerialIdentifier": "", "polarizationMode": "HH", "processingLevel": null, "productIdentifier": "/eodata/auxdata/CopDEM/COP-DEM_GLO-30-DGED/DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP.DEM", "productType": "DGE_30", "publicationDate": "2021-06-21T22:01:44.918Z", "quicklook": null, "resolution": 30.0, "sensorMode": null, "sensorType": "ALTIMETRIC", "services": {"download": {"mimeType": "application/octet-stream", "size": 0, "url": "https://datahub.creodias.eu/download/e2d2e6dd-198f-53b0-9b93-f898ab28cf8c"}}, "snowCover": 0, "startTimeFromAscendingNode": "2011-02-16T17:41:43.000Z", "storageStatus": "ONLINE", "thumbnail": "https://datahub.creodias.eu/get-object?path=/auxdata/CopDEM/COP-DEM_GLO-30-DGED/DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP.DEM/DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP-ql.jpg", "title": "DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP", "uid": "e2d2e6dd-198f-53b0-9b93-f898ab28cf8c"}, "type": "Feature"}, {"geometry": {"coordinates": [[[1.0, 43.0], [2.0, 43.0], [2.0, 44.0], [1.0, 44.0], [1.0, 43.0]]], "type": "Polygon"}, "id": "DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx", "properties": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. Data were acquired through the TanDEM-X mission between 2011 and 2015 [https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model].", "accessConstraint": {"description": {"shortName": "No license"}, "grantedCountries": null, "grantedFlags": null, "grantedOrganizationCountries": null, "hasToBeSigned": "never", "licenseId": "unlicensed", "signatureQuota": -1, "viewService": "public"}, "centroid": {"coordinates": [1.5, 43.5], "type": "Point"}, "completionTimeFromAscendingNode": "2014-09-06T17:42:15.000Z", "downloadLink": "https://zipper.creodias.eu/download/6aead0d1-5def-5c1d-a20d-f1597e1e4b6c", "eodag_product_type": "COP_DEM_GLO30_DGED", "eodag_provider": "creodias", "eodag_search_intersection": {"coordinates": [[[2.0, 43.2], [1.0, 43.2], [1.0, 43.9], [2.0, 43.9], [2.0, 43.2]]], "type": "Polygon"}, "gmlgeometry": "\\u003cgml:Polygon srsName=\\"EPSG:4326\\"\\u003e\\u003cgml:outerBoundaryIs\\u003e\\u003cgml:LinearRing\\u003e\\u003cgml:coordinates\\u003e1,43 2,43 2,44 1,44 1,43\\u003c/gml:coordinates\\u003e\\u003c/gml:LinearRing\\u003e\\u003c/gml:outerBoundaryIs\\u003e\\u003c/gml:Polygon\\u003e", "instrument": null, "keywords": "TerraSAR,TanDEM-X,COP-DEM,DEM,surface,GLO-30,DSM,GDGED", "license": "proprietary", "links": [{"href": "https://datahub.creodias.eu/resto/collections/COP-DEM/6aead0d1-5def-5c1d-a20d-f1597e1e4b6c.json", "rel": "self", "title": "GeoJSON link for 6aead0d1-5def-5c1d-a20d-f1597e1e4b6c", "type": "application/json"}], "missionStartDate": "2010-06-21T00:00:00Z", "modificationDate": "2021-06-21T19:59:05.813Z", "orbitNumber": 0, "organisationName": "ESA", "parentIdentifier": null, "platform": "COP-DEM", "platformSerialIdentifier": "", "polarizationMode": "HH", "processingLevel": null, "productIdentifier": "/eodata/auxdata/CopDEM/COP-DEM_GLO-30-DGED/DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx.DEM", "productType": "DGE_30", "publicationDate": "2021-06-21T19:59:05.813Z", "quicklook": null, "resolution": 30.0, "sensorMode": null, "sensorType": "ALTIMETRIC", "services": {"download": {"mimeType": "application/octet-stream", "size": 0, "url": "https://datahub.creodias.eu/download/6aead0d1-5def-5c1d-a20d-f1597e1e4b6c"}}, "snowCover": 0, "startTimeFromAscendingNode": "2011-04-17T17:50:32.000Z", "storageStatus": "ONLINE", "thumbnail": "https://datahub.creodias.eu/get-object?path=/auxdata/CopDEM/COP-DEM_GLO-30-DGED/DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx.DEM/DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx-ql.jpg", "title": "DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx", "uid": "6aead0d1-5def-5c1d-a20d-f1597e1e4b6c"}, "type": "Feature"}, {"geometry": {"coordinates": [[[-0.0, 43.0], [1.0, 43.0], [1.0, 44.0], [-0.0, 44.0], [-0.0, 43.0]]], "type": "Polygon"}, "id": "DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX", "properties": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. Data were acquired through the TanDEM-X mission between 2011 and 2015 [https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model].", "accessConstraint": {"description": {"shortName": "No license"}, "grantedCountries": null, "grantedFlags": null, "grantedOrganizationCountries": null, "hasToBeSigned": "never", "licenseId": "unlicensed", "signatureQuota": -1, "viewService": "public"}, "centroid": {"coordinates": [0.5, 43.5], "type": "Point"}, "completionTimeFromAscendingNode": "2014-08-31T17:50:54.000Z", "downloadLink": "https://zipper.creodias.eu/download/2f73582e-a3ea-53c9-8578-73f333d9e14b", "eodag_product_type": "COP_DEM_GLO30_DGED", "eodag_provider": "creodias", "eodag_search_intersection": {"coordinates": [[[1.0, 43.2], [0.25, 43.2], [0.25, 43.9], [1.0, 43.9], [1.0, 43.2]]], "type": "Polygon"}, "gmlgeometry": "\\u003cgml:Polygon srsName=\\"EPSG:4326\\"\\u003e\\u003cgml:outerBoundaryIs\\u003e\\u003cgml:LinearRing\\u003e\\u003cgml:coordinates\\u003e0,43 1,43 1,44 0,44 0,43\\u003c/gml:coordinates\\u003e\\u003c/gml:LinearRing\\u003e\\u003c/gml:outerBoundaryIs\\u003e\\u003c/gml:Polygon\\u003e", "instrument": null, "keywords": "TerraSAR,TanDEM-X,COP-DEM,DEM,surface,GLO-30,DSM,GDGED", "license": "proprietary", "links": [{"href": "https://datahub.creodias.eu/resto/collections/COP-DEM/2f73582e-a3ea-53c9-8578-73f333d9e14b.json", "rel": "self", "title": "GeoJSON link for 2f73582e-a3ea-53c9-8578-73f333d9e14b", "type": "application/json"}], "missionStartDate": "2010-06-21T00:00:00Z", "modificationDate": "2021-06-21T19:39:33.821Z", "orbitNumber": 0, "organisationName": "ESA", "parentIdentifier": null, "platform": "COP-DEM", "platformSerialIdentifier": "", "polarizationMode": "HH", "processingLevel": null, "productIdentifier": "/eodata/auxdata/CopDEM/COP-DEM_GLO-30-DGED/DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX.DEM", "productType": "DGE_30", "publicationDate": "2021-06-21T19:39:33.821Z", "quicklook": null, "resolution": 30.0, "sensorMode": null, "sensorType": "ALTIMETRIC", "services": {"download": {"mimeType": "application/octet-stream", "size": 0, "url": "https://datahub.creodias.eu/download/2f73582e-a3ea-53c9-8578-73f333d9e14b"}}, "snowCover": 0, "startTimeFromAscendingNode": "2011-04-28T17:49:52.000Z", "storageStatus": "ONLINE", "thumbnail": "https://datahub.creodias.eu/get-object?path=/auxdata/CopDEM/COP-DEM_GLO-30-DGED/DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX.DEM/DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX-ql.jpg", "title": "DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX", "uid": "2f73582e-a3ea-53c9-8578-73f333d9e14b"}, "type": "Feature"}], "type": "FeatureCollection"});\n", + "\n", + " \n", + " \n", + " geo_json_a1a84a7e944c3478f83d093c02ad9c6e.bindTooltip(\n", + " function(layer){\n", + " let div = L.DomUtil.create('div');\n", + " \n", + " let handleObject = feature=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;\n", + " let fields = ["title"];\n", + " let aliases = ["title"];\n", + " let table = '<table>' +\n", + " String(\n", + " fields.map(\n", + " (v,i)=>\n", + " `<tr>\n", + " <th>${aliases[i]}</th>\n", + " \n", + " <td>${handleObject(layer.feature.properties[v])}</td>\n", + " </tr>`).join(''))\n", + " +'</table>';\n", + " div.innerHTML=table;\n", + " \n", + " return div\n", + " }\n", + " ,{"className": "foliumtooltip", "sticky": true});\n", + " \n", + "</script>\n", + "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" + ], "text/plain": [ - "Map(center=[43.53, 1.33], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_ou…" + "<folium.folium.Map at 0x7ff440363760>" ] }, + "execution_count": 6, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "import ipyleaflet as ipyl\n", - "import ipywidgets as widgets\n", - "\n", - "center = [43.53, 1.33] # Toulouse\n", - "zoom = 6\n", - "m = ipyl.Map(center=center, zoom=zoom)\n", - "\n", - "def clear_m():\n", - " global drawn_polygons\n", - " drawn_polygons = set()\n", - "\n", - "clear_m()\n", - "\n", - "myDrawControl = ipyl.DrawControl(polygon={'shapeOptions':{'color':'#00F'}}, circlemarker={}, polyline={})\n", - "\n", - "def handle_draw(self, action, geo_json):\n", - " global drawn_polygons, m\n", - " polygon=[]\n", - " for coords in geo_json['geometry']['coordinates'][0][:-1][:]:\n", - " polygon.append(tuple(coords))\n", - " polygon = tuple(polygon)\n", - " if action == 'created':\n", - " drawn_polygons.add(polygon)\n", - " elif action == 'deleted':\n", - " drawn_polygons.discard(polygon)\n", - " # clear found tiles\n", - " for l in m.layers[1:]:\n", - " m.remove_layer(l)\n", + "import folium\n", "\n", - "myDrawControl.on_draw(handle_draw)\n", - "m.add_control(myDrawControl)\n", - "m" + "# Create a map zoomed over the search area\n", + "fmap = folium.Map([43.5, 1.5], zoom_start=8)\n", + "# Create a layer that represents the search area in red\n", + "folium.Rectangle(\n", + " bounds=[search_geom[0:2][::-1], search_geom[2:4][::-1]],\n", + " color=\"red\",\n", + " tooltip=\"Search extent\"\n", + ").add_to(fmap)\n", + "# Create a layer that maps the products found\n", + "folium.GeoJson(\n", + " data=search_result, # SearchResult has a __geo_interface__ interface used by folium to get its GeoJSON representation\n", + " tooltip=folium.GeoJsonTooltip(fields=[\"title\"])\n", + ").add_to(fmap)\n", + "fmap\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Filter using drawn geometry and show on map:" + "## Download the DEM files to workspace" ] }, { - "cell_type": "code", - "execution_count": 9, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 3 products intersecting given geometry\n" - ] - } - ], "source": [ - "from shapely.geometry import Polygon\n", - "\n", - "filtered_by_geom = []\n", - "if drawn_polygons:\n", - " drawn_polygon = Polygon(list(next(iter(drawn_polygons))))\n", - " \n", - " # Filter products\n", - " filtered_by_geom = items.filter_overlap(\n", - " intersects=True,\n", - " geometry=drawn_polygon\n", - " )\n", - " print(\"Found %s products intersecting given geometry\" % len(filtered_by_geom))\n", - " \n", - " # show on map\n", - " filtered_by_geom_layer = ipyl.GeoJSON(data=filtered_by_geom.as_geojson_object(), style=dict(color='green'))\n", - " m.add_layer(filtered_by_geom_layer)" + "Make sure credentials were set for the provider. Here we will download on `creodias` that needs an additionnal [TOTP](https://eodag.readthedocs.io/en/latest/getting_started_guide/configure.html?#use-otp-through-python-code) key:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 7, "metadata": {}, + "outputs": [], "source": [ - "## Download the filtered DEM files to workspace" + "dag.update_providers_config(\n", + " \"\"\"\n", + " creodias:\n", + " auth:\n", + " credentials:\n", + " totp: PLEASE_CHANGE_ME\n", + " \"\"\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "933fb7aeb68c4262958585dcb39aad00", + "model_id": "fd6a84d656e0487a8486add5d0649b64", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Downloaded products: 0%\| \| 0/3 [00:00<?, ?product/s]" + "Downloaded products: 0%\| …" ] }, "metadata": {}, @@ -313,12 +373,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "562166e129f946aca4bf7c084ec9c2be", + "model_id": "2fb43b78b98d47d6aa87b6fe3cdfb7eb", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Copernicus_DSM_COG_10_N42_00_E001_00_DEM: 0%\| \| 0.00/42.6M [00:00<?, ?B/s]" + "0.00B [00:00, ?B/s]" ] }, "metadata": {}, @@ -326,29 +386,64 @@ }, { "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "db221a0eb62e4a97b948582baf6c928b", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "['/home/sylvain/workspace/eodag/examples/eodag_workspace_copdem/Copernicus_DSM_COG_10_N42_00_E001_00_DEM',\n", - " '/home/sylvain/workspace/eodag/examples/eodag_workspace_copdem/Copernicus_DSM_COG_10_N43_00_E001_00_DEM',\n", - " '/home/sylvain/workspace/eodag/examples/eodag_workspace_copdem/Copernicus_DSM_COG_10_N42_00_E002_00_DEM']" + "0.00B [00:00, ?B/s]" ] }, - "execution_count": 10, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fddf46d0e3214180bc84a735a3b42920", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0.00B [00:00, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "['/home/sylvain/workspace/eodag/docs/notebooks/tutos/eodag_workspace_copdem/DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP/DEM1_SAR_DGE_30_20110216T174143_20140906T174215_ADS_000000_jSLP.DEM',\n", + " '/home/sylvain/workspace/eodag/docs/notebooks/tutos/eodag_workspace_copdem/DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX/DEM1_SAR_DGE_30_20110428T174952_20140831T175054_ADS_000000_Q3FX.DEM',\n", + " '/home/sylvain/workspace/eodag/docs/notebooks/tutos/eodag_workspace_copdem/DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx/DEM1_SAR_DGE_30_20110417T175032_20140906T174215_ADS_000000_PIJx.DEM']" + ] + }, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "paths = dag.download_all(\n", - " filtered_by_geom, \n", + " search_result, \n", " outputs_prefix=workspace,\n", ")\n", "paths" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -362,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { @@ -9577,5 +9672,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/eodag/resources/product_types.yml b/eodag/resources/product_types.yml index d5840879..bfab67b8 100644 --- a/eodag/resources/product_types.yml +++ b/eodag/resources/product_types.yml @@ -2923,22 +2923,69 @@ EFAS_SEASONAL_REFORECAST: missionEndDate: "2020-10-01T00:00:00Z" # COPERNICUS Digital Elevation Model -COP_DEM_GLO30: - abstract: \| - The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, - infrastructure and vegetation. We provide two instances of Copernicus DEM named GLO-30 Public and GLO-90. GLO-30 - provides worldwide coverage at 30 meters. Note that ocean areas do not have tiles, there one can assume height - values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Several releases are currently available for - all Copernicus DEM instances, only the last one is provided as 2022 release. A full collection of tiles can be - found via FTP and PANDA Catalogue under dataset names marked with '2022_1'. +COP_DEM_GLO30_DGED: + abstract: \| + Defence Gridded Elevation Data (DGED) formatted Copernicus DEM GLO-30 data. + The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth + including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, + GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission + between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. + instrument: + platform: TerraSAR + platformSerialIdentifier: + processingLevel: + keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,GDGED + sensorType: ALTIMETRIC + license: proprietary + title: Copernicus DEM GLO-30 DGED + missionStartDate: "2010-06-21T00:00:00Z" +COP_DEM_GLO30_DTED: + abstract: \| + Digital Terrain Elevation Data (DTED) formatted Copernicus DEM GLO-30 data. + The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth + including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, + GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission + between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. + instrument: + platform: TerraSAR + platformSerialIdentifier: + processingLevel: + keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,DTED + sensorType: ALTIMETRIC + license: proprietary + title: Copernicus DEM GLO-30 DTED + missionStartDate: "2010-06-21T00:00:00Z" +COP_DEM_GLO90_DGED: + abstract: \| + Defence Gridded Elevation Data (DGED) formatted Copernicus DEM GLO-90 data. + The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth + including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, + GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission + between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. + instrument: + platform: TerraSAR + platformSerialIdentifier: + processingLevel: + keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,GDGED + sensorType: ALTIMETRIC + license: proprietary + title: Copernicus DEM GLO-90 DGED + missionStartDate: "2010-06-21T00:00:00Z" +COP_DEM_GLO90_DTED: + abstract: \| + Digital Terrain Elevation Data (DTED) formatted Copernicus DEM GLO-90 data. + The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth + including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, + GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission + between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. instrument: platform: TerraSAR platformSerialIdentifier: processingLevel: - keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM + keywords: TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,DTED sensorType: ALTIMETRIC license: proprietary - title: COPERNICUS Digital Elevation Model (GLO-30) + title: Copernicus DEM GLO-90 DTED missionStartDate: "2010-06-21T00:00:00Z" # Copernicus Land Monitoring Service diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index 5fed056b..187a7680 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -1192,6 +1192,27 @@ S5P_L2_SO2: productType: L2__SO2___ collection: Sentinel5P + # COP DEM + COP_DEM_GLO30_DGED: + productType: DGE_30 + collection: CopDem + metadata_mapping: + cloudCover: '$.null' + COP_DEM_GLO30_DTED: + productType: DTE_30 + collection: CopDem + metadata_mapping: + cloudCover: '$.null' + COP_DEM_GLO90_DGED: + productType: DGE_90 + collection: CopDem + metadata_mapping: + cloudCover: '$.null' + COP_DEM_GLO90_DTED: + productType: DTE_90 + collection: CopDem + metadata_mapping: + cloudCover: '$.null' GENERIC_PRODUCT_TYPE: productType: '{productType}' collection: '{collection}' @@ -4043,7 +4064,7 @@ processingLevel: - '{{"stringChoiceValues": [{{"name": "processingLevel", "value": "{processingLevel}"}}]}}' - '{$.productInfo.product#get_processing_level_from_s5p_id}' - COP_DEM_GLO30: + COP_DEM_GLO30_DGED: productType: EO:DEM:DAT:COP-DEM_GLO-30-DGED__2022_1 metadata_mapping: id: @@ -4052,6 +4073,15 @@ startTimeFromAscendingNode: '$.productInfo.productStartDate' completionTimeFromAscendingNode: '$.productInfo.productEndDate' defaultGeometry: '-180 -90 180 90' + COP_DEM_GLO90_DGED: + productType: EO:DEM:DAT:COP-DEM_GLO-90-DGED__2022_1 + metadata_mapping: + id: + - '{{"boundingBoxValues": [{{"name": "bbox", "bbox": {id#split_cop_dem_id}}}]}}' + - '{$.productInfo.product#remove_extension}' + startTimeFromAscendingNode: '$.productInfo.productStartDate' + completionTimeFromAscendingNode: '$.productInfo.productEndDate' + defaultGeometry: '-180 -90 180 90' CLMS_CORINE: productType: EO:CLMS:DAT:CORINE providerProductType: "Corine Land Cover 2018" diff --git a/eodag/resources/user_conf_template.yml b/eodag/resources/user_conf_template.yml index 94720ee8..e605eae2 100644 --- a/eodag/resources/user_conf_template.yml +++ b/eodag/resources/user_conf_template.yml @@ -202,9 +202,9 @@ hydroweb_next: wekeo: priority: # Lower value means lower priority (Default: 0) search: - outputs_prefix: - credentials: - username: - password: download: outputs_prefix: + auth: + credentials: + username: + password:	Support COP-DEM (30 / 90m) retrieval from creodias Is your feature request related to a problem? Please describe. Creodias provides the COP DEM at 30 m and 90m on the finder: https://finder.creodias.eu/resto/api/collections/CopDem/search.json?maxRecords=10&resolution=30&geometry=POLYGON((1.2779813430646272+43.66703702416669%2C1.2685850331328663+43.496872606474625%2C1.5442101244645277+43.496872606474625%2C1.5473422277751145+43.65797373066451%2C1.2779813430646272+43.66703702416669))&sortParam=startDate&sortOrder=descending&status=all&dataset=ESA-DATASET I could be interesting for the users and the WorldCereal project to get this data with EODAG Additional context Linked to WorldCereal project	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index 430eb40c..421d2236 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -92,17 +92,50 @@ class TestCore(TestCoreBase): "CBERS4_PAN10M_L4": ["aws_eos"], "CBERS4_PAN5M_L2": ["aws_eos"], "CBERS4_PAN5M_L4": ["aws_eos"], - "ERA5_SL": ["cop_cds"], + "CLMS_CORINE": ["wekeo"], + "CLMS_GLO_DMP_333M": ["wekeo"], + "CLMS_GLO_FAPAR_333M": ["wekeo"], + "CLMS_GLO_FCOVER_333M": ["wekeo"], + "CLMS_GLO_GDMP_333M": ["wekeo"], + "CLMS_GLO_LAI_333M": ["wekeo"], + "CLMS_GLO_NDVI_1KM_LTS": ["wekeo"], + "CLMS_GLO_NDVI_333M": ["wekeo"], + "COP_DEM_GLO30_DGED": ["creodias", "wekeo"], + "COP_DEM_GLO30_DTED": ["creodias"], + "COP_DEM_GLO90_DGED": ["creodias", "wekeo"], + "COP_DEM_GLO90_DTED": ["creodias"], + "EEA_DAILY_SSM_1KM": ["wekeo"], + "EEA_DAILY_SWI_1KM": ["wekeo"], + "EEA_DAILY_VI": ["wekeo"], + "EFAS_FORECAST": ["wekeo"], + "EFAS_HISTORICAL": ["wekeo"], + "EFAS_REFORECAST": ["wekeo"], + "EFAS_SEASONAL": ["wekeo"], + "EFAS_SEASONAL_REFORECAST": ["wekeo"], + "ERA5_LAND": ["wekeo"], + "ERA5_LAND_MONTHLY": ["wekeo"], + "ERA5_PL": ["wekeo"], + "ERA5_PL_MONTHLY": ["wekeo"], + "ERA5_SL": ["cop_cds", "wekeo"], + "ERA5_SL_MONTHLY": ["wekeo"], + "FIRE_HISTORICAL": ["wekeo"], + "GLACIERS_DIST_RANDOLPH": ["wekeo"], + "GLACIERS_ELEVATION_AND_MASS_CHANGE": ["wekeo"], + "GLOFAS_FORECAST": ["wekeo"], + "GLOFAS_HISTORICAL": ["wekeo"], + "GLOFAS_REFORECAST": ["wekeo"], + "GLOFAS_SEASONAL": ["wekeo"], + "GLOFAS_SEASONAL_REFORECAST": ["wekeo"], "L57_REFLECTANCE": ["theia"], "L8_OLI_TIRS_C1L1": ["aws_eos", "earth_search", "earth_search_gcs", "onda"], "L8_REFLECTANCE": ["theia"], "LANDSAT_C2L1": [ "astraea_eod", + "planetary_computer", "usgs", "usgs_satapi_aws", - "planetary_computer", ], - "LANDSAT_C2L2": ["usgs", "planetary_computer"], + "LANDSAT_C2L2": ["planetary_computer", "usgs"], "LANDSAT_C2L2ALB_BT": ["usgs_satapi_aws"], "LANDSAT_C2L2ALB_SR": ["usgs_satapi_aws"], "LANDSAT_C2L2ALB_ST": ["usgs_satapi_aws"], @@ -132,12 +165,21 @@ class TestCore(TestCoreBase): "mundi", "onda", "peps", - "sara", "planetary_computer", + "sara", + "wekeo", ], "S1_SAR_OCN": ["cop_dataspace", "creodias", "mundi", "onda", "peps", "sara"], "S1_SAR_RAW": ["cop_dataspace", "creodias", "mundi", "onda"], - "S1_SAR_SLC": ["cop_dataspace", "creodias", "mundi", "onda", "peps", "sara"], + "S1_SAR_SLC": [ + "cop_dataspace", + "creodias", + "mundi", + "onda", + "peps", + "sara", + "wekeo", + ], "S2_MSI_L1C": [ "astraea_eod", "aws_eos", @@ -150,6 +192,7 @@ class TestCore(TestCoreBase): "peps", "sara", "usgs", + "wekeo", ], "S2_MSI_L2A": [ "astraea_eod", @@ -160,36 +203,67 @@ class TestCore(TestCoreBase): "mundi", "onda", "peps", - "sara", "planetary_computer", + "sara", + "wekeo", ], "S2_MSI_L2A_COG": ["earth_search_cog"], "S2_MSI_L2A_MAJA": ["theia"], "S2_MSI_L2B_MAJA_SNOW": ["theia"], "S2_MSI_L2B_MAJA_WATER": ["theia"], "S2_MSI_L3A_WASP": ["theia"], - "S3_EFR": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_ERR": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_LAN": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_OLCI_L2LFR": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_OLCI_L2LRR": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_OLCI_L2WFR": ["cop_dataspace", "creodias", "onda", "sara"], - "S3_OLCI_L2WRR": ["cop_dataspace", "creodias", "onda", "sara"], + "S3_EFR": ["cop_dataspace", "creodias", "mundi", "onda", "sara", "wekeo"], + "S3_ERR": ["cop_dataspace", "creodias", "mundi", "onda", "sara", "wekeo"], + "S3_LAN": ["cop_dataspace", "creodias", "mundi", "onda", "sara", "wekeo"], + "S3_OLCI_L2LFR": [ + "cop_dataspace", + "creodias", + "mundi", + "onda", + "sara", + "wekeo", + ], + "S3_OLCI_L2LRR": [ + "cop_dataspace", + "creodias", + "mundi", + "onda", + "sara", + "wekeo", + ], + "S3_OLCI_L2WFR": ["cop_dataspace", "creodias", "onda", "sara", "wekeo"], + "S3_OLCI_L2WRR": ["cop_dataspace", "creodias", "onda", "sara", "wekeo"], "S3_RAC": ["sara"], - "S3_SLSTR_L1RBT": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_SLSTR_L2AOD": ["cop_dataspace", "creodias", "sara"], - "S3_SLSTR_L2FRP": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], + "S3_SLSTR_L1RBT": [ + "cop_dataspace", + "creodias", + "mundi", + "onda", + "sara", + "wekeo", + ], + "S3_SLSTR_L2": ["wekeo"], + "S3_SLSTR_L2AOD": ["cop_dataspace", "creodias", "sara", "wekeo"], + "S3_SLSTR_L2FRP": [ + "cop_dataspace", + "creodias", + "mundi", + "onda", + "sara", + "wekeo", + ], "S3_SLSTR_L2LST": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_SLSTR_L2WST": ["cop_dataspace", "creodias", "onda", "sara"], - "S3_SRA": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_SRA_A": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_SRA_BS": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], + "S3_SLSTR_L2WST": ["cop_dataspace", "creodias", "onda", "sara", "wekeo"], + "S3_SRA": ["cop_dataspace", "creodias", "mundi", "onda", "sara", "wekeo"], + "S3_SRA_A": ["cop_dataspace", "creodias", "mundi", "onda", "sara", "wekeo"], + "S3_SRA_BS": ["cop_dataspace", "creodias", "mundi", "onda", "sara", "wekeo"], "S3_SY_AOD": ["cop_dataspace", "creodias", "onda", "sara"], - "S3_SY_SYN": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], + "S3_SY_SYN": ["cop_dataspace", "creodias", "mundi", "onda", "sara", "wekeo"], "S3_SY_V10": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], "S3_SY_VG1": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], "S3_SY_VGP": ["cop_dataspace", "creodias", "mundi", "onda", "sara"], - "S3_WAT": ["cop_dataspace", "creodias", "onda", "sara"], + "S3_WAT": ["cop_dataspace", "creodias", "onda", "sara", "wekeo"], + "S5P_L1B2_IR_ALL": ["wekeo"], "S5P_L1B_IR_SIR": ["creodias", "mundi"], "S5P_L1B_IR_UVN": ["creodias", "mundi"], "S5P_L1B_RA_BD1": ["cop_dataspace", "creodias", "mundi", "onda"], @@ -214,10 +288,21 @@ class TestCore(TestCoreBase): "S5P_L2_O3_PR": ["cop_dataspace", "creodias", "mundi", "onda"], "S5P_L2_O3_TCL": ["creodias", "mundi"], "S5P_L2_SO2": ["cop_dataspace", "creodias", "mundi", "onda"], + "SATELLITE_CARBON_DIOXIDE": ["wekeo"], + "SATELLITE_METHANE": ["wekeo"], + "SATELLITE_SEA_LEVEL_BLACK_SEA": ["wekeo"], + "SEASONAL_MONTHLY_PL": ["wekeo"], + "SEASONAL_MONTHLY_SL": ["wekeo"], + "SEASONAL_ORIGINAL_PL": ["wekeo"], + "SEASONAL_ORIGINAL_SL": ["wekeo"], + "SEASONAL_POSTPROCESSED_PL": ["wekeo"], + "SEASONAL_POSTPROCESSED_SL": ["wekeo"], + "SIS_HYDRO_MET_PROJ": ["wekeo"], "SPOT5_SPIRIT": ["theia"], "SPOT_SWH": ["theia"], "SPOT_SWH_OLD": ["theia"], "TIGGE_CF_SFC": ["ecmwf"], + "UERRA_EUROPE_SL": ["wekeo"], "VENUS_L1C": ["theia"], "VENUS_L2A_MAJA": ["theia"], "VENUS_L3A_MAJA": ["theia"], @@ -263,6 +348,7 @@ class TestCore(TestCoreBase): "cop_dataspace", "planetary_computer", "hydroweb_next", + "wekeo", ] def setUp(self): @@ -1172,6 +1258,7 @@ class TestCoreSearch(TestCoreBase): "S2_MSI_L2B_MAJA_SNOW", "S2_MSI_L2B_MAJA_WATER", "S2_MSI_L3A_WASP", + "EEA_DAILY_VI", ] self.assertEqual(actual, expected)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 3 }, "num_modified_files": 4 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@177d77120e1c5bda5c84af638bdaa49a07edf5f4#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.25.7 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.0b2.dev46+g177d7712 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.25.7 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs" ]	[]	[ "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreDownload::test_download_local_product" ]	[]	Apache License 2.0	null
CS-SI__eodag-918	4286d132c8500c968a82445c2e3509216c4a1e65	2023-11-13 13:50:05	0a6208a6e306461fca3f66c1653a41ffda54dfb6	github-actions[bot]: ## Test Results     3 files +    1     3 suites +1 3m 12s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "duration of all tests") + 1m 9s 431 tests +    1 427 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") +    1 3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±0 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 973 runs +422 966 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") +419 6 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") +3 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/18626106596). Results for commit f9c1e23e. ± Comparison against base commit f323366a. [test-results]:data:application/gzip;base64,H4sIAGYqUmUC/02MQQ6DIBAAv2I496BsBbefaSwsCalKg3Bq+vcuVanHmUnmLZyfaBW3Bi6NWLNPFWyOY/JhYexQsuCUSrxCd9B9zcYUJfVfPf1rX2zCjX4qkyooxhDZtGxiXsoTNexwLFGparZj5dPwx+efCfPsE4NwaDqSQA/UGqmXowE59IQWsCVwViFrpwbx+QKE/s9sBQEAAA== github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against f9c1e23eb9779e52ac3285e9d390e3fd6979ef68 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `53%` \| :x: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against f9c1e23eb9779e52ac3285e9d390e3fd6979ef68 </p> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports/data/871-staging-status-in-server-mode-download/./badge.svg) ## Code Coverage (Ubuntu) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ __init__.py 11 0 100.00% cli.py 300 47 84.33% 61, 647-686, 788-839, 843 config.py 318 27 91.51% 80-82, 91, 99, 103-105, 178, 190, 391-393, 457-460, 507-508, 517-518, 597, 666-671, 673 crunch.py 6 6 0.00% 18-24 api/__init__.py 1 0 100.00% api/core.py 727 76 89.55% 87-96, 369, 582, 626-629, 667, 772, 776-781, 807, 877, 954, 1068-1073, 1159-1171, 1211, 1213, 1217, 1238-1240, 1244-1255, 1268-1274, 1364-1367, 1396-1416, 1464, 1470-1473, 1482, 1848, 1881-1887, 2152, 2156-2159, 2173-2175, 2210 api/search_result.py 44 6 86.36% 33-35, 70, 79, 86, 100 api/product/__init__.py 6 0 100.00% api/product/_assets.py 44 5 88.64% 27-29, 79, 129 api/product/_product.py 211 32 84.83% 53-60, 64-66, 170-177, 261-262, 352, 388, 449, 463-466, 479, 503-506, 549-555 api/product/metadata_mapping.py 649 82 87.37% 67-69, 130-132, 233, 265-266, 318-330, 332, 343, 349-361, 402-405, 442, 463-466, 489, 497-498, 571-572, 596-597, 603-606, 621-622, 771, 817, 934-939, 1070, 1084-1104, 1124, 1129, 1239, 1261, 1275, 1288-1307, 1346, 1398, 1436-1440, 1459 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 8 2 75.00% 23, 41 plugins/__init__.py 1 0 100.00% plugins/base.py 23 3 86.96% 25, 48, 55 plugins/manager.py 127 14 88.98% 49-51, 95-100, 146, 185, 205, 231, 270-271 plugins/apis/__init__.py 1 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 99 15 84.85% 47-55, 156-158, 205-206, 232-234 plugins/apis/usgs.py 172 36 79.07% 59-64, 128, 201, 235, 270-272, 277, 303-304, 309, 339-346, 357-362, 384-390, 392-398, 421 plugins/authentication/__init__.py 7 1 85.71% 31 plugins/authentication/aws_auth.py 20 2 90.00% 25-27 plugins/authentication/base.py 19 3 84.21% 26, 34, 47 plugins/authentication/generic.py 16 3 81.25% 28, 40, 50 plugins/authentication/header.py 17 1 94.12% 27 plugins/authentication/keycloak.py 88 17 80.68% 32-34, 159-160, 190-212, 238-243 plugins/authentication/oauth.py 15 8 46.67% 25, 32-34, 38-41 plugins/authentication/openid_connect.py 103 64 37.86% 39-41, 138-150, 154-172, 180-222, 228-237, 246-286, 291-299, 304-305 plugins/authentication/qsauth.py 36 2 94.44% 32, 83 plugins/authentication/sas_auth.py 49 2 95.92% 32, 76 plugins/authentication/token.py 81 13 83.95% 35-37, 86, 99, 101, 114-117, 165-168 plugins/crunch/__init__.py 1 0 100.00% plugins/crunch/base.py 10 2 80.00% 25, 38 plugins/crunch/filter_date.py 62 15 75.81% 30, 53-58, 72, 81, 90, 93, 105-107, 116-118, 125 plugins/crunch/filter_latest_intersect.py 50 10 80.00% 32-34, 51-52, 71, 80-83, 85, 92-95 plugins/crunch/filter_latest_tpl_name.py 33 2 93.94% 28, 86 plugins/crunch/filter_overlap.py 68 17 75.00% 28-30, 33, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 33 8 75.76% 29, 60-65, 68-69, 85-89 plugins/download/__init__.py 1 0 100.00% plugins/download/aws.py 491 165 66.40% 77-83, 272, 285, 352-355, 369-373, 419-421, 425, 458-459, 465-469, 502, 537, 541, 548, 578-586, 590, 628-636, 643-645, 686-760, 778-839, 850-855, 871-884, 913, 928-930, 933, 943-951, 959-972, 982-1001, 1008-1020, 1061, 1087, 1132-1134, 1354 plugins/download/base.py 261 57 78.16% 58-64, 145, 180, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 538-542, 572-573, 581-598, 605-613, 615-619, 666, 689, 711, 719 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 458 94 79.48% 82-88, 122, 196-208, 211, 292-297, 321, 323, 387, 414-416, 426, 432, 434, 458, 497-501, 558, 644-700, 718, 751-760, 786-787, 814, 838, 846-851, 873-874, 881, 942-948, 1003-1004, 1010, 1020, 1086, 1090, 1104-1120 plugins/download/s3rest.py 117 27 76.92% 55-58, 124, 165, 199, 229-236, 239-241, 245, 258-264, 272-273, 276-280, 303, 324-327 plugins/search/__init__.py 1 0 100.00% plugins/search/base.py 127 9 92.91% 49-54, 108, 112, 275, 295, 378 plugins/search/build_search_result.py 179 22 87.71% 62, 97, 142-143, 149, 160, 290-293, 327, 384-396, 458, 461, 471, 488, 516, 518 plugins/search/creodias_s3.py 54 3 94.44% 55, 73, 107 plugins/search/csw.py 107 83 22.43% 43-45, 57-58, 62-63, 74-122, 128-141, 149-181, 199-240 plugins/search/data_request_search.py 198 65 67.17% 52, 89-92, 108, 120, 124-125, 139, 144, 149, 156, 169-172, 226-227, 231, 241-247, 252, 280-283, 291-302, 319, 321, 328-329, 331-332, 350-354, 387, 394, 405, 418, 424-436, 441 plugins/search/qssearch.py 561 65 88.41% 89, 361-367, 375-376, 483-489, 544-547, 620-621, 662, 680, 695, 748, 769, 772-773, 782, 793, 802, 825, 885-890, 894-895, 923, 994, 1013-1030, 1069, 1145-1149, 1215-1216, 1237, 1262, 1268, 1299-1300, 1310-1316, 1359, 1373, 1393, 1483 plugins/search/static_stac_search.py 47 3 93.62% 39-40, 82 rest/__init__.py 1 0 100.00% rest/core.py 193 18 90.67% 252, 275-277, 283-286, 318, 570, 572, 575-577, 649, 656-660 rest/server.py 299 54 81.94% 80-81, 107, 130-131, 242-244, 260, 300-301, 313-329, 415-420, 451, 612-619, 651, 694-695, 783-785, 802-807, 837, 839, 843-844, 848-849 rest/stac.py 436 95 78.21% 59-61, 229-231, 273, 286-295, 314-320, 365, 402-404, 427, 462-463, 549-594, 639, 659-660, 840, 905-907, 1126, 1136-1148, 1161-1183, 1197-1242, 1401-1402 rest/types/__init__.py 1 0 100.00% rest/types/eodag_search.py 185 9 95.14% 52-55, 231-235, 288, 291, 359 rest/types/stac_queryables.py 30 2 93.33% 28, 114 rest/types/stac_search.py 122 10 91.80% 48-51, 167, 182-184, 192, 196 rest/utils/__init__.py 116 14 87.93% 53, 108-109, 128-130, 180, 190-204, 236 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 31 3 90.32% 78, 90, 92 types/__init__.py 76 6 92.11% 53, 87, 152, 172, 177, 185 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 104 types/whoosh.py 15 0 100.00% utils/__init__.py 496 43 91.33% 83, 88, 109-111, 189-190, 199-226, 229, 243, 325-329, 405-409, 430-432, 514, 519, 529, 567-568, 964-967, 975-976, 1017-1018, 1098, 1182, 1200, 1375 utils/constraints.py 123 42 65.85% 31, 84-93, 134, 139, 143, 154, 177-178, 189-197, 206, 220-236, 245-256 utils/exceptions.py 37 2 94.59% 23, 93 utils/import_system.py 30 20 33.33% 27, 67-81, 93-103 utils/logging.py 29 1 96.55% 123 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/stac_reader.py 91 28 69.23% 55-56, 63-86, 93-95, 99, 141, 155-158 TOTAL 8691 1535 82.34% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ------------------------------------- ------- ------ ------- plugins/authentication/base.py 0 +1 -5.26% plugins/download/http.py +15 +1 +0.47% plugins/search/build_search_result.py +1 0 +0.07% plugins/search/qssearch.py +1 +1 -0.16% rest/core.py +24 +8 -3.41% rest/server.py +1 0 +0.06% utils/__init__.py +1 0 +0.02% TOTAL +43 +11 -0.04% ``` Results for commit: 4142cd1296b4af91d62719d926266d28d50acd6f _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-ubuntu-latest/coverage.xml --> github-actions[bot]: ![badge](https://raw.githubusercontent.com/CS-SI/eodag/_xml_coverage_reports_win/data/871-staging-status-in-server-mode-download/./badge.svg) ## Code Coverage (Windows) <details> ``` Filename Stmts Miss Cover Missing ----------------------------------------- ------- ------ ------- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- __init__.py 11 0 100.00% cli.py 300 47 84.33% 61, 647-686, 788-839, 843 config.py 318 28 91.19% 80-82, 91, 99, 103-105, 178, 190, 391-393, 457-460, 507-508, 517-518, 597, 631, 666-671, 673 crunch.py 6 6 0.00% 18-24 api/__init__.py 1 0 100.00% api/core.py 727 76 89.55% 87-96, 369, 582, 626-629, 667, 772, 776-781, 807, 877, 954, 1068-1073, 1159-1171, 1211, 1213, 1217, 1238-1240, 1244-1255, 1268-1274, 1364-1367, 1396-1416, 1464, 1470-1473, 1482, 1848, 1881-1887, 2152, 2156-2159, 2173-2175, 2210 api/search_result.py 44 6 86.36% 33-35, 70, 79, 86, 100 api/product/__init__.py 6 0 100.00% api/product/_assets.py 44 5 88.64% 27-29, 79, 129 api/product/_product.py 211 32 84.83% 53-60, 64-66, 170-177, 261-262, 352, 388, 449, 463-466, 479, 503-506, 549-555 api/product/metadata_mapping.py 649 83 87.21% 67-69, 130-132, 233, 265-266, 318-330, 332, 343, 349-361, 402-405, 442, 463-466, 489, 497-498, 571-572, 596-597, 603-606, 621-622, 771, 817, 934-939, 1070, 1084-1104, 1124, 1129, 1239, 1261, 1275, 1288-1307, 1346, 1398, 1421, 1436-1440, 1459 api/product/drivers/__init__.py 6 0 100.00% api/product/drivers/base.py 8 2 75.00% 23, 41 plugins/__init__.py 1 0 100.00% plugins/base.py 23 4 82.61% 25, 48, 55, 68 plugins/manager.py 127 14 88.98% 49-51, 95-100, 146, 185, 205, 231, 270-271 plugins/apis/__init__.py 1 0 100.00% plugins/apis/base.py 4 0 100.00% plugins/apis/ecmwf.py 99 15 84.85% 47-55, 156-158, 205-206, 232-234 plugins/apis/usgs.py 172 36 79.07% 59-64, 128, 201, 235, 270-272, 277, 303-304, 309, 339-346, 357-362, 384-390, 392-398, 421 plugins/authentication/__init__.py 7 1 85.71% 31 plugins/authentication/aws_auth.py 20 2 90.00% 25-27 plugins/authentication/base.py 19 3 84.21% 26, 34, 47 plugins/authentication/generic.py 16 3 81.25% 28, 40, 50 plugins/authentication/header.py 17 1 94.12% 27 plugins/authentication/keycloak.py 88 17 80.68% 32-34, 159-160, 190-212, 238-243 plugins/authentication/oauth.py 15 8 46.67% 25, 32-34, 38-41 plugins/authentication/openid_connect.py 103 64 37.86% 39-41, 138-150, 154-172, 180-222, 228-237, 246-286, 291-299, 304-305 plugins/authentication/qsauth.py 36 2 94.44% 32, 83 plugins/authentication/sas_auth.py 49 2 95.92% 32, 76 plugins/authentication/token.py 81 13 83.95% 35-37, 86, 99, 101, 114-117, 165-168 plugins/crunch/__init__.py 1 0 100.00% plugins/crunch/base.py 10 2 80.00% 25, 38 plugins/crunch/filter_date.py 62 15 75.81% 30, 53-58, 72, 81, 90, 93, 105-107, 116-118, 125 plugins/crunch/filter_latest_intersect.py 50 35 30.00% 32-34, 48-53, 69-114 plugins/crunch/filter_latest_tpl_name.py 33 2 93.94% 28, 86 plugins/crunch/filter_overlap.py 68 17 75.00% 28-30, 33, 82-85, 91, 99, 110-126 plugins/crunch/filter_property.py 33 8 75.76% 29, 60-65, 68-69, 85-89 plugins/download/__init__.py 1 0 100.00% plugins/download/aws.py 491 165 66.40% 77-83, 272, 285, 352-355, 369-373, 419-421, 425, 458-459, 465-469, 502, 537, 541, 548, 578-586, 590, 628-636, 643-645, 686-760, 778-839, 850-855, 871-884, 913, 928-930, 933, 943-951, 959-972, 982-1001, 1008-1020, 1061, 1087, 1132-1134, 1354 plugins/download/base.py 261 59 77.39% 58-64, 145, 180, 250-252, 319-320, 340-346, 377-381, 387-388, 432, 435-449, 461, 465, 538-542, 572-573, 581-598, 605-613, 615-619, 666, 689, 711, 719 plugins/download/creodias_s3.py 17 9 47.06% 44-58 plugins/download/http.py 458 94 79.48% 82-88, 122, 196-208, 211, 292-297, 321, 323, 387, 414-416, 426, 432, 434, 458, 497-501, 558, 644-700, 718, 751-760, 786-787, 814, 838, 846-851, 873-874, 881, 942-948, 1003-1004, 1010, 1020, 1086, 1090, 1104-1120 plugins/download/s3rest.py 117 27 76.92% 55-58, 124, 165, 199, 229-236, 239-241, 245, 258-264, 272-273, 276-280, 303, 324-327 plugins/search/__init__.py 1 0 100.00% plugins/search/base.py 127 9 92.91% 49-54, 108, 112, 275, 295, 378 plugins/search/build_search_result.py 179 29 83.80% 62, 97, 142-143, 149, 160, 290-293, 327, 384-396, 458, 461, 471, 488, 508-523 plugins/search/creodias_s3.py 54 3 94.44% 55, 73, 107 plugins/search/csw.py 107 83 22.43% 43-45, 57-58, 62-63, 74-122, 128-141, 149-181, 199-240 plugins/search/data_request_search.py 198 65 67.17% 52, 89-92, 108, 120, 124-125, 139, 144, 149, 156, 169-172, 226-227, 231, 241-247, 252, 280-283, 291-302, 319, 321, 328-329, 331-332, 350-354, 387, 394, 405, 418, 424-436, 441 plugins/search/qssearch.py 561 89 84.14% 89, 361-367, 375-376, 483-489, 544-547, 620-621, 662, 680, 695, 748, 769, 772-773, 782, 793, 802, 825, 885-890, 894-895, 923, 994, 1013-1030, 1069, 1145-1149, 1215-1216, 1237, 1262, 1268, 1299-1300, 1310-1316, 1359, 1373, 1393, 1443-1512 plugins/search/static_stac_search.py 47 3 93.62% 39-40, 82 rest/__init__.py 1 0 100.00% rest/core.py 193 82 57.51% 149, 151, 153, 159-160, 177-185, 191-197, 244-320, 474-501, 519, 569-608, 649, 656-660 rest/server.py 299 299 0.00% 18-862 rest/stac.py 436 154 64.68% 59-61, 214, 229-231, 273, 286-295, 314-320, 365, 402-404, 427, 462-463, 549-594, 639, 647-648, 652-660, 782, 840, 905-907, 924-926, 934-936, 949-951, 965-982, 992-1013, 1023-1045, 1053-1070, 1093-1116, 1126, 1136-1148, 1161-1183, 1197-1242, 1395-1421 rest/types/__init__.py 1 0 100.00% rest/types/eodag_search.py 185 18 90.27% 52-55, 231-235, 268-270, 288, 291, 297, 301, 359, 371-374 rest/types/stac_queryables.py 30 6 80.00% 28, 53-58, 114 rest/types/stac_search.py 122 12 90.16% 48-51, 167, 182-184, 192, 196, 240, 243 rest/utils/__init__.py 116 31 73.28% 53, 79-85, 105, 108-109, 128-130, 147, 173-181, 188-209, 236 rest/utils/cql_evaluate.py 48 5 89.58% 69, 76, 90, 97, 105 rest/utils/rfc3339.py 31 5 83.87% 73-74, 78, 90, 92 types/__init__.py 76 11 85.53% 53, 87, 125, 152, 162-164, 172, 177, 185, 195 types/bbox.py 43 19 55.81% 46-61, 72-74, 85-87, 99-101, 113-115, 123 types/download_args.py 9 0 100.00% types/queryables.py 81 0 100.00% types/search_args.py 70 18 74.29% 60-64, 71-88, 104 types/whoosh.py 15 0 100.00% utils/__init__.py 496 44 91.13% 83, 88, 109-111, 189-190, 199-226, 229, 243, 325-329, 405-409, 430-432, 514, 519, 529, 567-568, 964-967, 975-976, 1017-1018, 1098, 1181-1182, 1200, 1375 utils/constraints.py 123 42 65.85% 31, 84-93, 134, 139, 143, 154, 177-178, 189-197, 206, 220-236, 245-256 utils/exceptions.py 37 2 94.59% 23, 93 utils/import_system.py 30 20 33.33% 27, 67-81, 93-103 utils/logging.py 29 1 96.55% 123 utils/notebook.py 44 23 47.73% 25-29, 36-41, 58-62, 72-78, 83-87 utils/stac_reader.py 91 28 69.23% 55-56, 63-86, 93-95, 99, 141, 155-158 TOTAL 8691 2004 76.94% ``` </details> ### Diff against develop ``` Filename Stmts Miss Cover ------------------------------------- ------- ------ -------- plugins/download/http.py +15 +1 +0.47% plugins/search/build_search_result.py +1 0 +0.09% plugins/search/qssearch.py +1 +1 -0.15% rest/core.py +24 +21 -6.40% rest/server.py +1 +1 +100.00% utils/__init__.py +1 0 +0.02% TOTAL +43 +24 -0.16% ``` Results for commit: 4142cd1296b4af91d62719d926266d28d50acd6f _Minimum allowed coverage is `70%`_ :recycle: This comment has been updated with latest results <!-- Sticky Pull Request Commentartifacts/unit-test-results-python3.12-windows-latest/coverage.xml -->	diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 140f1e12..c1b03a36 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -130,7 +130,7 @@ class HTTPDownload(Download): product: EOProduct, auth: Optional[AuthBase] = None, *kwargs: Unpack[DownloadConf], - ) -> None: + ) -> Optional[Dict[str, Any]]: """Send product order request. It will be executed once before the download retry loop, if the product is OFFLINE @@ -156,6 +156,8 @@ class HTTPDownload(Download): :type auth: Optional[AuthBase] :param kwargs: download additional kwargs :type kwargs: Union[str, bool, dict] + :returns: the returned json status response + :rtype: dict """ product.properties["storageStatus"] = STAGING_STATUS @@ -205,15 +207,20 @@ class HTTPDownload(Download): ) self._check_auth_exception(e) - self.order_response_process(response, product) + return self.order_response_process(response, product) + return None - def order_response_process(self, response: Response, product: EOProduct) -> None: + def order_response_process( + self, response: Response, product: EOProduct + ) -> Optional[Dict[str, Any]]: """Process order response :param response: The order response :type response: :class:`~requests.Response` :param product: The orderd EO product :type product: :class:`~eodag.api.product._product.EOProduct` + :returns: the returned json status response + :rtype: dict """ order_metadata_mapping = getattr(self.config, "order_on_response", {}).get( "metadata_mapping", {} @@ -223,8 +230,11 @@ class HTTPDownload(Download): order_metadata_mapping_jsonpath = mtd_cfg_as_conversion_and_querypath( order_metadata_mapping, ) + + json_response = response.json() + properties_update = properties_from_json( - response.json(), + json_response, order_metadata_mapping_jsonpath, ) properties_update = { @@ -235,8 +245,12 @@ class HTTPDownload(Download): product.remote_location = product.location = product.properties[ "downloadLink" ] + product.properties["storageStatus"] = ONLINE_STATUS logger.debug(f"Product location updated to {product.location}") + return json_response + return None + def orderDownloadStatus( self, product: EOProduct, @@ -582,6 +596,19 @@ class HTTPDownload(Download): ) return stream_size + def _check_product_filename(self, product: EOProduct) -> str: + filename = None + asset_content_disposition = self.stream.headers.get("content-disposition", None) + if asset_content_disposition: + filename = cast( + Optional[str], + parse_header(asset_content_disposition).get_param("filename", None), + ) + if not filename: + # default filename extracted from path + filename = os.path.basename(self.stream.url) + return filename + def _stream_download_dict( self, product: EOProduct, @@ -823,7 +850,11 @@ class HTTPDownload(Download): product.properties["storageStatus"] = "ORDERED" self._process_exception(None, product, ordered_message) stream_size = self._check_stream_size(product) or None + filename = self._check_product_filename(product) or None product.headers = self.stream.headers + product.headers[ + "content-disposition" + ] = f"attachment; filename={filename}" progress_callback.reset(total=stream_size) for chunk in self.stream.iter_content(chunk_size=64 1024): if chunk: diff --git a/eodag/plugins/search/build_search_result.py b/eodag/plugins/search/build_search_result.py index 396b5dd0..8e5f9e9e 100644 --- a/eodag/plugins/search/build_search_result.py +++ b/eodag/plugins/search/build_search_result.py @@ -175,8 +175,9 @@ class BuildPostSearchResult(PostJsonSearch): query_hash = hashlib.sha1(str(qs).encode("UTF-8")).hexdigest() - # update result with search args if not None (and not auth) + # update result with product_type_def_params and search args if not None (and not auth) kwargs.pop("auth", None) + result.update(self.product_type_def_params) result = dict(result, {k: v for k, v in kwargs.items() if v is not None}) # parse porperties diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index 7c97fc86..9e8c5a2d 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -1144,6 +1144,11 @@ class PostJsonSearch(QueryStringSearch): if _dc_qs is not None: qs = unquote_plus(unquote_plus(_dc_qs)) qp = geojson.loads(qs) + + # provider product type specific conf + self.product_type_def_params = self.get_product_type_def_params( + product_type, kwargs + ) else: keywords = { k: v for k, v in kwargs.items() if k != "auth" and v is not None diff --git a/eodag/rest/core.py b/eodag/rest/core.py index 729e8f99..df5aa6c6 100644 --- a/eodag/rest/core.py +++ b/eodag/rest/core.py @@ -15,20 +15,28 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + import datetime import logging import os import re -from typing import Any, Callable, Dict, List, Optional, cast +from typing import TYPE_CHECKING, cast +from unittest.mock import Mock import dateutil -from fastapi import Request -from fastapi.responses import StreamingResponse +from fastapi.responses import ORJSONResponse, StreamingResponse from pydantic import ValidationError as pydanticValidationError +from requests.models import Response as RequestsResponse import eodag from eodag import EOProduct -from eodag.api.product.metadata_mapping import OSEO_METADATA_MAPPING +from eodag.api.product.metadata_mapping import ( + NOT_AVAILABLE, + ONLINE_STATUS, + OSEO_METADATA_MAPPING, + STAGING_STATUS, +) from eodag.api.search_result import SearchResult from eodag.config import load_stac_config from eodag.plugins.crunch.filter_latest_intersect import FilterLatestIntersect @@ -37,7 +45,6 @@ from eodag.plugins.crunch.filter_overlap import FilterOverlap from eodag.rest.stac import StacCatalog, StacCollection, StacCommon, StacItem from eodag.rest.types.eodag_search import EODAGSearch from eodag.rest.types.stac_queryables import StacQueryableProperty, StacQueryables -from eodag.rest.types.stac_search import SearchPostRequest from eodag.rest.utils import ( Cruncher, file_to_stream, @@ -51,6 +58,7 @@ from eodag.utils import ( _deprecated, deepcopy, dict_items_recursive_apply, + urlencode, ) from eodag.utils.exceptions import ( MisconfiguredError, @@ -59,6 +67,16 @@ from eodag.utils.exceptions import ( ValidationError, ) +if TYPE_CHECKING: + from typing import Any, Callable, Dict, List, Optional, Union + + from fastapi import Request + from requests.auth import AuthBase + from starlette.responses import Response + + from eodag.rest.types.stac_search import SearchPostRequest + + eodag_api = eodag.EODataAccessGateway() logger = logging.getLogger("eodag.rest.core") @@ -212,12 +230,13 @@ def search_stac_items( def download_stac_item( + request: Request, catalogs: List[str], item_id: str, provider: Optional[str] = None, asset: Optional[str] = None, kwargs: Any, -) -> StreamingResponse: +) -> Response: """Download item :param catalogs: Catalogs list (only first is used as product_type) @@ -229,7 +248,7 @@ def download_stac_item( :param kwargs: additional download parameters :type kwargs: Any :returns: a stream of the downloaded data (zip file) - :rtype: StreamingResponse + :rtype: Response """ product_type = catalogs[0] @@ -243,12 +262,19 @@ def download_stac_item( f"Could not find {item_id} item in {product_type} collection" + (f" for provider {provider}" if provider else "") ) + auth = product.downloader_auth.authenticate() try: + _order_and_update(product, auth, kwargs) + download_stream = cast( StreamResponse, product.downloader._stream_download_dict( - product, auth=product.downloader_auth.authenticate(), asset=asset + product, + auth=auth, + asset=asset, + wait=-1, + timeout=-1, ), ) except NotImplementedError: @@ -259,6 +285,19 @@ def download_stac_item( download_stream = file_to_stream( eodag_api.download(product, extract=False, asset=asset) ) + except NotAvailableError: + if product.properties.get("storageStatus") != ONLINE_STATUS: + download_link = f"{request.state.url}?{urlencode(dict(kwargs, {'provider': provider}), doseq=True)}" + return ORJSONResponse( + status_code=202, + headers={"Location": download_link}, + content={ + "description": "Product is not available yet, please try again using given updated location", + "location": download_link, + }, + ) + else: + raise return StreamingResponse( content=download_stream.content, @@ -267,6 +306,40 @@ def download_stac_item( ) +def _order_and_update( + product: EOProduct, auth: Union[AuthBase, Dict[str, str]], kwargs: Any +) -> None: + """Order product if needed and update given kwargs with order-status-dict""" + if product.properties.get("storageStatus") != ONLINE_STATUS and hasattr( + product.downloader, "order_response_process" + ): + # update product (including orderStatusLink) if product was previously ordered + logger.debug("Use given download query arguments to parse order link") + response = Mock(spec=RequestsResponse) + response.status_code = 200 + response.json.return_value = kwargs + product.downloader.order_response_process(response, product) + + if ( + product.properties.get("storageStatus") != ONLINE_STATUS + and NOT_AVAILABLE in product.properties.get("orderStatusLink", "") + and hasattr(product.downloader, "orderDownload") + ): + # first order + logger.debug("Order product") + order_status_dict = product.downloader.orderDownload(product=product, auth=auth) + kwargs.update(order_status_dict or {}) + + if product.properties.get("storageStatus") == STAGING_STATUS and hasattr( + product.downloader, "orderDownloadStatus" + ): + # check order status if needed + logger.debug("Checking product order status") + product.downloader.orderDownloadStatus(product=product, auth=auth) + if product.properties.get("storageStatus") != ONLINE_STATUS: + raise NotAvailableError("Product is not available yet") + + def get_detailled_collections_list( provider: Optional[str] = None, fetch_providers: bool = True ) -> List[Dict[str, Any]]: diff --git a/eodag/rest/server.py b/eodag/rest/server.py index 3aedb6a9..21139461 100755 --- a/eodag/rest/server.py +++ b/eodag/rest/server.py @@ -39,7 +39,7 @@ from fastapi import FastAPI, HTTPException, Request from fastapi.encoders import jsonable_encoder from fastapi.middleware.cors import CORSMiddleware from fastapi.openapi.utils import get_openapi -from fastapi.responses import ORJSONResponse, StreamingResponse +from fastapi.responses import ORJSONResponse from pydantic import ValidationError as pydanticValidationError from pygeofilter.backends.cql2_json import to_cql2 from pygeofilter.parsers.cql2_text import parse as parse_cql2_text @@ -80,6 +80,8 @@ if TYPE_CHECKING: from fastapi.types import DecoratedCallable from requests import Response +from starlette.responses import Response as StarletteResponse + logger = logging.getLogger("eodag.rest.server") ERRORS_WITH_500_STATUS_CODE = { "MisconfiguredError", @@ -385,7 +387,7 @@ def stac_extension_oseo(request: Request) -> Any: ) def stac_collections_item_download( collection_id: str, item_id: str, request: Request -) -> StreamingResponse: +) -> StarletteResponse: """STAC collection item download""" logger.debug("URL: %s", request.url) @@ -393,7 +395,11 @@ def stac_collections_item_download( provider = arguments.pop("provider", None) return download_stac_item( - catalogs=[collection_id], item_id=item_id, provider=provider, arguments + request=request, + catalogs=[collection_id], + item_id=item_id, + provider=provider, + arguments, ) @@ -412,6 +418,7 @@ def stac_collections_item_download_asset( provider = arguments.pop("provider", None) return download_stac_item( + request=request, catalogs=[collection_id], item_id=item_id, provider=provider, @@ -575,7 +582,7 @@ def collections(request: Request) -> Any: ) def stac_catalogs_item_download( catalogs: str, item_id: str, request: Request -) -> StreamingResponse: +) -> StarletteResponse: """STAC Catalog item download""" logger.debug("URL: %s", request.url) @@ -585,7 +592,11 @@ def stac_catalogs_item_download( list_catalog = catalogs.strip("/").split("/") return download_stac_item( - catalogs=list_catalog, item_id=item_id, provider=provider, arguments + request=request, + catalogs=list_catalog, + item_id=item_id, + provider=provider, + arguments, ) @@ -606,6 +617,7 @@ def stac_catalogs_item_download_asset( list_catalog = catalogs.strip("/").split("/") return download_stac_item( + request=request, catalogs=list_catalog, item_id=item_id, provider=provider, diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 6471d0db..b46a838a 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -1433,6 +1433,7 @@ class StreamResponse: content: Iterator[bytes] headers: Optional[Mapping[str, str]] = None media_type: Optional[str] = None + status_code: Optional[int] = None def guess_file_type(file: str) -> Optional[str]:	staging status in server mode download When downloading an OFFLINE product in server mode, use min values for `wait` and `time` and return a 202 status until product is available	CS-SI/eodag	diff --git a/tests/__init__.py b/tests/__init__.py index 2bd14e8a..59ce5692 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -335,6 +335,7 @@ class EODagTestCase(unittest.TestCase): response.__zip_buffer = io.BytesIO(fh.read()) cl = response.__zip_buffer.getbuffer().nbytes response.headers = {"content-length": cl} + response.url = "http://foo.bar" def __enter__(response): return response diff --git a/tests/context.py b/tests/context.py index 2e6aeec7..f697bb45 100644 --- a/tests/context.py +++ b/tests/context.py @@ -33,6 +33,7 @@ from eodag.api.product.metadata_mapping import ( format_metadata, OFFLINE_STATUS, ONLINE_STATUS, + STAGING_STATUS, properties_from_json, NOT_AVAILABLE, ) diff --git a/tests/units/test_download_plugins.py b/tests/units/test_download_plugins.py index 832c63f7..3f2f79ef 100644 --- a/tests/units/test_download_plugins.py +++ b/tests/units/test_download_plugins.py @@ -172,6 +172,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): self.__zip_buffer = io.BytesIO(fh.read()) cl = self.__zip_buffer.getbuffer().nbytes self.headers = {"content-length": cl} + self.url = "http://foo.bar" def __enter__(self): return self @@ -1008,12 +1009,12 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): # empty product download directory should have been removed self.assertFalse(Path(os.path.join(self.output_dir, "dummy_product")).exists()) - def test_plugins_download_http_order_download_cds( + def test_plugins_download_http_order_download_cop_ads( self, ): """HTTPDownload.download must order the product if needed""" - self.product.provider = "cop_cds" + self.product.provider = "cop_ads" self.product.product_type = "CAMS_EAC4" product_dataset = "cams-global-reanalysis-eac4" @@ -1075,6 +1076,7 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): # expected values expected_remote_location = "http://somewhere/download/dummy_request_id" + expected_order_status_link = f"{endpoint}/tasks/dummy_request_id" expected_path = os.path.join( output_data_path, self.product.properties["title"] ) @@ -1085,6 +1087,12 @@ class TestDownloadPluginHttp(BaseDownloadPluginTest): timeout=0.2 / 60, ) self.assertEqual(self.product.remote_location, expected_remote_location) + self.assertEqual( + self.product.properties["downloadLink"], expected_remote_location + ) + self.assertEqual( + self.product.properties["orderStatusLink"], expected_order_status_link + ) self.assertEqual(path, expected_path) self.assertEqual(self.product.location, path_to_uri(expected_path)) diff --git a/tests/units/test_http_server.py b/tests/units/test_http_server.py index 9571d73f..5d72bed1 100644 --- a/tests/units/test_http_server.py +++ b/tests/units/test_http_server.py @@ -25,7 +25,7 @@ import unittest from pathlib import Path from tempfile import TemporaryDirectory from typing import Any, Dict, List, Optional, Union -from unittest.mock import Mock +from unittest.mock import MagicMock, Mock import geojson import httpx @@ -36,11 +36,15 @@ from eodag.config import PluginConfig from eodag.plugins.authentication.base import Authentication from eodag.plugins.download.base import Download from eodag.utils import USER_AGENT, MockResponse, StreamResponse -from eodag.utils.exceptions import TimeOutError +from eodag.utils.exceptions import NotAvailableError, TimeOutError from tests import mock from tests.context import ( DEFAULT_ITEMS_PER_PAGE, HTTP_REQ_TIMEOUT, + NOT_AVAILABLE, + OFFLINE_STATUS, + ONLINE_STATUS, + STAGING_STATUS, TEST_RESOURCES_PATH, AuthenticationError, SearchResult, @@ -436,6 +440,13 @@ class RequestTestCase(unittest.TestCase): self.assertIn("description", response_content) self.assertIn("NotAvailableError", response_content["description"]) + def _request_accepted(self, url: str): + response = self.app.get(url, follow_redirects=True) + response_content = json.loads(response.content.decode("utf-8")) + self.assertEqual(202, response.status_code) + self.assertIn("description", response_content) + self.assertIn("location", response_content) + def test_request_params(self): self._request_not_valid(f"search?collections={self.tested_product_type}&bbox=1") self._request_not_valid( @@ -1047,6 +1058,10 @@ class RequestTestCase(unittest.TestCase): response_filename = header_content_disposition.get_param("filename", None) self.assertEqual(response_filename, expected_file) + @mock.patch( + "eodag.plugins.authentication.base.Authentication.authenticate", + autospec=True, + ) @mock.patch( "eodag.plugins.download.base.Download._stream_download_dict", autospec=True, @@ -1056,7 +1071,7 @@ class RequestTestCase(unittest.TestCase): autospec=True, ) def test_download_item_from_collection_no_stream( - self, mock_download: Mock, mock_stream_download: Mock + self, mock_download: Mock, mock_stream_download: Mock, mock_auth: Mock ): """Download through eodag server catalog should return a valid response""" # download should be performed locally then deleted if streaming is not available @@ -1073,6 +1088,71 @@ class RequestTestCase(unittest.TestCase): expected_file ), f"File {expected_file} should have been deleted" + @mock.patch( + "eodag.rest.core.eodag_api.search", + autospec=True, + ) + def test_download_offline_item_from_catalog(self, mock_search): + """Download an offline item through eodag server catalog should return a + response with HTTP Status 202""" + # mock_search_result returns 2 search results, only keep one + two_results = self.mock_search_result() + product = two_results[0][0] + mock_search.return_value = ( + [ + product, + ], + 1, + ) + product.downloader_auth = MagicMock() + product.downloader.orderDownload = MagicMock() + product.downloader.orderDownloadStatus = MagicMock() + product.downloader.order_response_process = MagicMock() + product.downloader._stream_download_dict = MagicMock( + side_effect=NotAvailableError("Product offline. Try again later.") + ) + product.properties["orderStatusLink"] = f"{NOT_AVAILABLE}?foo=bar" + + # ONLINE product with error + product.properties["storageStatus"] = ONLINE_STATUS + # status 404 and no order try + self._request_not_found( + f"catalogs/{self.tested_product_type}/items/foo/download" + ) + product.downloader.orderDownload.assert_not_called() + product.downloader.orderDownloadStatus.assert_not_called() + product.downloader.order_response_process.assert_not_called() + product.downloader._stream_download_dict.assert_called_once() + product.downloader._stream_download_dict.reset_mock() + + # OFFLINE product with error + product.properties["storageStatus"] = OFFLINE_STATUS + # status 202 and order once and no status check + self._request_accepted( + f"catalogs/{self.tested_product_type}/items/foo/download" + ) + product.downloader.orderDownload.assert_called_once() + product.downloader.orderDownload.reset_mock() + product.downloader.orderDownloadStatus.assert_not_called() + product.downloader.order_response_process.assert_called() + product.downloader.order_response_process.reset_mock() + product.downloader._stream_download_dict.assert_called_once() + product.downloader._stream_download_dict.reset_mock() + + # STAGING product and available orderStatusLink + product.properties["storageStatus"] = STAGING_STATUS + product.properties["orderStatusLink"] = "http://somewhere?foo=bar" + # status 202 and no order but status checked and no download try + self._request_accepted( + f"catalogs/{self.tested_product_type}/items/foo/download" + ) + product.downloader.orderDownload.assert_not_called() + product.downloader.orderDownloadStatus.assert_called_once() + product.downloader.orderDownloadStatus.reset_mock() + product.downloader.order_response_process.assert_called() + product.downloader.order_response_process.reset_mock() + product.downloader._stream_download_dict.assert_not_called() + def test_conformance(self): """Request to /conformance should return a valid response""" self._request_valid("conformance", check_links=False)	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 1 }, "num_modified_files": 6 }	2.12	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 backports.tarfile==1.2.0 beautifulsoup4==4.13.3 boto3==1.37.23 boto3-stubs==1.37.23 botocore==1.37.23 botocore-stubs==1.37.23 cachetools==5.5.2 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 cssselect==1.3.0 dateparser==1.2.1 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@4286d132c8500c968a82445c2e3509216c4a1e65#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lark==1.2.2 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 mypy-boto3-cloudformation==1.37.22 mypy-boto3-dynamodb==1.37.12 mypy-boto3-ec2==1.37.16 mypy-boto3-lambda==1.37.16 mypy-boto3-rds==1.37.21 mypy-boto3-s3==1.37.0 mypy-boto3-sqs==1.37.0 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 pygeofilter==0.3.1 pygeoif==1.5.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 regex==2024.11.6 requests==2.32.3 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 starlette==0.46.1 stdlib-list==0.11.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-awscrt==0.24.2 types-html5lib==1.1.11.20241018 types-lxml==2025.3.30 types-PyYAML==6.0.12.20250326 types-s3transfer==0.11.4 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 tzlocal==5.3.1 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - backports-tarfile==1.2.0 - beautifulsoup4==4.13.3 - boto3==1.37.23 - boto3-stubs==1.37.23 - botocore==1.37.23 - botocore-stubs==1.37.23 - cachetools==5.5.2 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - cssselect==1.3.0 - dateparser==1.2.1 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.12.2.dev36+g4286d132 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lark==1.2.2 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - mypy-boto3-cloudformation==1.37.22 - mypy-boto3-dynamodb==1.37.12 - mypy-boto3-ec2==1.37.16 - mypy-boto3-lambda==1.37.16 - mypy-boto3-rds==1.37.21 - mypy-boto3-s3==1.37.0 - mypy-boto3-sqs==1.37.0 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygeofilter==0.3.1 - pygeoif==1.5.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - regex==2024.11.6 - requests==2.32.3 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - soupsieve==2.6 - starlette==0.46.1 - stdlib-list==0.11.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-awscrt==0.24.2 - types-html5lib==1.1.11.20241018 - types-lxml==2025.3.30 - types-pyyaml==6.0.12.20250326 - types-s3transfer==0.11.4 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - tzlocal==5.3.1 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_http_server.py::RequestTestCase::test_download_offline_item_from_catalog" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_dir_permission" ]	[ "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_collision_avoidance", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_existing", "tests/units/test_download_plugins.py::TestDownloadPluginBase::test_plugins_download_base_prepare_download_no_url", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_asset_filter", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_head", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_filename_from_href", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_resume", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_assets_size", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_file_without_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_ignore_assets_without_ssl", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_nonzip_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_one_local_asset", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_download_cop_ads", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_get", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_post", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_order_status_search_again", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_several_local_assets", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_tar_file_with_zip_extension_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttp::test_plugins_download_http_zip_file_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_error_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_notready_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_ok", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_once_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_short_timeout", "tests/units/test_download_plugins.py::TestDownloadPluginHttpRetry::test_plugins_download_http_retry_timeout_disabled", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_get_bucket_prefix", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_no_safe_build_no_flatten_top_dirs", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build", "tests/units/test_download_plugins.py::TestDownloadPluginAws::test_plugins_download_aws_safe_build_assets", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_offline", "tests/units/test_download_plugins.py::TestDownloadPluginS3Rest::test_plugins_download_s3rest_online", "tests/units/test_download_plugins.py::TestDownloadPluginCreodiasS3::test_plugins_download_creodias_s3", "tests/units/test_http_server.py::RequestTestCase::test_auth_error", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse", "tests/units/test_http_server.py::RequestTestCase::test_catalog_browse_date_search", "tests/units/test_http_server.py::RequestTestCase::test_cloud_cover_post_search", "tests/units/test_http_server.py::RequestTestCase::test_collection", "tests/units/test_http_server.py::RequestTestCase::test_collection_free_text_search", "tests/units/test_http_server.py::RequestTestCase::test_conformance", "tests/units/test_http_server.py::RequestTestCase::test_cql_post_search", "tests/units/test_http_server.py::RequestTestCase::test_date_post_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_catalog_items", "tests/units/test_http_server.py::RequestTestCase::test_date_search_from_items", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_catalog_stream", "tests/units/test_http_server.py::RequestTestCase::test_download_item_from_collection_no_stream", "tests/units/test_http_server.py::RequestTestCase::test_filter", "tests/units/test_http_server.py::RequestTestCase::test_forward", "tests/units/test_http_server.py::RequestTestCase::test_ids_post_search", "tests/units/test_http_server.py::RequestTestCase::test_intersects_post_search", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_nok", "tests/units/test_http_server.py::RequestTestCase::test_list_product_types_ok", "tests/units/test_http_server.py::RequestTestCase::test_not_found", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_from_constraints", "tests/units/test_http_server.py::RequestTestCase::test_product_type_queryables_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_queryables", "tests/units/test_http_server.py::RequestTestCase::test_queryables_with_provider", "tests/units/test_http_server.py::RequestTestCase::test_request_params", "tests/units/test_http_server.py::RequestTestCase::test_route", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_catalog", "tests/units/test_http_server.py::RequestTestCase::test_search_item_id_from_collection", "tests/units/test_http_server.py::RequestTestCase::test_search_provider_in_downloadlink", "tests/units/test_http_server.py::RequestTestCase::test_search_response_contains_pagination_info", "tests/units/test_http_server.py::RequestTestCase::test_service_desc", "tests/units/test_http_server.py::RequestTestCase::test_service_doc", "tests/units/test_http_server.py::RequestTestCase::test_stac_extension_oseo", "tests/units/test_http_server.py::RequestTestCase::test_timeout_error" ]	[]	Apache License 2.0	null
CS-SI__eodag-927	72e7a48eba23f7debb89903f31d67555b53fcbce	2023-11-16 13:22:12	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: ## Test Results       4 files ±0       4 suites ±0 3m 32s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "duration of all tests") -1s   431 tests +1   428 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") +1   3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 1 724 runs +4 1 648 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") +4 76 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 Results for commit ef417f45. ± Comparison against base commit 09a9b79e. [test-results]:data:application/gzip;base64,H4sIAGAYVmUC/03MSw6DMAxF0a2gjDvAxkmgm6lQiKWofKqQjKruvYZCysz3WHpvxWH0q7pXdKvUmkMqMeTYp7DMkggoIK+0Pxs467Fm5zbC9k/P8BJqCnAfRoG6gI9xiYfEPG+bYJGOOjfBUFvot2nNCZfNva+TbpmmkCSUZwLLpLVFi7UZ6s4R+KFhOR1b1NixQwL1+QJoniJECAEAAA== github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against ef417f455727206d09c41ed3f6d0cf72529fc241 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `76%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against ef417f455727206d09c41ed3f6d0cf72529fc241 </p>	diff --git a/docs/_static/product_types_information.csv b/docs/_static/product_types_information.csv index 21b10020..61464555 100644 --- a/docs/_static/product_types_information.csv +++ b/docs/_static/product_types_information.csv @@ -28,10 +28,10 @@ CLMS_GLO_GDMP_333M,"Gross dry matter Productivity (GDMP) is an indication of the CLMS_GLO_LAI_333M,"LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds therefore to the total green LAI, including the contribution of the green elements of the understory. The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods. ","OLCI,PROBA-V",Sentinel-3,,,"Land,Leaf-area-index,LAI,OLCI,PROBA-V,Sentinel-3",,proprietary,Global 10-daily Leaf Area Index 333m,2014-01-10T00:00:00Z,,,,,,,,,,,,,,,,,,,,,available CLMS_GLO_NDVI_1KM_LTS,"The Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the reflectance in the red band. The time series of dekadal (10-daily) NDVI 1km version 2 observations over the period 1999-2017 is used to calculate Long Term Statistics (LTS) for each of the 36 10-daily periods (dekads) of the year. The calculated LTS include the minimum, median, maximum, average, standard deviation and the number of observations in the covered time series period. These LTS can be used as a reference for actual NDVI observations, which allows evaluating whether vegetation conditions deviate from a 'normal' situation. ","VEGETATION,PROBA-V",SPOT,,,"Land,NDVI,LTS,SPOT,VEGETATION,PROBA-V",,proprietary,"Normalized Difference Vegetation Index: global Long Term Statistics (raster 1km) - version 2, Apr 2019",1999-01-01T00:00:00Z,,,,,,,,,,,,,,,,,,,,,available CLMS_GLO_NDVI_333M,"The Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the reflectance in the red band. It is closely related to FAPAR and is little scale dependant. ",PROBA-V,,,,"Land,NDVI,PROBA-V",,proprietary,Global 10-daily Normalized Difference Vegetation Index 333M,2014-01-01T00:00:00Z,,,,,,,,,,,,,,,,,,,,,available -COP_DEM_GLO30_DGED,"Defence Gridded Elevation Data (DGED) formatted Copernicus DEM GLO-30 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,GDGED",ALTIMETRIC,proprietary,Copernicus DEM GLO-30 DGED,2010-06-21T00:00:00Z,,,,,,available,,,,,,,,,,,,,,,available -COP_DEM_GLO30_DTED,"Digital Terrain Elevation Data (DTED) formatted Copernicus DEM GLO-30 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,DTED",ALTIMETRIC,proprietary,Copernicus DEM GLO-30 DTED,2010-06-21T00:00:00Z,,,,,,available,,,,,,,,,,,,,,, -COP_DEM_GLO90_DGED,"Defence Gridded Elevation Data (DGED) formatted Copernicus DEM GLO-90 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,GDGED",ALTIMETRIC,proprietary,Copernicus DEM GLO-90 DGED,2010-06-21T00:00:00Z,,,,,,available,,,,,,,,,,,,,,,available -COP_DEM_GLO90_DTED,"Digital Terrain Elevation Data (DTED) formatted Copernicus DEM GLO-90 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,DTED",ALTIMETRIC,proprietary,Copernicus DEM GLO-90 DTED,2010-06-21T00:00:00Z,,,,,,available,,,,,,,,,,,,,,, +COP_DEM_GLO30_DGED,"Defence Gridded Elevation Data (DGED) formatted Copernicus DEM GLO-30 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,GDGED",ALTIMETRIC,proprietary,Copernicus DEM GLO-30 DGED,2010-06-21T00:00:00Z,,,,,,available,available,,,,,,,,,,,,,,available +COP_DEM_GLO30_DTED,"Digital Terrain Elevation Data (DTED) formatted Copernicus DEM GLO-30 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-30 provides worldwide coverage at 30 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-30,DSM,DTED",ALTIMETRIC,proprietary,Copernicus DEM GLO-30 DTED,2010-06-21T00:00:00Z,,,,,,available,available,,,,,,,,,,,,,, +COP_DEM_GLO90_DGED,"Defence Gridded Elevation Data (DGED) formatted Copernicus DEM GLO-90 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,GDGED",ALTIMETRIC,proprietary,Copernicus DEM GLO-90 DGED,2010-06-21T00:00:00Z,,,,,,available,available,,,,,,,,,,,,,,available +COP_DEM_GLO90_DTED,"Digital Terrain Elevation Data (DTED) formatted Copernicus DEM GLO-90 data. The Copernicus Digital Elevation Model is a Digital Surface Model (DSM) that represents the surface of the Earth including buildings, infrastructure and vegetation. The Copernicus DEM is provided in 3 different instances: EEA-10, GLO-30 and GLO-90. GLO-90 provides worldwide coverage at 90 meters.Data were acquired through the TanDEM-X mission between 2011 and 2015. The datasets were made available for use in 2019 and will be maintained until 2026. ",,TerraSAR,,,"TerraSAR,TanDEM-X,DEM,surface,GLO-90,DSM,DTED",ALTIMETRIC,proprietary,Copernicus DEM GLO-90 DTED,2010-06-21T00:00:00Z,,,,,,available,available,,,,,,,,,,,,,, EEA_DAILY_SSM_1KM,"Surface Soil Moisture (SSM) is the relative water content of the top few centimetres soil, describing how wet or dry the soil is in its topmost layer, expressed in percent saturation. It is measured by satellite radar sensors and allows insights in local precipitation impacts and soil conditions. SSM is a key driver of water and heat fluxes between the ground and the atmosphere, regulating air temperature and humidity. Moreover, in its role as water supply, it is vital to vegetation health. Vice versa, SSM is very sensitive to external forcing in the form of precipitation, temperature, solar irradiation, humidity, and wind. SSM is thus both an integrator of climatic conditions and a driver of local weather and climate, and plays a major role in global water-, energy- and carbon- cycles. Knowledge on the dynamics of soil moisture is important in the understanding of processes in many environmental and socio-economic fields, e.g., its impact on vegetation vitality, crop yield, droughts or exposure to flood threats. ","C-SAR,Metop ASCAT",Sentinel-1,,,"SSM,C-SAR,Metop-ASCAT,Sentinel-1",RADAR,proprietary,"Surface Soil Moisture: continental Europe daily (raster 1km) - version 1, Apr 2019",2015-01-01T00:00:00Z,,,,,,,,,,,,,,,,,,,,,available EEA_DAILY_SWI_1KM,"The Soil Water Index (SWI) quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation. Soil moisture is a key parameter in numerous environmental studies including hydrology, meteorology and agriculture, and is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). The SWI product provides daily information about moisture conditions in different soil layers. It includes a quality flag (QFLAG) indicating the availability of SSM measurements for SWI calculations, and a Surface State Flag (SSF) indicating frozen or snow covered soils. ","C-SAR,Metop ASCAT",Sentinel-1,,,"SWI,QFLAG,SSF,C-SAR,Metop-ASCAT,Sentinel-1",RADAR,proprietary,"Soil Water Index: continental Europe daily (raster 1km) - version 1, Apr 2019",2015-01-01T00:00:00Z,,,,,,,,,,,,,,,,,,,,,available EEA_DAILY_VI,"Vegetation Indices (VI) comprises four daily vegetation indices (PPI, NDVI, LAI and FAPAR) and quality information, that are part of the Copernicus Land Monitoring Service (CLMS) HR-VPP product suite. The 10m resolution, daily updated Plant Phenology Index (PPI), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are derived from Copernicus Sentinel-2 satellite observations. They are provided together with a related quality indicator (QFLAG2) that flags clouds, shadows, snow, open water and other areas where the VI retrieval is less reliable. These Vegetation Indices are made available as a set of raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from 2017 until today, with daily updates. The Vegetation Indices are part of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). ",,Sentinel-2,"S2A, S2B",,"Land,Plant-phenology-index,Phenology,Vegetation,Sentinel-2,S2A,S2B",RADAR,proprietary,"Vegetation Indices, daily, UTM projection",,,,,,,,,,,,,,,,,,,,,,available diff --git a/docs/getting_started_guide/configure.rst b/docs/getting_started_guide/configure.rst index ac0559c5..4601b6ae 100644 --- a/docs/getting_started_guide/configure.rst +++ b/docs/getting_started_guide/configure.rst @@ -137,6 +137,7 @@ Core configuration using environment variables Some EODAG core settings can be overriden using environment variables: +* ``EODAG_CFG_DIR`` customized configuration directory in place of ``~/.config/eodag`` * ``EODAG_CFG_FILE`` for defining the desired path to the `user configuration file\ <https://eodag.readthedocs.io/en/stable/getting_started_guide/configure.html#yaml-user-configuration-file>`_ * ``EODAG_LOCS_CFG_FILE`` for defining the desired path to the diff --git a/eodag/api/core.py b/eodag/api/core.py index bcd5d3e5..81a69d27 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -21,6 +21,7 @@ import logging import os import re import shutil +import tempfile from operator import itemgetter from typing import ( TYPE_CHECKING, @@ -129,8 +130,27 @@ class EODataAccessGateway: self.product_types_config_md5 = obj_md5sum(self.product_types_config.source) self.providers_config = load_default_config() - self.conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") - makedirs(self.conf_dir) + env_var_cfg_dir = "EODAG_CFG_DIR" + self.conf_dir = os.getenv( + env_var_cfg_dir, + default=os.path.join(os.path.expanduser("~"), ".config", "eodag"), + ) + try: + makedirs(self.conf_dir) + except OSError as e: + logger.debug(e) + tmp_conf_dir = os.path.join(tempfile.gettempdir(), ".config", "eodag") + logger.warning( + f"Cannot create configuration directory {self.conf_dir}. " + + f"Falling back to temporary directory {tmp_conf_dir}." + ) + if os.getenv(env_var_cfg_dir) is None: + logger.warning( + "You can set the path of the configuration directory " + + f"with the environment variable {env_var_cfg_dir}" + ) + self.conf_dir = tmp_conf_dir + makedirs(self.conf_dir) self._plugins_manager = PluginManager(self.providers_config) # use updated providers_config	EODataAccessGateway constructor fails on AWS Lambda Describe the bug `makedirs` [invocation](https://github.com/CS-SI/eodag/blob/develop/eodag/api/core.py#L98) within the EODataAccessGateway constructor causes `OSError: [Errno 30] Read-only file system` when run on AWS Lambda Code To Reproduce CLI commands or Python code snippet to reproduce the bug. Please use maximum verbosity using: ```py from aws_lambda_powertools.logging import Logger from aws_lambda_powertools.tracing import Tracer from eodag import EODataAccessGateway logger = Logger() tracer = Tracer() @tracer.capture_lambda_handler @logger.inject_lambda_context def lambda_handler(event, context): EODataAccessGateway() ``` Output Compete output obtained with maximal verbosity. ``` [ERROR] OSError: [Errno 30] Read-only file system: '/home/sbx_user1051' Traceback (most recent call last): File "/opt/python/aws_lambda_powertools/tracing/tracer.py", line 305, in decorate response = lambda_handler(event, context, *kwargs) File "/opt/python/aws_lambda_powertools/logging/logger.py", line 438, in decorate return lambda_handler(event, context, args, kwargs) File "/var/task/<my file, my line>, in lambda_handler EODataAccessGateway() File "/opt/python/eodag/api/core.py", line 91, in __init__ makedirs(self.conf_dir) File "/opt/python/eodag/utils/__init__.py", line 496, in makedirs os.makedirs(dirpath) File "/var/lang/lib/python3.9/os.py", line 215, in makedirs makedirs(head, exist_ok=exist_ok) File "/var/lang/lib/python3.9/os.py", line 215, in makedirs makedirs(head, exist_ok=exist_ok) File "/var/lang/lib/python3.9/os.py", line 225, in makedirs mkdir(name, mode) ``` Environment: - Python version: 3.9 - EODAG version: 2.5.2 - Runtime: AWS Lambda Additional context** File system on AWS Lambda is read-only. It has "ephemeral storage" that could be accessed in `/tmp` ([1](https://aws.amazon.com/blogs/aws/aws-lambda-now-supports-up-to-10-gb-ephemeral-storage/), [2](https://aws.amazon.com/blogs/aws/aws-lambda-now-supports-up-to-10-gb-ephemeral-storage/)), however this class does not accept any configuration to where to point to `makedirs`. The workaround that heavily relies on the [current implementation](https://github.com/CS-SI/eodag/blob/develop/eodag/api/core.py#L97) is to change `$HOME` env var before invoking the constructor. But it is barely acceptable and extremely inconvenient, since this variable is used by many tools locally and in automated tools along the way.	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index f5f118c4..46fc1bed 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -22,6 +22,7 @@ import json import logging import os import shutil +import tempfile import unittest import uuid from pathlib import Path @@ -1178,6 +1179,20 @@ class TestCoreConfWithEnvVar(TestCoreBase): class TestCoreInvolvingConfDir(unittest.TestCase): + @classmethod + def setUpClass(cls): + super(TestCoreInvolvingConfDir, cls).setUpClass() + cls.dag = EODataAccessGateway() + # mock os.environ to empty env + cls.mock_os_environ = mock.patch.dict(os.environ, {}, clear=True) + cls.mock_os_environ.start() + + @classmethod + def tearDownClass(cls): + super(TestCoreInvolvingConfDir, cls).tearDownClass() + # stop os.environ + cls.mock_os_environ.stop() + def setUp(self): super(TestCoreInvolvingConfDir, self).setUp() self.dag = EODataAccessGateway() @@ -1194,14 +1209,15 @@ class TestCoreInvolvingConfDir(unittest.TestCase): except OSError: shutil.rmtree(old_path) - def execution_involving_conf_dir(self, inspect=None): + def execution_involving_conf_dir(self, inspect=None, conf_dir=None): """Check that the path(s) inspected (str, list) are created after the instantation of EODataAccessGateway. If they were already there, rename them (.old), instantiate, check, delete the new files, and restore the existing files to there previous name.""" if inspect is not None: + if conf_dir is None: + conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") if isinstance(inspect, str): inspect = [inspect] - conf_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") olds = [] currents = [] for inspected in inspect: @@ -1233,6 +1249,49 @@ class TestCoreInvolvingConfDir(unittest.TestCase): """The core object must create a locations config file and a shp dir in standard user config location on instantiation""" # noqa self.execution_involving_conf_dir(inspect=["locations.yml", "shp"]) + def test_read_only_home_dir(self): + # standard directory + home_dir = os.path.join(os.path.expanduser("~"), ".config", "eodag") + self.execution_involving_conf_dir(inspect="eodag.yml", conf_dir=home_dir) + + # user defined directory + user_dir = os.path.join(os.path.expanduser("~"), ".config", "another_eodag") + os.environ["EODAG_CFG_DIR"] = user_dir + self.execution_involving_conf_dir(inspect="eodag.yml", conf_dir=user_dir) + shutil.rmtree(user_dir) + del os.environ["EODAG_CFG_DIR"] + + # fallback temporary folder + def makedirs_side_effect(dir): + if dir == os.path.join(os.path.expanduser("~"), ".config", "eodag"): + raise OSError("Mock makedirs error") + else: + return makedirs(dir) + + with mock.patch( + "eodag.api.core.makedirs", side_effect=makedirs_side_effect + ) as mock_makedirs: + # backup temp_dir if exists + temp_dir = temp_dir_old = os.path.join( + tempfile.gettempdir(), ".config", "eodag" + ) + if os.path.exists(temp_dir): + temp_dir_old = f"{temp_dir}.old" + shutil.move(temp_dir, temp_dir_old) + + EODataAccessGateway() + expected = [unittest.mock.call(home_dir), unittest.mock.call(temp_dir)] + mock_makedirs.assert_has_calls(expected) + self.assertTrue(os.path.exists(temp_dir)) + + # restore temp_dir + if temp_dir_old != temp_dir: + try: + shutil.rmtree(temp_dir) + except OSError: + os.unlink(temp_dir) + shutil.move(temp_dir_old, temp_dir) + class TestCoreGeometry(TestCoreBase): @classmethod	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 0, "test_score": 1 }, "num_modified_files": 3 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@72e7a48eba23f7debb89903f31d67555b53fcbce#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-PyYAML==6.0.12.20250326 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.1.dev38+g72e7a48e - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-pyyaml==6.0.12.20250326 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCoreInvolvingConfDir::test_read_only_home_dir" ]	[]	[ "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_list_queryables", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available_with_alias", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreDownload::test_download_local_product", "tests/units/test_core.py::TestCoreProductAlias::test_get_alias_from_product_type", "tests/units/test_core.py::TestCoreProductAlias::test_get_product_type_from_alias" ]	[]	Apache License 2.0	null
CS-SI__eodag-961	0dcc36d255600669d69fd757f7af6d20e5a4faf1	2023-12-13 13:17:57	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: ## Test Results     2 files - 1     2 suites - 1 1m 30s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "duration of all tests") -1s 440 tests ±0 436 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") ±0   3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±0 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 689 runs +7 653 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") +7 35 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±0 1 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 For more details on these failures, see [this check](https://github.com/CS-SI/eodag/runs/19599283533). Results for commit b4f3f2a5. ± Comparison against base commit 0dcc36d2. [test-results]:data:application/gzip;base64,H4sIALqveWUC/03MSw7CIBSF4a00jB0ob9yM4XUTYlsMhZFx715Mix3+30nOm0Ca40buE71MZGupjgit2Jryimmu2LjUvnE+6rE17zsx+adneiGxAWDTjHAbEEvJBaXflLb2T6nNHselFGzI/igOOD3++nzo87KkikEcBwbUCqeilY55CBAsZYKCj0oHZcArG4Mmny/gD4/6BQEAAA==	diff --git a/eodag/rest/stac.py b/eodag/rest/stac.py index 4114e3c5..8f84ddea 100644 --- a/eodag/rest/stac.py +++ b/eodag/rest/stac.py @@ -275,20 +275,28 @@ class StacItem(StacCommon): "href" ] = f"{without_arg_url}?{urlencode(query_dict, doseq=True)}" - # add origin assets to product assets - origin_assets = product_item["assets"].pop("origin_assets") + # move origin asset urls to alternate links and replace with eodag-server ones + origin_assets = product_item["assets"].pop("origin_assets", {}) if getattr(product, "assets", False): # replace origin asset urls with eodag-server ones for asset_key, asset_value in origin_assets.items(): - origin_assets[asset_key]["href"] = without_arg_url + # use origin asset as default + product_item["assets"][asset_key] = asset_value + # origin assets as alternate link + product_item["assets"][asset_key]["alternate"] = { + "origin": { + "title": "Origin asset link", + "href": asset_value["href"], + } + } + # use server-mode assets download links + asset_value["href"] = without_arg_url if query_dict: - origin_assets[asset_key][ + product_item["assets"][asset_key][ "href" ] += f"/{asset_key}?{urlencode(query_dict, doseq=True)}" else: - origin_assets[asset_key]["href"] += f"/{asset_key}" - - product_item["assets"] = dict(product_item["assets"], **origin_assets) + product_item["assets"][asset_key]["href"] += f"/{asset_key}" # apply conversion if needed for prop_key, prop_val in need_conversion.items():	eodag-authenticated and origin raw assets both available in server mode In server mode, once #721 is done, make available both eodag-authenticated and origin raw assets using https://github.com/stac-extensions/alternate-assets: ```json { "features": [ { "id": "some-product", "assets": { "B01": { "title": "Download link", "href": "http://127.0.0.1:5000/collections/some-collection/items/some-product/download/B01", "alternate": { "origin": { "title": "Origin asset link", "href": "s3://path/to/B01-asset" } } } } } ] } ```	CS-SI/eodag	diff --git a/tests/units/test_stac_utils.py b/tests/units/test_stac_utils.py index 6b3fd0f1..7e03f4ab 100644 --- a/tests/units/test_stac_utils.py +++ b/tests/units/test_stac_utils.py @@ -453,10 +453,16 @@ class TestStacUtils(unittest.TestCase): "assets" ].items(): self.assertIn(k, response["features"][0]["assets"].keys()) + # check asset server-mode download link self.assertEqual( response["features"][0]["assets"][k]["href"], f"http://foo/collections/S2_MSI_L2A/items/{product_id}/download/{k}?provider=earth_search", ) + # check asset origin download link + self.assertEqual( + response["features"][0]["assets"][k]["alternate"]["origin"]["href"], + self.earth_search_resp_search_json["features"][0]["assets"][k]["href"], + ) # preferred provider should not be changed self.assertEqual("peps", self.rest_utils.eodag_api.get_preferred_provider()[0])	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_issue_reference" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 3, "test_score": 0 }, "num_modified_files": 1 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "tox" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@0dcc36d255600669d69fd757f7af6d20e5a4faf1#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-PyYAML==6.0.12.20250326 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.30.0 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.1.dev15+g0dcc36d2 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-pyyaml==6.0.12.20250326 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.30.0 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_stac_utils.py::TestStacUtils::test_search_stac_items_with_stac_providers" ]	[]	[ "tests/units/test_stac_utils.py::TestStacUtils::test_detailled_collections_list", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products_filter_misuse_raise_error", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products_missing_additional_parameters_raise_error", "tests/units/test_stac_utils.py::TestStacUtils::test_filter_products_unknown_cruncher_raise_error", "tests/units/test_stac_utils.py::TestStacUtils::test_format_product_types", "tests/units/test_stac_utils.py::TestStacUtils::test_get_arguments_query_paths", "tests/units/test_stac_utils.py::TestStacUtils::test_get_criterias_from_metadata_mapping", "tests/units/test_stac_utils.py::TestStacUtils::test_get_date", "tests/units/test_stac_utils.py::TestStacUtils::test_get_datetime", "tests/units/test_stac_utils.py::TestStacUtils::test_get_geometry", "tests/units/test_stac_utils.py::TestStacUtils::test_get_int", "tests/units/test_stac_utils.py::TestStacUtils::test_get_metadata_query_paths", "tests/units/test_stac_utils.py::TestStacUtils::test_get_pagination_info", "tests/units/test_stac_utils.py::TestStacUtils::test_get_product_types", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_catalogs", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_collection_by_id", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_collections", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_conformance", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_extension_oseo", "tests/units/test_stac_utils.py::TestStacUtils::test_get_stac_item_by_id", "tests/units/test_stac_utils.py::TestStacUtils::test_get_templates_path", "tests/units/test_stac_utils.py::TestStacUtils::test_home_page_content", "tests/units/test_stac_utils.py::TestStacUtils::test_search_bbox", "tests/units/test_stac_utils.py::TestStacUtils::test_search_product_by_id", "tests/units/test_stac_utils.py::TestStacUtils::test_search_products", "tests/units/test_stac_utils.py::TestStacUtils::test_search_stac_items_get", "tests/units/test_stac_utils.py::TestStacUtils::test_search_stac_items_post", "tests/units/test_stac_utils.py::TestStacUtils::test_search_stac_items_with_non_stac_providers" ]	[]	Apache License 2.0	swerebench/sweb.eval.x86_64.cs-si_1776_eodag-961
CS-SI__eodag-969	40fe987ec3a0644e660653a7a330dbaefca9d610	2023-12-20 17:10:27	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: ## Test Results       4 files ±0       4 suites ±0 4m 4s [:stopwatch:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "duration of all tests") -1s   441 tests ±0   438 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") ±0   3 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 1 764 runs ±0 1 686 [:heavy_check_mark:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "passed tests") ±0 78 [:zzz:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "skipped / disabled tests") ±0 0 [:x:](https://github.com/EnricoMi/publish-unit-test-result-action/blob/v2.11.0/README.md#the-symbols "failed tests") ±0 Results for commit 4f2bc269. ± Comparison against base commit 40fe987e. [test-results]:data:application/gzip;base64,H4sIAM4gg2UC/03MQQ6DIBCF4asY1l0UHEfpZRocICFVaRBWpncvWKUu/+8lb2PWTWZljwZuDVuTizV0Cio6v+QUUCBPcR+Bn/VcE1GhdvjTy70ztRWsclOGewUTgg+HhLSUT94jHHV+chyw0u+zH064fO59vSQ/zy7mYGDFSAKlBm06S5pAQgeGS0GklFZIYlQILft8AYRMzQQIAQAA github-actions[bot]: <strong>Code Coverage (Ubuntu)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `82%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 4f2bc269d4de5fcdc49454e192ccaada6c2ba643 </p> github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `76%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against 4f2bc269d4de5fcdc49454e192ccaada6c2ba643 </p>	diff --git a/docs/_static/product_types_information.csv b/docs/_static/product_types_information.csv index a693c35f..9762a9fc 100644 --- a/docs/_static/product_types_information.csv +++ b/docs/_static/product_types_information.csv @@ -79,7 +79,7 @@ S1_SAR_OCN,"Level-2 OCN products include components for Ocean Swell spectra (OSW S1_SAR_RAW,"The SAR Level-0 products consist of the sequence of Flexible Dynamic Block Adaptive Quantization (FDBAQ) compressed unfocused SAR raw data. For the data to be usable, it will need to be decompressed and processed using a SAR processor. SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification ",SAR,SENTINEL1,"S1A,S1B",L0,"SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L0,RAW,SAFE",RADAR,proprietary,SENTINEL1 SAR Level-0,2014-04-03T00:00:00Z,,,,,available,available,,,,,,,available,,,,,,,available S1_SAR_SLC,"Level-1 Single Look Complex (SLC) products consist of focused SAR data geo-referenced using orbit and attitude data from the satellite and provided in zero-Doppler slant-range geometry. The products include a single look in each dimension using the full transmit signal bandwidth and consist of complex samples preserving the phase information. SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/data-formats/safe-specification ",SAR,SENTINEL1,"S1A,S1B",L1,"SAR,SENTINEL,SENTINEL1,S1,S1A,S1B,L1,SLC,SAFE",RADAR,proprietary,SENTINEL1 Level-1 Single Look Complex,2014-04-03T00:00:00Z,,,,,available,available,,,,,,,available,available,,available,,,,available S2_MSI_L1C,"The Level-1C product is composed of 100x100 km2 tiles (ortho-images in UTM/WGS84 projection). It results from using a Digital Elevation Model (DEM) to project the image in cartographic geometry. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances along with the parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 meters depending on the native resolution of the different spectral bands. In Level-1C products, pixel coordinates refer to the upper left corner of the pixel. Level-1C products will additionally include Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats ",MSI,SENTINEL2,"S2A,S2B",L1,"MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L1,L1C,SAFE",OPTICAL,proprietary,SENTINEL2 Level-1C,2015-06-23T00:00:00Z,available,available,,,available,available,available,,available,,,,available,available,,available,,available,,available -S2_MSI_L2A,"The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection). SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats ",MSI,SENTINEL2,"S2A,S2B",L2,"MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,SAFE",OPTICAL,proprietary,SENTINEL2 Level-2A,2018-03-26T00:00:00Z,available,available,,,available,available,available,,,,,,available,available,available,available,,,,available +S2_MSI_L2A,"The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection). SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats ",MSI,SENTINEL2,"S2A,S2B",L2,"MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,SAFE",OPTICAL,proprietary,SENTINEL2 Level-2A,2018-03-26T00:00:00Z,available,available,,,available,available,available,,,,,,available,,available,available,,,,available S2_MSI_L2AP,"The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection). SAFE formatted product, see https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/data-formats. Level-2AP are the pilot products of Level-2A product generated by ESA until March 2018. After March, they are operational products ",MSI,SENTINEL2,"S2A,S2B",L2,"MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,SAFE, pilot",OPTICAL,proprietary,SENTINEL2 Level-2A pilot,2017-05-23T00:00:00Z,,,,,,,,,,,,,,,,,,,,available S2_MSI_L2A_COG,"The Level-2A product provides Bottom Of Atmosphere (BOA) reflectance images derived from the associated Level-1C products. Each Level-2A product is composed of 100x100 km2 tiles in cartographic geometry (UTM/WGS84 projection). Product containing Cloud Optimized GeoTIFF images, without SAFE formatting. ",MSI,SENTINEL2,"S2A,S2B",L2,"MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,COG",OPTICAL,proprietary,SENTINEL2 Level-2A,2015-06-23T00:00:00Z,,,,,,,,available,,,,,,,,,,,, S2_MSI_L2A_MAJA,"The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows using MAJA. MAJA uses MUSCATE processing center at CNES, in the framework of THEIA land data center. Sentinel-2 level 1C data are downloaded from PEPS. The full description of the product format is available at https://theia.cnes.fr/atdistrib/documents/PSC-NT-411-0362-CNES_01_00_SENTINEL-2A_L2A_Products_Description.pdf ",MSI,SENTINEL2,"S2A,S2B",L2,"MSI,SENTINEL,SENTINEL2,S2,S2A,S2B,L2,L2A,MAJA",OPTICAL,proprietary,SENTINEL2 Level-2A,2015-06-23T00:00:00Z,,,,,,,,,,,,,,,,,available,,, diff --git a/eodag/resources/providers.yml b/eodag/resources/providers.yml index f29e49a0..39da2a7a 100644 --- a/eodag/resources/providers.yml +++ b/eodag/resources/providers.yml @@ -850,9 +850,6 @@ S2_MSI_L1C: collection: S2ST productType: S2MSI1C - S2_MSI_L2A: - collection: S2ST - productType: S2MSI2A GENERIC_PRODUCT_TYPE: productType: '{productType}' collection: '{collection}'	remove S2_MSI_L2A from peps Remove `S2_MSI_L2A` from the product types available through `peps`	CS-SI/eodag	diff --git a/tests/units/test_core.py b/tests/units/test_core.py index f483ba47..9b186289 100644 --- a/tests/units/test_core.py +++ b/tests/units/test_core.py @@ -214,7 +214,6 @@ class TestCore(TestCoreBase): "creodias", "earth_search", "onda", - "peps", "planetary_computer", "sara", "wekeo",	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_short_problem_statement", "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 1, "test_score": 0 }, "num_modified_files": 2 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio" ], "pre_install": null, "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@40fe987ec3a0644e660653a7a330dbaefca9d610#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 pandas==2.2.3 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.1 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-asyncio==0.26.0 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-PyYAML==6.0.12.20250326 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xarray==2024.7.0 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.1.dev18+g40fe987e - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - pandas==2.2.3 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.1 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-asyncio==0.26.0 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-pyyaml==6.0.12.20250326 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xarray==2024.7.0 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/units/test_core.py::TestCore::test_list_product_types_for_provider_ok" ]	[]	[ "tests/units/test_core.py::TestCore::test_build_index_ko", "tests/units/test_core.py::TestCore::test_core_object_locations_file_not_found", "tests/units/test_core.py::TestCore::test_core_object_open_index_if_exists", "tests/units/test_core.py::TestCore::test_core_object_set_default_locations_config", "tests/units/test_core.py::TestCore::test_discover_product_types", "tests/units/test_core.py::TestCore::test_discover_product_types_with_api_plugin", "tests/units/test_core.py::TestCore::test_discover_product_types_without_plugin", "tests/units/test_core.py::TestCore::test_fetch_product_types_list", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_disabled", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_updated_system_conf", "tests/units/test_core.py::TestCore::test_fetch_product_types_list_without_ext_conf", "tests/units/test_core.py::TestCore::test_get_queryables", "tests/units/test_core.py::TestCore::test_get_version", "tests/units/test_core.py::TestCore::test_list_product_types_fetch_providers", "tests/units/test_core.py::TestCore::test_list_product_types_for_unsupported_provider", "tests/units/test_core.py::TestCore::test_list_product_types_ok", "tests/units/test_core.py::TestCore::test_prune_providers_list", "tests/units/test_core.py::TestCore::test_prune_providers_list_for_search_without_auth", "tests/units/test_core.py::TestCore::test_prune_providers_list_without_api_or_search_plugin", "tests/units/test_core.py::TestCore::test_rebuild_index", "tests/units/test_core.py::TestCore::test_set_preferred_provider", "tests/units/test_core.py::TestCore::test_supported_product_types_in_unit_test", "tests/units/test_core.py::TestCore::test_supported_providers_in_unit_test", "tests/units/test_core.py::TestCore::test_update_product_types_list", "tests/units/test_core.py::TestCore::test_update_product_types_list_unknown_provider", "tests/units/test_core.py::TestCore::test_update_product_types_list_with_api_plugin", "tests/units/test_core.py::TestCore::test_update_product_types_list_without_plugin", "tests/units/test_core.py::TestCore::test_update_providers_config", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_config_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_locations_file_in_envvar", "tests/units/test_core.py::TestCoreConfWithEnvVar::test_core_object_prioritize_providers_file_in_envvar", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_config_standard_location", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_index_if_not_exist", "tests/units/test_core.py::TestCoreInvolvingConfDir::test_core_object_creates_locations_standard_location", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_geometry_and_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_no_match_raises_error", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_kwargs", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_locations_with_wrong_location_name_in_locations_dict", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_no_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations", "tests/units/test_core.py::TestCoreGeometry::test_get_geometry_from_various_only_locations_regex", "tests/units/test_core.py::TestCoreSearch::test__do_search_can_raise_errors", "tests/units/test_core.py::TestCoreSearch::test__do_search_counts_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_does_not_raise_by_default", "tests/units/test_core.py::TestCoreSearch::test__do_search_doest_not_register_downloader_if_no_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_fuzzy_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_no_count_returned", "tests/units/test_core.py::TestCoreSearch::test__do_search_paginated_handle_null_count", "tests/units/test_core.py::TestCoreSearch::test__do_search_query_products_must_be_a_list", "tests/units/test_core.py::TestCoreSearch::test__do_search_register_downloader_if_search_intersection", "tests/units/test_core.py::TestCoreSearch::test__do_search_support_itemsperpage_higher_than_maximum", "tests/units/test_core.py::TestCoreSearch::test__do_search_without_count", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_dates", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_geom", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_locations", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_parameters", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_no_plugins_when_search_by_id", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_available_with_alias", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_peps_plugins_product_not_available", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_preserve_additional_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_guess_it", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_product_type_provided", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_remove_guess_kwargs", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_generic_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_search_plugin_has_known_product_properties", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test__prepare_search_with_id", "tests/units/test_core.py::TestCoreSearch::test__search_by_id", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_has_no_limit", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_with_kwargs", "tests/units/test_core.py::TestCoreSearch::test_guess_product_type_without_kwargs", "tests/units/test_core.py::TestCoreSearch::test_search_all_must_collect_them_all", "tests/units/test_core.py::TestCoreSearch::test_search_all_unknown_product_type", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_default_value", "tests/units/test_core.py::TestCoreSearch::test_search_all_use_max_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_all_user_items_per_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_does_not_handle_query_errors", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_and_quit_early", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_exhaust_get_all_pages_no_products_last_page", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_must_reset_next_attrs_if_next_mechanism", "tests/units/test_core.py::TestCoreSearch::test_search_iter_page_returns_iterator", "tests/units/test_core.py::TestCoreDownload::test_download_local_product", "tests/units/test_core.py::TestCoreProductAlias::test_get_alias_from_product_type", "tests/units/test_core.py::TestCoreProductAlias::test_get_product_type_from_alias" ]	[]	Apache License 2.0	null
CS-SI__eodag-987	fe2a51497fa806f856e9bbb4e3911e7c78dad17e	2024-01-12 19:03:03	49209ff081784ca7d31121899ce1eb8eda7d294c	github-actions[bot]: ## Test Results 1 files - 3 1 suites - 3 18s :stopwatch: - 3m 52s 36 tests - 412 0 :white_check_mark: - 445 0 :zzz: - 3 0 :x: ±0 36 :fire: + 36 165 runs - 1 627 0 :white_check_mark: - 1 714 0 :zzz: - 78 0 :x: ±0 165 :fire: +165 For more details on these errors, see [this check](https://github.com/CS-SI/eodag/runs/20437640306). Results for commit 06858612. ± Comparison against base commit 0f094d51. <details> <summary>This pull request <b>removes</b> 448 and <b>adds</b> 36 tests. <i>Note that renamed tests count towards both.</i></summary> ``` eodag.types.__init__ ‑ eodag.types.json_field_definition_to_python eodag.types.__init__ ‑ eodag.types.json_type_to_python eodag.types.__init__ ‑ eodag.types.model_fields_to_annotated_tuple eodag.types.__init__ ‑ eodag.types.python_field_definition_to_json eodag.types.__init__ ‑ eodag.types.python_type_to_json eodag.types.queryables ‑ eodag.types.queryables.CommonQueryables.get_queryable_from_alias eodag.utils.__init__ ‑ eodag.utils.cached_parse eodag.utils.__init__ ‑ eodag.utils.dict_items_recursive_apply eodag.utils.__init__ ‑ eodag.utils.dict_items_recursive_sort eodag.utils.__init__ ‑ eodag.utils.format_dict_items … ``` ``` eodag.types.__init__ eodag.types.bbox eodag.types.queryables eodag.types.search_args eodag.utils.__init__ eodag.utils.exceptions eodag.utils.import_system eodag.utils.logging eodag.utils.notebook eodag.utils.stac_reader … ``` </details> [test-results]:data:application/gzip;base64,H4sIAGaNoWUC/1XNyw6DIBCF4VcxrLtgECn2ZRoumkyq0nBZmb57EUvV5Xcm+WclI05DII8Gbg0JCeMfNnkV0S0bZXa+xO3WiopnSMbkhR7DC9+XYVQ4XYbBe+drxaelfBPdD+fi7iNYfOoV19yeMG6eMWYSKmQnBTB2l6Ds0AMXIDnQVnFlmTbANadW9+TzBQrSdBICAQAA github-actions[bot]: <strong>Code Coverage (Windows)</strong> \| File \| Coverage \| \| \| - \| :-: \| :-: \| \| All files \| `76%` \| :white_check_mark: \| _Minimum allowed coverage is `70%`_ <p align="right">Generated by :monkey: cobertura-action against e35358ed56c513d2df4da473fe9e2e32ec7ee8aa </p>	diff --git a/eodag/api/core.py b/eodag/api/core.py index 5bd91ddc..a7a08e78 100644 --- a/eodag/api/core.py +++ b/eodag/api/core.py @@ -83,6 +83,7 @@ from eodag.utils.exceptions import ( from eodag.utils.stac_reader import fetch_stac_items if TYPE_CHECKING: + from pydantic.fields import FieldInfo from shapely.geometry.base import BaseGeometry from whoosh.index import Index @@ -2086,7 +2087,7 @@ class EODataAccessGateway: self, provider: Optional[str] = None, product_type: Optional[str] = None, - ) -> Dict[str, Tuple[Annotated, Any]]: + ) -> Dict[str, Tuple[Annotated[Any, FieldInfo], Any]]: """Fetch the queryable properties for a given product type and/or provider. :param provider: (optional) The provider. @@ -2096,7 +2097,7 @@ class EODataAccessGateway: :returns: A dict containing the EODAG queryable properties, associating parameters to a tuple containing their annotaded type and default value - :rtype: Dict[str, Tuple[Annotated, Any]] + :rtype: Dict[str, Tuple[Annotated[Any, FieldInfo], Any]] """ # unknown product type if product_type is not None and product_type not in self.list_product_types( @@ -2106,7 +2107,7 @@ class EODataAccessGateway: # dictionary of the queryable properties of the providers supporting the given product type providers_available_queryables: Dict[ - str, Dict[str, Tuple[Annotated, Any]] + str, Dict[str, Tuple[Annotated[Any, FieldInfo], Any]] ] = dict() if provider is None and product_type is None: diff --git a/eodag/config.py b/eodag/config.py index 237ad87c..bba53bad 100644 --- a/eodag/config.py +++ b/eodag/config.py @@ -20,8 +20,8 @@ from __future__ import annotations import logging import os import tempfile +from inspect import isclass from typing import ( - TYPE_CHECKING, Any, Dict, ItemsView, @@ -32,6 +32,7 @@ from typing import ( TypedDict, Union, ValuesView, + get_type_hints, ) import orjson @@ -39,13 +40,16 @@ import requests import yaml import yaml.constructor import yaml.parser +from jsonpath_ng import JSONPath from pkg_resources import resource_filename +from requests.auth import AuthBase from eodag.utils import ( HTTP_REQ_TIMEOUT, USER_AGENT, cached_yaml_load, cached_yaml_load_all, + cast_scalar_value, deepcopy, dict_items_recursive_apply, merge_mappings, @@ -56,10 +60,6 @@ from eodag.utils import ( ) from eodag.utils.exceptions import ValidationError -if TYPE_CHECKING: - from jsonpath_ng import JSONPath - from requests.auth import AuthBase - logger = logging.getLogger("eodag.config") EXT_PRODUCT_TYPES_CONF_URI = ( @@ -250,7 +250,7 @@ class PluginConfig(yaml.YAMLObject): need_auth: bool result_type: str results_entry: str - pagination: Pagination + pagination: PluginConfig.Pagination query_params_key: str discover_metadata: Dict[str, str] discover_product_types: Dict[str, Any] @@ -266,7 +266,7 @@ class PluginConfig(yaml.YAMLObject): merge_responses: bool # PostJsonSearch for aws_eos collection: bool # PostJsonSearch for aws_eos max_connections: int # StaticStacSearch - timeout: int # StaticStacSearch + timeout: float # StaticStacSearch # download ------------------------------------------------------------------------- base_uri: str @@ -275,7 +275,7 @@ class PluginConfig(yaml.YAMLObject): order_enabled: bool # HTTPDownload order_method: str # HTTPDownload order_headers: Dict[str, str] # HTTPDownload - order_status_on_success: OrderStatusOnSuccess + order_status_on_success: PluginConfig.OrderStatusOnSuccess bucket_path_level: int # S3RestDownload # auth ----------------------------------------------------------------------------- @@ -471,13 +471,38 @@ def override_config_from_env(config: Dict[str, Any]) -> None: :type mapping: dict """ parts = env_var.split("__") - if len(parts) == 1: + iter_parts = iter(parts) + env_type = get_type_hints(PluginConfig).get(next(iter_parts, ""), str) + child_env_type = ( + get_type_hints(env_type).get(next(iter_parts, ""), None) + if isclass(env_type) + else None + ) + if len(parts) == 2 and child_env_type: + # for nested config (pagination, ...) + # try converting env_value type from type hints + try: + env_value = cast_scalar_value(env_value, child_env_type) + except TypeError: + logger.warning( + f"Could not convert {parts} value {env_value} to {child_env_type}" + ) + mapping.setdefault(parts[0], {}) + mapping[parts[0]][parts[1]] = env_value + elif len(parts) == 1: + # try converting env_value type from type hints + try: + env_value = cast_scalar_value(env_value, env_type) + except TypeError: + logger.warning( + f"Could not convert {parts[0]} value {env_value} to {env_type}" + ) mapping[parts[0]] = env_value else: new_map = mapping.setdefault(parts[0], {}) build_mapping_from_env("__".join(parts[1:]), env_value, new_map) - mapping_from_env = {} + mapping_from_env: Dict[str, Any] = {} for env_var in os.environ: if env_var.startswith("EODAG__"): build_mapping_from_env( @@ -500,7 +525,6 @@ def override_config_from_mapping( :type mapping: dict """ for provider, new_conf in mapping.items(): - new_conf: Dict[str, Any] old_conf: Optional[Dict[str, Any]] = config.get(provider) if old_conf is not None: old_conf.update(new_conf) diff --git a/eodag/plugins/apis/base.py b/eodag/plugins/apis/base.py index cd65bb26..ab7f1497 100644 --- a/eodag/plugins/apis/base.py +++ b/eodag/plugins/apis/base.py @@ -21,6 +21,8 @@ import logging from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple if TYPE_CHECKING: + from pydantic.fields import FieldInfo + from eodag.api.product import EOProduct from eodag.api.search_result import SearchResult from eodag.config import PluginConfig @@ -95,7 +97,7 @@ class Api(PluginTopic): def discover_queryables( self, product_type: Optional[str] = None - ) -> Optional[Dict[str, Tuple[Annotated, Any]]]: + ) -> Optional[Dict[str, Tuple[Annotated[Any, FieldInfo], Any]]]: """Fetch queryables list from provider using `discover_queryables` conf""" return None diff --git a/eodag/plugins/apis/usgs.py b/eodag/plugins/apis/usgs.py index 69443435..b306f2e3 100644 --- a/eodag/plugins/apis/usgs.py +++ b/eodag/plugins/apis/usgs.py @@ -327,7 +327,9 @@ class UsgsApi(Download, Api): stream.raise_for_status() except RequestException as e: if e.response and hasattr(e.response, "content"): - error_message = f"{e.response.content} - {e}" + error_message = ( + f"{e.response.content.decode('utf-8')} - {e}" + ) else: error_message = str(e) raise NotAvailableError(error_message) @@ -341,7 +343,7 @@ class UsgsApi(Download, Api): progress_callback(len(chunk)) except requests.exceptions.Timeout as e: if e.response and hasattr(e.response, "content"): - error_message = f"{e.response.content} - {e}" + error_message = f"{e.response.content.decode('utf-8')} - {e}" else: error_message = str(e) raise NotAvailableError(error_message) diff --git a/eodag/plugins/download/http.py b/eodag/plugins/download/http.py index 2d419fcf..0f5515c6 100644 --- a/eodag/plugins/download/http.py +++ b/eodag/plugins/download/http.py @@ -104,7 +104,9 @@ class HTTPDownload(Download): super(HTTPDownload, self).__init__(provider, config) if not hasattr(self.config, "base_uri"): raise MisconfiguredError( - "{} plugin require a base_uri configuration key".format(self.__name__) + "{} plugin require a base_uri configuration key".format( + type(self).__name__ + ) ) def orderDownload( @@ -166,10 +168,14 @@ class HTTPDownload(Download): logger.debug(ordered_message) logger.info("%s was ordered", product.properties["title"]) except RequestException as e: + if e.response and hasattr(e.response, "content"): + error_message = f"{e.response.content.decode('utf-8')} - {e}" + else: + error_message = str(e) logger.warning( "%s could not be ordered, request returned %s", product.properties["title"], - f"{e.response.content} - {e}", + error_message, ) order_metadata_mapping = getattr(self.config, "order_on_response", {}).get( @@ -177,9 +183,8 @@ class HTTPDownload(Download): ) if order_metadata_mapping: logger.debug("Parsing order response to update product metada-mapping") - order_metadata_mapping_jsonpath = {} order_metadata_mapping_jsonpath = mtd_cfg_as_conversion_and_querypath( - order_metadata_mapping, order_metadata_mapping_jsonpath + order_metadata_mapping, ) properties_update = properties_from_json( response.json(), diff --git a/eodag/plugins/search/base.py b/eodag/plugins/search/base.py index 02420873..fed7ca11 100644 --- a/eodag/plugins/search/base.py +++ b/eodag/plugins/search/base.py @@ -33,6 +33,8 @@ from eodag.utils import ( ) if TYPE_CHECKING: + from pydantic.fields import FieldInfo + from eodag.api.product import EOProduct from eodag.config import PluginConfig from eodag.utils import Annotated @@ -90,7 +92,7 @@ class Search(PluginTopic): def discover_queryables( self, product_type: Optional[str] = None - ) -> Optional[Dict[str, Tuple[Annotated, Any]]]: + ) -> Optional[Dict[str, Tuple[Annotated[Any, FieldInfo], Any]]]: """Fetch queryables list from provider using `discover_queryables` conf""" return None diff --git a/eodag/plugins/search/qssearch.py b/eodag/plugins/search/qssearch.py index ea7a8cc4..f398b44b 100644 --- a/eodag/plugins/search/qssearch.py +++ b/eodag/plugins/search/qssearch.py @@ -1270,7 +1270,7 @@ class StacSearch(PostJsonSearch): def discover_queryables( self, product_type: Optional[str] = None - ) -> Optional[Dict[str, Tuple[Annotated, Any]]]: + ) -> Optional[Dict[str, Tuple[Annotated[Any, FieldInfo], Any]]]: """Fetch queryables list from provider using `discover_queryables` conf :param product_type: (optional) product type diff --git a/eodag/rest/types/stac_queryables.py b/eodag/rest/types/stac_queryables.py index 6a4139fe..e395ea97 100644 --- a/eodag/rest/types/stac_queryables.py +++ b/eodag/rest/types/stac_queryables.py @@ -17,13 +17,16 @@ # limitations under the License. from __future__ import annotations -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union from pydantic import BaseModel, Field from eodag.types import python_field_definition_to_json from eodag.utils import Annotated +if TYPE_CHECKING: + from pydantic.fields import FieldInfo + class StacQueryableProperty(BaseModel): """A class representing a queryable property. @@ -49,7 +52,7 @@ class StacQueryableProperty(BaseModel): @classmethod def from_python_field_definition( - cls, id: str, python_field_definition: Tuple[Annotated, Any] + cls, id: str, python_field_definition: Tuple[Annotated[Any, FieldInfo], Any] ) -> StacQueryableProperty: """Build Model from python_field_definition""" def_dict = python_field_definition_to_json(python_field_definition) diff --git a/eodag/types/__init__.py b/eodag/types/__init__.py index 02b5f98f..0e7b1b5a 100644 --- a/eodag/types/__init__.py +++ b/eodag/types/__init__.py @@ -122,7 +122,7 @@ def json_field_definition_to_python( def python_field_definition_to_json( - python_field_definition: Tuple[Annotated, Any] + python_field_definition: Tuple[Annotated[Any, FieldInfo], Any] ) -> Dict[str, Any]: """Get json field definition from python `typing.Annotated` @@ -144,7 +144,8 @@ def python_field_definition_to_json( or len(python_field_definition) != 2 ): raise ValidationError( - "%s must be an instance of Tuple[Annotated, Any]" % python_field_definition + "%s must be an instance of Tuple[Annotated[Any, FieldInfo], Any]" + % python_field_definition ) python_field_annotated = python_field_definition[0] @@ -196,7 +197,7 @@ def python_field_definition_to_json( def model_fields_to_annotated_tuple( model_fields: Dict[str, FieldInfo] -) -> Dict[str, Tuple[Annotated, Any]]: +) -> Dict[str, Tuple[Annotated[Any, FieldInfo], Any]]: """Convert BaseModel.model_fields from FieldInfo to Annotated tuple usable as create_model argument >>> from pydantic import create_model diff --git a/eodag/utils/__init__.py b/eodag/utils/__init__.py index 08827ee8..e5918e53 100644 --- a/eodag/utils/__init__.py +++ b/eodag/utils/__init__.py @@ -373,29 +373,9 @@ def merge_mappings(mapping1: Dict[Any, Any], mapping2: Dict[Any, Any]) -> None: and current_value_type == list ): mapping1[m1_keys_lowercase.get(key, key)] = value - elif isinstance(value, str): - # Bool is a type with special meaning in Python, thus the special - # case - if current_value_type is bool: - if value.capitalize() not in ("True", "False"): - raise ValueError( - "Only true or false strings (case insensitive) are " - "allowed for booleans" - ) - # Get the real Python value of the boolean. e.g: value='tRuE' - # => eval(value.capitalize())=True. - # str.capitalize() transforms the first character of the string - # to a capital letter - mapping1[m1_keys_lowercase.get(key, key)] = eval( - value.capitalize() - ) - else: - mapping1[ - m1_keys_lowercase.get(key, key) - ] = current_value_type(value) else: - mapping1[m1_keys_lowercase.get(key, key)] = current_value_type( - value + mapping1[m1_keys_lowercase.get(key, key)] = cast_scalar_value( + value, current_value_type ) except (TypeError, ValueError): # Ignore any override value that does not have the same type @@ -1398,3 +1378,34 @@ def parse_header(header: str) -> Message: m = Message() m["content-type"] = header return m + + +def cast_scalar_value(value: Any, new_type: Any) -> Any: + """Convert a scalar (not nested) value type to the given one + + >>> cast_scalar_value('1', int) + 1 + >>> cast_scalar_value(1, str) + '1' + >>> cast_scalar_value('false', bool) + False + + :param value: the scalar value to convert + :param new_type: the wanted type + :returns: scalar value converted to new_type + """ + if isinstance(value, str) and new_type is bool: + # Bool is a type with special meaning in Python, thus the special + # case + if value.capitalize() not in ("True", "False"): + raise ValueError( + "Only true or false strings (case insensitive) are " + "allowed for booleans" + ) + # Get the real Python value of the boolean. e.g: value='tRuE' + # => eval(value.capitalize())=True. + # str.capitalize() transforms the first character of the string + # to a capital letter + return eval(value.capitalize()) + + return new_type(value)	convert timeout type when loaded from environment variable Following #973, ```py import os from eodag import EODataAccessGateway os.environ["EODAG__HYDROWEB_NEXT__SEARCH__TIMEOUT"] = "10" dag = EODataAccessGateway() dag.search_all(productType="HYDROWEB_RIVERS") ``` raises ``` Traceback (most recent call last): File "/home/msordi/git/git-hysope2/deployment/misc/stac/test_download_hydroweb0.py", line 75, in <module> search_results = dag.search_all( File "/home/msordi/.local/lib/python3.10/site-packages/eodag/api/core.py", line 1219, in search_all self.fetch_product_types_list() File "/home/msordi/.local/lib/python3.10/site-packages/eodag/api/core.py", line 638, in fetch_product_types_list provider_ext_product_types_conf = self.discover_product_types( File "/home/msordi/.local/lib/python3.10/site-packages/eodag/api/core.py", line 699, in discover_product_types ] = search_plugin.discover_product_types() File "/home/msordi/.local/lib/python3.10/site-packages/eodag/plugins/search/qssearch.py", line 286, in discover_product_types response = QueryStringSearch._request( File "/home/msordi/.local/lib/python3.10/site-packages/eodag/plugins/search/qssearch.py", line 800, in _request response = requests.get( File "/usr/lib/python3/dist-packages/requests/api.py", line 76, in get return request('get', url, params=params, kwargs) File "/usr/lib/python3/dist-packages/requests/api.py", line 61, in request return session.request(method=method, url=url, kwargs) File "/usr/lib/python3/dist-packages/requests/sessions.py", line 544, in request resp = self.send(prep, send_kwargs) File "/usr/lib/python3/dist-packages/requests/sessions.py", line 657, in send r = adapter.send(request, kwargs) File "/usr/lib/python3/dist-packages/requests/adapters.py", line 435, in send timeout = TimeoutSauce(connect=timeout, read=timeout) File "/usr/lib/python3/dist-packages/urllib3/util/timeout.py", line 103, in __init__ self._connect = self._validate_timeout(connect, "connect") File "/usr/lib/python3/dist-packages/urllib3/util/timeout.py", line 158, in _validate_timeout raise ValueError( ValueError: Timeout value connect was 10, but it must be an int, float or None. ``` - [x] `timeout` loaded from environment variable needs to be converted to `float`	CS-SI/eodag	diff --git a/tests/test_config.py b/tests/test_config.py index 65b302d5..e819986e 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -437,6 +437,11 @@ class TestConfigFunctions(unittest.TestCase): "EODAG__AWS_EOS__AUTH__CREDENTIALS__AWS_SECRET_ACCESS_KEY" ] = "secret-access-key" os.environ["EODAG__PEPS__DOWNLOAD__OUTPUTS_PREFIX"] = "/data" + # check a parameter that has not been set yet + self.assertFalse(hasattr(default_config["peps"].search, "timeout")) + self.assertNotIn("start_page", default_config["peps"].search.pagination) + os.environ["EODAG__PEPS__SEARCH__TIMEOUT"] = "3.1" + os.environ["EODAG__PEPS__SEARCH__PAGINATION__START_PAGE"] = "2" config.override_config_from_env(default_config) usgs_conf = default_config["usgs"] @@ -457,6 +462,8 @@ class TestConfigFunctions(unittest.TestCase): peps_conf = default_config["peps"] self.assertEqual(peps_conf.download.outputs_prefix, "/data") + self.assertEqual(peps_conf.search.timeout, 3.1) + self.assertEqual(peps_conf.search.pagination["start_page"], 2) @mock.patch("requests.get", autospec=True) def test_get_ext_product_types_conf(self, mock_get):	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_issue_reference", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 1 }, "num_modified_files": 10 }	2.11	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov pytest-xdist pytest-mock" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements/base.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	annotated-types==0.7.0 anyio==4.9.0 attrs==25.3.0 backports.tarfile==1.2.0 boto3==1.37.23 botocore==1.37.23 cachetools==5.5.2 cdsapi==0.7.5 certifi==2025.1.31 cffi==1.17.1 cfgv==3.4.0 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 coverage==7.8.0 cryptography==44.0.2 datapi==0.3.0 decorator==5.2.1 distlib==0.3.9 dnspython==2.7.0 docutils==0.21.2 ecmwf-api-client==1.6.5 email_validator==2.2.0 -e git+https://github.com/CS-SI/eodag.git@fe2a51497fa806f856e9bbb4e3911e7c78dad17e#egg=eodag exceptiongroup==1.2.2 execnet==2.1.1 Faker==37.1.0 fastapi==0.115.12 fastapi-cli==0.0.7 filelock==3.18.0 flake8==7.2.0 geojson==3.2.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 id==1.5.0 identify==2.6.9 idna==3.10 importlib_metadata==8.6.1 iniconfig==2.1.0 itsdangerous==2.2.0 jaraco.classes==3.4.0 jaraco.context==6.0.1 jaraco.functools==4.1.0 jeepney==0.9.0 Jinja2==3.1.6 jmespath==1.0.1 jsonpath-ng==1.5.3 keyring==25.6.0 lxml==5.3.1 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mccabe==0.7.0 mdurl==0.1.2 more-itertools==10.6.0 moto==5.1.2 multiurl==0.3.5 nh3==0.2.21 nodeenv==1.9.1 numpy==2.0.2 orjson==3.10.16 OWSLib==0.31.0 packaging==24.2 platformdirs==4.3.7 pluggy==1.5.0 ply==3.11 pre_commit==4.2.0 py==1.11.0 pycodestyle==2.13.0 pycparser==2.22 pycryptodome==3.22.0 pydantic==2.11.1 pydantic-extra-types==2.10.3 pydantic-settings==2.8.1 pydantic_core==2.33.0 pyflakes==3.3.2 Pygments==2.19.1 pyproj==3.6.1 pyproject-api==1.9.0 pyshp==2.3.1 pystac==1.10.1 pytest==8.3.5 pytest-cov==6.0.0 pytest-html==3.1.1 pytest-instafail==0.5.0 pytest-metadata==3.1.1 pytest-mock==3.14.0 pytest-socket==0.7.0 pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 python-dotenv==1.1.0 python-multipart==0.0.20 pytz==2025.2 PyYAML==6.0.2 readme_renderer==44.0 requests==2.32.3 requests-ftp==0.3.1 requests-futures==1.0.2 requests-toolbelt==1.0.0 responses==0.23.3 rfc3986==2.0.0 rich==14.0.0 rich-toolkit==0.14.1 s3transfer==0.11.4 SecretStorage==3.3.3 shapely==2.0.7 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 starlette==0.46.1 stream-zip==0.0.83 tomli==2.2.1 tox==4.25.0 tqdm==4.67.1 twine==6.1.0 typer==0.15.2 types-PyYAML==6.0.12.20250326 typing-inspection==0.4.0 typing_extensions==4.13.0 tzdata==2025.2 ujson==5.10.0 urllib3==1.26.20 usgs==0.3.5 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 watchfiles==1.0.4 websockets==15.0.1 Werkzeug==3.1.3 Whoosh==2.7.4 xmltodict==0.14.2 zipp==3.21.0	name: eodag channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - annotated-types==0.7.0 - anyio==4.9.0 - attrs==25.3.0 - backports-tarfile==1.2.0 - boto3==1.37.23 - botocore==1.37.23 - cachetools==5.5.2 - cdsapi==0.7.5 - certifi==2025.1.31 - cffi==1.17.1 - cfgv==3.4.0 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - coverage==7.8.0 - cryptography==44.0.2 - datapi==0.3.0 - decorator==5.2.1 - distlib==0.3.9 - dnspython==2.7.0 - docutils==0.21.2 - ecmwf-api-client==1.6.5 - email-validator==2.2.0 - eodag==2.11.1.dev26+gfe2a5149 - exceptiongroup==1.2.2 - execnet==2.1.1 - faker==37.1.0 - fastapi==0.115.12 - fastapi-cli==0.0.7 - filelock==3.18.0 - flake8==7.2.0 - geojson==3.2.0 - h11==0.14.0 - httpcore==1.0.7 - httptools==0.6.4 - httpx==0.28.1 - id==1.5.0 - identify==2.6.9 - idna==3.10 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - itsdangerous==2.2.0 - jaraco-classes==3.4.0 - jaraco-context==6.0.1 - jaraco-functools==4.1.0 - jeepney==0.9.0 - jinja2==3.1.6 - jmespath==1.0.1 - jsonpath-ng==1.5.3 - keyring==25.6.0 - lxml==5.3.1 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mccabe==0.7.0 - mdurl==0.1.2 - more-itertools==10.6.0 - moto==5.1.2 - multiurl==0.3.5 - nh3==0.2.21 - nodeenv==1.9.1 - numpy==2.0.2 - orjson==3.10.16 - owslib==0.31.0 - packaging==24.2 - platformdirs==4.3.7 - pluggy==1.5.0 - ply==3.11 - pre-commit==4.2.0 - py==1.11.0 - pycodestyle==2.13.0 - pycparser==2.22 - pycryptodome==3.22.0 - pydantic==2.11.1 - pydantic-core==2.33.0 - pydantic-extra-types==2.10.3 - pydantic-settings==2.8.1 - pyflakes==3.3.2 - pygments==2.19.1 - pyproj==3.6.1 - pyproject-api==1.9.0 - pyshp==2.3.1 - pystac==1.10.1 - pytest==8.3.5 - pytest-cov==6.0.0 - pytest-html==3.1.1 - pytest-instafail==0.5.0 - pytest-metadata==3.1.1 - pytest-mock==3.14.0 - pytest-socket==0.7.0 - pytest-xdist==3.6.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - python-multipart==0.0.20 - pytz==2025.2 - pyyaml==6.0.2 - readme-renderer==44.0 - requests==2.32.3 - requests-ftp==0.3.1 - requests-futures==1.0.2 - requests-toolbelt==1.0.0 - responses==0.23.3 - rfc3986==2.0.0 - rich==14.0.0 - rich-toolkit==0.14.1 - s3transfer==0.11.4 - secretstorage==3.3.3 - shapely==2.0.7 - shellingham==1.5.4 - six==1.17.0 - sniffio==1.3.1 - starlette==0.46.1 - stream-zip==0.0.83 - tomli==2.2.1 - tox==4.25.0 - tqdm==4.67.1 - twine==6.1.0 - typer==0.15.2 - types-pyyaml==6.0.12.20250326 - typing-extensions==4.13.0 - typing-inspection==0.4.0 - tzdata==2025.2 - ujson==5.10.0 - urllib3==1.26.20 - usgs==0.3.5 - uvicorn==0.34.0 - uvloop==0.21.0 - virtualenv==20.29.3 - watchfiles==1.0.4 - websockets==15.0.1 - werkzeug==3.1.3 - whoosh==2.7.4 - xmltodict==0.14.2 - zipp==3.21.0 prefix: /opt/conda/envs/eodag	[ "tests/test_config.py::TestConfigFunctions::test_override_config_from_env" ]	[]	[ "tests/test_config.py::TestProviderConfig::test_provider_config_merge", "tests/test_config.py::TestProviderConfig::test_provider_config_name", "tests/test_config.py::TestProviderConfig::test_provider_config_update", "tests/test_config.py::TestProviderConfig::test_provider_config_valid", "tests/test_config.py::TestPluginConfig::test_plugin_config_update", "tests/test_config.py::TestPluginConfig::test_plugin_config_valid", "tests/test_config.py::TestConfigFunctions::test_get_ext_product_types_conf", "tests/test_config.py::TestConfigFunctions::test_load_default_config", "tests/test_config.py::TestConfigFunctions::test_override_config_from_file", "tests/test_config.py::TestConfigFunctions::test_override_config_from_str", "tests/test_config.py::TestStacProviderConfig::test_custom_stac_provider_conf", "tests/test_config.py::TestStacProviderConfig::test_existing_stac_provider_conf" ]	[]	Apache License 2.0	null
CSCfi__beacon-python-26	49539e81197255007a6978c102b4a7eb1cf16b9f	2018-10-29 13:14:17	8e6c74787741d3025897cc6f744a13449a78041e		diff --git a/beacon_api/__init__.py b/beacon_api/__init__.py index f20f8a5..42894f9 100644 --- a/beacon_api/__init__.py +++ b/beacon_api/__init__.py @@ -28,4 +28,4 @@ __org_address__ = 'empty' __org_welcomeUrl__ = 'https://www.csc.fi/' __org_contactUrl__ = 'empty' __org_logoUrl__ = 'https://www.csc.fi/documents/10180/161914/CSC_2012_LOGO_RGB_72dpi.jpg' -__org_info__ = [{'author': __author__}] +__org_info__ = {'author': __author__} diff --git a/beacon_api/api/info.py b/beacon_api/api/info.py index 341c89f..631cab1 100644 --- a/beacon_api/api/info.py +++ b/beacon_api/api/info.py @@ -71,7 +71,7 @@ async def beacon_info(host, pool): 'updateDateTime': __updatetime__, 'datasets': beacon_dataset, 'sampleAlleleRequests': sample_allele_request, - 'info': [{"key": "value"}] + 'info': {} } return beacon_info diff --git a/beacon_api/schemas/info.json b/beacon_api/schemas/info.json index b973857..cef1abc 100644 --- a/beacon_api/schemas/info.json +++ b/beacon_api/schemas/info.json @@ -58,10 +58,7 @@ "pattern": "^(.)$" }, "info": { - "type": "array", - "items": { - "type": "object" - } + "type": "object" } } }, @@ -146,10 +143,7 @@ "pattern": "^(.)$" }, "info": { - "type": "array", - "items": { - "type": "object" - } + "type": "object" }, "dataUseConditions": { "type": "object", @@ -831,10 +825,7 @@ } }, "info": { - "type": "array", - "items": { - "type": "object" - } + "type": "object" } } } diff --git a/beacon_api/schemas/response.json b/beacon_api/schemas/response.json index 060d078..ee2ad67 100644 --- a/beacon_api/schemas/response.json +++ b/beacon_api/schemas/response.json @@ -110,6 +110,14 @@ "exists": { "type": "boolean" }, + "referenceBases": { + "type": "string", + "pattern": "^([ACGT]+)$" + }, + "alternateBases": { + "type": "string", + "pattern": "^([ACGT]+)$" + }, "error": { "type": "object", "required": [ @@ -152,11 +160,7 @@ "pattern": "^(.*)$" }, "info": { - "type": "array", - "default": [], - "items": { - "type": "object" - } + "type": "object" } } } diff --git a/beacon_api/utils/data_query.py b/beacon_api/utils/data_query.py index 35107ee..84312f8 100644 --- a/beacon_api/utils/data_query.py +++ b/beacon_api/utils/data_query.py @@ -19,8 +19,10 @@ def sql_tuple(array): def transform_record(record): """Format the record we got from the database to adhere to the response schema.""" response = dict(record) + response["referenceBases"] = response.pop("referenceBases") + response["alternateBases"] = response.pop("alternateBases") response["frequency"] = round(response.pop("frequency"), 9) - response["info"] = [{"accessType": response.pop("accessType")}] + response["info"] = {"accessType": response.pop("accessType")} # Error is not required and should not be shown # otherwise schema validation will fail # response["error"] = None @@ -31,11 +33,13 @@ def transform_record(record): def transform_misses(record): """Format the missed datasets record we got from the database to adhere to the response schema.""" response = dict(record) + response["referenceBases"] = '' + response["alternateBases"] = '' response["frequency"] = 0 response["variantCount"] = 0 response["callCount"] = 0 response["sampleCount"] = 0 - response["info"] = [{"accessType": response.pop("accessType")}] + response["info"] = {"accessType": response.pop("accessType")} # Error is not required and should not be shown # otherwise schema validation will fail # response["error"] = None @@ -46,7 +50,7 @@ def transform_misses(record): def transform_metadata(record): """Format the metadata record we got from the database to adhere to the response schema.""" response = dict(record) - response["info"] = [{"accessType": response.pop("accessType")}] + response["info"] = {"accessType": response.pop("accessType")} # TO DO test with null date if 'createDateTime' in response and isinstance(response["createDateTime"], datetime): response["createDateTime"] = response.pop("createDateTime").strftime('%Y-%m-%dT%H:%M:%SZ') @@ -128,7 +132,7 @@ async def fetch_filtered_dataset(db_pool, position, chromosome, reference, alter # UBER QUERY - TBD if it is what we need query = f"""SELECT {"DISTINCT ON (a.datasetId)" if misses else ''} a.datasetId as "datasetId", b.accessType as "accessType", - a.chromosome as "referenceName", + a.chromosome as "referenceName", a.reference as "referenceBases", a.alternate as "alternateBases", b.externalUrl as "externalUrl", b.description as "note", a.variantCount as "variantCount", a.callCount as "callCount", b.sampleCount as "sampleCount",	Differentiate wildcard results #### Proposed solution Currently in a wildcard search it is difficult to differentiate between wildcard results, as illustrated. ![image](https://user-images.githubusercontent.com/47524/47644992-5c8b6d80-db78-11e8-813f-e4454c681819.png) The idea is to add values in the `info` key to differentiate between such results. Results of search are: ``` CT > AT (allele count: 1) CT > TC (allele count: 1) CT > TT (allele count: 118) ``` `info` key could contain something like: ``` "info": { "referenceBases": "CT", "alternateBases": "AT" } ``` #### DoD (Definition of Done) Added values to `info` key to be used in UI. Updated JSON schema. #### Testing Unit test updated.	CSCfi/beacon-python	diff --git a/tests/test_data_query.py b/tests/test_data_query.py index f87248c..a12ad10 100644 --- a/tests/test_data_query.py +++ b/tests/test_data_query.py @@ -13,14 +13,17 @@ class Record: Mimic asyncpg Record object. """ - def __init__(self, accessType, frequency=None, createDateTime=None, updateDateTime=None): + def __init__(self, accessType, frequency=None, createDateTime=None, updateDateTime=None, referenceBases=None, alternateBases=None): """Initialise things.""" self.data = {"accessType": accessType} + if referenceBases: + self.data.update({"referenceBases": referenceBases}) + if alternateBases: + self.data.update({"alternateBases": alternateBases}) if frequency: self.data.update({"frequency": frequency}) if createDateTime: self.data.update({"createDateTime": createDateTime}) - if updateDateTime: self.data.update({"updateDateTime": updateDateTime}) @@ -77,15 +80,17 @@ class TestDataQueryFunctions(asynctest.TestCase): def test_transform_record(self): """Test transform DB record.""" - response = {"frequency": 0.009112876, "info": [{"accessType": "PUBLIC"}]} - record = Record("PUBLIC", 0.009112875989879) + response = {"frequency": 0.009112876, "info": {"accessType": "PUBLIC"}, + "referenceBases": "CT", "alternateBases": "AT"} + record = Record("PUBLIC", 0.009112875989879, referenceBases="CT", alternateBases="AT") result = transform_record(record) self.assertEqual(result, response) def test_transform_misses(self): """Test transform misses record.""" - response = {"frequency": 0, "callCount": 0, "sampleCount": 0, "variantCount": 0, - "info": [{"accessType": "PUBLIC"}]} + response = {"referenceBases": '', "alternateBases": '', + "frequency": 0, "callCount": 0, "sampleCount": 0, "variantCount": 0, + "info": {"accessType": "PUBLIC"}} record = Record("PUBLIC") result = transform_misses(record) print(result) @@ -94,9 +99,9 @@ class TestDataQueryFunctions(asynctest.TestCase): def test_transform_metadata(self): """Test transform medata record.""" response = {"createDateTime": "2018-10-20T20:33:40Z", "updateDateTime": "2018-10-20T20:33:40Z", - "info": [{"accessType": "PUBLIC"}]} - record = Record("PUBLIC", None, datetime.strptime("2018-10-20 20:33:40+00", '%Y-%m-%d %H:%M:%S+00'), - datetime.strptime("2018-10-20 20:33:40+00", '%Y-%m-%d %H:%M:%S+00')) + "info": {"accessType": "PUBLIC"}} + record = Record("PUBLIC", createDateTime=datetime.strptime("2018-10-20 20:33:40+00", '%Y-%m-%d %H:%M:%S+00'), + updateDateTime=datetime.strptime("2018-10-20 20:33:40+00", '%Y-%m-%d %H:%M:%S+00')) result = transform_metadata(record) print(result) self.assertEqual(result, response) diff --git a/tests/test_response.py b/tests/test_response.py index d3efd52..1e18e38 100644 --- a/tests/test_response.py +++ b/tests/test_response.py @@ -20,7 +20,7 @@ mock_dataset_metadata = {"id": "id1", "callCount": 0, "sampleCount": 2534, "version": "v0.4", - "info": [{"accessType": "PUBLIC"}], + "info": {"accessType": "PUBLIC"}, "createDateTime": "2013-05-02T12:00:00Z", "updateDateTime": "2013-05-02T12:00:00Z"} @@ -34,7 +34,7 @@ mock_data = [{"datasetId": "id1", "sampleCount": 2534, "exists": True, "frequency": 0.001183899, - "info": [{"accessType": "PUBLIC"}]}, + "info": {"accessType": "PUBLIC"}}, {"datasetId": "id1", "referenceName": "MT", "externalUrl": "url", @@ -44,7 +44,7 @@ mock_data = [{"datasetId": "id1", "sampleCount": 0, "exists": False, "frequency": 0, - "info": [{"accessType": "REGISTERED"}]}] + "info": {"accessType": "REGISTERED"}}] class TestBasicFunctions(asynctest.TestCase):	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_media", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 0, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 5 }	1.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[test]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "coveralls", "testfixtures", "flake8", "flake8-docstrings", "asynctest", "aioresponses" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	aiohttp==3.8.6 aioresponses==0.7.6 aiosignal==1.2.0 async-timeout==4.0.2 asyncpg==0.26.0 asynctest==0.13.0 attrs==22.2.0 -e git+https://github.com/CSCfi/beacon-python.git@49539e81197255007a6978c102b4a7eb1cf16b9f#egg=beacon_api certifi==2021.5.30 cffi==1.15.1 charset-normalizer==2.0.12 click==8.0.4 coloredlogs==15.0.1 coverage==6.2 coveralls==3.3.1 cryptography==40.0.2 Cython==3.0.12 cyvcf2==0.30.18 distlib==0.3.9 docopt==0.6.2 filelock==3.4.1 flake8==5.0.4 flake8-docstrings==1.6.0 frozenlist==1.2.0 humanfriendly==10.0 idna==3.10 idna-ssl==1.1.0 importlib-metadata==4.2.0 importlib-resources==5.4.0 iniconfig==1.1.1 jsonschema==3.0.0a3 mccabe==0.7.0 multidict==5.2.0 numpy==1.19.5 packaging==21.3 platformdirs==2.4.0 pluggy==1.0.0 py==1.11.0 pycodestyle==2.9.1 pycparser==2.21 pydocstyle==6.3.0 pyflakes==2.5.0 PyJWT==2.4.0 pyparsing==3.1.4 pyrsistent==0.18.0 pytest==7.0.1 pytest-cov==4.0.0 requests==2.27.1 six==1.17.0 snowballstemmer==2.2.0 testfixtures==7.2.2 toml==0.10.2 tomli==1.2.3 tox==3.28.0 typing_extensions==4.1.1 urllib3==1.26.20 virtualenv==20.16.2 yarl==1.7.2 zipp==3.6.0	name: beacon-python channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - aiohttp==3.8.6 - aioresponses==0.7.6 - aiosignal==1.2.0 - async-timeout==4.0.2 - asyncpg==0.26.0 - asynctest==0.13.0 - attrs==22.2.0 - cffi==1.15.1 - charset-normalizer==2.0.12 - click==8.0.4 - coloredlogs==15.0.1 - coverage==6.2 - coveralls==3.3.1 - cryptography==40.0.2 - cython==3.0.12 - cyvcf2==0.30.18 - distlib==0.3.9 - docopt==0.6.2 - filelock==3.4.1 - flake8==5.0.4 - flake8-docstrings==1.6.0 - frozenlist==1.2.0 - humanfriendly==10.0 - idna==3.10 - idna-ssl==1.1.0 - importlib-metadata==4.2.0 - importlib-resources==5.4.0 - iniconfig==1.1.1 - jsonschema==3.0.0a3 - mccabe==0.7.0 - multidict==5.2.0 - numpy==1.19.5 - packaging==21.3 - platformdirs==2.4.0 - pluggy==1.0.0 - py==1.11.0 - pycodestyle==2.9.1 - pycparser==2.21 - pydocstyle==6.3.0 - pyflakes==2.5.0 - pyjwt==2.4.0 - pyparsing==3.1.4 - pyrsistent==0.18.0 - pytest==7.0.1 - pytest-cov==4.0.0 - requests==2.27.1 - six==1.17.0 - snowballstemmer==2.2.0 - testfixtures==7.2.2 - toml==0.10.2 - tomli==1.2.3 - tox==3.28.0 - typing-extensions==4.1.1 - urllib3==1.26.20 - virtualenv==20.16.2 - yarl==1.7.2 - zipp==3.6.0 prefix: /opt/conda/envs/beacon-python	[ "Test" ]	[]	[]	[]	Apache License 2.0	null
CSCfi__beacon-python-54	0f810a3789be6bc54614ddf17085655c106e486b	2018-12-19 13:34:39	8e6c74787741d3025897cc6f744a13449a78041e		diff --git a/beacon_api/conf/config.py b/beacon_api/conf/config.py index 832143e..b3aba49 100644 --- a/beacon_api/conf/config.py +++ b/beacon_api/conf/config.py @@ -8,19 +8,6 @@ At this point we also initialize a connection pool that the API is going to use import os import asyncpg -URL = os.environ.get('DATABASE_URL', 'postgresql://localhost:5432').split('/')[2] -POSTGRES = { - 'user': os.environ.get('DATABASE_USER', 'beacon'), - 'password': os.environ.get('DATABASE_PASSWORD', 'beacon'), - 'database': os.environ.get('DATABASE_NAME', 'beacondb'), - 'host': URL, -} - -DB_URL = 'postgresql://{user}:{pw}@{url}/{db}'.format(user=POSTGRES['user'], - pw=POSTGRES['password'], - url=POSTGRES['host'], - db=POSTGRES['database']) - DB_SCHEMA = os.environ.get('DATABASE_SCHEMA', '') DB_SCHEMA += '.' if DB_SCHEMA else '' @@ -30,7 +17,11 @@ async def init_db_pool(): As we will have frequent requests to the database it is recommended to create a connection pool. """ - return await asyncpg.create_pool(dsn=DB_URL, + return await asyncpg.create_pool(host=os.environ.get('DATABASE_URL', 'localhost'), + port=os.environ.get('DATABASE_PORT', '5432'), + user=os.environ.get('DATABASE_USER', 'beacon'), + password=os.environ.get('DATABASE_PASSWORD', 'beacon'), + database=os.environ.get('DATABASE_NAME', 'beacondb'), # initializing with 0 connections allows the web server to # start and also continue to live min_size=0, diff --git a/beacon_api/utils/db_load.py b/beacon_api/utils/db_load.py index 3a9f049..f837616 100644 --- a/beacon_api/utils/db_load.py +++ b/beacon_api/utils/db_load.py @@ -47,20 +47,16 @@ from cyvcf2 import VCF from datetime import datetime -from ..conf.config import DB_URL from .logging import LOG class BeaconDB: """Database connection and operations.""" - def __init__(self, db_url): + def __init__(self): """Start database routines.""" LOG.info('Start database routines') self._conn = None - LOG.info('Fetch database URL from config') - self._db_url = db_url - LOG.info('Database URL has been set -> Connections can now be made') def _transform_vt(self, vt, variant): """Transform variant types.""" @@ -131,7 +127,11 @@ class BeaconDB: """Connect to the database.""" LOG.info('Establish a connection to database') try: - self._conn = await asyncpg.connect(self._db_url) + self._conn = await asyncpg.connect(host=os.environ.get('DATABASE_URL', 'localhost'), + port=os.environ.get('DATABASE_PORT', '5432'), + user=os.environ.get('DATABASE_USER', 'beacon'), + password=os.environ.get('DATABASE_PASSWORD', 'beacon'), + database=os.environ.get('DATABASE_NAME', 'beacondb')) LOG.info('Database connection has been established') except Exception as e: LOG.error(f'AN ERROR OCCURRED WHILE ATTEMPTING TO CONNECT TO DATABASE -> {e}') @@ -266,7 +266,7 @@ async def init_beacon_db(arguments=None): args = parse_arguments(arguments) # Initialise the database connection - db = BeaconDB(DB_URL) + db = BeaconDB() # Connect to the database await db.connection() diff --git a/deploy/docker-compose.yml b/deploy/docker-compose.yml index f766395..3e3780c 100644 --- a/deploy/docker-compose.yml +++ b/deploy/docker-compose.yml @@ -18,7 +18,7 @@ services: depends_on: - postgres environment: - DATABASE_URL: postgresql://postgres:5432 + DATABASE_URL: postgres links: - postgres:postgres ports: diff --git a/docs/instructions.rst b/docs/instructions.rst index c8895b8..b95d387 100644 --- a/docs/instructions.rst +++ b/docs/instructions.rst @@ -17,7 +17,9 @@ the table below. +---------------------+-------------------------------+--------------------------------------------------+ \| ENV \| Default \| Description \| +---------------------+-------------------------------+--------------------------------------------------+ -\| `DATABASE_URL` \| `postgresql://localhost:5432` \| The URL for the PostgreSQL server. \| +\| `DATABASE_URL` \| `localhost` \| The URL for the PostgreSQL server. \| ++---------------------+-------------------------------+--------------------------------------------------+ +\| `DATABASE_PORT` \| `5432` \| The port for the PostgreSQL server. \| +---------------------+-------------------------------+--------------------------------------------------+ \| `DATABASE_NAME` \| `beacondb` \| Name of the database. \| +---------------------+-------------------------------+--------------------------------------------------+ @@ -42,7 +44,8 @@ Setting the necessary environment variables can be done e.g. via the command li .. code-block:: console - $ export DATABASE_URL=postgresql://localhost:5432 + $ export DATABASE_URL=localhost + $ export DATABASE_PORT=5434 $ export DATABASE_NAME=beacondb $ export DATABASE_USER=beacon $ export DATABASE_PASSWORD=beacon	Special characters in database password #### Describe the bug If your db password contains certain special characters , the call to `asyncpg.create_pool` will fail, as some characters are interpreted as a url delimiters. #### To Reproduce Steps to reproduce the behaviour: 1. Have a password containing special characters. 2. Run the beacon. 3. Make a call to the beacon. 4. Get `.../.local/lib/python3.6/site-packages/asyncpg/connect_utils.py", line 187, in _parse_hostlist hostlist_ports.append(int(hostspec_port))` #### Possible solution In `beacon_api/conf/config.py`: ```py async def init_db_pool(): return await asyncpg.create_pool(host=POSTGRES['host'], # eg "localhost" port=POSTGRES['port'], user=POSTGRES['user'], password=POSTGRES['password'], database=POSTGRES['database'], # initializing with 0 connections allows the web server to ... ```	CSCfi/beacon-python	diff --git a/tests/test_db_load.py b/tests/test_db_load.py index 66a0439..ac01147 100644 --- a/tests/test_db_load.py +++ b/tests/test_db_load.py @@ -95,8 +95,7 @@ class DatabaseTestCase(asynctest.TestCase): def setUp(self): """Initialise BeaconDB object.""" - self._db_url = 'http://url.fi' - self._db = BeaconDB(self._db_url) + self._db = BeaconDB() self._dir = TempDirectory() self.data = """##fileformat=VCFv4.0 ##fileDate=20090805 @@ -133,14 +132,14 @@ class DatabaseTestCase(asynctest.TestCase): async def test_connection(self, db_mock): """Test database URL fetching.""" await self._db.connection() - db_mock.connect.assert_called_with(self._db_url) + db_mock.connect.assert_called() @asynctest.mock.patch('beacon_api.utils.db_load.asyncpg.connect') async def test_check_tables(self, db_mock): """Test checking tables.""" db_mock.return_value = Connection() await self._db.connection() - db_mock.assert_called_with(self._db_url) + db_mock.assert_called() result = await self._db.check_tables(['DATATSET1', 'DATATSET2']) # No Missing tables assert result == [] @@ -155,7 +154,7 @@ class DatabaseTestCase(asynctest.TestCase): PRIMARY KEY (id));""" db_mock.return_value = Connection() await self._db.connection() - db_mock.assert_called_with(self._db_url) + db_mock.assert_called() sql_file = self._dir.write('sql.init', sql.encode('utf-8')) await self._db.create_tables(sql_file) # Should assert logs @@ -177,7 +176,7 @@ class DatabaseTestCase(asynctest.TestCase): "accessType": "PUBLIC"}""" db_mock.return_value = Connection() await self._db.connection() - db_mock.assert_called_with(self._db_url) + db_mock.assert_called() metafile = self._dir.write('data.json', metadata.encode('utf-8')) vcf = asynctest.mock.MagicMock(name='samples') vcf.samples.return_value = [1, 2, 3] @@ -195,7 +194,7 @@ class DatabaseTestCase(asynctest.TestCase): vcf.return_value = [{'record': 1}, {'record': 2}, {'records': 3}] vcf.samples.return_value = [{'record': 1}, {'record': 2}, {'records': 3}] await self._db.connection() - db_mock.assert_called_with(self._db_url) + db_mock.assert_called() await self._db.load_datafile(vcf, self.datafile, 'DATASET1') # Should assert logs mock_log.info.mock_calls = [f'Read data from {self.datafile}', @@ -207,16 +206,17 @@ class DatabaseTestCase(asynctest.TestCase): """Test load_datafile.""" db_mock.return_value = Connection() await self._db.connection() - db_mock.assert_called_with(self._db_url) + db_mock.assert_called() await self._db.insert_variants('DATASET1', ['C'], 1) # Should assert logs mock_log.info.mock_calls = [f'Received 1 variants for insertion to DATASET1', 'Insert variants into the database'] - def test_bad_init(self): - """Capture error in case of anything wrong with initializing BeaconDB.""" - with self.assertRaises(TypeError): - BeaconDB() + # This was the case when BeaconDB() was initiated with a URL parameter, now it happens with environment variables + # def test_bad_init(self): + # """Capture error in case of anything wrong with initializing BeaconDB.""" + # with self.assertRaises(TypeError): + # BeaconDB() @asynctest.mock.patch('beacon_api.utils.db_load.LOG') @asynctest.mock.patch('beacon_api.utils.db_load.asyncpg.connect')	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 3 }, "num_modified_files": 4 }	1.0	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio", "testfixtures", "flake8", "flake8-docstrings", "asynctest", "aioresponses" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.6", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	aiocache==0.12.3 aiohttp==3.8.6 aiohttp-cors==0.7.0 aiomcache==0.6.0 aioresponses==0.7.6 aiosignal==1.2.0 async-timeout==4.0.2 asyncpg==0.26.0 asynctest==0.13.0 attrs==22.2.0 -e git+https://github.com/CSCfi/beacon-python.git@0f810a3789be6bc54614ddf17085655c106e486b#egg=beacon_api certifi==2021.5.30 cffi==1.15.1 charset-normalizer==3.0.1 click==8.0.4 coloredlogs==15.0.1 coverage==6.2 cryptography==40.0.2 Cython==3.0.12 cyvcf2==0.30.18 distlib==0.3.9 ecdsa==0.19.1 execnet==1.9.0 filelock==3.4.1 flake8==5.0.4 flake8-docstrings==1.6.0 frozenlist==1.2.0 gunicorn==21.2.0 humanfriendly==10.0 idna==3.10 idna-ssl==1.1.0 importlib-metadata==4.2.0 importlib-resources==5.4.0 iniconfig==1.1.1 jsonschema==3.0.0a3 mccabe==0.7.0 multidict==5.2.0 numpy==1.19.5 packaging==21.3 platformdirs==2.4.0 pluggy==1.0.0 py==1.11.0 pyasn1==0.4.8 pycodestyle==2.9.1 pycparser==2.21 pydocstyle==6.3.0 pyflakes==2.5.0 pyparsing==3.1.4 pyrsistent==0.18.0 pytest==7.0.1 pytest-asyncio==0.16.0 pytest-cov==4.0.0 pytest-mock==3.6.1 pytest-xdist==3.0.2 python-jose==3.4.0 rsa==4.9 six==1.17.0 snowballstemmer==2.2.0 testfixtures==7.2.2 toml==0.10.2 tomli==1.2.3 tox==3.28.0 typing_extensions==4.1.1 ujson==4.3.0 uvloop==0.14.0 virtualenv==20.17.1 yarl==1.7.2 zipp==3.6.0	name: beacon-python channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2021.5.30=py36h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=21.2.2=py36h06a4308_0 - python=3.6.13=h12debd9_1 - readline=8.2=h5eee18b_0 - setuptools=58.0.4=py36h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.37.1=pyhd3eb1b0_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - aiocache==0.12.3 - aiohttp==3.8.6 - aiohttp-cors==0.7.0 - aiomcache==0.6.0 - aioresponses==0.7.6 - aiosignal==1.2.0 - async-timeout==4.0.2 - asyncpg==0.26.0 - asynctest==0.13.0 - attrs==22.2.0 - cffi==1.15.1 - charset-normalizer==3.0.1 - click==8.0.4 - coloredlogs==15.0.1 - coverage==6.2 - cryptography==40.0.2 - cython==3.0.12 - cyvcf2==0.30.18 - distlib==0.3.9 - ecdsa==0.19.1 - execnet==1.9.0 - filelock==3.4.1 - flake8==5.0.4 - flake8-docstrings==1.6.0 - frozenlist==1.2.0 - gunicorn==21.2.0 - humanfriendly==10.0 - idna==3.10 - idna-ssl==1.1.0 - importlib-metadata==4.2.0 - importlib-resources==5.4.0 - iniconfig==1.1.1 - jsonschema==3.0.0a3 - mccabe==0.7.0 - multidict==5.2.0 - numpy==1.19.5 - packaging==21.3 - platformdirs==2.4.0 - pluggy==1.0.0 - py==1.11.0 - pyasn1==0.4.8 - pycodestyle==2.9.1 - pycparser==2.21 - pydocstyle==6.3.0 - pyflakes==2.5.0 - pyparsing==3.1.4 - pyrsistent==0.18.0 - pytest==7.0.1 - pytest-asyncio==0.16.0 - pytest-cov==4.0.0 - pytest-mock==3.6.1 - pytest-xdist==3.0.2 - python-jose==3.4.0 - rsa==4.9 - six==1.17.0 - snowballstemmer==2.2.0 - testfixtures==7.2.2 - toml==0.10.2 - tomli==1.2.3 - tox==3.28.0 - typing-extensions==4.1.1 - ujson==4.3.0 - uvloop==0.14.0 - virtualenv==20.17.1 - yarl==1.7.2 - zipp==3.6.0 prefix: /opt/conda/envs/beacon-python	[ "Test" ]	[]	[]	[]	Apache License 2.0	null
CSCfi__swift-browser-ui-20	316d76f77f93701289188d0d461d4fc30fdccf96	2019-08-21 12:19:43	316d76f77f93701289188d0d461d4fc30fdccf96		diff --git a/swift_browser_ui/middlewares.py b/swift_browser_ui/middlewares.py index 3a857ab8..3f08bf77 100644 --- a/swift_browser_ui/middlewares.py +++ b/swift_browser_ui/middlewares.py @@ -5,41 +5,40 @@ from aiohttp import web from .settings import setd +def return_error_response(error_code): + """Return the correct error page with correct status code.""" + with open( + setd["static_directory"] + "/" + str(error_code) + ".html" + ) as resp: + return web.Response( + body="".join(resp.readlines()), + status=error_code, + content_type="text/html", + headers={ + "Cache-Control": "no-cache, no-store, must-revalidate", + "Pragma": "no-cache", + "Expires": "0" + } + ) + + @web.middleware async def error_middleware(request, handler): """Return the correct HTTP Error page.""" try: response = await handler(request) if response.status == 401: - return web.FileResponse( - setd["static_directory"] + "/401.html", - status=401 - ) + return return_error_response(401) if response.status == 403: - return web.FileResponse( - setd["static_directory"] + "/403.html", - status=403 - ) + return return_error_response(403) if response.status == 404: - return web.FileResponse( - setd["static_directory"] + "/404.html", - status=404 - ) + return return_error_response(404) return response except web.HTTPException as ex: if ex.status == 401: - return web.FileResponse( - setd["static_directory"] + "/401.html", - status=401 - ) + return return_error_response(401) if ex.status == 403: - return web.FileResponse( - setd["static_directory"] + "/403.html", - status=403 - ) + return return_error_response(403) if ex.status == 404: - return web.FileResponse( - setd["static_directory"] + "/404.html", - status=404 - ) + return return_error_response(404) raise diff --git a/tox.ini b/tox.ini index ab953fc3..d983294d 100644 --- a/tox.ini +++ b/tox.ini @@ -16,7 +16,7 @@ deps = pydocstyle==3.0.0 flake8 flake8-docstrings -commands = flake8 . +commands = flake8 swift_browser_ui tests ui_tests [testenv:docs] ; skip_install = true	Failure to retrieve containers after switching to a a project with no containers #### Describe the bug Failure to retrieve containers after switching to a a project with no containers #### To Reproduce Steps to reproduce the behaviour: 1. Log in 2.1 If there is a project with some containers available switch to one without anything 2.2 If there is a project with no containers available switch to one with some containers 4. See error #### Expected behavior Display the available containers if they are present. #### Screenshots ![Peek 2019-08-19 15-18](https://user-images.githubusercontent.com/47524/63264800-a45cbc00-c294-11e9-8368-698b6b43b2ba.gif) #### Additional context <!-- Add any other context about the problem here. -->	CSCfi/swift-browser-ui	diff --git a/tests/test_middlewares.py b/tests/test_middlewares.py index 84bad576..3c54417d 100644 --- a/tests/test_middlewares.py +++ b/tests/test_middlewares.py @@ -52,37 +52,37 @@ class MiddlewareTestClass(asynctest.TestCase): """Test 401 middleware when the 401 status is returned.""" resp = await error_middleware(None, return_401_handler) self.assertEqual(resp.status, 401) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_401_exception(self): """Test 401 middleware when the 401 status is risen.""" resp = await error_middleware(True, return_401_handler) self.assertEqual(resp.status, 401) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_403_return(self): """Test 403 middleware when the 403 status is returned.""" resp = await error_middleware(None, return_403_handler) self.assertEqual(resp.status, 403) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_403_exception(self): """Test 403 middleware when the 403 status is risen.""" resp = await error_middleware(True, return_403_handler) self.assertEqual(resp.status, 403) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_404_return(self): """Test 404 middleware when the 404 status is returned.""" resp = await error_middleware(None, return_404_handler) self.assertEqual(resp.status, 404) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_404_exception(self): """Test 404 middlewrae when the 404 status is risen.""" resp = await error_middleware(True, return_404_handler) self.assertEqual(resp.status, 404) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_error_middleware_no_error(self): """Test the general error middleware with correct status."""	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_media", "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 2 }	0.3	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[test]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.7", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	aiohttp==3.8.6 aiosignal==1.3.1 alabaster==0.7.13 async-timeout==4.0.3 asynctest==0.13.0 attrs==24.2.0 Babel==2.14.0 certifi @ file:///croot/certifi_1671487769961/work/certifi cffi==1.15.1 charset-normalizer==3.4.1 click==8.1.8 coverage==6.5.0 coveralls==3.3.1 cryptography==44.0.2 distlib==0.3.9 docopt==0.6.2 docutils==0.19 exceptiongroup==1.2.2 execnet==2.0.2 filelock==3.12.2 flake8==5.0.4 flake8-docstrings==1.7.0 frozenlist==1.3.3 gunicorn==23.0.0 idna==3.10 imagesize==1.4.1 importlib-metadata==4.2.0 iniconfig==2.0.0 iso8601==2.1.0 Jinja2==3.1.6 keystoneauth1==5.1.3 MarkupSafe==2.1.5 mccabe==0.7.0 multidict==6.0.5 os-service-types==1.7.0 packaging==24.0 pbr==6.1.1 platformdirs==2.6.2 pluggy==1.2.0 py==1.11.0 pycodestyle==2.9.1 pycparser==2.21 pydocstyle==6.3.0 pyflakes==2.5.0 Pygments==2.17.2 pytest==7.4.4 pytest-aiohttp==1.0.5 pytest-asyncio==0.21.2 pytest-cov==4.1.0 pytest-xdist==3.5.0 python-swiftclient==4.7.0 pytz==2025.2 requests==2.31.0 six==1.17.0 snowballstemmer==2.2.0 Sphinx==5.3.0 sphinx-rtd-theme==2.0.0 sphinxcontrib-applehelp==1.0.2 sphinxcontrib-devhelp==1.0.2 sphinxcontrib-htmlhelp==2.0.0 sphinxcontrib-jquery==4.1 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.5 stevedore==3.5.2 -e git+https://github.com/CSCfi/swift-browser-ui.git@316d76f77f93701289188d0d461d4fc30fdccf96#egg=swift_browser_ui tomli==2.0.1 tox==3.28.0 typing_extensions==4.7.1 urllib3==2.0.7 uvloop==0.18.0 virtualenv==20.16.2 yarl==1.9.4 zipp==3.15.0	name: swift-browser-ui channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - aiohttp==3.8.6 - aiosignal==1.3.1 - alabaster==0.7.13 - async-timeout==4.0.3 - asynctest==0.13.0 - attrs==24.2.0 - babel==2.14.0 - cffi==1.15.1 - charset-normalizer==3.4.1 - click==8.1.8 - coverage==6.5.0 - coveralls==3.3.1 - cryptography==44.0.2 - distlib==0.3.9 - docopt==0.6.2 - docutils==0.19 - exceptiongroup==1.2.2 - execnet==2.0.2 - filelock==3.12.2 - flake8==5.0.4 - flake8-docstrings==1.7.0 - frozenlist==1.3.3 - gunicorn==23.0.0 - idna==3.10 - imagesize==1.4.1 - importlib-metadata==4.2.0 - iniconfig==2.0.0 - iso8601==2.1.0 - jinja2==3.1.6 - keystoneauth1==5.1.3 - markupsafe==2.1.5 - mccabe==0.7.0 - multidict==6.0.5 - os-service-types==1.7.0 - packaging==24.0 - pbr==6.1.1 - platformdirs==2.6.2 - pluggy==1.2.0 - py==1.11.0 - pycodestyle==2.9.1 - pycparser==2.21 - pydocstyle==6.3.0 - pyflakes==2.5.0 - pygments==2.17.2 - pytest==7.4.4 - pytest-aiohttp==1.0.5 - pytest-asyncio==0.21.2 - pytest-cov==4.1.0 - pytest-xdist==3.5.0 - python-swiftclient==4.7.0 - pytz==2025.2 - requests==2.31.0 - six==1.17.0 - snowballstemmer==2.2.0 - sphinx==5.3.0 - sphinx-rtd-theme==2.0.0 - sphinxcontrib-applehelp==1.0.2 - sphinxcontrib-devhelp==1.0.2 - sphinxcontrib-htmlhelp==2.0.0 - sphinxcontrib-jquery==4.1 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==1.0.3 - sphinxcontrib-serializinghtml==1.1.5 - stevedore==3.5.2 - tomli==2.0.1 - tox==3.28.0 - typing-extensions==4.7.1 - urllib3==2.0.7 - uvloop==0.18.0 - virtualenv==20.16.2 - yarl==1.9.4 - zipp==3.15.0 prefix: /opt/conda/envs/swift-browser-ui	[ "tests/test_middlewares.py::MiddlewareTestClass::test_401_exception", "tests/test_middlewares.py::MiddlewareTestClass::test_401_return", "tests/test_middlewares.py::MiddlewareTestClass::test_403_exception", "tests/test_middlewares.py::MiddlewareTestClass::test_403_return", "tests/test_middlewares.py::MiddlewareTestClass::test_404_exception", "tests/test_middlewares.py::MiddlewareTestClass::test_404_return" ]	[]	[ "tests/test_middlewares.py::MiddlewareTestClass::test_error_middleware_no_error", "tests/test_middlewares.py::MiddlewareTestClass::test_error_middleware_non_handled_raise" ]	[]	MIT License	swerebench/sweb.eval.x86_64.cscfi_1776_swift-browser-ui-20
CSCfi__swift-browser-ui-21	316d76f77f93701289188d0d461d4fc30fdccf96	2019-08-21 13:00:14	316d76f77f93701289188d0d461d4fc30fdccf96		diff --git a/docs/source/conf.py b/docs/source/conf.py index 861c746a..c2766881 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -28,7 +28,7 @@ copyright = f'{current_year}, CSC Developers' author = 'CSC Developers' # The full version, including alpha/beta/rc tags -version = release = '0.3.2' +version = release = '0.3.3' # -- General configuration --------------------------------------------------- diff --git a/swift_browser_ui/__init__.py b/swift_browser_ui/__init__.py index a1d3d29a..330337d2 100644 --- a/swift_browser_ui/__init__.py +++ b/swift_browser_ui/__init__.py @@ -7,6 +7,6 @@ with the object storage. __name__ = 'swift_browser_ui' -__version__ = '0.3.2' +__version__ = '0.3.3' __author__ = 'CSC Developers' __license__ = 'MIT License' diff --git a/swift_browser_ui/middlewares.py b/swift_browser_ui/middlewares.py index 3a857ab8..3f08bf77 100644 --- a/swift_browser_ui/middlewares.py +++ b/swift_browser_ui/middlewares.py @@ -5,41 +5,40 @@ from aiohttp import web from .settings import setd +def return_error_response(error_code): + """Return the correct error page with correct status code.""" + with open( + setd["static_directory"] + "/" + str(error_code) + ".html" + ) as resp: + return web.Response( + body="".join(resp.readlines()), + status=error_code, + content_type="text/html", + headers={ + "Cache-Control": "no-cache, no-store, must-revalidate", + "Pragma": "no-cache", + "Expires": "0" + } + ) + + @web.middleware async def error_middleware(request, handler): """Return the correct HTTP Error page.""" try: response = await handler(request) if response.status == 401: - return web.FileResponse( - setd["static_directory"] + "/401.html", - status=401 - ) + return return_error_response(401) if response.status == 403: - return web.FileResponse( - setd["static_directory"] + "/403.html", - status=403 - ) + return return_error_response(403) if response.status == 404: - return web.FileResponse( - setd["static_directory"] + "/404.html", - status=404 - ) + return return_error_response(404) return response except web.HTTPException as ex: if ex.status == 401: - return web.FileResponse( - setd["static_directory"] + "/401.html", - status=401 - ) + return return_error_response(401) if ex.status == 403: - return web.FileResponse( - setd["static_directory"] + "/403.html", - status=403 - ) + return return_error_response(403) if ex.status == 404: - return web.FileResponse( - setd["static_directory"] + "/404.html", - status=404 - ) + return return_error_response(404) raise diff --git a/tox.ini b/tox.ini index ab953fc3..d983294d 100644 --- a/tox.ini +++ b/tox.ini @@ -16,7 +16,7 @@ deps = pydocstyle==3.0.0 flake8 flake8-docstrings -commands = flake8 . +commands = flake8 swift_browser_ui tests ui_tests [testenv:docs] ; skip_install = true	Failure to retrieve containers after switching to a a project with no containers #### Describe the bug Failure to retrieve containers after switching to a a project with no containers #### To Reproduce Steps to reproduce the behaviour: 1. Log in 2.1 If there is a project with some containers available switch to one without anything 2.2 If there is a project with no containers available switch to one with some containers 4. See error #### Expected behavior Display the available containers if they are present. #### Screenshots ![Peek 2019-08-19 15-18](https://user-images.githubusercontent.com/47524/63264800-a45cbc00-c294-11e9-8368-698b6b43b2ba.gif) #### Additional context <!-- Add any other context about the problem here. -->	CSCfi/swift-browser-ui	diff --git a/tests/test_middlewares.py b/tests/test_middlewares.py index 84bad576..3c54417d 100644 --- a/tests/test_middlewares.py +++ b/tests/test_middlewares.py @@ -52,37 +52,37 @@ class MiddlewareTestClass(asynctest.TestCase): """Test 401 middleware when the 401 status is returned.""" resp = await error_middleware(None, return_401_handler) self.assertEqual(resp.status, 401) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_401_exception(self): """Test 401 middleware when the 401 status is risen.""" resp = await error_middleware(True, return_401_handler) self.assertEqual(resp.status, 401) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_403_return(self): """Test 403 middleware when the 403 status is returned.""" resp = await error_middleware(None, return_403_handler) self.assertEqual(resp.status, 403) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_403_exception(self): """Test 403 middleware when the 403 status is risen.""" resp = await error_middleware(True, return_403_handler) self.assertEqual(resp.status, 403) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_404_return(self): """Test 404 middleware when the 404 status is returned.""" resp = await error_middleware(None, return_404_handler) self.assertEqual(resp.status, 404) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_404_exception(self): """Test 404 middlewrae when the 404 status is risen.""" resp = await error_middleware(True, return_404_handler) self.assertEqual(resp.status, 404) - self.assertIsInstance(resp, FileResponse) + self.assertIsInstance(resp, Response) async def test_error_middleware_no_error(self): """Test the general error middleware with correct status."""	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_media", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 1, "issue_text_score": 1, "test_score": 1 }, "num_modified_files": 4 }	0.3	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[test]", "log_parser": "parse_log_pytest_v2", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "pytest-asyncio" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.7", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest -xvs" }	aiohttp==3.8.6 aiosignal==1.3.1 alabaster==0.7.13 async-timeout==4.0.3 asynctest==0.13.0 attrs==24.2.0 Babel==2.14.0 certifi @ file:///croot/certifi_1671487769961/work/certifi cffi==1.15.1 charset-normalizer==3.4.1 click==8.1.8 coverage==6.5.0 coveralls==3.3.1 cryptography==44.0.2 distlib==0.3.9 docopt==0.6.2 docutils==0.19 exceptiongroup==1.2.2 execnet==2.0.2 filelock==3.12.2 flake8==5.0.4 flake8-docstrings==1.7.0 frozenlist==1.3.3 gunicorn==23.0.0 idna==3.10 imagesize==1.4.1 importlib-metadata==4.2.0 iniconfig==2.0.0 iso8601==2.1.0 Jinja2==3.1.6 keystoneauth1==5.1.3 MarkupSafe==2.1.5 mccabe==0.7.0 multidict==6.0.5 os-service-types==1.7.0 packaging==24.0 pbr==6.1.1 platformdirs==2.6.2 pluggy==1.2.0 py==1.11.0 pycodestyle==2.9.1 pycparser==2.21 pydocstyle==6.3.0 pyflakes==2.5.0 Pygments==2.17.2 pytest==7.4.4 pytest-aiohttp==1.0.5 pytest-asyncio==0.21.2 pytest-cov==4.1.0 pytest-mock==3.11.1 pytest-xdist==3.5.0 python-swiftclient==4.7.0 pytz==2025.2 requests==2.31.0 six==1.17.0 snowballstemmer==2.2.0 Sphinx==5.3.0 sphinx-rtd-theme==2.0.0 sphinxcontrib-applehelp==1.0.2 sphinxcontrib-devhelp==1.0.2 sphinxcontrib-htmlhelp==2.0.0 sphinxcontrib-jquery==4.1 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.5 stevedore==3.5.2 -e git+https://github.com/CSCfi/swift-browser-ui.git@316d76f77f93701289188d0d461d4fc30fdccf96#egg=swift_browser_ui tomli==2.0.1 tox==3.28.0 typing_extensions==4.7.1 urllib3==2.0.7 uvloop==0.18.0 virtualenv==20.16.2 yarl==1.9.4 zipp==3.15.0	name: swift-browser-ui channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - certifi=2022.12.7=py37h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=22.3.1=py37h06a4308_0 - python=3.7.16=h7a1cb2a_0 - readline=8.2=h5eee18b_0 - setuptools=65.6.3=py37h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.38.4=py37h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - aiohttp==3.8.6 - aiosignal==1.3.1 - alabaster==0.7.13 - async-timeout==4.0.3 - asynctest==0.13.0 - attrs==24.2.0 - babel==2.14.0 - cffi==1.15.1 - charset-normalizer==3.4.1 - click==8.1.8 - coverage==6.5.0 - coveralls==3.3.1 - cryptography==44.0.2 - distlib==0.3.9 - docopt==0.6.2 - docutils==0.19 - exceptiongroup==1.2.2 - execnet==2.0.2 - filelock==3.12.2 - flake8==5.0.4 - flake8-docstrings==1.7.0 - frozenlist==1.3.3 - gunicorn==23.0.0 - idna==3.10 - imagesize==1.4.1 - importlib-metadata==4.2.0 - iniconfig==2.0.0 - iso8601==2.1.0 - jinja2==3.1.6 - keystoneauth1==5.1.3 - markupsafe==2.1.5 - mccabe==0.7.0 - multidict==6.0.5 - os-service-types==1.7.0 - packaging==24.0 - pbr==6.1.1 - platformdirs==2.6.2 - pluggy==1.2.0 - py==1.11.0 - pycodestyle==2.9.1 - pycparser==2.21 - pydocstyle==6.3.0 - pyflakes==2.5.0 - pygments==2.17.2 - pytest==7.4.4 - pytest-aiohttp==1.0.5 - pytest-asyncio==0.21.2 - pytest-cov==4.1.0 - pytest-mock==3.11.1 - pytest-xdist==3.5.0 - python-swiftclient==4.7.0 - pytz==2025.2 - requests==2.31.0 - six==1.17.0 - snowballstemmer==2.2.0 - sphinx==5.3.0 - sphinx-rtd-theme==2.0.0 - sphinxcontrib-applehelp==1.0.2 - sphinxcontrib-devhelp==1.0.2 - sphinxcontrib-htmlhelp==2.0.0 - sphinxcontrib-jquery==4.1 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==1.0.3 - sphinxcontrib-serializinghtml==1.1.5 - stevedore==3.5.2 - tomli==2.0.1 - tox==3.28.0 - typing-extensions==4.7.1 - urllib3==2.0.7 - uvloop==0.18.0 - virtualenv==20.16.2 - yarl==1.9.4 - zipp==3.15.0 prefix: /opt/conda/envs/swift-browser-ui	[ "tests/test_middlewares.py::MiddlewareTestClass::test_401_exception", "tests/test_middlewares.py::MiddlewareTestClass::test_401_return", "tests/test_middlewares.py::MiddlewareTestClass::test_403_exception", "tests/test_middlewares.py::MiddlewareTestClass::test_403_return", "tests/test_middlewares.py::MiddlewareTestClass::test_404_exception", "tests/test_middlewares.py::MiddlewareTestClass::test_404_return", "tests/test_middlewares.py::MiddlewareTestClass::test_error_middleware_no_error", "tests/test_middlewares.py::MiddlewareTestClass::test_error_middleware_non_handled_raise" ]	[]	[]	[]	MIT License	swerebench/sweb.eval.x86_64.cscfi_1776_swift-browser-ui-21
CSCfi__swift-browser-ui-394	2434fc7b1907f113f516da55eb9de62b1e452541	2021-11-12 14:55:00	26a360f4c39d0c2161c9651554090a9610d4cbce		diff --git a/swift_browser_ui/ui/api.py b/swift_browser_ui/ui/api.py index 9fa283b5..0da1405b 100644 --- a/swift_browser_ui/ui/api.py +++ b/swift_browser_ui/ui/api.py @@ -67,11 +67,6 @@ async def swift_list_buckets(request: aiohttp.web.Request) -> aiohttp.web.Respon # response at once serv = request.app["Sessions"][session]["ST_conn"].list() [_unpack(i, cont, request) for i in serv] - # for a bucket with no objects - if not cont: - # return empty object - request.app["Log"].debug("Empty container list.") - raise aiohttp.web.HTTPNotFound() return aiohttp.web.json_response(cont) except SwiftError: request.app["Log"].error("SwiftError occured return empty container list.") diff --git a/swift_browser_ui_frontend/src/components/Breadcrumb.vue b/swift_browser_ui_frontend/src/components/Breadcrumb.vue new file mode 100644 index 00000000..457defe0 --- /dev/null +++ b/swift_browser_ui_frontend/src/components/Breadcrumb.vue @@ -0,0 +1,22 @@ +<template> + <li class="breadcrumb-element"> + <router-link + class="breadcrumb-link" + :to="address" + > + {{ alias \| truncate(100) }} + </router-link> + </li> +</template> + +<script> +export default { + name: "BreadcrumbListElement", + filters: { + truncate(value, length) { + return value.length > length ? value.substr(0, length) + "..." : value; + }, + }, + props: ["address", "alias"], +}; +</script> diff --git a/swift_browser_ui_frontend/src/entries/main.js b/swift_browser_ui_frontend/src/entries/main.js index 64e708db..9e8579e8 100644 --- a/swift_browser_ui_frontend/src/entries/main.js +++ b/swift_browser_ui_frontend/src/entries/main.js @@ -7,6 +7,7 @@ import VueI18n from "vue-i18n"; // Project Vue components import BrowserNavbar from "@/components/BrowserNavbar.vue"; +import BreadcrumbListElement from "@/components/Breadcrumb.vue"; // Project JS functions import getLangCookie from "@/common/conv"; @@ -48,6 +49,7 @@ new Vue({ store, components: { BrowserNavbar, + BreadcrumbListElement, ProgressBar, }, computed: { diff --git a/swift_browser_ui_frontend/src/pages/BrowserPage.vue b/swift_browser_ui_frontend/src/pages/BrowserPage.vue index 70f25229..e2e82dc7 100644 --- a/swift_browser_ui_frontend/src/pages/BrowserPage.vue +++ b/swift_browser_ui_frontend/src/pages/BrowserPage.vue @@ -6,18 +6,22 @@ :projects="projects" /> <ProgressBar v-if="isUploading \|\| isChunking" /> - <b-breadcrumb - style="margin-left:5%;margin-top:1%;font-size:1rem;" + <div + class="breadcrumb" + aria-label="breadcrumbs" > - <b-breadcrumb-item - v-for="item in getRouteAsList ()" - :key="item.alias" - :to="item.address" - tag="router-link" + <ul + id="breadcrumb-list" + style="margin-left:5%;margin-top:1%;" > - {{ item.alias \| truncate(100) }} - </b-breadcrumb-item> - </b-breadcrumb> + <BreadcrumbListElement + v-for="item in getRouteAsList ()" + :key="item.alias" + :alias="item.alias" + :address="item.address" + /> + </ul> + </div> <router-view /> <b-loading :is-full-page="isFullPage" @@ -43,17 +47,6 @@ </div> </template> -<script> -export default { - name: "BrowserPage", - filters: { - truncate(value, length) { - return value.length > length ? value.substr(0, length) + "..." : value; - }, - }, -}; -</script> - <style> html, body { height: 100%;	Deleting the last bucket does not refresh the browser view #### Describe the bug If you delete the last bucket in the project a notification is shown that the bucket is deleted, but the view does not get updated and the bucket is still visible and you can still open the bucket. #### To Reproduce Steps to reproduce the behaviour: 1. Select a project with one bucket (or one without any and create a new bucket). 2. Select the bucket and click "Delete". 3. A notification pops up saying bucket was deleted. 4. The bucket is still visible the browser and is still accessible, you can click "Delete" again and you can even open the bucket. #### Expected behavior Along with the notification in step 3 above, the browser view should be refreshed so that the bucket disappears. #### Screenshots ![image](https://user-images.githubusercontent.com/10267750/137113134-65433be8-0810-4da0-be9f-1752998a71c1.png) #### Additional context Tested with latest Firefox (93.0 for macOS).	CSCfi/swift-browser-ui	diff --git a/tests/ui_unit/test_api.py b/tests/ui_unit/test_api.py index 5348e7d0..f116ab0f 100644 --- a/tests/ui_unit/test_api.py +++ b/tests/ui_unit/test_api.py @@ -107,10 +107,11 @@ class APITestClass(asynctest.TestCase): ) async def test_list_without_containers(self): - """Test function list buckets on a project without object storage.""" + """Test function list buckets on a project without containers.""" self.request.app["Sessions"][self.cookie]["ST_conn"].init_with_data(containers=0) - with self.assertRaises(HTTPNotFound): - _ = await swift_list_buckets(self.request) + response = await swift_list_buckets(self.request) + objects = json.loads(response.text) + self.assertEqual(objects, []) async def test_list_with_invalid_container(self): """Test function list objects with an invalid container id."""	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_hyperlinks", "has_media", "has_added_files", "has_many_modified_files", "has_many_hunks", "has_pytest_match_arg" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 3, "issue_text_score": 0, "test_score": 0 }, "num_modified_files": 3 }	1.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest pytest-cov flake8 flake8-docstrings pytest-xdist asynctest", "pytest" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.8", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	aiohttp==3.7.4.post0 alabaster==0.7.13 async-timeout==3.0.1 asyncpg==0.24.0 asynctest==0.13.0 attrs==25.3.0 babel==2.17.0 certifi==2021.10.8 cffi==1.17.1 chardet==4.0.0 charset-normalizer==3.4.1 click==8.0.3 coverage==7.6.1 cryptography==35.0.0 docutils==0.17.1 exceptiongroup==1.2.2 execnet==2.1.1 flake8==7.1.2 flake8-docstrings==1.7.0 gunicorn==23.0.0 idna==3.10 imagesize==1.4.1 iniconfig==2.1.0 iso8601==2.1.0 Jinja2==3.1.6 keystoneauth1==4.4.0 MarkupSafe==2.1.5 mccabe==0.7.0 multidict==6.1.0 os-service-types==1.7.0 packaging==24.2 pbr==6.1.1 pluggy==1.5.0 propcache==0.2.0 pycodestyle==2.12.1 pycparser==2.22 pydocstyle==6.3.0 pyflakes==3.2.0 Pygments==2.19.1 pytest==8.3.5 pytest-cov==5.0.0 pytest-xdist==3.6.1 python-swiftclient==3.12.0 pytz==2025.2 requests==2.32.3 six==1.17.0 snowballstemmer==2.2.0 Sphinx==4.2.0 sphinx-rtd-theme==1.0.0 sphinxcontrib-applehelp==1.0.4 sphinxcontrib-devhelp==1.0.2 sphinxcontrib-htmlhelp==2.0.1 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.5 stevedore==5.3.0 -e git+https://github.com/CSCfi/swift-browser-ui.git@2434fc7b1907f113f516da55eb9de62b1e452541#egg=swift_browser_ui tomli==2.2.1 typing_extensions==4.13.0 urllib3==2.2.3 uvloop==0.16.0 yarl==1.15.2	name: swift-browser-ui channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=24.2=py38h06a4308_0 - python=3.8.20=he870216_0 - readline=8.2=h5eee18b_0 - setuptools=75.1.0=py38h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.44.0=py38h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - aiohttp==3.7.4.post0 - alabaster==0.7.13 - async-timeout==3.0.1 - asyncpg==0.24.0 - asynctest==0.13.0 - attrs==25.3.0 - babel==2.17.0 - certifi==2021.10.8 - cffi==1.17.1 - chardet==4.0.0 - charset-normalizer==3.4.1 - click==8.0.3 - coverage==7.6.1 - cryptography==35.0.0 - docutils==0.17.1 - exceptiongroup==1.2.2 - execnet==2.1.1 - flake8==7.1.2 - flake8-docstrings==1.7.0 - gunicorn==23.0.0 - idna==3.10 - imagesize==1.4.1 - iniconfig==2.1.0 - iso8601==2.1.0 - jinja2==3.1.6 - keystoneauth1==4.4.0 - markupsafe==2.1.5 - mccabe==0.7.0 - multidict==6.1.0 - os-service-types==1.7.0 - packaging==24.2 - pbr==6.1.1 - pluggy==1.5.0 - propcache==0.2.0 - pycodestyle==2.12.1 - pycparser==2.22 - pydocstyle==6.3.0 - pyflakes==3.2.0 - pygments==2.19.1 - pytest==8.3.5 - pytest-cov==5.0.0 - pytest-xdist==3.6.1 - python-swiftclient==3.12.0 - pytz==2025.2 - requests==2.32.3 - six==1.17.0 - snowballstemmer==2.2.0 - sphinx==4.2.0 - sphinx-rtd-theme==1.0.0 - sphinxcontrib-applehelp==1.0.4 - sphinxcontrib-devhelp==1.0.2 - sphinxcontrib-htmlhelp==2.0.1 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==1.0.3 - sphinxcontrib-serializinghtml==1.1.5 - stevedore==5.3.0 - tomli==2.2.1 - typing-extensions==4.13.0 - urllib3==2.2.3 - uvloop==0.16.0 - yarl==1.15.2 prefix: /opt/conda/envs/swift-browser-ui	[ "tests/ui_unit/test_api.py::APITestClass::test_list_without_containers" ]	[]	[ "tests/ui_unit/test_api.py::APITestClass::test_get_container_meta_swift", "tests/ui_unit/test_api.py::APITestClass::test_get_object_meta_s3", "tests/ui_unit/test_api.py::APITestClass::test_get_object_meta_swift", "tests/ui_unit/test_api.py::APITestClass::test_get_object_meta_swift_whole", "tests/ui_unit/test_api.py::APITestClass::test_get_os_active_project", "tests/ui_unit/test_api.py::APITestClass::test_get_os_user", "tests/ui_unit/test_api.py::APITestClass::test_get_project_metadata", "tests/ui_unit/test_api.py::APITestClass::test_list_containers_correct", "tests/ui_unit/test_api.py::APITestClass::test_list_containers_swift_error", "tests/ui_unit/test_api.py::APITestClass::test_list_objects_correct", "tests/ui_unit/test_api.py::APITestClass::test_list_objects_with_unicode_nulls", "tests/ui_unit/test_api.py::APITestClass::test_list_with_invalid_container", "tests/ui_unit/test_api.py::APITestClass::test_list_without_objects", "tests/ui_unit/test_api.py::APITestClass::test_os_list_projects", "tests/ui_unit/test_api.py::APITestClass::test_swift_download_object", "tests/ui_unit/test_api.py::TestProxyFunctions::test_swift_check_object_chunk", "tests/ui_unit/test_api.py::TestProxyFunctions::test_swift_download_container", "tests/ui_unit/test_api.py::TestProxyFunctions::test_swift_download_share_object", "tests/ui_unit/test_api.py::TestProxyFunctions::test_swift_replicate_container", "tests/ui_unit/test_api.py::TestProxyFunctions::test_swift_upload_object_chunk" ]	[]	MIT License	swerebench/sweb.eval.x86_64.cscfi_1776_swift-browser-ui-394
CSCfi__swift-browser-ui-541	26a360f4c39d0c2161c9651554090a9610d4cbce	2022-03-01 13:50:01	26a360f4c39d0c2161c9651554090a9610d4cbce		diff --git a/.github/config/.finnishwords.txt b/.github/config/.finnishwords.txt index 3629ed43..397cc63b 100644 --- a/.github/config/.finnishwords.txt +++ b/.github/config/.finnishwords.txt @@ -1,5 +1,7 @@ + ajaksi alapuolisesta +alla aloitettiin annetut apin @@ -24,6 +26,7 @@ erillinen erissä esimerkiksi esitetty +estetty etsi etsittyä että @@ -32,6 +35,7 @@ etusivun hakea haku hakuindeksi +haluat haluatko halutako haluttu @@ -65,9 +69,11 @@ johtaa johtua jonka jos +jota julkinen julkiset julkisia +kaikki kansio kansioille kansioina @@ -86,6 +92,7 @@ käyttäjän käyttäjänimestä käyttöä käyttöliittymä +käyttöliittymän käyttörajoista kehittänyt kelpaa @@ -96,6 +103,8 @@ keskus kestää kiellettyä kirjaudu +kirjautuaksesi +kirjautumalla kirjautumisen kirjoitus kirjoitusoikeus @@ -162,6 +171,7 @@ mikä mikäli minuutin mitätöi +muiden muita mukaan muokataan @@ -172,7 +182,9 @@ muussa muutaman muuttaa muuttaaksesi +näkyvissä nämä +nappia navigointia näytä näytetään @@ -205,6 +217,7 @@ omistavan ongelmiin onnistui onnistunut +onnistuu operaatio operaation osoite @@ -237,13 +250,18 @@ polku postamista poudan projekteihin +projekteja projekti +projektia +projektien +projektien projektilla projektille projektilta projektin projektissa projektista +projektit projektitunnisteet projektitunnisteille pudota @@ -253,6 +271,13 @@ pyydetty pyydetyn pyyntö rajan +rajattu +rajatun +rajoitettua +rajoitettuihin +rajoittamattomat +rajoittamattomia +rajoittamattomien rajoitteiden resurssienkäyttö resurssit @@ -274,8 +299,12 @@ salausratkaisun salli sallittu selaamiseen +selailla +selailu selain selainta +selatessa +siirto sisään sisäänkirjauksen sisällön @@ -336,6 +365,7 @@ tilan tilankäyttö tilapäinen tilapäisesti +toiseen toteuttaa tueta tuettu @@ -358,8 +388,10 @@ uusi uusia vaatii vähintään +vaihto väliaikaiseen valikossa +valinnan valinnat valitse valittu diff --git a/CHANGELOG.md b/CHANGELOG.md index 4a588edb..94ec91a1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +### Added + +- (GH #468) add support for project isolation for projects marked as restricted + - discovery for restricted projects pending, implemented logic only for now + - foced restricted mode can be configured on + + ## [v2.0.0] ### Added diff --git a/swift_browser_ui/ui/api.py b/swift_browser_ui/ui/api.py index 333bb818..a5402619 100644 --- a/swift_browser_ui/ui/api.py +++ b/swift_browser_ui/ui/api.py @@ -37,7 +37,14 @@ async def os_list_projects(request: aiohttp.web.Request) -> aiohttp.web.Response ) # Filter out the tokens contained in session token return aiohttp.web.json_response( - [{"name": v["name"], "id": v["id"]} for _, v in session["projects"].items()] + [ + { + "name": v["name"], + "id": v["id"], + "tainted": v["tainted"], + } + for _, v in session["projects"].items() + ] ) diff --git a/swift_browser_ui/ui/front.py b/swift_browser_ui/ui/front.py index 67342312..ce7ee52f 100644 --- a/swift_browser_ui/ui/front.py +++ b/swift_browser_ui/ui/front.py @@ -22,6 +22,18 @@ async def browse(_: aiohttp.web.Request) -> aiohttp.web.FileResponse: ) +async def select(_: aiohttp.web.Request) -> aiohttp.web.FileResponse: + """Serve a project selection for users with tainted projects.""" + return aiohttp.web.FileResponse( + str(setd["static_directory"]) + "/select.html", + headers={ + "Cache-Control": "no-cache, no-store, must-revalidate", + "Pragma": "no-cache", + "Expires": "0", + }, + ) + + async def index( request: typing.Optional[aiohttp.web.Request], ) -> typing.Union[aiohttp.web.Response, aiohttp.web.FileResponse]: diff --git a/swift_browser_ui/ui/login.py b/swift_browser_ui/ui/login.py index 2bea8d7d..58726639 100644 --- a/swift_browser_ui/ui/login.py +++ b/swift_browser_ui/ui/login.py @@ -186,6 +186,8 @@ async def login_with_token( session["referer"] = request.url.host uname = "" + taint = True if setd["force_restricted_mode"] else False + # Check token availability avail = await get_availability_from_token(token, client) session["projects"] = {} @@ -242,12 +244,23 @@ async def login_with_token( "name": project["name"], "endpoint": endpoint["url"], "token": scoped, + "tainted": True if setd["force_restricted_mode"] else False, } session["token"] = token session["uname"] = uname + if taint: + session["taint"] = True + else: + session["taint"] = False + session.changed() + + if taint: + response.headers["Location"] = "/select" + return response + # Redirect to the browse page if "NAV_TO" in request.cookies.keys(): response.headers["Location"] = request.cookies["NAV_TO"] @@ -258,6 +271,50 @@ async def login_with_token( return response +async def handle_project_lock(request: aiohttp.web.Request) -> aiohttp.web.Response: + """Lock down to a specific project.""" + log = request.app["Log"] + log.info("Call for locking down the project.") + + session = await aiohttp_session.get_session(request) + + project = request.match_info["project"] + + # Ditch all projects that aren't the one specified if project is defined + if project in session["projects"]: + session["projects"] = { + k: v + for k, v in filter( + lambda val: val[0] == project, + session["projects"].items(), + ) + } + # If the project doesn't exist, allow all untainted projects + else: + session["projects"] = { + k: v + for k, v in filter( + lambda val: not val[1]["tainted"], session["projects"].items() + ) + } + + if not session["projects"]: + session.invalidate() + raise aiohttp.web.HTTPForbidden(reason="No untainted projects available.") + + # The session is no longer tainted if it's been locked + session["taint"] = False + + session.changed() + return aiohttp.web.Response( + status=303, + body=None, + headers={ + "Location": "/browse", + }, + ) + + async def handle_logout(request: aiohttp.web.Request) -> aiohttp.web.Response: """Properly kill the session for the user.""" log = request.app["Log"] diff --git a/swift_browser_ui/ui/middlewares.py b/swift_browser_ui/ui/middlewares.py index dbb2ba56..8b17ac90 100644 --- a/swift_browser_ui/ui/middlewares.py +++ b/swift_browser_ui/ui/middlewares.py @@ -9,6 +9,7 @@ import aiohttp_session from swift_browser_ui.ui.settings import setd + AiohttpHandler = typing.Callable[ [web.Request], typing.Coroutine[typing.Awaitable, typing.Any, web.Response] ] @@ -46,6 +47,30 @@ async def check_session_at( return await handler(request) [email protected] +async def check_session_taintness( + request: web.Request, + handler: AiohttpHandler, +) -> web.StreamResponse: + """Override tainted sessions with project selection until scoped.""" + session = await aiohttp_session.get_session(request) + if ( + "taint" in session + and session["taint"] + and "select" not in request.path + and "projects" not in request.path + and "lock" not in request.path + and "static" not in request.path + ): + return web.Response( + status=303, + headers={ + "Location": "/select", + }, + ) + return await handler(request) + + @web.middleware async def error_middleware(request: web.Request, handler: AiohttpHandler) -> web.Response: """Return the correct HTTP Error page.""" diff --git a/swift_browser_ui/ui/server.py b/swift_browser_ui/ui/server.py index 6b549fa7..5e69e311 100644 --- a/swift_browser_ui/ui/server.py +++ b/swift_browser_ui/ui/server.py @@ -20,13 +20,14 @@ import aiohttp_session.redis_storage import aioredis -from swift_browser_ui.ui.front import index, browse, loginpassword +from swift_browser_ui.ui.front import index, browse, loginpassword, select from swift_browser_ui.ui.login import ( handle_login, handle_logout, sso_query_begin, sso_query_end, credentials_login_end, + handle_project_lock, ) from swift_browser_ui.ui.api import ( swift_get_metadata_container, @@ -82,6 +83,7 @@ async def servinit( middlewares = [ swift_browser_ui.ui.middlewares.error_middleware, swift_browser_ui.ui.middlewares.check_session_at, + swift_browser_ui.ui.middlewares.check_session_taintness, ] if inject_middleware: middlewares = middlewares + inject_middleware @@ -136,6 +138,14 @@ async def servinit( # Route all URLs prefixed by /browse to the browser page, as this is # an spa aiohttp.web.get("/browse/{tail:.*}", browse), + aiohttp.web.get("/select", select), + ] + ) + + # Add lock routes + app.add_routes( + [ + aiohttp.web.get("/lock/{project}", handle_project_lock), ] ) diff --git a/swift_browser_ui/ui/settings.py b/swift_browser_ui/ui/settings.py index 4596d234..57bbdc0e 100644 --- a/swift_browser_ui/ui/settings.py +++ b/swift_browser_ui/ui/settings.py @@ -70,6 +70,7 @@ setd: Dict[str, Union[str, int, None]] = { "has_trust": environ.get("BROWSER_START_HAS_TRUST", False), "set_origin_address": environ.get("BROWSER_START_SET_ORIGIN_ADDRESS", None), "os_user_domain": environ.get("OS_USER_DOMAIN_NAME", "Default"), + "force_restricted_mode": environ.get("SWIFT_UI_FORCE_RESTRICTED_MODE", False), "logfile": None, "port": 8080, "verbose": False, diff --git a/swift_browser_ui_frontend/src/common/lang.js b/swift_browser_ui_frontend/src/common/lang.js index e908adc8..bfc44ff8 100644 --- a/swift_browser_ui_frontend/src/common/lang.js +++ b/swift_browser_ui_frontend/src/common/lang.js @@ -300,6 +300,20 @@ let default_translations = { buildingIndex: "This project has a large number of objects. Please, " + "wait while the search index is ready, and try again.", }, + select: { + heading: "Select project for logging in", + description: "The user account used for logging in contains " + + "projects flagged as restricted. The interface scope is limited " + + "when a restricted project is opened, i.e. only the restricted " + + "project is visible during a restricted session. This means you " + + "cannot copy or move items across projects or view items in " + + "other projects available to you. Select a project you want to " + + "use from the following listing, after selection you will not be " + + "able to change the project without logging out. If you want to " + + "browse unrestricted projects, use the unrestricted projects " + + "button below.", + unrestricted: "All unrestricted projects", + }, }, }, fi: { @@ -594,6 +608,19 @@ let default_translations = { buildingIndex: "Tässä projektissa on suuri määrä kohteita. Odota, " + "kunnes hakuindeksi on valmis, ja yritä uudelleen.", }, + select: { + heading: "Valitse projekti kirjautuaksesi sisään", + description: "Käyttäjällä on pääsy rajoitettuihin projekteihin. " + + "Selatessa rajoitettua projektia käyttöliittymän pääsy on " + + "rajattu, eli vain rajatun projektin sisältö on näkyvissä. " + + "Tiedostojen kopiointi ja siirto projektista toiseen, ja " + + "muiden projektien sisällön selailu on estetty. Valitse " + + "projekti, jota haluat käyttää. Valinnan jälkeen projektin " + + "vaihto onnistuu vain kirjautumalla ulos. Mikäli haluat " + + "selailla vain rajoittamattomia projekteja, paina " + + "rajoittamattomien projektien nappia alla.", + unrestricted: "Kaikki rajoittamattomat projektit", + }, }, }, }; diff --git a/swift_browser_ui_frontend/src/entries/select.js b/swift_browser_ui_frontend/src/entries/select.js new file mode 100644 index 00000000..4c1d7e6c --- /dev/null +++ b/swift_browser_ui_frontend/src/entries/select.js @@ -0,0 +1,51 @@ +import Vue from "vue"; +import App from "@/pages/SelectPage.vue"; +import Buefy from "buefy"; + +import getLangCookie from "@/common/conv"; +import translations from "@/common/lang"; + +import { getProjects } from "@/common/api"; + +// Import project css +import "@/css/prod.scss"; + +import VueI18n from "vue-i18n"; + +Vue.use(VueI18n); +Vue.use(Buefy); + + +const i18n = new VueI18n({ + locale: getLangCookie(), + messages: translations, +}); + +new Vue ({ + i18n, + data: { + formname: "Token id:", + loginformname: "Openstack account:", + idb: true, + projects: [], + langs: [{ph: "In English", value: "en"}, {ph: "Suomeksi", value: "fi"}], + }, + created() { + document.title = this.$t("message.program_name"); + this.updateProjects(); + }, + methods: { + "updateProjects": function () { + getProjects().then(ret => this.projects = ret); + }, + setCookieLang: function() { + const expiryDate = new Date(); + expiryDate.setMonth(expiryDate.getMonth() + 1); + document.cookie = "OBJ_UI_LANG=" + + i18n.locale + + "; path=/; expires=" + + expiryDate.toUTCString(); + }, + }, + ...App, +}).$mount("#app"); diff --git a/swift_browser_ui_frontend/src/pages/SelectPage.vue b/swift_browser_ui_frontend/src/pages/SelectPage.vue new file mode 100644 index 00000000..939bbbcf --- /dev/null +++ b/swift_browser_ui_frontend/src/pages/SelectPage.vue @@ -0,0 +1,73 @@ +<template> + <div class="selectpage"> + <div class="content has-text-centered"> + <h2>{{ $t("message.select.heading" ) }}</h2> + <p class="maintext"> + {{ $t("message.select.description") }} + </p> + <p + v-for="project in projects.filter(pro => pro.tainted)" + :key="project.id" + > + <b-button + tag="a" + expanded + :href="'/lock/'.concat(project.id)" + size="is-large" + type="is-primary is-outlined" + > + {{ project.name }} + </b-button> + </p> + <p> + <b-button + tag="a" + expanded + href="/lock/none" + size="is-large" + type="is-primary is-outlined" + > + {{ $t("message.select.unrestricted" ) }} + </b-button> + </p> + <p> + <b-field class="locale-changed block center"> + <b-select + v-model="$i18n.locale" + placeholder="Language" + icon="earth" + expanded + @input="setCookieLang ()" + > + <option + v-for="lang in langs" + :key="lang.value" + :value="lang.value" + > + {{ lang.ph }} + </option> + </b-select> + </b-field> + </p> + </div> + </div> +</template> + +<style> +html, body { + height: 100%; +} +.selectpage { + width: 40%; + height: 100%; + display: flex; + flex-direction: column; + margin: auto; + align-content: center; + justify-content: center;; +} +.center { + width: 50%; + margin: auto; +} +</style> diff --git a/swift_browser_ui_frontend/vue.config.js b/swift_browser_ui_frontend/vue.config.js index 0ba8009b..0eaeed64 100644 --- a/swift_browser_ui_frontend/vue.config.js +++ b/swift_browser_ui_frontend/vue.config.js @@ -17,6 +17,13 @@ module.exports = { // eslint-disable-line title: "Swift browser UI", chunks: ["chunk-vendors", "chunk-common", "index"], }, + select: { + entry: "src/entries/select.js", + template: "public/select.html", + filename: "select.html", + title: "Select a project to isolate", + chunks: ["chunk-vendors", "chunk-common", "select"], + }, badrequest: { entry: "src/entries/badrequest.js", template: "public/index.html",	Support project isolation for token #### Proposed solution <!-- A clear and concise description of what you want to happen. --> There should be a possibility to limit project scope when necessary. Some services will require this due to regulation. #### DoD (Definition of Done) <!-- How to know this is implemented. Preferably one short sentence. --> Project scoping can be configured to limit scope to a single project per session, when necessary. #### Testing <!-- How can someone else verify the task, can be test-suite or something else. --> UI tests can be written if necessary.	CSCfi/swift-browser-ui	diff --git a/tests/common/mockups.py b/tests/common/mockups.py index f2f76f7b..e3177d45 100644 --- a/tests/common/mockups.py +++ b/tests/common/mockups.py @@ -32,12 +32,14 @@ class APITestBase(unittest.IsolatedAsyncioTestCase): "name": "test-name-0", "token": "test-token-0", "endpoint": "https://test-endpoint-0/v1/AUTH_test-id-0", + "tainted": False, }, "test-id-1": { "id": "test-id-1", "name": "test-name-1", "token": "test-token-1", "endpoint": "https://test-endpoint-1/v1/AUTH_test-id-1", + "tainted": False, }, } self.aiohttp_session_get_session_mock = unittest.mock.AsyncMock() diff --git a/tests/ui_unit/test_api.py b/tests/ui_unit/test_api.py index 1b01ad52..8423e3bb 100644 --- a/tests/ui_unit/test_api.py +++ b/tests/ui_unit/test_api.py @@ -35,10 +35,12 @@ class APITestClass(tests.common.mockups.APITestBase): { "id": "test-id-0", "name": "test-name-0", + "tainted": False, }, { "id": "test-id-1", "name": "test-name-1", + "tainted": False, }, ] ) diff --git a/tests/ui_unit/test_login.py b/tests/ui_unit/test_login.py index 3ffda3b2..d1f8fa1d 100644 --- a/tests/ui_unit/test_login.py +++ b/tests/ui_unit/test_login.py @@ -197,6 +197,7 @@ class LoginTestClass(tests.common.mockups.APITestBase): """ self.setd_mock["session_lifetime"] = 28800 self.setd_mock["history_lifetime"] = 2592000 + self.setd_mock["force_restricted_mode"] = False self.setd_mock["swift_endpoint_url"] = ("http://obj.exampleosep.com:443/v1",) patch1 = unittest.mock.patch( "swift_browser_ui.ui.login.setd",	{ "commit_name": "head_commit", "failed_lite_validators": [ "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 2, "test_score": 2 }, "num_modified_files": 10 }	1.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[test,docs,ui_test]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "requirements.txt", "pip_packages": [ "pytest", "pytest-cov", "pytest-xdist", "coveragepy-lcov" ], "pre_install": [ "apt-get update", "apt-get install -y gcc" ], "python": "3.9", "reqs_path": [ "requirements.txt" ], "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	aiohttp==3.8.1 aiohttp-session==2.11.0 aioredis==2.0.1 aiosignal==1.3.2 alabaster==0.7.16 async-timeout==4.0.3 asyncpg==0.25.0 attrs==25.3.0 babel==2.17.0 black==22.1.0 certifi==2021.10.8 cffi==1.17.1 charset-normalizer==2.1.1 click==8.0.4 coverage==6.3.2 coveragepy-lcov==0.1.2 cryptography==36.0.1 distlib==0.3.9 docutils==0.17.1 exceptiongroup==1.2.2 execnet==2.1.1 filelock==3.18.0 flake8==4.0.1 flake8-docstrings==1.6.0 frozenlist==1.5.0 gunicorn==23.0.0 idna==3.10 imagesize==1.4.1 importlib_metadata==8.6.1 iniconfig==2.1.0 iso8601==2.1.0 Jinja2==3.1.6 keystoneauth1==4.4.0 MarkupSafe==3.0.2 mccabe==0.6.1 multidict==6.2.0 mypy-extensions==1.0.0 os-service-types==1.7.0 packaging==24.2 pathspec==0.12.1 pbr==6.1.1 platformdirs==4.3.7 pluggy==1.5.0 propcache==0.3.1 py==1.11.0 pycodestyle==2.8.0 pycparser==2.22 pydocstyle==6.3.0 pyflakes==2.4.0 Pygments==2.19.1 pytest==7.0.1 pytest-cov==3.0.0 pytest-forked==1.6.0 pytest-timeout==2.1.0 pytest-xdist==2.5.0 python-swiftclient==3.13.1 requests==2.32.3 six==1.17.0 snowballstemmer==2.2.0 Sphinx==4.4.0 sphinx-rtd-theme==1.0.0 sphinxcontrib-applehelp==2.0.0 sphinxcontrib-devhelp==2.0.0 sphinxcontrib-htmlhelp==2.1.0 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==2.0.0 sphinxcontrib-serializinghtml==2.0.0 stevedore==5.4.1 -e git+https://github.com/CSCfi/swift-browser-ui.git@26a360f4c39d0c2161c9651554090a9610d4cbce#egg=swift_browser_ui toml==0.10.2 tomli==2.2.1 tox==3.24.5 typing_extensions==4.13.0 urllib3==2.3.0 uvloop==0.16.0 virtualenv==20.29.3 yarl==1.18.3 zipp==3.21.0	name: swift-browser-ui channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - pip=25.0=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - aiohttp==3.8.1 - aiohttp-session==2.11.0 - aioredis==2.0.1 - aiosignal==1.3.2 - alabaster==0.7.16 - async-timeout==4.0.3 - asyncpg==0.25.0 - attrs==25.3.0 - babel==2.17.0 - black==22.1.0 - certifi==2021.10.8 - cffi==1.17.1 - charset-normalizer==2.1.1 - click==8.0.4 - coverage==6.3.2 - coveragepy-lcov==0.1.2 - cryptography==36.0.1 - distlib==0.3.9 - docutils==0.17.1 - exceptiongroup==1.2.2 - execnet==2.1.1 - filelock==3.18.0 - flake8==4.0.1 - flake8-docstrings==1.6.0 - frozenlist==1.5.0 - gunicorn==23.0.0 - idna==3.10 - imagesize==1.4.1 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - iso8601==2.1.0 - jinja2==3.1.6 - keystoneauth1==4.4.0 - markupsafe==3.0.2 - mccabe==0.6.1 - multidict==6.2.0 - mypy-extensions==1.0.0 - os-service-types==1.7.0 - packaging==24.2 - pathspec==0.12.1 - pbr==6.1.1 - platformdirs==4.3.7 - pluggy==1.5.0 - propcache==0.3.1 - py==1.11.0 - pycodestyle==2.8.0 - pycparser==2.22 - pydocstyle==6.3.0 - pyflakes==2.4.0 - pygments==2.19.1 - pytest==7.0.1 - pytest-cov==3.0.0 - pytest-forked==1.6.0 - pytest-timeout==2.1.0 - pytest-xdist==2.5.0 - python-swiftclient==3.13.1 - requests==2.32.3 - six==1.17.0 - snowballstemmer==2.2.0 - sphinx==4.4.0 - sphinx-rtd-theme==1.0.0 - sphinxcontrib-applehelp==2.0.0 - sphinxcontrib-devhelp==2.0.0 - sphinxcontrib-htmlhelp==2.1.0 - sphinxcontrib-jsmath==1.0.1 - sphinxcontrib-qthelp==2.0.0 - sphinxcontrib-serializinghtml==2.0.0 - stevedore==5.4.1 - toml==0.10.2 - tomli==2.2.1 - tox==3.24.5 - typing-extensions==4.13.0 - urllib3==2.3.0 - uvloop==0.16.0 - virtualenv==20.29.3 - yarl==1.18.3 - zipp==3.21.0 prefix: /opt/conda/envs/swift-browser-ui	[ "tests/ui_unit/test_api.py::APITestClass::test_os_list_projects" ]	[]	[ "tests/ui_unit/test_api.py::APITestClass::test_add_project_container_acl", "tests/ui_unit/test_api.py::APITestClass::test_batch_swift_update_object_metadata", "tests/ui_unit/test_api.py::APITestClass::test_batch_swift_update_object_metadata_no_objects", "tests/ui_unit/test_api.py::APITestClass::test_get_access_control_metadata", "tests/ui_unit/test_api.py::APITestClass::test_get_os_user", "tests/ui_unit/test_api.py::APITestClass::test_remove_container_acl", "tests/ui_unit/test_api.py::APITestClass::test_remove_project_container_acl", "tests/ui_unit/test_api.py::APITestClass::test_swift_batch_get_object_meta", "tests/ui_unit/test_api.py::APITestClass::test_swift_batch_get_object_meta_through_container", "tests/ui_unit/test_api.py::APITestClass::test_swift_create_container", "tests/ui_unit/test_api.py::APITestClass::test_swift_delete_container", "tests/ui_unit/test_api.py::APITestClass::test_swift_delete_containers_to_objects", "tests/ui_unit/test_api.py::APITestClass::test_swift_delete_objects", "tests/ui_unit/test_api.py::APITestClass::test_swift_delete_objects_toomany", "tests/ui_unit/test_api.py::APITestClass::test_swift_download_object", "tests/ui_unit/test_api.py::APITestClass::test_swift_get_container_acl_wrapper", "tests/ui_unit/test_api.py::APITestClass::test_swift_get_container_meta", "tests/ui_unit/test_api.py::APITestClass::test_swift_get_met_wrapper", "tests/ui_unit/test_api.py::APITestClass::test_swift_get_meta_fail", "tests/ui_unit/test_api.py::APITestClass::test_swift_get_project_metadata", "tests/ui_unit/test_api.py::APITestClass::test_swift_get_project_metadata_fail", "tests/ui_unit/test_api.py::APITestClass::test_swift_get_shared_container_address", "tests/ui_unit/test_api.py::APITestClass::test_swift_list_containers", "tests/ui_unit/test_api.py::APITestClass::test_swift_list_containers_fail_ostack", "tests/ui_unit/test_api.py::APITestClass::test_swift_list_containers_no_access", "tests/ui_unit/test_api.py::APITestClass::test_swift_list_objects", "tests/ui_unit/test_api.py::APITestClass::test_swift_update_container_metadata", "tests/ui_unit/test_api.py::APITestClass::test_swift_update_object_meta_wrapper", "tests/ui_unit/test_api.py::APITestClass::test_swift_update_object_metadata_via_container", "tests/ui_unit/test_api.py::ProxyFunctionsTestClass::test_get_upload_session", "tests/ui_unit/test_api.py::ProxyFunctionsTestClass::test_swift_download_container", "tests/ui_unit/test_api.py::ProxyFunctionsTestClass::test_swift_download_share_object", "tests/ui_unit/test_api.py::ProxyFunctionsTestClass::test_swift_replicate_container", "tests/ui_unit/test_login.py::LoginTestClass::test_credentials_login_end", "tests/ui_unit/test_login.py::LoginTestClass::test_handle_login", "tests/ui_unit/test_login.py::LoginTestClass::test_handle_logout", "tests/ui_unit/test_login.py::LoginTestClass::test_login_with_token", "tests/ui_unit/test_login.py::LoginTestClass::test_sso_query_begin_with_trust", "tests/ui_unit/test_login.py::LoginTestClass::test_sso_query_begin_without_trust", "tests/ui_unit/test_login.py::LoginTestClass::test_sso_query_end", "tests/ui_unit/test_login.py::LoginTestClass::test_test_token" ]	[]	MIT License	null
CTFd__ctfcli-148	26bcec897fcc1f5f23c8117e2a225a130a8b0dea	2024-03-07 11:52:44	704a86447656564369e41881af1f692d45cf4f40	ColdHeat: Pardon my ignorance here but I don't see what the point is of the `--load` switch? Do you want to have the image output as a tar file? Additional arguments could be good but I think it would need to be generic. Perhaps something like `--extra-args="--switch1=123"` pl4nty: `--load` loads the image into local storage. same as current behaviour, because the `docker` driver implicitly uses `--load` if no output is specified (for backwards compat). but other drivers require an explicit output argument, and will fail at the moment. generic arguments sound great, I'm happy to send another PR if you'd like.	diff --git a/ctfcli/cli/challenges.py b/ctfcli/cli/challenges.py index b56525c..51755e6 100644 --- a/ctfcli/cli/challenges.py +++ b/ctfcli/cli/challenges.py @@ -1,3 +1,4 @@ +import contextlib import logging import os import subprocess @@ -203,167 +204,253 @@ class ChallengeCommand: click.secho(f"Could not process the challenge path: '{repo}'", fg="red") return 1 - def push(self, challenge: str = None) -> int: + def push(self, challenge: str = None, no_auto_pull: bool = False, quiet=False) -> int: log.debug(f"push: (challenge={challenge})") config = Config() - challenge_path = Path.cwd() if challenge: - challenge_path = config.project_path / Path(challenge) + challenge_instance = self._resolve_single_challenge(challenge) + if not challenge_instance: + return 1 - # Get a relative path from project root to the challenge - # As this is what git subtree push requires - challenge_path = challenge_path.relative_to(config.project_path) - challenge_repo = config.challenges.get(str(challenge_path), None) + challenges = [challenge_instance] + else: + challenges = self._resolve_all_challenges() - # if we don't find the challenge by the directory, - # check if it's saved with a direct path to challenge.yml - if not challenge_repo: - challenge_repo = config.challenges.get(str(challenge_path / "challenge.yml"), None) + failed_pushes = [] - if not challenge_repo: - click.secho( - f"Could not find added challenge '{challenge_path}' " - "Please check that the challenge is added to .ctf/config and that your path matches", - fg="red", - ) - return 1 + if quiet or len(challenges) <= 1: + context = contextlib.nullcontext(challenges) + else: + context = click.progressbar(challenges, label="Pushing challenges") - if not challenge_repo.endswith(".git"): - click.secho( - f"Cannot push challenge '{challenge_path}', as it's not a git-based challenge", - fg="yellow", - ) - return 1 + with context as context_challenges: + for challenge_instance in context_challenges: + click.echo() - head_branch = get_git_repo_head_branch(challenge_repo) + # Get a relative path from project root to the challenge + # As this is what git subtree push requires + challenge_path = challenge_instance.challenge_directory.resolve().relative_to(config.project_path) + challenge_repo = config.challenges.get(str(challenge_path), None) - log.debug(f"call(['git', 'add', '.'], cwd='{config.project_path / challenge_path}')") - git_add = subprocess.call(["git", "add", "."], cwd=config.project_path / challenge_path) + # if we don't find the challenge by the directory, + # check if it's saved with a direct path to challenge.yml + if not challenge_repo: + challenge_repo = config.challenges.get(str(challenge_path / "challenge.yml"), None) - log.debug( - f"call(['git', 'commit', '-m', 'Pushing changes to {challenge_path}'], " - f"cwd='{config.project_path / challenge_path}')" - ) - git_commit = subprocess.call( - ["git", "commit", "-m", f"Pushing changes to {challenge_path}"], - cwd=config.project_path / challenge_path, - ) + if not challenge_repo: + click.secho( + f"Could not find added challenge '{challenge_path}' " + "Please check that the challenge is added to .ctf/config and that your path matches", + fg="red", + ) + failed_pushes.append(challenge_instance) + continue - if any(r != 0 for r in [git_add, git_commit]): - click.secho( - "Could not commit the challenge changes. " "Please check git error messages above.", - fg="red", - ) - return 1 + if not challenge_repo.endswith(".git"): + click.secho( + f"Cannot push challenge '{challenge_path}', as it's not a git-based challenge", + fg="yellow", + ) + failed_pushes.append(challenge_instance) + continue - log.debug( - f"call(['git', 'subtree', 'push', '--prefix', '{challenge_path}', '{challenge_repo}', '{head_branch}'], " - f"cwd='{config.project_path / challenge_path}')" - ) - git_subtree_push = subprocess.call( - [ - "git", - "subtree", - "push", - "--prefix", - challenge_path, - challenge_repo, - head_branch, - ], - cwd=config.project_path, - ) + click.secho(f"Pushing '{challenge_path}' to '{challenge_repo}'", fg="blue") + head_branch = get_git_repo_head_branch(challenge_repo) - if git_subtree_push != 0: - click.secho( - "Could not push the challenge subtree. " "Please check git error messages above.", - fg="red", - ) - return 1 + log.debug( + f"call(['git', 'status', '--porcelain'], cwd='{config.project_path / challenge_path}'," + f" stdout=subprocess.PIPE, text=True)" + ) + git_status = subprocess.run( + ["git", "status", "--porcelain"], + cwd=config.project_path / challenge_path, + stdout=subprocess.PIPE, + text=True, + ) - return 0 + if git_status.stdout.strip() == "" and git_status.returncode == 0: + click.secho(f"No changes to be pushed for {challenge_path}", fg="green") + continue + + log.debug(f"call(['git', 'add', '.'], cwd='{config.project_path / challenge_path}')") + git_add = subprocess.call(["git", "add", "."], cwd=config.project_path / challenge_path) + + log.debug( + f"call(['git', 'commit', '-m', 'Pushing changes to {challenge_path}'], " + f"cwd='{config.project_path / challenge_path}')" + ) + git_commit = subprocess.call( + ["git", "commit", "-m", f"Pushing changes to {challenge_path}"], + cwd=config.project_path / challenge_path, + ) + + if any(r != 0 for r in [git_add, git_commit]): + click.secho( + "Could not commit the challenge changes. " "Please check git error messages above.", + fg="red", + ) + failed_pushes.append(challenge_instance) + continue + + log.debug( + f"call(['git', 'subtree', 'push', '--prefix', '{challenge_path}', '{challenge_repo}', " + f"'{head_branch}'], cwd='{config.project_path / challenge_path}')" + ) + git_subtree_push = subprocess.call( + [ + "git", + "subtree", + "push", + "--prefix", + challenge_path, + challenge_repo, + head_branch, + ], + cwd=config.project_path, + ) + + if git_subtree_push != 0: + click.secho( + "Could not push the challenge subtree. " "Please check git error messages above.", + fg="red", + ) + failed_pushes.append(challenge_instance) + continue + + # if auto pull is not disabled + if not no_auto_pull: + self.pull(str(challenge_path), quiet=True) - def pull(self, challenge: str = None) -> int: + if len(failed_pushes) == 0: + if not quiet: + click.secho("Success! All challenges pushed!", fg="green") + + return 0 + + if not quiet: + click.secho("Push failed for:", fg="red") + for challenge in failed_pushes: + click.echo(f" - {challenge}") + + return 1 + + def pull(self, challenge: str = None, quiet=False) -> int: log.debug(f"pull: (challenge={challenge})") config = Config() - challenge_path = Path.cwd() if challenge: - challenge_path = config.project_path / Path(challenge) + challenge_instance = self._resolve_single_challenge(challenge) + if not challenge_instance: + return 1 - # Get a relative path from project root to the challenge - # As this is what git subtree push requires - challenge_path = challenge_path.relative_to(config.project_path) - challenge_repo = config.challenges.get(str(challenge_path), None) + challenges = [challenge_instance] + else: + challenges = self._resolve_all_challenges() - # if we don't find the challenge by the directory, - # check if it's saved with a direct path to challenge.yml - if not challenge_repo: - challenge_repo = config.challenges.get(str(challenge_path / "challenge.yml"), None) + if quiet or len(challenges) <= 1: + context = contextlib.nullcontext(challenges) + else: + context = click.progressbar(challenges, label="Pulling challenges") - if not challenge_repo: - click.secho( - f"Could not find added challenge '{challenge_path}' " - "Please check that the challenge is added to .ctf/config and that your path matches", - fg="red", - ) - return 1 + failed_pulls = [] + with context as context_challenges: + for challenge_instance in context_challenges: + click.echo() - if not challenge_repo.endswith(".git"): - click.secho( - f"Cannot pull challenge '{challenge_path}', as it's not a git-based challenge", - fg="yellow", - ) - return 1 + # Get a relative path from project root to the challenge + # As this is what git subtree push requires + challenge_path = challenge_instance.challenge_directory.resolve().relative_to(config.project_path) + challenge_repo = config.challenges.get(str(challenge_path), None) - click.secho(f"Pulling latest '{challenge_repo}' to '{challenge_path}'", fg="blue") - head_branch = get_git_repo_head_branch(challenge_repo) + # if we don't find the challenge by the directory, + # check if it's saved with a direct path to challenge.yml + if not challenge_repo: + challenge_repo = config.challenges.get(str(challenge_path / "challenge.yml"), None) - log.debug( - f"call(['git', 'subtree', 'pull', '--prefix', '{challenge_path}', " - f"'{challenge_repo}', '{head_branch}', '--squash'], cwd='{config.project_path}')" - ) - git_subtree_pull = subprocess.call( - [ - "git", - "subtree", - "pull", - "--prefix", - challenge_path, - challenge_repo, - head_branch, - "--squash", - ], - cwd=config.project_path, - ) + if not challenge_repo: + click.secho( + f"Could not find added challenge '{challenge_path}' " + "Please check that the challenge is added to .ctf/config and that your path matches", + fg="red", + ) + failed_pulls.append(challenge_instance) + continue - if git_subtree_pull != 0: - click.secho( - f"Could not pull the subtree for challenge '{challenge_path}'. " - "Please check git error messages above.", - fg="red", - ) - return 1 + if not challenge_repo.endswith(".git"): + click.secho( + f"Cannot pull challenge '{challenge_path}', as it's not a git-based challenge", + fg="yellow", + ) + failed_pulls.append(challenge_instance) + continue - log.debug(f"call(['git', 'mergetool'], cwd='{config.project_path / challenge_path}')") - git_mergetool = subprocess.call(["git", "mergetool"], cwd=config.project_path / challenge_path) + click.secho(f"Pulling latest '{challenge_repo}' to '{challenge_path}'", fg="blue") + head_branch = get_git_repo_head_branch(challenge_repo) - log.debug(f"call(['git', 'clean', '-f'], cwd='{config.project_path / challenge_path}')") - git_clean = subprocess.call(["git", "clean", "-f"], cwd=config.project_path / challenge_path) + log.debug( + f"call(['git', 'subtree', 'pull', '--prefix', '{challenge_path}', " + f"'{challenge_repo}', '{head_branch}', '--squash'], cwd='{config.project_path}')" + ) - log.debug(f"call(['git', 'commit', '--no-edit'], cwd='{config.project_path / challenge_path}')") - subprocess.call(["git", "commit", "--no-edit"], cwd=config.project_path / challenge_path) + pull_env = os.environ.copy() + pull_env["GIT_MERGE_AUTOEDIT"] = "no" + + git_subtree_pull = subprocess.call( + [ + "git", + "subtree", + "pull", + "--prefix", + challenge_path, + challenge_repo, + head_branch, + "--squash", + ], + cwd=config.project_path, + env=pull_env, + ) - # git commit is allowed to return a non-zero code because it would also mean that there's nothing to commit - if any(r != 0 for r in [git_mergetool, git_clean]): - click.secho( - f"Could not commit the subtree for challenge '{challenge_path}'. " - "Please check git error messages above.", - fg="red", - ) - return 1 + if git_subtree_pull != 0: + click.secho( + f"Could not pull the subtree for challenge '{challenge_path}'. " + "Please check git error messages above.", + fg="red", + ) + failed_pulls.append(challenge_instance) + continue - return 0 + log.debug(f"call(['git', 'mergetool'], cwd='{config.project_path / challenge_path}')") + git_mergetool = subprocess.call(["git", "mergetool"], cwd=config.project_path / challenge_path) + + log.debug(f"call(['git', 'commit', '--no-edit'], cwd='{config.project_path / challenge_path}')") + subprocess.call(["git", "commit", "--no-edit"], cwd=config.project_path / challenge_path) + + log.debug(f"call(['git', 'clean', '-f'], cwd='{config.project_path / challenge_path}')") + git_clean = subprocess.call(["git", "clean", "-f"], cwd=config.project_path / challenge_path) + + # git commit is allowed to return a non-zero code + # because it would also mean that there's nothing to commit + if any(r != 0 for r in [git_mergetool, git_clean]): + click.secho( + f"Could not commit the subtree for challenge '{challenge_path}'. " + "Please check git error messages above.", + fg="red", + ) + failed_pulls.append(challenge_instance) + continue + + if len(failed_pulls) == 0: + if not quiet: + click.secho("Success! All challenges pulled!", fg="green") + return 0 + + if not quiet: + click.secho("Pull failed for:", fg="red") + for challenge in failed_pulls: + click.echo(f" - {challenge}") + + return 1 def restore(self, challenge: str = None) -> int: log.debug(f"restore: (challenge={challenge})") diff --git a/ctfcli/core/image.py b/ctfcli/core/image.py index e66e6e7..025187f 100644 --- a/ctfcli/core/image.py +++ b/ctfcli/core/image.py @@ -24,7 +24,9 @@ class Image: self.built = False def build(self) -> Optional[str]: - docker_build = subprocess.call(["docker", "build", "-t", self.name, "."], cwd=self.build_path.absolute()) + docker_build = subprocess.call( + ["docker", "build", "--load", "-t", self.name, "."], cwd=self.build_path.absolute() + ) if docker_build != 0: return	Support Buildkit drivers for image builds [Buildkit drivers](https://docs.docker.com/build/drivers/) offer a lot of additional features over the default `docker` driver, like multi-arch images and remote builders. They could be supported by moving tag/push to `docker build --push`, depending on docker buildx which became the default for docker engine in Feb 2023. Buildx/buildkit has other benefits too eg native caching for CI environments, that would be useful to support in a future PR. Happy to send a PR for this if you want.	CTFd/ctfcli	diff --git a/tests/core/test_image.py b/tests/core/test_image.py index 2e773f4..8e6ab37 100644 --- a/tests/core/test_image.py +++ b/tests/core/test_image.py @@ -56,7 +56,9 @@ class TestImage(unittest.TestCase): self.assertTrue(image.built) self.assertEqual(image_name, "test-challenge") - mock_call.assert_called_once_with(["docker", "build", "-t", "test-challenge", "."], cwd=build_path.absolute()) + mock_call.assert_called_once_with( + ["docker", "build", "--load", "-t", "test-challenge", "."], cwd=build_path.absolute() + ) @mock.patch("ctfcli.core.image.subprocess.call", return_value=1) def test_build_returns_none_if_failed(self, mock_call: MagicMock): @@ -68,7 +70,9 @@ class TestImage(unittest.TestCase): self.assertFalse(image.built) self.assertIsNone(image_name) - mock_call.assert_called_once_with(["docker", "build", "-t", "test-challenge", "."], cwd=build_path.absolute()) + mock_call.assert_called_once_with( + ["docker", "build", "--load", "-t", "test-challenge", "."], cwd=build_path.absolute() + ) @mock.patch("ctfcli.core.image.subprocess.call", return_value=0) def test_push_built_image(self, mock_call: MagicMock): @@ -150,7 +154,7 @@ class TestImage(unittest.TestCase): mock_call.assert_has_calls( [ call( - ["docker", "build", "-t", "test-challenge", "."], + ["docker", "build", "--load", "-t", "test-challenge", "."], cwd=build_path.absolute(), ), call( @@ -224,7 +228,7 @@ class TestImage(unittest.TestCase): mock_call.assert_has_calls( [ call( - ["docker", "build", "-t", "test-challenge", "."], + ["docker", "build", "--load", "-t", "test-challenge", "."], cwd=build_path.absolute(), ), call(	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_hyperlinks", "has_many_modified_files" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 1, "test_score": 2 }, "num_modified_files": 2 }	0.1	{ "env_vars": null, "env_yml_path": null, "install": "pip install -e .[dev]", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.9", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	appdirs==1.4.4 arrow==1.3.0 binaryornot==0.4.4 certifi==2025.1.31 chardet==5.2.0 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 cookiecutter==2.6.0 -e git+https://github.com/CTFd/ctfcli.git@26bcec897fcc1f5f23c8117e2a225a130a8b0dea#egg=ctfcli exceptiongroup @ file:///croot/exceptiongroup_1706031385326/work fire==0.5.0 idna==3.10 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work Jinja2==3.1.6 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mdurl==0.1.2 packaging @ file:///croot/packaging_1734472117206/work pluggy @ file:///croot/pluggy_1733169602837/work Pygments==2.19.1 pytest @ file:///croot/pytest_1738938843180/work python-dateutil==2.9.0.post0 python-frontmatter==1.1.0 python-slugify==8.0.4 PyYAML==6.0.2 requests==2.32.3 rich==14.0.0 six==1.17.0 termcolor==3.0.0 text-unidecode==1.3 tomli @ file:///opt/conda/conda-bld/tomli_1657175507142/work types-python-dateutil==2.9.0.20241206 typing_extensions==4.13.0 urllib3==2.3.0	name: ctfcli channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - ca-certificates=2025.2.25=h06a4308_0 - exceptiongroup=1.2.0=py39h06a4308_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.2=py39h06a4308_0 - pip=25.0=py39h06a4308_0 - pluggy=1.5.0=py39h06a4308_0 - pytest=8.3.4=py39h06a4308_0 - python=3.9.21=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py39h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tomli=2.0.1=py39h06a4308_0 - tzdata=2025a=h04d1e81_0 - wheel=0.45.1=py39h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - appdirs==1.4.4 - arrow==1.3.0 - binaryornot==0.4.4 - certifi==2025.1.31 - chardet==5.2.0 - charset-normalizer==3.4.1 - click==8.1.8 - colorama==0.4.6 - cookiecutter==2.6.0 - ctfcli==0.1.2 - fire==0.5.0 - idna==3.10 - jinja2==3.1.6 - markdown-it-py==3.0.0 - markupsafe==3.0.2 - mdurl==0.1.2 - pygments==2.19.1 - python-dateutil==2.9.0.post0 - python-frontmatter==1.1.0 - python-slugify==8.0.4 - pyyaml==6.0.2 - requests==2.32.3 - rich==14.0.0 - six==1.17.0 - termcolor==3.0.0 - text-unidecode==1.3 - types-python-dateutil==2.9.0.20241206 - typing-extensions==4.13.0 - urllib3==2.3.0 prefix: /opt/conda/envs/ctfcli	[ "tests/core/test_image.py::TestImage::test_build", "tests/core/test_image.py::TestImage::test_build_returns_none_if_failed", "tests/core/test_image.py::TestImage::test_builds_image_before_export", "tests/core/test_image.py::TestImage::test_builds_image_before_push" ]	[]	[ "tests/core/test_image.py::TestImage::test_accepts_path_as_string_and_pathlike", "tests/core/test_image.py::TestImage::test_assigns_attributes", "tests/core/test_image.py::TestImage::test_export_built_image", "tests/core/test_image.py::TestImage::test_extracts_correct_basename", "tests/core/test_image.py::TestImage::test_get_exposed_port", "tests/core/test_image.py::TestImage::test_get_exposed_port_returns_first_port_if_multiple_exposed", "tests/core/test_image.py::TestImage::test_get_exposed_port_returns_none_if_no_ports_exposed", "tests/core/test_image.py::TestImage::test_pull", "tests/core/test_image.py::TestImage::test_push_built_image", "tests/core/test_image.py::TestImage::test_push_returns_none_if_failed" ]	[]	Apache License 2.0	null
CWorthy-ocean__C-Star-206	b67a5f9f18fcb0aae6c8eaae6463310b9d9ad638	2024-12-23 17:01:00	70c35baf4e6664de208500686b5a0a9c1af22e73		diff --git a/cstar/base/component.py b/cstar/base/component.py index 1128546..191cc43 100644 --- a/cstar/base/component.py +++ b/cstar/base/component.py @@ -6,7 +6,7 @@ from cstar.base.base_model import BaseModel if TYPE_CHECKING: from cstar.base.additional_code import AdditionalCode from cstar.base.discretization import Discretization - from cstar.scheduler.job import SchedulerJob + from cstar.execution.handler import ExecutionHandler class Component(ABC): @@ -179,7 +179,7 @@ class Component(ABC): """ @abstractmethod - def run(self) -> Optional["SchedulerJob"]: + def run(self) -> "ExecutionHandler": """Run this component. This abstract method will be implemented differently by different Component diff --git a/cstar/case.py b/cstar/case.py index cc271ec..bd9f429 100644 --- a/cstar/case.py +++ b/cstar/case.py @@ -14,7 +14,7 @@ from cstar.roms.component import ROMSComponent from cstar.marbl.component import MARBLComponent if TYPE_CHECKING: - from cstar.scheduler.job import SchedulerJob + from cstar.execution.handler import ExecutionHandler class Case: @@ -535,7 +535,7 @@ class Case: walltime: Optional[str] = None, queue_name: Optional[str] = None, job_name: Optional[str] = None, - ) -> Optional["SchedulerJob"]: + ) -> "ExecutionHandler": """Run the case by calling `component.run(caseroot)` on the primary component (to which others are coupled).""" diff --git a/cstar/execution/handler.py b/cstar/execution/handler.py new file mode 100644 index 0000000..46ea117 --- /dev/null +++ b/cstar/execution/handler.py @@ -0,0 +1,164 @@ +import time +from abc import ABC, abstractmethod +from enum import Enum, auto +from pathlib import Path + + +class ExecutionStatus(Enum): + """Enum representing possible states of a process to be executed. + + Attributes + ---------- + UNSUBMITTED : ExecutionStatus + The task is to be handled by a scheduler, but has not yet been submitted + PENDING : ExecutionStatus + The task is to be handled by a scheduler, but is waiting to start + RUNNING : ExecutionStatus + The task is currently executing. + COMPLETED : ExecutionStatus + The task finished successfully. + CANCELLED : ExecutionStatus + The task was cancelled before completion. + FAILED : ExecutionStatus + The task finished unsuccessfully. + HELD : ExecutionStatus + The task is to be handled by a scheduler, but is currently on hold pending release + ENDING : ExecutionStatus + The task is in the process of ending but not fully completed. + UNKNOWN : ExecutionStatus + The task state is unknown or not recognized. + """ + + UNSUBMITTED = auto() + PENDING = auto() + RUNNING = auto() + COMPLETED = auto() + CANCELLED = auto() + FAILED = auto() + HELD = auto() + ENDING = auto() + UNKNOWN = auto() + + def __str__(self) -> str: + return self.name.lower() # Convert enum name to lowercase for display + + +class ExecutionHandler(ABC): + """Abstract base class for managing the execution of a task or process. + + This class defines the interface and common behavior for handling task + execution, such as monitoring the status and providing live updates + from an output file. + + Attributes + ---------- + status : ExecutionStatus + Represents the current status of the task (e.g., RUNNING, COMPLETED). + This is an abstract property that must be implemented by subclasses. + + Methods + ------- + updates(seconds=10) + Stream live updates from the task's output file for a specified duration. + """ + + @property + @abstractmethod + def status(self) -> ExecutionStatus: + """Abstract property representing the current status of the task. + + Subclasses must implement this property to query the underlying + process and return the appropriate `ExecutionStatus` enum value. + + Returns + ------- + ExecutionStatus + The current status of the task, such as `ExecutionStatus.RUNNING`, + `ExecutionStatus.COMPLETED`, or other valid states defined in + the `ExecutionStatus` enum. + + Notes + ----- + The specific implementation should query the underlying process or + system to determine the task's status. + """ + + pass + + @property + @abstractmethod + def output_file(self) -> Path: + """Abstract property representing the output file. + + Returns + ------- + output_file (Path): + Path to the file in which stdout and stderr will be written. + """ + + pass + + def updates(self, seconds: int = 10, confirm_indefinite: bool = True): + """Stream live updates from the task's output file. + + This method streams updates from the task's output file for the + specified duration. If the task is not running, a message will + indicate the inability to provide updates. If the output file + exists and the task has already finished, the method provides a + reference to the output file for review. + + Parameters + ---------- + seconds : int, optional, default = 10 + The duration (in seconds) for which updates should be streamed. + If set to 0, updates will be streamed indefinitely until + interrupted by the user. + confirm_indefinite: bool, optional, default = True + If 'seconds' is set to 0, the user will be prompted to confirm + whether they want to continue with an indefinite update stream + if confirm_indefinite is set to True + Notes + ----- + - This method moves to the end of the output file and streams only + new lines appended during the specified duration. + - When streaming indefinitely (`seconds=0`), user confirmation is + required before proceeding. + """ + + if self.status != ExecutionStatus.RUNNING: + print( + f"This job is currently not running ({self.status}). Live updates cannot be provided." + ) + if (self.status in {ExecutionStatus.FAILED, ExecutionStatus.COMPLETED}) or ( + self.status == ExecutionStatus.CANCELLED and self.output_file.exists() + ): + print(f"See {self.output_file.resolve()} for job output") + return + + if (seconds == 0) and (confirm_indefinite): + # Confirm indefinite tailing + confirmation = ( + input( + "This will provide indefinite updates to your job. You can stop it anytime using Ctrl+C. " + "Do you want to continue? (y/n): " + ) + .strip() + .lower() + ) + if confirmation not in {"y", "yes"}: + return + + try: + with open(self.output_file, "r") as f: + f.seek(0, 2) # Move to the end of the file + start_time = time.time() + while seconds == 0 or (time.time() - start_time < seconds): + line = f.readline() + if self.status != ExecutionStatus.RUNNING: + return + elif line: + print(line, end="") + else: + time.sleep(0.1) # 100ms delay between updates + except KeyboardInterrupt: + print("\nLive status updates stopped by user.") diff --git a/cstar/execution/local_process.py b/cstar/execution/local_process.py new file mode 100644 index 0000000..deb5749 --- /dev/null +++ b/cstar/execution/local_process.py @@ -0,0 +1,196 @@ +import subprocess +from pathlib import Path +from typing import Optional +from datetime import datetime +from cstar.execution.handler import ExecutionHandler, ExecutionStatus + + +class LocalProcess(ExecutionHandler): + """Execution handler for managing and monitoring local subprocesses. + + This class handles the execution of commands as local subprocesses, + allowing users to monitor their status, stream output, and manage + lifecycle events such as cancellation. + + Attributes + ---------- + commands : str + The shell command(s) to be executed as a subprocess. + run_path : Path + The directory from which the subprocess will be executed. + output_file : Path + The file where the subprocess's standard output and error will + be written. + status : ExecutionStatus + The current status of the subprocess, represented as an + `ExecutionStatus` enum value. + + Methods + ------- + start() + Start the subprocess using the specified command. + cancel() + Cancel the running subprocess. + updates(seconds=10) + Stream live updates from the subprocess's output file for a + specified duration. + """ + + def __init__( + self, + commands: str, + output_file: Optional[str \| Path] = None, + run_path: Optional[str \| Path] = None, + ): + """Initialize a `LocalProcess` instance. + + Parameters + ---------- + commands : str + The shell command(s) to execute as a subprocess. + output_file : str or Path, optional + The file path where the subprocess's standard output and error + will be written. Defaults to an auto-generated file in the + `run_path` directory. + run_path : str or Path, optional + The directory from which the subprocess will be executed. + Defaults to the current working directory. + """ + + self.commands = commands + self.run_path = Path(run_path) if run_path is not None else Path.cwd() + self._default_name = ( + f"cstar_process_{datetime.strftime(datetime.now(), '%Y%m%d_%H%M%S')}" + ) + self._output_file = ( + Path(output_file) if output_file is not None else output_file + ) + + self._output_file_handle = None + self._process = None + self._cancelled = False + + def start(self): + """Start the local process. + + This method initiates the execution of the command specified during + initialization. The command runs in a subprocess, with its standard + output and error directed to the output file. + + Notes + ----- + - The output file is opened and actively written to during execution. + - Use the `status` property to monitor the current state of the + subprocess. + + See Also + -------- + cancel : Terminates the running subprocess. + """ + + # Open the output file to write to + self._output_file_handle = open(self.output_file, "w") + local_process = subprocess.Popen( + self.commands.split(), + # shell=True, + cwd=self.run_path, + stdin=subprocess.PIPE, + stdout=self._output_file_handle, + stderr=subprocess.STDOUT, + ) + self._process = local_process + + @property + def output_file(self) -> Path: + """The file in which to write this task's STDOUT and STDERR.""" + return ( + self.run_path / f"{self._default_name}.out" + if self._output_file is None + else self._output_file + ) + + @property + def status(self) -> ExecutionStatus: + """Retrieve the current status of the local process. + + This property determines the status of the process based on its + lifecycle and return code. The possible statuses are represented by + the `ExecutionStatus` enum. + + Returns + ------- + ExecutionStatus + The current status of the process. Possible values include: + - `ExecutionStatus.UNSUBMITTED`: The task has not been started. + - `ExecutionStatus.RUNNING`: The task is currently executing. + - `ExecutionStatus.COMPLETED`: The task finished successfully. + - `ExecutionStatus.FAILED`: The task finished unsuccessfully. + - `ExecutionStatus.CANCELLED`: The task was cancelled using LocalProcess.cancel() + - `ExecutionStatus.UNKNOWN`: The task status could not be determined. + """ + + if self._cancelled: + return ExecutionStatus.CANCELLED + if self._process is None: + return ExecutionStatus.UNSUBMITTED + if self._process.poll() is None: + return ExecutionStatus.RUNNING + if self._process.returncode == 0: + if self._output_file_handle: + self._output_file_handle.close() + self._output_file_handle = None + return ExecutionStatus.COMPLETED + elif self._process.returncode is not None: + if self._output_file_handle: + self._output_file_handle.close() + self._output_file_handle = None + return ExecutionStatus.FAILED + return ExecutionStatus.UNKNOWN + + def cancel(self): + """Cancel the local process. + + This method terminates the subprocess if it is currently running. + A graceful shutdown is attempted using `terminate` (SIGTERM). If the + subprocess does not terminate within a timeout period, it is forcefully + killed using `kill` (SIGKILL). + + Notes + ----- + - The status of the subprocess is updated to `ExecutionStatus.CANCELLED` + after termination or killing. + - The method ensures that the output file handle is closed after + cancelling the process. + + See Also + -------- + wait : Wait for the local process to finish + """ + + if self._process and self.status == ExecutionStatus.RUNNING: + try: + self._process.terminate() # Send SIGTERM to allow graceful shutdown + self._process.wait(timeout=5) # Wait for it to terminate + except subprocess.TimeoutExpired: + self._process.kill() # Forcefully kill if it doesn't terminate + finally: + if self._output_file_handle: + self._output_file_handle.close() + self._output_file_handle = None + self._cancelled = True + else: + print(f"Cannot cancel job with status '{self.status}'") + return + + def wait(self): + """Wait for the local process to finish. + + See Also + -------- + cancel : end the current process + """ + + if self._process and self.status == ExecutionStatus.RUNNING: + self._process.wait() + else: + print(f"cannot wait for process with execution status '{self.status}'") diff --git a/cstar/scheduler/job.py b/cstar/execution/scheduler_job.py similarity index 85% rename from cstar/scheduler/job.py rename to cstar/execution/scheduler_job.py index f6621d1..bd0454c 100644 --- a/cstar/scheduler/job.py +++ b/cstar/execution/scheduler_job.py @@ -1,15 +1,14 @@ import os import re -import time import json import warnings import subprocess from math import ceil -from enum import Enum, auto from abc import ABC, abstractmethod from pathlib import Path from datetime import datetime, timedelta from typing import Optional, Tuple +from cstar.execution.handler import ExecutionStatus, ExecutionHandler from cstar.system.manager import cstar_sysmgr from cstar.system.scheduler import ( Queue, @@ -110,49 +109,7 @@ def create_scheduler_job( ) -class JobStatus(Enum): - """Enum representing possible states of a job in the scheduler. - - Each state corresponds to a stage in the lifecycle of a job, from submission - to completion or failure. - - Attributes - ---------- - UNSUBMITTED : JobStatus - The job has not been submitted to the scheduler yet. - PENDING : JobStatus - The job has been submitted but is waiting to start. - RUNNING : JobStatus - The job is currently executing. - COMPLETED : JobStatus - The job finished successfully. - CANCELLED : JobStatus - The job was cancelled before completion. - FAILED : JobStatus - The job finished unsuccessfully. - HELD : JobStatus - The job is on hold and will not run until released. - ENDING : JobStatus - The job is in the process of ending but not fully completed. - UNKNOWN : JobStatus - The job state is unknown or not recognized. - """ - - UNSUBMITTED = auto() - PENDING = auto() - RUNNING = auto() - COMPLETED = auto() - CANCELLED = auto() - FAILED = auto() - HELD = auto() - ENDING = auto() - UNKNOWN = auto() - - def __str__(self) -> str: - return self.name.lower() # Convert enum name to lowercase for display - - -class SchedulerJob(ABC): +class SchedulerJob(ExecutionHandler, ABC): """Abstract base class for representing a job submitted to a scheduler. This class defines the structure and common behavior for jobs managed by @@ -193,7 +150,7 @@ class SchedulerJob(ABC): The maximum walltime for the job, in the format "HH:MM:SS". id : int or None The unique job ID assigned by the scheduler. None if the job has not been submitted. - status: JobStatus + status: ExecutionStatus A representation of the current status of the job, e.g. RUNNING or CANCELLED script: str The job script to be submitted to the scheduler. @@ -277,19 +234,19 @@ class SchedulerJob(ABC): self._commands = commands self._cpus = cpus - default_name = f"cstar_job_{datetime.strftime(datetime.now(), '%Y%m%d_%H%M%S')}" + self._default_name = ( + f"cstar_job_{datetime.strftime(datetime.now(), '%Y%m%d_%H%M%S')}" + ) self.script_path = ( - Path.cwd() / f"{default_name}.sh" + Path.cwd() / f"{self._default_name}.sh" if script_path is None else Path(script_path) ) self.run_path = self.script_path.parent if run_path is None else Path(run_path) - self._job_name = default_name if job_name is None else job_name - self.output_file = ( - self.run_path / f"{default_name}.out" - if output_file is None - else output_file + self._job_name = self._default_name if job_name is None else job_name + self._output_file = ( + Path(output_file) if output_file is not None else output_file ) self._queue_name = ( scheduler.primary_queue_name if queue_name is None else queue_name @@ -385,6 +342,15 @@ class SchedulerJob(ABC): self._account_key = account_key self._id: Optional[int] = None + @property + def output_file(self) -> Path: + """The file in which to write this job's STDOUT and STDERR.""" + return ( + self.run_path / f"{self._default_name}.out" + if self._output_file is None + else self._output_file + ) + @property def account_key(self) -> str: """The account key associated with the job for resource tracking.""" @@ -498,12 +464,12 @@ class SchedulerJob(ABC): @property @abstractmethod - def status(self) -> JobStatus: + def status(self) -> ExecutionStatus: """Retrieve the current status of the job. This method queries the underlying scheduler to determine the current status of the job (e.g., PENDING, RUNNING, COMPLETED). Subclasses must implement this method - to interact with the specific scheduler and map its state to a `JobStatus` enum. + to interact with the specific scheduler and map its state to a `ExecutionStatus` enum. Parameters ---------- @@ -511,55 +477,11 @@ class SchedulerJob(ABC): Returns ------- - status: JobStatus + status: ExecutionStatus An enumeration value representing the current status of the job. """ pass - def updates(self, seconds=10): - """Provides updates from the job's output file as a live stream for `seconds` - seconds (default 10). - - If `seconds` is 0, updates are provided indefinitely until the user interrupts the stream. - """ - - if self.status != JobStatus.RUNNING: - print( - f"This job is currently not running ({self.status}). Live updates cannot be provided." - ) - if (self.status in {JobStatus.FAILED, JobStatus.COMPLETED}) or ( - self.status == JobStatus.CANCELLED and self.output_file.exists() - ): - print(f"See {self.output_file.resolve()} for job output") - return - - if seconds == 0: - # Confirm indefinite tailing - confirmation = ( - input( - "This will provide indefinite updates to your job. You can stop it anytime using Ctrl+C. " - "Do you want to continue? (y/n): " - ) - .strip() - .lower() - ) - if confirmation not in {"y", "yes"}: - return - - try: - with open(self.output_file, "r") as f: - f.seek(0, 2) # Move to the end of the file - start_time = time.time() - - while seconds == 0 or (time.time() - start_time < seconds): - line = f.readline() - if line: - print(line, end="") - else: - time.sleep(0.1) # 100ms delay between updates - except KeyboardInterrupt: - print("\nLive status updates stopped by user.") - def _calculate_node_distribution( self, n_cores_required: int, tot_cores_per_node: int ) -> Tuple[int, int]: @@ -631,7 +553,7 @@ class SlurmJob(SchedulerJob): The maximum walltime for the job, in the format "HH:MM:SS". id : int or None The unique job ID assigned by the SLURM scheduler. None if the job has not been submitted. - status : JobStatus + status : ExecutionStatus The current status of the job, retrieved from SLURM. script : str The SLURM-specific job script, including directives and commands. @@ -645,22 +567,22 @@ class SlurmJob(SchedulerJob): """ @property - def status(self) -> JobStatus: + def status(self) -> ExecutionStatus: """Retrieve the current status of the job from the SLURM scheduler. This property queries SLURM using the `sacct` command to determine the job's - state and maps it to a corresponding `JobStatus` enumeration. + state and maps it to a corresponding `ExecutionStatus` enumeration. Returns ------- - status: JobStatus + status: ExecutionStatus The current status of the job. Possible values include: - - `JobStatus.PENDING`: The job has been submitted but is waiting to start. - - `JobStatus.RUNNING`: The job is currently executing. - - `JobStatus.COMPLETED`: The job finished successfully. - - `JobStatus.CANCELLED`: The job was cancelled before completion. - - `JobStatus.FAILED`: The job finished unsuccessfully. - - `JobStatus.UNKNOWN`: The job state could not be determined. + - `ExecutionStatus.PENDING`: The job has been submitted but is waiting to start. + - `ExecutionStatus.RUNNING`: The job is currently executing. + - `ExecutionStatus.COMPLETED`: The job finished successfully. + - `ExecutionStatus.CANCELLED`: The job was cancelled before completion. + - `ExecutionStatus.FAILED`: The job finished unsuccessfully. + - `ExecutionStatus.UNKNOWN`: The job state could not be determined. Raises ------ @@ -669,7 +591,7 @@ class SlurmJob(SchedulerJob): """ if self.id is None: - return JobStatus.UNSUBMITTED + return ExecutionStatus.UNSUBMITTED else: sacct_cmd = f"sacct -j {self.id} --format=State%20 --noheader" result = subprocess.run( @@ -681,20 +603,20 @@ class SlurmJob(SchedulerJob): f"STDOUT: {result.stdout}, STDERR: {result.stderr}" ) - # Map sacct states to JobStatus enum + # Map sacct states to ExecutionStatus enum sacct_status_map = { - "PENDING": JobStatus.PENDING, - "RUNNING": JobStatus.RUNNING, - "COMPLETED": JobStatus.COMPLETED, - "CANCELLED": JobStatus.CANCELLED, - "FAILED": JobStatus.FAILED, + "PENDING": ExecutionStatus.PENDING, + "RUNNING": ExecutionStatus.RUNNING, + "COMPLETED": ExecutionStatus.COMPLETED, + "CANCELLED": ExecutionStatus.CANCELLED, + "FAILED": ExecutionStatus.FAILED, } for state, status in sacct_status_map.items(): if state in result.stdout: return status # Fallback if no known state is found - return JobStatus.UNKNOWN + return ExecutionStatus.UNKNOWN @property def script(self) -> str: @@ -803,8 +725,8 @@ class SlurmJob(SchedulerJob): If the `scancel` command fails or returns a non-zero exit code. """ - if self.status not in {JobStatus.RUNNING, JobStatus.PENDING}: - print(f"Cannot cancel job with status {self.status}") + if self.status not in {ExecutionStatus.RUNNING, ExecutionStatus.PENDING}: + print(f"Cannot cancel job with status '{self.status}'") return result = subprocess.run( @@ -862,7 +784,7 @@ class PBSJob(SchedulerJob): The maximum walltime for the job, in the format "HH:MM:SS". id : int or None The unique job ID assigned by the PBS scheduler. None if the job has not been submitted. - status : JobStatus + status : ExecutionStatus The current status of the job, retrieved from PBS. script : str The PBS-specific job script, including directives and commands. @@ -876,7 +798,7 @@ class PBSJob(SchedulerJob): """ @property - def script(self): + def script(self) -> str: """Generate the PBS-specific job script to be submitted to the scheduler. Includes standard Slurm scheduler directives as well as scheduler-specific directives specified by the scheduler.other_scheduler_directives attribute. @@ -901,7 +823,7 @@ class PBSJob(SchedulerJob): for ( key, value, - ) in cstar_sysmgr.scheduler.other_scheduler_directives.items(): + ) in self.scheduler.other_scheduler_directives.items(): scheduler_script += f"\n#PBS {key} {value}" scheduler_script += "\ncd ${PBS_O_WORKDIR}" @@ -909,24 +831,24 @@ class PBSJob(SchedulerJob): return scheduler_script @property - def status(self) -> JobStatus: + def status(self) -> ExecutionStatus: """Retrieve the current status of the job from the PBS scheduler. This property queries PBS using the `qstat` command to determine the job's - state and maps it to a corresponding `JobStatus` enumeration. + state and maps it to a corresponding `ExecutionStatus` enumeration. Returns ------- - status : JobStatus + status : ExecutionStatus The current status of the job. Possible values include: - - `JobStatus.PENDING`: The job has been submitted but is waiting to start. - - `JobStatus.RUNNING`: The job is currently executing. - - `JobStatus.COMPLETED`: The job finished successfully. - - `JobStatus.CANCELLED`: The job was cancelled before completion. - - `JobStatus.FAILED`: The job finished unsuccessfully. - - `JobStatus.HELD`: The job is on hold. - - `JobStatus.ENDING`: The job is in the process of ending. - - `JobStatus.UNKNOWN`: The job state could not be determined. + - `ExecutionStatus.PENDING`: The job has been submitted but is waiting to start. + - `ExecutionStatus.RUNNING`: The job is currently executing. + - `ExecutionStatus.COMPLETED`: The job finished successfully. + - `ExecutionStatus.CANCELLED`: The job was cancelled before completion. + - `ExecutionStatus.FAILED`: The job finished unsuccessfully. + - `ExecutionStatus.HELD`: The job is on hold. + - `ExecutionStatus.ENDING`: The job is in the process of ending. + - `ExecutionStatus.UNKNOWN`: The job state could not be determined. Raises ------ @@ -935,7 +857,7 @@ class PBSJob(SchedulerJob): """ if self.id is None: - return JobStatus.UNSUBMITTED + return ExecutionStatus.UNSUBMITTED qstat_cmd = f"qstat -x -f -F json {self.id}" result = subprocess.run(qstat_cmd, capture_output=True, text=True, shell=True) @@ -957,20 +879,24 @@ class PBSJob(SchedulerJob): # Extract the job state job_state = job_info["job_state"] pbs_status_map = { - "Q": JobStatus.PENDING, - "R": JobStatus.RUNNING, - "C": JobStatus.COMPLETED, - "H": JobStatus.HELD, - "E": JobStatus.ENDING, + "Q": ExecutionStatus.PENDING, + "R": ExecutionStatus.RUNNING, + "C": ExecutionStatus.COMPLETED, + "H": ExecutionStatus.HELD, + "E": ExecutionStatus.ENDING, } # Handle specific cases for "F" (Finished) if job_state == "F": exit_status = job_info.get("Exit_status", 1) - return JobStatus.COMPLETED if exit_status == 0 else JobStatus.FAILED + return ( + ExecutionStatus.COMPLETED + if exit_status == 0 + else ExecutionStatus.FAILED + ) else: # Default to UNKNOWN for unmapped states - return pbs_status_map.get(job_state, JobStatus.UNKNOWN) + return pbs_status_map.get(job_state, ExecutionStatus.UNKNOWN) except json.JSONDecodeError as e: raise RuntimeError(f"Failed to parse JSON from qstat output: {e}") @@ -1032,7 +958,11 @@ class PBSJob(SchedulerJob): If the `qdel` command fails or returns a non-zero exit code. """ - if self.status not in {JobStatus.RUNNING, JobStatus.PENDING, JobStatus.HELD}: + if self.status not in { + ExecutionStatus.RUNNING, + ExecutionStatus.PENDING, + ExecutionStatus.HELD, + }: print(f"Cannot cancel job with status {self.status}") return diff --git a/cstar/marbl/component.py b/cstar/marbl/component.py index 803c26c..aeffb5e 100644 --- a/cstar/marbl/component.py +++ b/cstar/marbl/component.py @@ -61,8 +61,8 @@ class MARBLComponent(Component): print("No pre-processing steps to be completed for MARBLComponent") pass - def run(self) -> None: - print("MARBL must be run in the context of a parent model") + def run(self): + raise ValueError("MARBL must be run in the context of a parent model") def post_run(self) -> None: print("No post-processing steps to be completed for MARBLComponent") diff --git a/cstar/roms/component.py b/cstar/roms/component.py index d8db87a..2a9e634 100644 --- a/cstar/roms/component.py +++ b/cstar/roms/component.py @@ -5,7 +5,9 @@ from pathlib import Path from datetime import datetime from typing import Optional, TYPE_CHECKING, List -from cstar.scheduler.job import create_scheduler_job +from cstar.execution.scheduler_job import create_scheduler_job +from cstar.execution.local_process import LocalProcess +from cstar.execution.handler import ExecutionStatus from cstar.base.utils import _replace_text_in_file from cstar.base.component import Component from cstar.roms.base_model import ROMSBaseModel @@ -24,7 +26,7 @@ from cstar.system.manager import cstar_sysmgr if TYPE_CHECKING: from cstar.roms import ROMSBaseModel - from cstar.scheduler.job import SchedulerJob + from cstar.execution.handler import ExecutionHandler class ROMSComponent(Component): @@ -151,6 +153,8 @@ class ROMSComponent(Component): self.exe_path: Optional[Path] = None self.partitioned_files: List[Path] \| None = None + self._execution_handler: Optional["ExecutionHandler"] = None + @property def in_file(self) -> Path: """Find the .in file associated with this ROMSComponent in @@ -853,7 +857,7 @@ class ROMSComponent(Component): walltime: Optional[str] = None, queue_name: Optional[str] = None, job_name: Optional[str] = None, - ) -> Optional["SchedulerJob"]: + ) -> "ExecutionHandler": """Runs the executable created by `build()` This method creates a temporary file to be submitted to the job scheduler (if any) @@ -923,7 +927,7 @@ class ROMSComponent(Component): output_dir.mkdir(parents=True, exist_ok=True) - ## 2: RUN ON THIS MACHINE + ## 2: RUN ROMS roms_exec_cmd = ( f"{cstar_sysmgr.environment.mpi_exec_prefix} -n {self.discretization.n_procs_tot} {self.exe_path} " @@ -952,85 +956,14 @@ class ROMSComponent(Component): ) job_instance.submit() + self._execution_handler = job_instance return job_instance else: # cstar_sysmgr.scheduler is None - import time - - romsprocess = subprocess.Popen( - roms_exec_cmd, - shell=True, - cwd=run_path, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - ) - - # Process stdout line-by-line - tstep0 = 0 - roms_init_string = "" - if romsprocess.stdout is None: - raise RuntimeError("ROMS is not producing stdout") - - # 2024-09-21 : the following is included as in some instances ROMS - # will exit with code 0 even if a fatal error occurs, see: - # https://github.com/CESR-lab/ucla-roms/issues/42 - - debugging = False # Print raw output if true - if debugging: - while True: - output = romsprocess.stdout.readline() - if output == "" and romsprocess.poll() is not None: - break - if output: - print(output.strip()) - else: - for line in romsprocess.stdout: - # Split the line by whitespace - parts = line.split() - - # Check if there are exactly 9 parts and the first one is an integer - if len(parts) == 9: - try: - # Try to convert the first part to an integer - tstep = int(parts[0]) - if tstep0 == 0: - tstep0 = tstep - T0 = time.time() - # Capture the first integer and print it - else: - ETA = (n_time_steps - (tstep - tstep0)) * ( - (time.time() - T0) / (tstep - tstep0) - ) - total_print_statements = 50 - print_frq = n_time_steps // total_print_statements + 1 - if ((tstep - tstep0) % print_frq) == 0: - print( - f"Running ROMS: time-step {tstep-tstep0} of {n_time_steps} ({time.time()-T0:.1f}s elapsed; ETA {ETA:.1f}s)" - ) - except ValueError: - pass - elif tstep0 == 0 and len(roms_init_string) == 0: - roms_init_string = "Running ROMS: Initializing run..." - print(roms_init_string) - - romsprocess.wait() - if romsprocess.returncode != 0: - import datetime as dt - - errlog = ( - output_dir - / f"ROMS_STDERR_{dt.datetime.now().strftime('%Y%m%d_%H%M%S')}.log" - ) - if romsprocess.stderr is not None: - with open(errlog, "w") as F: - F.write(romsprocess.stderr.read()) - raise RuntimeError( - f"ROMS terminated with errors. See {errlog} for further information." - ) - return None - - return None + romsprocess = LocalProcess(commands=roms_exec_cmd, run_path=run_path) + self._execution_handler = romsprocess + romsprocess.start() + return romsprocess def post_run(self, output_dir: Optional[str \| Path] = None) -> None: """Performs post-processing steps associated with this ROMSComponent object. @@ -1043,6 +976,17 @@ class ROMSComponent(Component): output_dir: str \| Path The directory in which output was produced by the run """ + + if self._execution_handler is None: + raise RuntimeError( + "Cannot call 'ROMSComponent.post_run()' before calling 'ROMSComponent.run()'" + ) + elif self._execution_handler.status != ExecutionStatus.COMPLETED: + raise RuntimeError( + "Cannot call 'ROMSComponent.post_run()' until the ROMS run is completed, " + + f"but current execution status is '{self._execution_handler.status}'" + ) + if output_dir is None: # This should not be necessary, it allows output_dir to appear # optional for signature compatibility in linting, see diff --git a/docs/5_handling_jobs_on_hpc_systems.ipynb b/docs/5_tracking_runs_executed_as_jobs_on_hpc_systems.ipynb similarity index 99% rename from docs/5_handling_jobs_on_hpc_systems.ipynb rename to docs/5_tracking_runs_executed_as_jobs_on_hpc_systems.ipynb index 9c947e2..cf01145 100644 --- a/docs/5_handling_jobs_on_hpc_systems.ipynb +++ b/docs/5_tracking_runs_executed_as_jobs_on_hpc_systems.ipynb @@ -20,7 +20,7 @@ "id": "53ab3d32-9292-44b6-a1f5-fa331b654307", "metadata": {}, "source": [ - "# Handling jobs on HPC systems\n", + "# Tracking runs executed as jobs on HPC systems\n", "On this page, we will look at how to use C-Star on supported HPC systems with job schedulers, including:\n", "\n", "- Submitting a job to a scheduler queue\n", @@ -689,7 +689,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/docs/6_tracking_runs_executed_locally.ipynb b/docs/6_tracking_runs_executed_locally.ipynb new file mode 100644 index 0000000..b7c89e0 --- /dev/null +++ b/docs/6_tracking_runs_executed_locally.ipynb @@ -0,0 +1,409 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "970997a3-caeb-42b8-923d-9b86f8c4d94c", + "metadata": {}, + "source": [ + "> [!Warning] \n", + "> This project is still in an early phase of development.\n", + ">\n", + "> The [python API](https://c-star.readthedocs.io/en/latest/api.html) is not yet stable, and some aspects of the schema for the [blueprint](https://c-star.readthedocs.io/en/latest/terminology.html#term-blueprint) will likely evolve. \n", + "> Therefore whilst you are welcome to try out using the package, we cannot yet guarantee backwards compatibility. \n", + "We expect to reach a more stable version in Q1 2025.\n", + ">\n", + "> To see which systems C-Star has been tested on so far, see [Supported Systems](https://c-star.readthedocs.io/en/latest/machines.html)." + ] + }, + { + "cell_type": "markdown", + "id": "53ab3d32-9292-44b6-a1f5-fa331b654307", + "metadata": {}, + "source": [ + "# Tracking runs executed locally\n", + "On this page, we will look at how to monitor processes created by C-Star where execution is handled locally in more detail.\n", + "If you are running C-Star on a supported HPC system with a job scheduler, see [the previous page](5_handling_jobs_on_hpc_systems.html). There are many features in common between jobs run locally and those submitted to a job scheduler, but the former is more simple.\n", + "\n", + "- Checking the status of a task created by C-Star\n", + "- Receiving live updates from a task created by C-Star\n", + "- Cancelling a task created by C-Star" + ] + }, + { + "cell_type": "markdown", + "id": "6f1d2be9-accc-4af6-849d-37d7f0257603", + "metadata": { + "tags": [] + }, + "source": [ + "## Importing an example Case and running it locally:\n", + "We will import and set up an example case similarly to the [previous example](2_importing_and_running_a_case_from_a_blueprint.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a301fe04-638c-4312-8068-cd4b26249c5f", + "metadata": {}, + "outputs": [], + "source": [ + "import cstar\n", + "\n", + "example_case_1 = cstar.Case.from_blueprint(blueprint = \"../examples/cstar_blueprint_roms_marbl_example.yaml\",\n", + " caseroot = \"../examples/example_case\", \n", + " start_date = \"2012-01-03 12:00:00\", \n", + " end_date = \"2012-01-06 12:00:00\")" + ] + }, + { + "cell_type": "markdown", + "id": "36a4e106-d605-4494-909a-b1c2cbc17eca", + "metadata": {}, + "source": [ + "## Starting a run locally\n", + "We can now set up and run the Case as in the [previous example](2_importing_and_running_a_case_from_a_blueprint.html), assigning the `LocalProcess` instance returned by `Case.run()` to a variable we can keep track of." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82cb820a-0353-4146-9e01-2e425a615fa2", + "metadata": {}, + "outputs": [], + "source": [ + "example_case_1.setup()\n", + "example_case_1.build()\n", + "example_case_1.pre_run()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "071b0e23-1f7f-402d-a2b5-1ae2fae90317", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Running ROMS... \n" + ] + } + ], + "source": [ + "cstar_task = example_case_1.run()" + ] + }, + { + "cell_type": "markdown", + "id": "c0aa814e-d5ef-4f0b-834b-c67d0f47a574", + "metadata": {}, + "source": [ + "## Tracking the local run\n" + ] + }, + { + "cell_type": "markdown", + "id": "30c673bb-9490-4feb-aeda-b434dcf1c687", + "metadata": {}, + "source": [ + "### Viewing the output file path\n", + "The output file contains the standard output and error streams returned by the run" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "216bbc79-f578-44d2-867b-7da38b252778", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/Users/dafyddstephenson/Code/my_c_star/examples/example_case/output/cstar_process_20241224_143121.out')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cstar_task.output_file" + ] + }, + { + "cell_type": "markdown", + "id": "68cf73c3-916f-4007-a9cb-c2e6761fb67c", + "metadata": {}, + "source": [ + "### Checking the status\n", + "We can check the run status using the `status` property. Possible values for a local run are:\n", + "\n", + "- `UNSUBMITTED`: the run has not yet started\n", + "- `RUNNING`: the run is underway\n", + "- `COMPLETED`: the run finished successfully\n", + "- `CANCELLED`: the run was cancelled by the user\n", + "- `FAILED`: the run finished unsuccessfully\n", + "- `UNKNOWN`: the status cannot be determined" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "41630183-ecde-4dc2-bfba-41a1457ede1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<ExecutionStatus.RUNNING: 3>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cstar_task.status" + ] + }, + { + "cell_type": "markdown", + "id": "b05c1c44-2d6f-45f3-84fc-4ac071456d6e", + "metadata": { + "tags": [] + }, + "source": [ + "### Receiving live updates from a local run\n", + "While the process is running, we can stream any new lines written to the output file using the `updates()` method. This method receives a `seconds` parameter, and will provide live updates for the number of seconds provided by the user (default 10). If the user specifies `seconds=0`, updates will be provided indefinitely until either the updates are stopped with a keyboard interruption (typically via `Ctrl-c`) or the process ends." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0a79a7cc-9281-4bd8-ad4e-ecb32e17a1c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2485 4385.8583 1.25835920793-02 1.0622096536-02 0.136952686070 0.136247868683 8 18 61\n", + " doing BGC with MARBL\n", + " 2486 4385.8625 1.26418727730-02 1.0677227692-02 0.138613564557 0.137943291663 8 18 61\n", + " doing BGC with MARBL\n", + " 2487 4385.8666 1.27018558957-02 1.0734782520-02 0.140313602622 0.139684982328 8 18 61\n", + " doing BGC with MARBL\n", + " 2488 4385.8708 1.27644276510-02 1.0795296451-02 0.142053378702 0.141473963532 8 18 61\n", + " doing BGC with MARBL\n", + " 2489 4385.8750 1.28303755595-02 1.0859155735-02 0.143837624076 0.143314536573 8 18 61\n", + " set_frc :: swrad input time (days) = 4385.92 rec = 71\n", + " set_frc :: lwrad input time (days) = 4385.92 rec = 71\n", + " set_frc :: uwnd input time (days) = 4385.92 rec = 71\n", + " set_frc :: vwnd input time (days) = 4385.92 rec = 71\n", + " set_frc :: Tair input time (days) = 4385.92 rec = 71\n", + " set_frc :: qair input time (days) = 4385.92 rec = 71\n", + " set_frc :: rain input time (days) = 4385.92 rec = 71\n", + " doing BGC with MARBL\n", + " 2490 4385.8791 1.29004414329-02 1.0926810649-02 0.145608490301 0.145146964406 8 18 61\n", + " doing BGC with MARBL\n", + " 2491 4385.8833 1.29708365192-02 1.0995773324-02 0.147131668685 0.146735361928 8 18 61\n", + " doing BGC with MARBL\n", + " 2492 4385.8875 1.30377943412-02 1.1063338488-02 0.148424019270 0.148095085576 8 18 61\n", + " doing BGC with MARBL\n", + " 2493 4385.8916 1.31023753611-02 1.1129874381-02 0.149523096643 0.149264579908 8 18 61\n", + " doing BGC with MARBL\n", + " 2494 4385.8958 1.31652198586-02 1.1195599618-02 0.150478728824 0.150294630798 8 18 61\n", + " doing BGC with MARBL\n", + " 2495 4385.9000 1.32268445879-02 1.1260610230-02 0.151333910835 0.151257155828 8 18 62\n", + " doing BGC with MARBL\n", + " 2496 4385.9041 1.32876049836-02 1.1325071888-02 0.152128252273 0.152128252273 8 18 62\n", + " doing BGC with MARBL\n", + " 2497 4385.9083 1.33478670990-02 1.1389121584-02 0.152959268208 0.152959268208 8 18 62\n", + " doing BGC with MARBL\n", + " 2498 4385.9125 1.34078950450-02 1.1452897611-02 0.153768111972 0.153768111972 8 18 62\n", + " doing BGC with MARBL\n", + " 2499 4385.9166 1.34680167142-02 1.1516533838-02 0.154567678966 0.154567678966 8 18 62\n", + " set_frc :: swrad input time (days) = 4385.96 rec = 72\n", + " set_frc :: lwrad input time (days) = 4385.96 rec = 72\n", + " set_frc :: uwnd input time (days) = 4385.96 rec = 72\n", + " set_frc :: vwnd input time (days) = 4385.96 rec = 72\n", + " set_frc :: Tair input time (days) = 4385.96 rec = 72\n", + " set_frc :: qair input time (days) = 4385.96 rec = 72\n", + " set_frc :: rain input time (days) = 4385.96 rec = 72\n", + " doing BGC with MARBL\n", + " 2500 4385.9208 1.35283165709-02 1.1580129351-02 0.155325624258 0.155221612013 8 18 61\n", + " doing BGC with MARBL\n", + " 2501 4385.9250 1.35828230769-02 1.1640319995-02 0.155700653053 0.155481340173 8 18 61\n", + " doing BGC with MARBL\n", + " 2502 4385.9291 1.36263388869-02 1.1693975898-02 0.155662438206 0.155322944376 8 18 61\n", + " doing BGC with MARBL\n", + " 2503 4385.9333 1.36600374443-02 1.1741283660-02 0.155263267688 0.154803506067 8 18 61\n", + " doing BGC with MARBL\n", + " 2504 4385.9375 1.36849625828-02 1.1782576915-02 0.154577909123 0.154000079067 8 18 61\n", + " doing BGC with MARBL\n", + " 2505 4385.9416 1.37020578756-02 1.1818261666-02 0.153670271592 0.152976916292 8 18 61\n", + " doing BGC with MARBL\n", + " 2506 4385.9458 1.37121832607-02 1.1848783946-02 0.152590160501 0.151783861959 8 18 61\n", + " doing BGC with MARBL\n", + " 2507 4385.9500 1.37161351454-02 1.1874569011-02 0.151374986137 0.150458733231 8 18 61\n", + " doing BGC with MARBL\n", + " 2508 4385.9541 1.37144734308-02 1.1896005050-02 0.150053002374 0.149030644412 8 18 61\n", + " doing BGC with MARBL\n", + " 2509 4385.9583 1.37078436551-02 1.1913471360-02 0.148646075415 0.147522693372 8 18 61\n", + " set_frc :: swrad input time (days) = 4386.00 rec = 73\n", + " set_frc :: lwrad input time (days) = 4386.00 rec = 73\n", + " set_frc :: uwnd input time (days) = 4386.00 rec = 73\n", + " set_frc :: vwnd input time (days) = 4386.00 rec = 73\n", + " set_frc :: Tair input time (days) = 4386.00 rec = 73\n", + " set_frc :: qair input time (days) = 4386.00 rec = 73\n", + " set_frc :: rain input time (days) = 4386.00 rec = 73\n", + " doing BGC with MARBL\n", + " 2510 4385.9625 1.36966772006-02 1.1927277505-02 0.147160964026 0.145942604721 8 18 61\n", + " doing BGC with MARBL\n", + " 2511 4385.9666 1.36809635410-02 1.1937393429-02 0.145575738835 0.144268441761 8 18 61\n", + " doing BGC with MARBL\n", + " 2512 4385.9708 1.36607130946-02 1.1943812910-02 0.143911766375 0.142521457819 8 18 61\n", + " doing BGC with MARBL\n", + " 2513 4385.9750 1.36363893415-02 1.1946799844-02 0.142189803708 0.140721996579 8 18 61\n", + " doing BGC with MARBL\n", + " 2514 4385.9791 1.36084937336-02 1.1946561437-02 0.140429539964 0.138961591747 8 18 62\n", + " doing BGC with MARBL\n", + " 2515 4385.9833 1.35771853114-02 1.1943254440-02 0.138650105839 0.137111897393 8 18 62\n", + " doing BGC with MARBL\n", + " 2516 4385.9875 1.35429113517-02 1.1937106844-02 0.136856769268 0.135251514662 8 18 62\n", + " doing BGC with MARBL\n", + " 2517 4385.9916 1.35060264336-02 1.1928413681-02 0.135055284864 0.133386889470 8 18 62\n", + " doing BGC with MARBL\n", + " 2518 4385.9958 1.34667019800-02 1.1917333838-02 0.133250058213 0.131523540883 8 18 62\n", + " doing BGC with MARBL\n", + " 2519 4386.0000 1.34251380663-02 1.1903898010-02 0.131446412425 0.129667318138 8 18 62\n", + " ocean_vars :: wrote history, tdays = 4386.0000 step = 2519 rec = 3\n", + " wrt_bgc_tracers :: history , trc = PO4 \n", + " wrt_bgc_tracers :: history , trc = NO3 \n", + " wrt_bgc_tracers :: history , trc = SiO3 \n", + " wrt_bgc_tracers :: history , trc = NH4 \n", + " wrt_bgc_tracers :: history , trc = Fe \n", + " wrt_bgc_tracers :: history , trc = Lig \n", + " wrt_bgc_tracers :: history , trc = O2 \n", + " wrt_bgc_tracers :: history , trc = DIC \n", + " wrt_bgc_tracers :: history , trc = DIC_ALT_CO2 \n", + " wrt_bgc_tracers :: history , trc = ALK \n", + " wrt_bgc_tracers :: history , trc = ALK_ALT_CO2 \n", + " wrt_bgc_tracers :: history , trc = DOC \n", + " wrt_bgc_tracers :: history , trc = DON \n", + " wrt_bgc_tracers :: history , trc = DOP \n", + " wrt_bgc_tracers :: history , trc = DOPr \n", + " wrt_bgc_tracers :: history , trc = DONr \n", + " wrt_bgc_tracers :: history , trc = DOCr \n", + " wrt_bgc_tracers :: history , trc = zooC \n", + " wrt_bgc_tracers :: history , trc = spChl \n", + " wrt_bgc_tracers :: history , trc = spC \n", + " wrt_bgc_tracers :: history , trc = spP \n", + " wrt_bgc_tracers :: history , trc = spFe \n", + " wrt_bgc_tracers :: history , trc = spCaCO3 \n", + " wrt_bgc_tracers :: history , trc = diatChl \n", + " wrt_bgc_tracers :: history , trc = diatC \n", + " wrt_bgc_tracers :: history , trc = diatP \n", + " wrt_bgc_tracers :: history , trc = diatFe \n", + " wrt_bgc_tracers :: history , trc = diatSi \n", + " wrt_bgc_tracers :: history , trc = diazChl \n", + " wrt_bgc_tracers :: history , trc = diazC \n", + " wrt_bgc_tracers :: history , trc = diazP \n", + " wrt_bgc_tracers :: history , trc = diazFe \n", + " bgc :: wrote history, tdays = 4386.0000 step = 2518 rec = 3\n", + " bgc diag :: wrote history, tdays = 4386.0000 step = 2519 rec = 3\n", + " set_frc :: zeta_east input time (days) = 4387.00 rec = 5\n", + " set_frc :: zeta_west input time (days) = 4387.00 rec = 5\n", + " set_frc :: zeta_south input time (days) = 4387.00 rec = 5\n", + " set_frc :: zeta_north input time (days) = 4387.00 rec = 5\n", + " set_frc :: swrad input time (days) = 4386.04 rec = 74\n", + " set_frc :: lwrad input time (days) = 4386.04 rec = 74\n", + " set_frc :: uwnd input time (days) = 4386.04 rec = 74\n", + " set_frc :: vwnd input time (days) = 4386.04 rec = 74\n", + " set_frc :: Tair input time (days) = 4386.04 rec = 74\n", + " set_frc :: qair input time (days) = 4386.04 rec = 74\n", + " set_frc :: rain input time (days) = 4386.04 rec = 74\n", + " doing BGC with MARBL\n", + " 2520 4386.0041 1.33813167950-02 1.1888112769-02 0.129656274071 0.127829968916 8 18 62\n", + " doing BGC with MARBL\n", + " 2521 4386.0083 1.33372723425-02 1.1871329181-02 0.127909737805 0.126040534783 8 18 62\n", + " doing BGC with MARBL\n", + " 2522 4386.0125 1.32937687605-02 1.1853843466-02 0.126215169511 0.124305538389 8 18 62\n", + " doing BGC with MARBL\n", + " 2523 4386.0166 1.32504548141-02 1.1835566947-02 0.124575269680 0.122625412446 8 18 62\n", + " doing BGC with MARBL\n", + " 2524 4386.0208 1.32071760708-02 1.1816443813-02 0.122986966618 0.120995584727 8 18 62\n" + ] + } + ], + "source": [ + "cstar_task.updates(seconds=2)" + ] + }, + { + "cell_type": "markdown", + "id": "86c42fba-256a-4950-b324-b58413c598e1", + "metadata": {}, + "source": [ + "### Cancelling a job\n", + "We can cancel the job using the `cancel` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "428f25ea-43d8-4462-9b80-d912552aa403", + "metadata": {}, + "outputs": [], + "source": [ + "cstar_task.cancel()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "10926495-2da3-4a20-b663-d238d6d96a43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<ExecutionStatus.CANCELLED: 5>" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cstar_task.status" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/api.rst b/docs/api.rst index f0127b5..f6a5670 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -67,9 +67,10 @@ Scheduler Job .. autosummary:: :toctree: generated/ - cstar.scheduler.job.SchedulerJob - cstar.scheduler.job.SlurmJob - cstar.scheduler.job.PBSJob + cstar.execution.scheduler_job.SchedulerJob + cstar.execution.scheduler_job.SlurmJob + cstar.execution.scheduler_job.PBSJob + cstar.execution.local_process.LocalProcess System ------ diff --git a/docs/index.rst b/docs/index.rst index aeddd08..de6b2b9 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -33,7 +33,8 @@ A key strength of C-Star lies in its ability to run regional simulations using a Importing and running a Case from a blueprint <2_importing_and_running_a_case_from_a_blueprint> Restarting and continuing a Case <3_restarting_and_continuing_a_case> Preparing input datasets for a Case with ROMS using roms-tools <4_preparing_roms_input_datasets> - Handling jobs on HPC systems <5_handling_jobs_on_hpc_systems> + Tracking runs executed as jobs on HPC systems <5_tracking_runs_executed_as_jobs_on_hpc_systems> + Tracking runs executed locally <6_tracking_runs_executed_locally> .. toctree:: :maxdepth: 1 diff --git a/docs/releases.rst b/docs/releases.rst index f76a0f7..cc4863f 100644 --- a/docs/releases.rst +++ b/docs/releases.rst @@ -1,5 +1,15 @@ Release notes ============= +.. _v0.0.6-alpha: + +v0.0.6-alpha () +--------------------------- + +New features: +~~~~~~~~~~~~~ +- Add ExecutionHandler class to track tasks run locally (LocalProcess subclass) or submitted to a job scheduler (SchedulerJob subclass) + + .. _v0.0.3-alpha: diff --git a/examples/cstar_example.py b/examples/cstar_example.py index ab2ef57..180fc0c 100644 --- a/examples/cstar_example.py +++ b/examples/cstar_example.py @@ -10,7 +10,7 @@ roms_marbl_case = cstar.Case.from_blueprint( blueprint="cstar_blueprint_roms_marbl_example.yaml", caseroot="roms_marbl_example_case/", start_date="20120103 12:00:00", - end_date="20120104 12:00:00", + end_date="20120103 18:00:00", ) ## In a python session, execute:	Create equivalent to `SchedulerJob` for local execution (`LocalProcess`?) Following #175 `ROMSComponent.run()` will return a `SchedulerJob` instance if the system uses a scheduler, or executes "live" while the user waits if not. For consistent behaviour we should return an instance of a class like `LocalProcess` that has a shared base class with `SchedulerJob` (something generic like `ExecutionHandler`). On the shared base class, common (abstract) methods like `updates` could be defined. This way, no matter which system the user calls `.run()` on, they get the same behaviour - an object is returned on which certain queries can be made about the state of their ROMS run. Steps: - [x] Move scheduler/job.py -> execution/scheduler_job.py - [x] Define ExecutionHandler base class in execution/handler.py - [x] Create LocalProcess subclass in execution/local_process.py - [x] Update ROMSComponent.run to create and return a LocalProcess instance on a non-scheduler machine - [x] Add ExecutionHandler returned by ROMSComponent.run() to a private attribute to prevent premature post_run call	CWorthy-ocean/C-Star	diff --git a/cstar/tests/integration_tests/test_cstar_test_blueprints.py b/cstar/tests/integration_tests/test_cstar_test_blueprints.py index f99db1b..3591b00 100644 --- a/cstar/tests/integration_tests/test_cstar_test_blueprints.py +++ b/cstar/tests/integration_tests/test_cstar_test_blueprints.py @@ -78,5 +78,6 @@ class TestCStar: cstar_test_case.to_blueprint(tmpdir / "test_blueprint_persistence.yaml") cstar_test_case.build() cstar_test_case.pre_run() - cstar_test_case.run() + test_process = cstar_test_case.run() + test_process.updates(seconds=0, confirm_indefinite=False) cstar_test_case.post_run() diff --git a/cstar/tests/unit_tests/execution/test_handler.py b/cstar/tests/unit_tests/execution/test_handler.py new file mode 100644 index 0000000..40c0b4a --- /dev/null +++ b/cstar/tests/unit_tests/execution/test_handler.py @@ -0,0 +1,251 @@ +import time +import threading +from unittest.mock import patch, PropertyMock +from pathlib import Path +from cstar.execution.handler import ExecutionHandler, ExecutionStatus + + +class MockExecutionHandler(ExecutionHandler): + """Mock implementation of `ExecutionHandler` for testing purposes.""" + + def __init__(self, status, output_file): + self._status = status + self._output_file = Path(output_file) + + @property + def status(self): + return self._status + + @property + def output_file(self): + return self._output_file + + +class TestExecutionHandlerUpdates: + """Tests for the `updates` method in the `ExecutionHandler` class. + + Tests + ----- + test_updates_non_running_job + Validates that `updates()` provides appropriate feedback when the job is not running. + test_updates_running_job_with_tmp_file + Verifies that `updates()` streams live updates from the output file when the job is running. + test_updates_indefinite_with_seconds_param_0 + Confirms that `updates()` runs indefinitely when `seconds=0` and allows termination via user interruption. + """ + + def test_updates_non_running_job(self, tmp_path): + """Validates that `updates()` provides appropriate feedback when the job is not + running. + + This test ensures that if the job status is not `RUNNING`, the method informs + the user and provides instructions for viewing the job output if applicable. + + Mocks + ----- + MockExecutionHandler.status + Mocked to return `ExecutionStatus.COMPLETED`, simulating a non-running job. + builtins.print + Mocked to capture printed messages for validation. + + Asserts + ------- + - That the user is informed the job is not running. + - That instructions for viewing the job output are provided if the job status + is `COMPLETED` or similar. + """ + handler = MockExecutionHandler( + ExecutionStatus.COMPLETED, tmp_path / "mock_output.log" + ) + + with patch("builtins.print") as mock_print: + handler.updates(seconds=10) + mock_print.assert_any_call( + "This job is currently not running (completed). Live updates cannot be provided." + ) + mock_print.assert_any_call( + f"See {handler.output_file.resolve()} for job output" + ) + + def test_updates_running_job_with_tmp_file(self, tmp_path): + """Verifies that `updates()` streams live updates from the job's output file + when the job is running. + + This test creates a temporary output file and pre-populates it with initial + content. Live updates are then appended to the file in real-time using a + background thread, simulating a running job. The `updates()` method is tested + to ensure it reads and prints both the pre-existing content and new updates. + + Mocks + ----- + MockExecutionHandler.status + Mocked to return `ExecutionStatus.RUNNING`, simulating a running job. + builtins.print + Mocked to capture printed messages for validation. + + Asserts + ------- + - That all live updates appended to the output file are printed correctly. + - That previously existing content in the file is also printed. + - That the method properly interacts with the output file in real-time. + """ + # Create a temporary output file + output_file = tmp_path / "output.log" + initial_content = ["First line\n"] + live_updates = ["Second line\n", "Third line\n", "Fourth line\n"] + + # Write initial content to the file + with output_file.open("w") as f: + f.writelines(initial_content) + + handler = MockExecutionHandler(ExecutionStatus.RUNNING, output_file) + + # Function to simulate appending live updates to the file + def append_live_updates(): + with output_file.open("a") as f: + for line in live_updates: + time.sleep(0.01) # Ensure `updates()` is actively reading + f.write(line) + f.flush() # Immediately write to disk + + # Start the live update simulation in a background thread + updater_thread = threading.Thread(target=append_live_updates, daemon=True) + updater_thread.start() + + # Run the `updates` method + with patch("builtins.print") as mock_print: + handler.updates(seconds=0.25) + + # Ensure both initial and live update lines are printed + for line in live_updates: + mock_print.assert_any_call(line, end="") + + # Ensure the thread finishes before the test ends + updater_thread.join() + + def test_updates_indefinite_with_seconds_param_0(self, tmp_path): + """Confirms that `updates()` runs indefinitely when `seconds=0` and allows + termination via user interruption. + + This test creates a temporary output file and pre-populates it with content. + Unlike `test_updates_running_job_with_tmp_file`, this test does not continue + to update the temporary file, so no updates are actually provided. The + stream is then killed via a simulated `KeyboardInterrupt` and the test + ensures a graceful exit. + + Mocks + ----- + MockExecutionHandler.status + Mocked to return `ExecutionStatus.RUNNING`, simulating a running job. + builtins.input + Mocked to simulate user responses to the confirmation prompt. + builtins.print + Mocked to capture printed messages for validation. + time.sleep + Mocked to simulate a `KeyboardInterrupt` during indefinite updates. + + Asserts + ------- + - That the user is prompted to confirm running the updates indefinitely. + - That the method handles user interruptions and prints the appropriate message. + - That no additional file operations occur if the user denies the confirmation. + """ + # Create a temporary output file + output_file = tmp_path / "output.log" + content = ["Line 1\n", "Line 2\n", "Line 3\n"] + + # Write initial content to the file + with output_file.open("w") as f: + f.writelines(content) + + handler = MockExecutionHandler(ExecutionStatus.RUNNING, output_file) + + # Mock the `status` property to return "running" + with patch.object( + MockExecutionHandler, "status", new_callable=PropertyMock + ) as mock_status: + mock_status.return_value = ExecutionStatus.RUNNING + + # Patch input to simulate the confirmation prompt + with patch("builtins.input", side_effect=["y"]) as mock_input: + # Replace `time.sleep` with a side effect that raises KeyboardInterrupt + with patch("builtins.print") as mock_print: + # Simulate a KeyboardInterrupt during the updates call + with patch("time.sleep", side_effect=KeyboardInterrupt): + handler.updates(seconds=0) # Run updates indefinitely + + # Assert that the "stopped by user" message was printed + mock_print.assert_any_call( + "\nLive status updates stopped by user." + ) + + # Verify that the prompt was displayed to the user + mock_input.assert_called_once_with( + "This will provide indefinite updates to your job. You can stop it anytime using Ctrl+C. " + "Do you want to continue? (y/n): " + ) + # Patch input to simulate the confirmation prompt + with patch("builtins.input", side_effect=["n"]) as mock_input: + with patch("builtins.open", create=True) as mock_open: + handler.updates(seconds=0) + mock_open.assert_not_called() + + def test_updates_stops_when_status_changes(self, tmp_path): + """Verifies that `updates()` stops execution when `status` changes to non- + RUNNING. + + This test ensures: + - The conditional block exits `updates` when `status` is not `RUNNING`. + - Only lines added while the job is `RUNNING` are streamed. + """ + # Create a temporary output file + output_file = tmp_path / "output.log" + initial_content = ["First line\n"] + running_updates = ["Second line\n", "Third line\n"] + completed_updates = ["Fourth line\n", "Fifth line\n"] + # Write initial content to the file + with output_file.open("w") as f: + f.writelines(initial_content) + + # Initialize the handler with status `RUNNING` + handler = MockExecutionHandler(ExecutionStatus.RUNNING, output_file) + + # Function to simulate appending live updates and changing status + def append_updates_and_change_status(): + with output_file.open("a") as f: + for line in running_updates: + time.sleep(0.1) # Ensure updates() is actively reading + f.write(line) + f.flush() + + # Change the status to `COMPLETED` after writing running updates + time.sleep(0.2) + handler._status = ExecutionStatus.COMPLETED + for line in completed_updates: + time.sleep(0.1) + f.write(line) + f.flush() + + # Start the background thread to append updates and change status + updater_thread = threading.Thread( + target=append_updates_and_change_status, daemon=True + ) + updater_thread.start() + + # Run the `updates` method + with patch("builtins.print") as mock_print: + handler.updates(seconds=0, confirm_indefinite=False) + + # Verify that only lines from `running_updates` were printed + printed_calls = [call[0][0] for call in mock_print.call_args_list] + # print(printed_calls) + + for line in running_updates: + assert line in printed_calls + + # Verify that lines from `completed_updates` were not printed + for line in completed_updates: + assert line not in printed_calls + + # Ensure the thread finishes before the test ends + updater_thread.join() diff --git a/cstar/tests/unit_tests/execution/test_local_process.py b/cstar/tests/unit_tests/execution/test_local_process.py new file mode 100644 index 0000000..ad50f05 --- /dev/null +++ b/cstar/tests/unit_tests/execution/test_local_process.py @@ -0,0 +1,392 @@ +from unittest.mock import MagicMock, patch +from pathlib import Path +from cstar.execution.local_process import LocalProcess, ExecutionStatus +import subprocess + + +class TestLocalProcess: + """Tests for the `LocalProcess` class. + + This test class covers the functionality of the `LocalProcess` class, which is a subclass + of `ExecutionHandler` designed to manage task execution in a local subprocess. It validates the + class's behavior across its lifecycle, including initialization, starting processes, + monitoring status, and cancellation. + + Tests + ----- + test_initialization_defaults + Ensures that default attributes are correctly assigned during initialization. + test_start_success + Verifies that the subprocess starts successfully with valid commands and updates the status. + test_status + Confirms the `status` property reflects the process's lifecycle states (e.g., RUNNING, COMPLETED, FAILED). + test_status_failed_closes_file_handle + Ensures that a failed process closes the output file handle and updates the status to FAILED. + test_status_unknown_no_returncode + Validates that the status is `UNKNOWN` when the subprocess state cannot be determined. + test_cancel_graceful_termination + Ensures that `cancel` gracefully terminates a running process using `terminate` without requiring `kill`. + test_cancel_forceful_termination + Confirms that `cancel` forcefully terminates a process using `kill` after `terminate` times out. + test_cancel_non_running_process + Validates that `cancel` does not attempt to terminate or kill a non-running process and provides feedback. + """ + + def setup_method(self, method): + """Sets up reusable parameters and paths for tests in `TestLocalProcess`. + + This method initializes common attributes for creating a `LocalProcess` + instance, including the commands to execute, the output file path, and + the run directory. + + Parameters + ---------- + method : Callable + The test method being executed. This parameter is part of the pytest + `setup_method` signature but is not used in this setup. + + Attributes + ---------- + - commands : str + The shell command(s) to execute. + """ + self.commands = "echo Hello, World" + + def test_initialization_defaults(self, tmp_path): + """Ensures that default attributes are correctly applied during initialization. + + This test verifies that if `run_path` and `output_file` are not explicitly + provided, `LocalProcess` assigns default values based on the current + working directory. + + Asserts + ------- + - That `run_path` defaults to `Path.cwd()`. + - That `output_file` is auto-generated with a timestamped name. + """ + process = LocalProcess(commands=self.commands, run_path=None, output_file=None) + assert process.run_path == Path.cwd() + assert process.output_file.name.startswith("cstar_process_") + assert process.status == ExecutionStatus.UNSUBMITTED + + @patch("subprocess.Popen") + def test_start_success(self, mock_popen, tmp_path): + """Verifies that the subprocess starts successfully with valid commands. + + This test ensures that the `start` method: + - Initializes a subprocess with the correct parameters (commands, working directory). + - Redirects the standard output and error to the specified output file. + - Updates the `status` property to `RUNNING`. + + Mocks + ----- + subprocess.Popen + Mocked to simulate a successful subprocess startup. + + Asserts + ------- + - That the subprocess is called with the correct arguments. + - That the `status` property reflects the `RUNNING` state after startup. + """ + mock_popen.return_value.poll.return_value = None # Simulate running process + process = LocalProcess( + commands=self.commands, + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + + with patch("builtins.open", MagicMock()) as mock_open: + process.start() + mock_open.assert_called_once_with(process.output_file, "w") + + mock_popen.assert_called_once_with( + self.commands.split(), + cwd=tmp_path, + stdin=subprocess.PIPE, + stdout=mock_open.return_value, + stderr=subprocess.STDOUT, + ) + assert process.status == ExecutionStatus.RUNNING + + @patch("subprocess.Popen") + def test_status(self, mock_popen, tmp_path): + """Tests that the `status` property reflects the subprocess lifecycle. + + This test covers the following states: + - `UNSUBMITTED`: Before `start()` is called. + - `RUNNING`: After `start()` is called and the process is active. + - `COMPLETED`: After the process terminates successfully. + - `FAILED`: After the process terminates with a non-zero return code. + - `UNKNOWN`: When the process state is indeterminate. + + Mocks + ----- + subprocess.Popen + Mocked to simulate the subprocess lifecycle. + + Asserts + ------- + - That the `status` property reflects the correct `ExecutionStatus` for + each stage of the process lifecycle. + """ + # Mock subprocess behavior + mock_process = MagicMock() + mock_popen.return_value = mock_process + + # Initial state: UNSUBMITTED + process = LocalProcess( + commands=self.commands, + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + assert process.status == ExecutionStatus.UNSUBMITTED + + # After starting: RUNNING + mock_process.poll.return_value = None # Simulate running process + process.start() + assert process.status == ExecutionStatus.RUNNING + + # After completion: COMPLETED + mock_process.poll.return_value = 0 # Simulate successful termination + mock_process.returncode = 0 + assert process.status == ExecutionStatus.COMPLETED + + # After failure: FAILED + mock_process.poll.return_value = 1 # Simulate unsuccessful termination + mock_process.returncode = 1 + assert process.status == ExecutionStatus.FAILED + + # Indeterminate state: UNKNOWN + mock_process.returncode = None # Indeterminate state + process._process = mock_process # Ensure process is assigned + assert process.status == ExecutionStatus.UNKNOWN + + @patch("subprocess.Popen") + def test_status_failed_closes_file_handle(self, mock_popen, tmp_path): + """Verifies that a failed process closes the output file handle. + + This test ensures: + - The `_output_file_handle` is closed and set to `None` after failure. + - The status is set to `ExecutionStatus.FAILED`. + + Mocks + ----- + subprocess.Popen + Mocked to simulate a failed subprocess. + + Asserts + ------- + - That `_output_file_handle.close()` is called. + - That `_output_file_handle` is set to `None`. + - That the status is `FAILED`. + """ + # Mock subprocess behavior + mock_process = MagicMock() + mock_popen.return_value = mock_process + mock_process.returncode = 1 # Process failed + + # Create the LocalProcess instance + process = LocalProcess( + commands=self.commands, + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + process._process = mock_process + + # Mock the output file handle + mock_file_handle = MagicMock() + process._output_file_handle = mock_file_handle + + # Check the status + assert process.status == ExecutionStatus.FAILED + mock_file_handle.close.assert_called_once() # Ensure the file handle is closed + assert process._output_file_handle is None # Ensure the file handle is cleared + + @patch("subprocess.Popen") + def test_cancel_graceful_termination(self, mock_popen, tmp_path): + """Ensures that the `cancel` method gracefully terminates a running process. + + This test verifies: + - `terminate()` is called to stop the process. + - `kill()` is not called when `terminate()` succeeds. + - The `status` property is updated to `CANCELLED`. + + Mocks + ----- + subprocess.Popen + Mocked to simulate the subprocess lifecycle. + + Asserts + ------- + - That `terminate()` is called to stop the process. + - That `kill()` is not called if `terminate()` succeeds. + - That the `status` property reflects the `CANCELLED` state after cancellation. + """ + # Mock subprocess behavior + mock_process = MagicMock() + mock_popen.return_value = mock_process + + # Simulate a running process + mock_process.poll.return_value = None # Process is active + process = LocalProcess( + commands=self.commands, + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + process.start() + + # Verify the process has started + assert process.status == ExecutionStatus.RUNNING + assert process._process is mock_process # Ensure the process was assigned + + # Test graceful termination + process.cancel() + mock_process.terminate.assert_called_once() # Ensure terminate() was called + mock_process.kill.assert_not_called() # kill() should not be called if terminate succeeds + assert process.status == ExecutionStatus.CANCELLED + + @patch("subprocess.Popen") + def test_cancel_forceful_termination(self, mock_popen, tmp_path): + """Ensures that the `cancel` method forcefully terminates a process if needed. + + This test verifies: + - `terminate()` is called but fails with a `TimeoutExpired` exception. + - `kill()` is called as a fallback to stop the process. + - The `status` property is updated to `CANCELLED`. + + Mocks + ----- + subprocess.Popen + Mocked to simulate the subprocess lifecycle. + + Asserts + ------- + - That `terminate()` is called to stop the process. + - That `kill()` is called if `terminate()` fails. + - That the `status` property reflects the `CANCELLED` state after cancellation. + """ + # Mock subprocess behavior + mock_process = MagicMock() + mock_popen.return_value = mock_process + + # Simulate a running process + mock_process.poll.return_value = None # Process is active + mock_process.terminate.side_effect = subprocess.TimeoutExpired( + cmd="mock", timeout=5 + ) # Simulate timeout + process = LocalProcess( + commands=self.commands, + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + process.start() + + # Verify the process has started + assert process.status == ExecutionStatus.RUNNING + assert process._process is mock_process # Ensure the process was assigned + + # Test forceful termination + process.cancel() + mock_process.terminate.assert_called_once() # Ensure terminate() was called + mock_process.kill.assert_called_once() # Ensure kill() was called after terminate timeout + assert process.status == ExecutionStatus.CANCELLED + + @patch("subprocess.Popen") + @patch("builtins.print") + def test_cancel_non_running_process(self, mock_print, mock_popen, tmp_path): + """Ensures that `cancel` does not attempt to terminate a non-running process. + + This test verifies: + - The `print` statement is invoked with the correct message. + - Neither `terminate` nor `kill` is called. + + Mocks + ----- + subprocess.Popen + Mocked to simulate the subprocess lifecycle. + builtins.print + Mocked to capture printed messages for validation. + + Asserts + ------- + - That the `print` statement outputs the correct message. + - That neither `terminate` nor `kill` is called. + """ + # Mock subprocess behavior + mock_process = MagicMock() + mock_popen.return_value = mock_process + + # Simulate a completed process + mock_process.poll.return_value = 0 # Process has completed + process = LocalProcess( + commands=self.commands, + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + process._process = mock_process # Directly assign the mock process + + # Test cancel on a completed process + process.cancel() + mock_print.assert_called_once_with( + f"Cannot cancel job with status '{process.status}'" + ) + mock_process.terminate.assert_not_called() + mock_process.kill.assert_not_called() + + @patch("subprocess.Popen") + @patch("builtins.print") + def test_wait_running_process(self, mock_print, mock_popen, tmp_path): + """Ensures that `wait` correctly waits for a running process to complete. + + This test verifies: + - `wait()` is called on the process when it is running. + - `print` is not called for valid process states. + - The process's final status is updated correctly. + """ + # Mock subprocess behavior + mock_process = MagicMock() + mock_popen.return_value = mock_process + + # Simulate a running process + mock_process.poll.return_value = None # Process is active + process = LocalProcess( + commands="sleep 1", + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + process._process = mock_process # Assign mock process + process._cancelled = False + + # Test wait on a running process + process.wait() + mock_process.wait.assert_called_once() # Ensure `wait` was called on the process + assert not mock_print.called # Confirm `print` was not called + + @patch("subprocess.Popen") + @patch("builtins.print") + def test_wait_non_running_process(self, mock_print, mock_popen, tmp_path): + """Ensures that `wait` does not perform actions for non-running processes. + + This test verifies: + - `wait()` prints an appropriate message for non-running processes. + - `wait()` does not attempt to wait on processes that are not running. + """ + # Mock subprocess behavior + mock_process = MagicMock() + mock_popen.return_value = mock_process + + # Test wait on a cancelled process + mock_process.poll.return_value = None # Process is active + process = LocalProcess( + commands="sleep 1", + run_path=tmp_path, + output_file=tmp_path / "output.log", + ) + process._process = mock_process # Assign mock process + process._cancelled = True + + process.wait() + mock_print.assert_called_once_with( + f"cannot wait for process with execution status '{ExecutionStatus.CANCELLED}'" + ) + mock_process.wait.assert_not_called() diff --git a/cstar/tests/unit_tests/scheduler/test_pbs_job.py b/cstar/tests/unit_tests/execution/test_pbs_job.py similarity index 89% rename from cstar/tests/unit_tests/scheduler/test_pbs_job.py rename to cstar/tests/unit_tests/execution/test_pbs_job.py index b6335c4..52267ec 100644 --- a/cstar/tests/unit_tests/scheduler/test_pbs_job.py +++ b/cstar/tests/unit_tests/execution/test_pbs_job.py @@ -2,8 +2,8 @@ import json import pytest from unittest.mock import MagicMock, patch, PropertyMock from cstar.system.scheduler import PBSScheduler, PBSQueue -from cstar.scheduler.job import ( - JobStatus, +from cstar.execution.scheduler_job import ( + ExecutionStatus, PBSJob, ) @@ -62,6 +62,7 @@ class TestPBSJob: self.scheduler = PBSScheduler( queues=[self.mock_queue], primary_queue_name="test_queue", + other_scheduler_directives={"mock_directive": "mock_value"}, ) # Define common job parameters @@ -77,20 +78,12 @@ class TestPBSJob: "queue_name": "test_queue", } - @patch( - "cstar.system.manager.CStarSystemManager.scheduler", new_callable=PropertyMock - ) - def test_script(self, mock_scheduler): + def test_script(self): # , mock_scheduler): """Verifies that the PBS job script is correctly generated with all directives. This test checks that the `script` property of `PBSJob` produces the expected job script, including user-provided parameters and any scheduler directives. - Mocks - ----- - CStarSystemManager.scheduler - Mocked to simulate a scheduler with specific directives (`mock_directive`). - Asserts ------- - That the generated job script matches the expected content, including: @@ -99,12 +92,6 @@ class TestPBSJob: - Commands to execute in the job script (`echo Hello, World`). """ - # Mock the scheduler and its attributes - mock_scheduler.return_value = MagicMock() - mock_scheduler.return_value.other_scheduler_directives = { - "mock_directive": "mock_value" - } - # Initialize a PBSJob job = PBSJob(self.common_job_params) @@ -129,10 +116,7 @@ class TestPBSJob: ), f"Script mismatch!\nExpected:\n{expected_script}\n\nGot:\n{job.script}" @patch("subprocess.run") - @patch( - "cstar.system.manager.CStarSystemManager.scheduler", new_callable=PropertyMock - ) - def test_submit(self, mock_scheduler, mock_subprocess, tmp_path): + def test_submit(self, mock_subprocess, tmp_path): """Ensures that the `submit` method properly submits a PBS job and extracts the job ID. @@ -145,8 +129,6 @@ class TestPBSJob: ----- subprocess.run Mocked to simulate the execution of the `qsub` command and its output. - CStarSystemManager.scheduler - Mocked to simulate a scheduler with no additional directives. Asserts ------- @@ -155,10 +137,6 @@ class TestPBSJob: - That the `qsub` command is executed with the correct arguments. """ - # Mock scheduler attributes - mock_scheduler.return_value = MagicMock() - mock_scheduler.return_value.other_scheduler_directives = {} - # Mock subprocess.run for qsub mock_subprocess.return_value = MagicMock( returncode=0, stdout="12345.mockserver\n", stderr="" @@ -186,12 +164,8 @@ class TestPBSJob: ], ) @patch("subprocess.run") - @patch( - "cstar.system.manager.CStarSystemManager.scheduler", new_callable=PropertyMock - ) def test_submit_raises( self, - mock_scheduler, mock_subprocess, returncode, stdout, @@ -214,18 +188,12 @@ class TestPBSJob: ----- subprocess.run Mocked to simulate the execution of the `qsub` command. - CStarSystemManager.scheduler - Mocked to simulate a scheduler with no additional directives. Asserts ------- - That a `RuntimeError` is raised with the expected error message when submission fails. """ - # Mock scheduler attributes - mock_scheduler.return_value = MagicMock() - mock_scheduler.return_value.other_scheduler_directives = {} - # Mock subprocess.run for qsub mock_subprocess.return_value = MagicMock( returncode=returncode, stdout=stdout, stderr=stderr @@ -247,7 +215,7 @@ class TestPBSJob: job.submit() @patch("subprocess.run") - @patch("cstar.scheduler.job.PBSJob.status", new_callable=PropertyMock) + @patch("cstar.execution.scheduler_job.PBSJob.status", new_callable=PropertyMock) def test_cancel_running_job(self, mock_status, mock_subprocess, tmp_path): """Tests that the `cancel` method successfully cancels a running PBS job. @@ -257,7 +225,7 @@ class TestPBSJob: Mocks ----- PBSJob.status - Mocked to return `JobStatus.RUNNING`, simulating a running job. + Mocked to return `ExecutionStatus.RUNNING`, simulating a running job. subprocess.run Mocked to simulate successful execution of the `qdel` command. @@ -268,7 +236,7 @@ class TestPBSJob: """ # Mock the status to "running" - mock_status.return_value = JobStatus.RUNNING + mock_status.return_value = ExecutionStatus.RUNNING # Mock subprocess.run for successful cancellation mock_subprocess.return_value = MagicMock(returncode=0, stdout="", stderr="") @@ -293,7 +261,7 @@ class TestPBSJob: ) @patch("subprocess.run") - @patch("cstar.scheduler.job.PBSJob.status", new_callable=PropertyMock) + @patch("cstar.execution.scheduler_job.PBSJob.status", new_callable=PropertyMock) @patch("builtins.print") def test_cancel_completed_job( self, mock_print, mock_status, mock_subprocess, tmp_path @@ -339,7 +307,7 @@ class TestPBSJob: ## @patch("subprocess.run") - @patch("cstar.scheduler.job.PBSJob.status", new_callable=PropertyMock) + @patch("cstar.execution.scheduler_job.PBSJob.status", new_callable=PropertyMock) def test_cancel_failure(self, mock_status, mock_subprocess, tmp_path): """Ensures that a `RuntimeError` is raised if the `qdel` command fails to cancel a job. @@ -351,7 +319,7 @@ class TestPBSJob: Mocks ----- PBSJob.status - Mocked to return `JobStatus.RUNNING`, simulating a running job. + Mocked to return `ExecutionStatus.RUNNING`, simulating a running job. subprocess.run Mocked to simulate a failed execution of the `qdel` command, returning a non-zero exit code. @@ -361,7 +329,7 @@ class TestPBSJob: - That the `qdel` command is called with the correct job ID and parameters. """ # Mock the status to "running" - mock_status.return_value = JobStatus.RUNNING + mock_status.return_value = ExecutionStatus.RUNNING # Mock subprocess.run for qdel failure mock_subprocess.return_value = MagicMock( @@ -388,7 +356,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "Q"}}}, None, - JobStatus.PENDING, + ExecutionStatus.PENDING, False, None, None, @@ -396,7 +364,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "R"}}}, None, - JobStatus.RUNNING, + ExecutionStatus.RUNNING, False, None, None, @@ -404,7 +372,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "C"}}}, None, - JobStatus.COMPLETED, + ExecutionStatus.COMPLETED, False, None, None, @@ -412,7 +380,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "H"}}}, None, - JobStatus.HELD, + ExecutionStatus.HELD, False, None, None, @@ -420,7 +388,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "F", "Exit_status": 1}}}, None, - JobStatus.FAILED, + ExecutionStatus.FAILED, False, None, None, @@ -428,7 +396,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "F", "Exit_status": 0}}}, None, - JobStatus.COMPLETED, + ExecutionStatus.COMPLETED, False, None, None, @@ -452,7 +420,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "E"}}}, None, - JobStatus.ENDING, + ExecutionStatus.ENDING, False, None, None, @@ -460,7 +428,7 @@ class TestPBSJob: ( {"Jobs": {"12345": {"job_state": "X"}}}, None, - JobStatus.UNKNOWN, + ExecutionStatus.UNKNOWN, False, None, None, diff --git a/cstar/tests/unit_tests/scheduler/test_scheduler_job.py b/cstar/tests/unit_tests/execution/test_scheduler_job.py similarity index 76% rename from cstar/tests/unit_tests/scheduler/test_scheduler_job.py rename to cstar/tests/unit_tests/execution/test_scheduler_job.py index 5deb475..72799a5 100644 --- a/cstar/tests/unit_tests/scheduler/test_scheduler_job.py +++ b/cstar/tests/unit_tests/execution/test_scheduler_job.py @@ -1,6 +1,4 @@ -import time import pytest -import threading from unittest.mock import MagicMock, patch, PropertyMock from cstar.system.scheduler import ( Scheduler, @@ -9,8 +7,7 @@ from cstar.system.scheduler import ( SlurmScheduler, SlurmQOS, ) -from cstar.scheduler.job import ( - JobStatus, +from cstar.execution.scheduler_job import ( SchedulerJob, PBSJob, SlurmJob, @@ -111,12 +108,6 @@ class TestSchedulerJobBase: system defaults. test_init_cpus_without_distribution_requirement Confirms that task distribution is skipped if the scheduler does not require it. - test_updates_non_running_job - Validates that `updates()` provides appropriate feedback when the job is not running. - test_updates_running_job_with_tmp_file - Verifies that `updates()` streams live updates from the output file when the job is running. - test_updates_indefinite_with_seconds_param_0 - Confirms that `updates()` runs indefinitely when `seconds=0` and allows termination via user interruption. """ def setup_method(self, method): @@ -417,173 +408,6 @@ class TestSchedulerJobBase: assert job.nodes == expected_nodes assert job.cpus_per_node == expected_cpus_per_node - def test_updates_non_running_job(self): - """Validates that `updates()` provides appropriate feedback when the job is not - running. - - This test ensures that if the job status is not `RUNNING`, the method informs - the user and provides instructions for viewing the job output if applicable. - - Mocks - ----- - MockSchedulerJob.status - Mocked to return `JobStatus.COMPLETED`, simulating a non-running job. - builtins.print - Mocked to capture printed messages for validation. - - Asserts - ------- - - That the user is informed the job is not running. - - That instructions for viewing the job output are provided if the job status - is `COMPLETED` or similar. - """ - - job = MockSchedulerJob(self.common_job_params) - - with patch.object( - MockSchedulerJob, "status", new_callable=PropertyMock - ) as mock_status: - mock_status.return_value = JobStatus.COMPLETED - with patch("builtins.print") as mock_print: - job.updates(seconds=10) - mock_print.assert_any_call( - "This job is currently not running (completed). Live updates cannot be provided." - ) - mock_print.assert_any_call( - f"See {job.output_file.resolve()} for job output" - ) - - def test_updates_running_job_with_tmp_file(self, tmp_path): - """Verifies that `updates()` streams live updates from the job's output file - when the job is running. - - This test creates a temporary output file and pre-populates it with initial - content. Live updates are then appended to the file in real-time using a - background thread, simulating a running job. The `updates()` method is tested - to ensure it reads and prints both the pre-existing content and new updates. - - Mocks - ----- - MockSchedulerJob.status - Mocked to return `JobStatus.RUNNING`, simulating a running job. - builtins.print - Mocked to capture printed messages for validation. - - Asserts - ------- - - That all live updates appended to the output file are printed correctly. - - That previously existing content in the file is also printed. - - That the method properly interacts with the output file in real-time. - """ - - # Create a temporary output file - output_file = tmp_path / "output.log" - initial_content = ["First line\n"] - live_updates = ["Second line\n", "Third line\n", "Fourth line\n"] - print(output_file) - # Write initial content to the file - with output_file.open("w") as f: - f.writelines(initial_content) - - job = MockSchedulerJob(self.common_job_params, output_file=output_file) - - # Mock the `status` property to return "running" - with patch.object( - MockSchedulerJob, "status", new_callable=PropertyMock - ) as mock_status: - mock_status.return_value = JobStatus.RUNNING - - # Function to simulate appending live updates to the file - def append_live_updates(): - with output_file.open("a") as f: - for line in live_updates: - time.sleep(0.01) # Ensure `updates()` is actively reading - f.write(line) - f.flush() # Immediately write to disk - - # Start the live update simulation in a background thread - updater_thread = threading.Thread(target=append_live_updates, daemon=True) - updater_thread.start() - - # Run the `updates` method - with patch("builtins.print") as mock_print: - job.updates(seconds=0.25) - - # Ensure both initial and live update lines are printed - for line in live_updates: - mock_print.assert_any_call(line, end="") - - # Ensure the thread finishes before the test ends - updater_thread.join() - - ## - - def test_updates_indefinite_with_seconds_param_0(self, tmp_path): - """Confirms that `updates()` runs indefinitely when `seconds=0` and allows - termination via user interruption. - - This test creates a temporary output file and pre-populates it with content. - Unlike `test_updates_running_job_with_tmp_file`, this test does not continue - to update the temporary file, so no updates are actually provided. The - stream is then killed via a simulated `KeyboardInterrupt` and the test - ensures a graceful exit - - Mocks - ----- - MockSchedulerJob.status - Mocked to return `JobStatus.RUNNING`, simulating a running job. - builtins.input - Mocked to simulate user responses to the confirmation prompt. - builtins.print - Mocked to capture printed messages for validation. - time.sleep - Mocked to simulate a `KeyboardInterrupt` during indefinite updates. - - Asserts - ------- - - That the user is prompted to confirm running the updates indefinitely. - - That the method handles user interruptions and prints the appropriate message. - - That no additional file operations occur if the user denies the confirmation. - """ - # Create a temporary output file - output_file = tmp_path / "output.log" - content = ["Line 1\n", "Line 2\n", "Line 3\n"] - - # Write initial content to the file - with output_file.open("w") as f: - f.writelines(content) - - job = MockSchedulerJob(self.common_job_params, output_file=output_file) - - # Mock the `status` property to return "running" - with patch.object( - MockSchedulerJob, "status", new_callable=PropertyMock - ) as mock_status: - mock_status.return_value = JobStatus.RUNNING - - # Patch input to simulate the confirmation prompt - with patch("builtins.input", side_effect=["y"]) as mock_input: - # Replace `time.sleep` with a side effect that raises KeyboardInterrupt - with patch("builtins.print") as mock_print: - # Simulate a KeyboardInterrupt during the updates call - with patch("time.sleep", side_effect=KeyboardInterrupt): - job.updates(seconds=0) # Run updates indefinitely - - # Assert that the "stopped by user" message was printed - mock_print.assert_any_call( - "\nLive status updates stopped by user." - ) - # Verify that the prompt was displayed to the user - mock_input.assert_called_once_with( - "This will provide indefinite updates to your job. You can stop it anytime using Ctrl+C. " - "Do you want to continue? (y/n): " - ) - # Patch input to simulate the confirmation prompt - with patch("builtins.input", side_effect=["n"]) as mock_input: - with patch("builtins.open", create=True) as mock_open: - job.updates(seconds=0) - mock_open.assert_not_called() - ## @pytest.mark.filterwarnings( diff --git a/cstar/tests/unit_tests/scheduler/test_slurm_job.py b/cstar/tests/unit_tests/execution/test_slurm_job.py similarity index 95% rename from cstar/tests/unit_tests/scheduler/test_slurm_job.py rename to cstar/tests/unit_tests/execution/test_slurm_job.py index de68b17..55d0285 100644 --- a/cstar/tests/unit_tests/scheduler/test_slurm_job.py +++ b/cstar/tests/unit_tests/execution/test_slurm_job.py @@ -1,7 +1,7 @@ import pytest from unittest.mock import MagicMock, patch, PropertyMock from cstar.system.scheduler import SlurmScheduler, SlurmQOS, SlurmPartition -from cstar.scheduler.job import SlurmJob, JobStatus +from cstar.execution.scheduler_job import SlurmJob, ExecutionStatus class TestSlurmJob: @@ -336,7 +336,7 @@ class TestSlurmJob: job.submit() @patch("subprocess.run") - @patch("cstar.scheduler.job.SlurmJob.status", new_callable=PropertyMock) + @patch("cstar.execution.scheduler_job.SlurmJob.status", new_callable=PropertyMock) def test_cancel(self, mock_status, mock_subprocess, tmp_path): """Verifies that the `cancel` method cancels a SLURM job and raises an exception if it fails. @@ -365,7 +365,7 @@ class TestSlurmJob: run_path = tmp_path # Mock the status to simulate a running job - mock_status.return_value = JobStatus.RUNNING + mock_status.return_value = ExecutionStatus.RUNNING # Mock the subprocess.run behavior for successful cancellation mock_subprocess.return_value = MagicMock(returncode=0, stdout="", stderr="") @@ -459,12 +459,24 @@ class TestSlurmJob: @pytest.mark.parametrize( "job_id, sacct_output, return_code, expected_status, should_raise", [ - (None, "", 0, JobStatus.UNSUBMITTED, False), # Unsubmitted job - (12345, "PENDING\n", 0, JobStatus.PENDING, False), # Pending job - (12345, "RUNNING\n", 0, JobStatus.RUNNING, False), # Running job - (12345, "COMPLETED\n", 0, JobStatus.COMPLETED, False), # Completed job - (12345, "CANCELLED\n", 0, JobStatus.CANCELLED, False), # Cancelled job - (12345, "FAILED\n", 0, JobStatus.FAILED, False), # Failed job + (None, "", 0, ExecutionStatus.UNSUBMITTED, False), # Unsubmitted job + (12345, "PENDING\n", 0, ExecutionStatus.PENDING, False), # Pending job + (12345, "RUNNING\n", 0, ExecutionStatus.RUNNING, False), # Running job + ( + 12345, + "COMPLETED\n", + 0, + ExecutionStatus.COMPLETED, + False, + ), # Completed job + ( + 12345, + "CANCELLED\n", + 0, + ExecutionStatus.CANCELLED, + False, + ), # Cancelled job + (12345, "FAILED\n", 0, ExecutionStatus.FAILED, False), # Failed job (12345, "", 1, None, True), # sacct command failure ], )	{ "commit_name": "merge_commit", "failed_lite_validators": [ "has_issue_reference", "has_added_files", "has_many_modified_files", "has_many_hunks" ], "has_test_patch": true, "is_lite": false, "llm_score": { "difficulty_score": 2, "issue_text_score": 0, "test_score": 3 }, "num_modified_files": 10 }	0.0	{ "env_vars": null, "env_yml_path": [ "ci/environment.yml" ], "install": "pip install -e .", "log_parser": "parse_log_pytest", "no_use_env": null, "packages": "pytest", "pip_packages": [ "pytest" ], "pre_install": null, "python": "3.10", "reqs_path": null, "test_cmd": "pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning" }	affine==2.4.0 attrs==25.3.0 bokeh==3.7.2 Bottleneck==1.4.2 Cartopy==0.24.1 certifi==2025.1.31 cftime==1.6.4.post1 charset-normalizer==3.4.1 click==8.1.8 click-plugins==1.1.1 cligj==0.7.2 cloudpickle==3.1.1 contourpy==1.3.1 -e git+https://github.com/CWorthy-ocean/C-Star.git@b67a5f9f18fcb0aae6c8eaae6463310b9d9ad638#egg=cstar_ocean cycler==0.12.1 dask==2025.3.0 exceptiongroup @ file:///croot/exceptiongroup_1706031385326/work fonttools==4.57.0 fsspec==2025.3.2 future==1.0.0 gcm_filters==0.5.1 geopandas==1.0.1 idna==3.10 importlib_metadata==8.6.1 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work Jinja2==3.1.6 kiwisolver==1.4.8 llvmlite==0.44.0 locket==1.0.0 MarkupSafe==3.0.2 matplotlib==3.10.1 narwhals==1.33.0 netCDF4==1.7.2 numba==0.61.0 numpy==2.1.3 packaging @ file:///croot/packaging_1734472117206/work pandas==2.2.3 partd==1.4.2 pillow==11.1.0 platformdirs==4.3.7 pluggy @ file:///croot/pluggy_1733169602837/work pooch==1.8.2 pyamg==5.2.1 pyogrio==0.10.0 pyparsing==3.2.3 pyproj==3.7.1 pyshp==2.3.1 pytest @ file:///croot/pytest_1738938843180/work python-dateutil==2.9.0.post0 python-dotenv==1.1.0 pytz==2025.2 PyYAML==6.0.2 rasterio==1.4.3 regionmask==0.13.0 requests==2.32.3 roms-tools==2.6.1 scipy==1.15.2 shapely==2.1.0 six==1.17.0 tomli @ file:///opt/conda/conda-bld/tomli_1657175507142/work toolz==1.0.0 tornado==6.4.2 tzdata==2025.2 urllib3==2.3.0 xarray==2025.3.1 xgcm==0.8.1 xyzservices==2025.1.0 zipp==3.21.0	name: C-Star channels: - defaults - https://repo.anaconda.com/pkgs/main - https://repo.anaconda.com/pkgs/r - conda-forge dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h5eee18b_6 - ca-certificates=2025.2.25=h06a4308_0 - exceptiongroup=1.2.0=py310h06a4308_0 - iniconfig=1.1.1=pyhd3eb1b0_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.4.4=h6a678d5_1 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.16=h5eee18b_0 - packaging=24.2=py310h06a4308_0 - pip=25.0=py310h06a4308_0 - pluggy=1.5.0=py310h06a4308_0 - pytest=8.3.4=py310h06a4308_0 - python=3.10.16=he870216_1 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py310h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - tomli=2.0.1=py310h06a4308_0 - wheel=0.45.1=py310h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - affine==2.4.0 - attrs==25.3.0 - bokeh==3.7.2 - bottleneck==1.4.2 - cartopy==0.24.1 - certifi==2025.1.31 - cftime==1.6.4.post1 - charset-normalizer==3.4.1 - click==8.1.8 - click-plugins==1.1.1 - cligj==0.7.2 - cloudpickle==3.1.1 - contourpy==1.3.1 - cstar-ocean==0.0.8.dev1+gb67a5f9 - cycler==0.12.1 - dask==2025.3.0 - fonttools==4.57.0 - fsspec==2025.3.2 - future==1.0.0 - gcm-filters==0.5.1 - geopandas==1.0.1 - idna==3.10 - importlib-metadata==8.6.1 - jinja2==3.1.6 - kiwisolver==1.4.8 - llvmlite==0.44.0 - locket==1.0.0 - markupsafe==3.0.2 - matplotlib==3.10.1 - narwhals==1.33.0 - netcdf4==1.7.2 - numba==0.61.0 - numpy==2.1.3 - pandas==2.2.3 - partd==1.4.2 - pillow==11.1.0 - platformdirs==4.3.7 - pooch==1.8.2 - pyamg==5.2.1 - pyogrio==0.10.0 - pyparsing==3.2.3 - pyproj==3.7.1 - pyshp==2.3.1 - python-dateutil==2.9.0.post0 - python-dotenv==1.1.0 - pytz==2025.2 - pyyaml==6.0.2 - rasterio==1.4.3 - regionmask==0.13.0 - requests==2.32.3 - roms-tools==2.6.1 - scipy==1.15.2 - shapely==2.1.0 - six==1.17.0 - toolz==1.0.0 - tornado==6.4.2 - tzdata==2025.2 - urllib3==2.3.0 - xarray==2025.3.1 - xgcm==0.8.1 - xyzservices==2025.1.0 - zipp==3.21.0 prefix: /opt/conda/envs/C-Star	[ "cstar/tests/unit_tests/execution/test_handler.py::TestExecutionHandlerUpdates::test_updates_non_running_job", "cstar/tests/unit_tests/execution/test_handler.py::TestExecutionHandlerUpdates::test_updates_running_job_with_tmp_file", "cstar/tests/unit_tests/execution/test_handler.py::TestExecutionHandlerUpdates::test_updates_indefinite_with_seconds_param_0", "cstar/tests/unit_tests/execution/test_handler.py::TestExecutionHandlerUpdates::test_updates_stops_when_status_changes", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_initialization_defaults", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_start_success", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_status", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_status_failed_closes_file_handle", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_cancel_graceful_termination", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_cancel_forceful_termination", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_cancel_non_running_process", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_wait_running_process", "cstar/tests/unit_tests/execution/test_local_process.py::TestLocalProcess::test_wait_non_running_process", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_script", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_submit", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_submit_raises[1--Error", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_submit_raises[0-InvalidJobIDFormat--Unexpected", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_cancel_running_job", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_cancel_completed_job", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_cancel_failure", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output0-None-pending-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output1-None-running-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output2-None-completed-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output3-None-held-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output4-None-failed-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output5-None-completed-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[None-1-None-True-RuntimeError-Failed", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output7-None-None-True-RuntimeError-Job", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output8-None-ending-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[qstat_output9-None-unknown-False-None-None]", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status[invalid_json-None-None-True-RuntimeError-Failed", "cstar/tests/unit_tests/execution/test_pbs_job.py::TestPBSJob::test_status_json_decode_error", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_initialization_defaults", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_no_walltime_and_no_queue_max_walltime", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_walltime_provided_but_no_queue_max_walltime", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_no_walltime_but_queue_max_walltime_provided", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_walltime_exceeds_max_walltime", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_without_nodes_but_with_cpus_per_node", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_with_nodes_but_without_cpus_per_node", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_without_nodes_or_cpus_per_node", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_cpus_without_distribution_requirement[3-8-3-8]", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_cpus_without_distribution_requirement[None-8-None-8]", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_cpus_without_distribution_requirement[3-None-3-None]", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestSchedulerJobBase::test_init_cpus_without_distribution_requirement[None-None-None-None]", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCalculateNodeDistribution::test_exact_division", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCalculateNodeDistribution::test_partial_division", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCalculateNodeDistribution::test_single_node", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCalculateNodeDistribution::test_minimum_cores", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCreateSchedulerJob::test_create_slurm_job", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCreateSchedulerJob::test_create_pbs_job", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCreateSchedulerJob::test_unsupported_scheduler", "cstar/tests/unit_tests/execution/test_scheduler_job.py::TestCreateSchedulerJob::test_missing_arguments", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_script_task_distribution_not_required", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_script_task_distribution_required", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_submit", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_submit_raises[-1-Non-zero", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_submit_raises[Submitted", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_cancel", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_save_script", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_status[None--0-unsubmitted-False]", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_status[12345-PENDING\\n-0-pending-False]", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_status[12345-RUNNING\\n-0-running-False]", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_status[12345-COMPLETED\\n-0-completed-False]", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_status[12345-CANCELLED\\n-0-cancelled-False]", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_status[12345-FAILED\\n-0-failed-False]", "cstar/tests/unit_tests/execution/test_slurm_job.py::TestSlurmJob::test_status[12345--1-None-True]" ]	[ "cstar/tests/integration_tests/test_cstar_test_blueprints.py::TestCStar::test_cstar[test_case_remote_with_netcdf_datasets]", "cstar/tests/integration_tests/test_cstar_test_blueprints.py::TestCStar::test_cstar[test_case_remote_with_yaml_datasets]", "cstar/tests/integration_tests/test_cstar_test_blueprints.py::TestCStar::test_cstar[test_case_local_with_netcdf_datasets]", "cstar/tests/integration_tests/test_cstar_test_blueprints.py::TestCStar::test_cstar[test_case_local_with_yaml_datasets]" ]	[]	[]	Apache License 2.0	null

Subsets and Splits

Unique Repositories in Tests

Lists unique repository names from the dataset, providing a basic overview of the repositories present.

Filter Test Data by Instance ID

Retrieves all records for a specific instance, providing limited insight into the data structure but no broader analytical value.